INNOVATIVE HEALTHCARE FINANCING AND EQUITY …

i

INNOVATIVE HEALTHCARE FINANCING AND EQUITY THROUGH

COMMUNITY BASED HEALTH INSURANCE SCHEMES (CBHIS) IN KENYA

BY

JANE WANGUI GITAHI

UNITED STATES INTERNATIONAL UNIVERSITY - AFRICA

SPRING 2017

ii

INNOVATIVE HEALTHCARE FINANCING AND EQUITY THROUGH

COMMUNITY BASED HEALTH INSURANCE SCHEMES (CBHIS) IN KENYA

BY

JANE WANGUI GITAHI

A Dissertation Report submitted to the Chandaria School of Business in Partial

Fulfillment of the Requirement for the Degree of Doctorate in Business Administration

(DBA)

UNITED STATES INTERNATIONAL UNIVERSITY - AFRICA

SPRING 2017

iii

STUDENT’S DECLARATION

I, the undersigned, declare that this is my original work and has not been submitted to any

other college, institution or university other than the United States International University

for academic credit.

Signed: ________________________ Date: _____________________

Jane Wangui Gitahi (ID 627753)

This project has been presented for examination with my approval as the appointed

supervisors

Signed: ________________________ Date: _____________________

Prof. Amos Njuguna

Signed: ________________________ Date: _____________________

Dr. Timothy C. Okech

Signed: ________________________ Date: _____________________

Dean Chandaria School of Business

Signed: ________________________ Date: _____________________

Deputy Vice, Chancellor Academic and Students Affairs

Signed: ________________________ Date: _____________________

iv

COPYRIGHT

All rights reserved. No part of this report may be photocopied, recorded or otherwise

reproduced, stored in retrieval system or transmitted in any electronic or mechanical means

without prior permission of USIU-A or the author.

Jane W. Gitahi © 2017

v

ABSTRACT

The purpose of this study was to examine innovative healthcare financing and equity through

CBHIs in Kenya. Particularly, the study explored the effect of the enrolment, mix of

contributions; risk pooling, strategic purchasing and the moderating effect of government

stewardship on equity in healthcare in Kenyan CBHIs. Out of the 115 CBHIs that are

registered with Kenya Community Based Health Financing Association, data was collected

from a sample of 79 CBHIs which had complete and consistent data. Responses were sought

from four members of each CBHIs management team. Data was collected by use of

questionnaires and data collection sheets for secondary data. 224 usable questionnaires were

collected, representing 71% percent response rate. Data was cleaned and analyzed using

Excel, SPSS version 20 and SmartPLS version 3. The study used descriptive statistics, factor

analysis, path analysis and multivariate regression analysis in structural modeling equation

(SEM) to investigate the relationship among variables and measure the strength and direction

of relationships between constructs.

Empirical results revealed that there exist a positive relationship between enrolment in

CBHIs and equity in healthcare in Kenya, a negative relationship between mix of

contributions in CBHIs and equity in healthcare in Kenya, positive relationship between risk

pooling in CBHIs and equity in healthcare in Kenya and positive relationship between

strategic purchasing in CBHIs and equity in healthcare in Kenya. Government stewardship

has a positive influence on the relationship between enrolment and strategic and equity in

healthcare.

The major conclusion drawn from the study is that CBHIs stimulates enrolment by focusing

on social capital and stimulating willingness to pay in excluded segments of the population.

The current mix of contributions in CBHIs does not offer an optimal mix of funds necessary

for increased access to care and financial risk protection for precluded groups. The study

recommends that for realization of equity goals in the healthcare in Kenya government and

sectoral partners should define the place of CBHIs with the national health policy by enacting

the requisite legal and regulatory framework to guide CBHIs administrative and fiscal

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structures. The government should also encourage the players in health financing to

consolidate the pooled funds to enhance risk equalization.

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ACKNOWLEDEGMENTS

The journey of completing this doctoral research would not have been possible without the

direction, encouragement and support of particular people. I am highly indebted to my two

supervisors Prof. Amos Njuguna and Dr. Timothy Okech for their great insight, valuable

guidance, encouragement and mentorship throughout the process of writing this thesis.

The realization of my dream of pursuing doctoral studies would not have been a reality were

it not for United States International University-Africa admissions office and the vetting

board under the leadership of Dr. George Achoki and Prof. Amos Njuguna. I cannot forget

my colleagues in DBA one for continuous encouragement and Ephantus Mutitu for his

invaluable support during data analysis.

Special thanks goes to my husband Isaac and my sister Silvia for their prayers and unfailing

encouragement – you believed in me and endured long hours of absence in my pursuit of this

degree. Special thanks to my brother Jesse for encouragement and support. To my friends,

thank you for your prayers and support.

Above all, am thankful to God.

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DEDICATIONS

Dedicated to Isaac and Sylvia

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TABLE OF CONTENTS

STUDENT’S DECLARATION ............................................................................................ iii

COPYRIGHT ......................................................................................................................... iv

ABSTRACT ............................................................................................................................. v

ACKNOWLEDEGMENTS ................................................................................................. vii

DEDICATIONS ................................................................................................................... viii

TABLE OF CONTENTS ...................................................................................................... ix

LIST OF ABBREVIATIONS .............................................................................................. xv

CHAPTER ONE ..................................................................................................................... 1

1.0 INTRODUCTION............................................................................................................. 1

1.1 Background of the Study .................................................................................................... 1

1.2 Statement of the Problem .................................................................................................. 10

1.3 General Objective of the study ......................................................................................... 11

1.4 Specific Objectives ........................................................................................................... 11

1.5 Hypotheses: Null and alternative hypotheses ................................................................... 11

1.6 Justification of the Study .................................................................................................. 12

1.7 Scope of the Study ............................................................................................................ 13

1.8 Definition of Terms........................................................................................................... 13

CHAPTER TWO .................................................................................................................. 16

2.0 LITERATURE REVIEW .............................................................................................. 16

2.1 Introduction ....................................................................................................................... 16

2.2 Theoretical Review ........................................................................................................... 16

2.3 Conceptual Framework ..................................................................................................... 22

2.4 Empirical Review.............................................................................................................. 67

2.5 Chapter Summary ........................................................................................................... 109

CHAPTER THREE ............................................................................................................ 110

3.0 RESEARCH METHODOLOGY ................................................................................ 110

3.1 Introduction ..................................................................................................................... 110

3.2 Research Philosophy and Research Paradigm ................................................................ 110

3.3 Research Design.............................................................................................................. 112

3.4 Population ....................................................................................................................... 113

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3.5 Sampling Design ............................................................................................................. 113

3.6 Data Collection Methods ................................................................................................ 114

3.7 Research Procedures ....................................................................................................... 117

3.8 Data Analysis Methods ................................................................................................... 123

3.9 Chapter Summary ........................................................................................................... 139

CHAPTER FOUR ............................................................................................................... 140

4.0 FINDINGS ..................................................................................................................... 140

4.1 Introduction ..................................................................................................................... 140

4.2 General Information ........................................................................................................ 140

4.3 Effect of Enrolment on Equity in Healthcare ................................................................. 155

4.4 Effect of Mix of Contributions on Equity in Healthcare ................................................ 166

4.5 Effect of Risk Pooling in CBHIs on Equity in Healthcare Indicators ............................ 171

4.6 Effect of Strategic Purchasing in CBHIs on Equity in Healthcare Indicators ................ 177

4.7 Moderating effect of Government Stewardship on Equity in Healthcare ...................... 183

4.8 Overall Model ................................................................................................................. 190

4.9 Chapter Summary ........................................................................................................... 199

CHAPTER FIVE ................................................................................................................ 201

5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS .............................. 201

5.1 Introduction ..................................................................................................................... 201

5.3 Discussion of Results ...................................................................................................... 203

5.4 Conclusions ..................................................................................................................... 213

5.5 Recommendations ........................................................................................................... 216

REFERENCES .................................................................................................................... 219

APPENDICES ..................................................................................................................... 246

APPENDIX 1: APPROVAL LETTER FROM USIU-A ...................................................... 246

APPENDIX 2: APPROVAL LETTER FROM NACOSTI .................................................. 247

APPENDIX 3: NACOSTI RESEARCH CLEARANCE PERMIT ...................................... 248

APPENDIX 4: QUESTIONNAIRE ..................................................................................... 249

APPENDIX 5: SECONDARY DATA SHEET .................................................................... 258

APPENDIX 6: LIST OF CBHIS AND THEIR RESPECTIVE NETWORKS .................... 262

APPENDIX 7: CROSS LOADINGS OF CONSTRUCTS .................................................. 263

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LIST OF TABLES

Table 3.1 Type of data and data collection tools .................................................................. 115

Table 3.2: Reliability Test of Constructs .............................................................................. 119

Table 3.3: Kaiser-Meyer-Olkin and Bartlett's test ................................................................ 120

Table 3.4: Measures to Fit PLS Model ................................................................................. 131

Table 3.5 Items of measure for study constructs .................................................................. 133

Table 3.6: Summary of Hypotheses Testing ......................................................................... 138

Table 4.1 Descriptive Analysis - Extent Effect of Equity in Healthcare- Healthcare Access

............................................................................................................................................... 148

Table 4.2 Descriptive Analysis - Extent Effect of Equity in Healthcare - Equity in

Contributions......................................................................................................................... 149

Table 4.3 Descriptive Analysis – Extent Effect of Equity in Healthcare - Quality of Care . 149

Table 4.4 Descriptive Analysis - Extent Effect of Equity in Healthcare - Sustainability

(Administrative and Managerial Capability) ........................................................................ 151

Table 4.5 Descriptive Analysis - Extent Effect of Equity in Healthcare – Sustainability

(Financial Sustainability) ...................................................................................................... 153

Table 4.6 Cronbach‘s Alpha Coefficients, AVE and KMO values for Equity in Healthcare

(Healthcare Access, Equity in Contributions, Quality of Care and Sustainability) .............. 155

Table 4.7 Extent Effect of Affordability on Equity in Healthcare ........................................ 156

Table 4.8 Extent Effect of Unit of Membership on Equity in Healthcare ............................ 157

Table 4.9 Extent Effect of Timing of Collections on Equity in Healthcare ......................... 158

Table 4.10 Extent Effect of Trust on Equity in Healthcare .................................................. 159

Table 4.11 Correlation between Affordability and Healthcare Access ................................ 160

Table 4.12 Correlation between Affordability and Equity in Contributions ........................ 160

Table 4.13 Correlation between Affordability and Quality of Care ..................................... 160

Table 4.14 Correlation between Affordability and Sustainability ........................................ 161

Table 4.15 Correlation between Timing of Collections and Healthcare Access .................. 161

Table 4.16 Correlation between Timing of Collections and Equity in Contributions .......... 161

Table 4.17 Correlation between Timing of Collections and Quality of Care ....................... 162

Table 4.18 Correlation between Timings of Collections and Sustainability ........................ 162

Table 4.19 Correlation between Trust and Healthcare Access ............................................. 162

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Table 4.20 Correlation between Trust and Equity in Contributions ..................................... 163

Table 4.21 Correlation between Trust and Quality of Care .................................................. 163

Table 4.22 Correlation between Trust and Sustainability ..................................................... 163

Table 4.23 Cronbach‘s Alpha Coefficients, AVE and KMO values for Enrolment ............ 164

Table 4.24 Path Coefficients (Mean, STDEV, t-value) ........................................................ 166

Table 4.25 Descriptive Analysis - Extent Effect of Mix of Contributions in CBHIs on Equity

in Healthcare ......................................................................................................................... 167

Table 4.26 Correlation between Mix of Contributions and Healthcare Access ................... 168

Table 4.27 Correlation between Mix of Contributions and Equity in Contributions ........... 168

Table 4.28 Correlation between Mix of Contributions and Quality of Care ........................ 169

Table 4.29 Correlation between Mix of Contributions and Sustainability ........................... 169

Table 4.30 Cronbach‘s Alpha Coefficients, AVE and KMO values for Mix of Contributions

............................................................................................................................................... 169

Table 4.31 Path Coefficients (Mean, STDEV, t-values) ...................................................... 171

Table 4.32 Extent Effect of Risk Pooling in CBHIs on Equity in Healthcare ...................... 172

Table 4.33 Correlation between Risk pooling in CBHIs and Healthcare Access ................. 174

Table 4.34 Correlation between Risk Pooling and Equity in Contributions......................... 174

Table 4.35 Correlation between Risk Pooling and Quality of Care ..................................... 175

Table 4.36 Correlation between Risk Pooling and Sustainability ........................................ 175

Table 4.37 Cronbach‘s Alpha Coefficients, AVE and KMO values for Risk Pooling ......... 175


Table 4.39 Descriptive Analysis - Extent Effect of Strategic Purchasing on Equity in

Healthcare ............................................................................................................................. 178

Table 4.40 Correlation between Strategic Purchasing and Healthcare Access .................... 179

Table 4.41 Correlation between Strategic Purchasing and Equity in Contributions ............ 179

Table 4.43 Correlation between Strategic Purchasing and Sustainability ............................ 180

Table 4.44 Table 4.44 Cronbach‘s Alpha Coefficients, AVE and KMO values for Strategic

Purchasing ............................................................................................................................. 182


Table 4.46 Descriptive Analysis - Extent Effect of Government Stewardship on Equity in

Healthcare - Advisory Role on Design ................................................................................. 184

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Healthcare- Monitoring ......................................................................................................... 184


Healthcare- Training ............................................................................................................. 184


Healthcare- Co-financing ...................................................................................................... 186

Table 4.50 Cronbach‘s Alpha Coefficients, AVE and KMO values for Government

Stewardship - Design ............................................................................................................ 186


Stewardship - Monitoring ..................................................................................................... 188


Stewardship - Training .......................................................................................................... 188


Stewardship - Co-financing .................................................................................................. 189

Table 4.54 Multicollinearity Test ......................................................................................... 190

Table 4.55 Construct reliability ............................................................................................ 190

Table 4.55 Convergent Validity of outer model ................................................................... 190

Table 4.57 Measures of Discriminant Validity ..................................................................... 192

Table 4.58 Latent Variable Correlations / Correlation matrix of constructs ........................ 192

Table 4.59 Path Coefficients (Mean, STDEV, t-Values)...................................................... 196


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LIST OF FIGURES

Figure 2.1 Conceptual Framework ......................................................................................... 23

Figure 3.1 Path model for Equity in Healthcare ................................................................... 132

Figure 4.1 Figure 4.1 Response Rate .................................................................................... 132

Figure 4.2 Responses based on Years of Operation ............................................................. 141

Figure 4.3 Households targeted by CBHIs ........................................................................... 142

Figure 4.5 Relationships between the Number of Households Targeted and Those Covered

by CBHIs .............................................................................................................................. 143

Figure 4.6 Benefits Package, Premiums and Products Uptake in CBHIs ............................. 144

Figure 4.7 Methods of Payment: Inpatient and Outpatient services ..................................... 145

Figure 4.8 Distance to the Nearest Contracted Service Provider.......................................... 146

Figure 4.9 Average mix of contributions in CBHIs .............................................................. 146

Figure 4.10 Trends of Total Premiums collected, healthcare cost reimbursements,

administration cost and deficit/surplus in CBHIs between 2010-2015 ................................ 147

Figure 4.11 Path coefficients for effect of enrolment in CBHIs on equity in health care .... 165

Figure 4.12 t-values for effect of enrolment in CBHIs on equity in healthcare ................... 165

Figure 4.13 Path coefficients for effect of mix of contributions on equity in healthcare in

CBHIs ................................................................................................................................. 1713

Figure 4.14 t-values for effect of mix of contributions on equity in healthcare in CBHIs ... 171

Figure 4.15 Path coefficients for effect of Risk pooling in CBHIs on equity in healthcare . 176

Figure 4.16 t-values for effect of Risk pooling in CBHIs on equity in healthcare ............... 176

Figure 4.17 Path coefficients for effect of Strategic purchasing in CBHIs on equity in

healthcare ............................................................................................................................ 1824

Figure 4.18 t-values for effect of Strategic purchasing in CBHIs on equity in health care .. 182

Figure 4.19 Path coefficients for the optimum model without moderation .......................... 194

Figure 4.20 t- values for the optimum model without moderation ....................................... 195

Figure 4.21 Path coefficients for the optimum moderated model ........................................ 198

Figure 4.22 t-values for the optimum moderated model....................................................... 197

xv

LIST OF ABBREVIATIONS

CBHI Community Based Health Insurance

CHAT Choosing Healthplan All Together

DFID Department for International Development

GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit

GTZ Deutsche Gesellschaft für Technische Zusammenarbeit

GDP Gross Domestic Product

GNI Gross National Income

HLTIIFHS High- level Taskforce on Innovative International Financing for

Health Systems

IMF International Monetary Fund

KIHBS Kenya Integrated Household Budget Survey

KHHUES Kenya Household Health Expenditure and Utilization Survey

KNBS Kenya National Bureau of Statistics

LMICs Low and Middle Income Countries

MDG Millennium Development Goal

MLSSS Ministry of Labour Social Security and Services

MoH Ministry of Health

MOMS Ministry for Medical Services

NHA National Health Accounts

NHIF National Hospital Insurance Fund

NPISH Nonprofit Institutions Serving Households

NSHIF National Social Health Insurance Fund

ODA Official Development Assistance

OECD Organization for Economic Co-operation and development

OECD-DAC Organization for Economic Co-operation and development -

Development Co-operation Directorate

OOP Out-of-Pocket

SDGs Sustainable Development Goals

SEAR South East Asia Regions

SWAp Sector-Wide Approach

xvi

THE Total health expenditure

UHC Universal Health Coverage

UN United Nations

VAT Value Added Taxes

WHO World Health Organization

WHA World Health Assembly

1

CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the Study

The subject of disparities and inequities in healthcare is increasingly being recognized as a

central issue in current policy debates on healthcare. Huge differences in healthcare access

and high levels of financial risks associated with healthcare payments have been

documented; most of the cases are widespread in low and middle income countries (LMICs).

Globally an estimated 400 million people lack access to essential health services, 17% of the

people are impoverished or pushed deeper into poverty by healthcare costs (Starfield, 2011;

WHO, 2015a; Asante, Price, Hayen, Jan & Wiseman, 2016) whilst almost a third of

households in Africa and South East Asia regions (SEAR) of WHO are forced to borrow

money or sell assets to pay for healthcare at the point of use (Kruk, Godmann & Galea,

2009).

Enormous discrepancies in healthcare expenditure are evident among countries with LIMCs

relying heavily on out of pocket (OOP) expenditure to finance healthcare. In 2013,

households in LIMCs contributed 42.3% and 40.6% respectively of Total Health Expenditure

(THE) compared to 21.2% in high income countries (WHO, 2016). Additionally, while

poorer countries in African and SEAR of WHO account for over half of global burden of

disease and 39% of world‘s population they spent only 3% of world health resources in 2012

(WHO, 2015b). Similarly, the WHO African region and SEAR are deprived of access to

quality healthcare due to large deficits of skilled health workers (4.2 million and 6.9 million

respectively). Within countries, the disparities in access are driven by differences in

socioeconomic status with Sub-Saharan Africa and SEAR having the highest child mortality

rates (WHO, 2016). The ensuing disparities in access to quality healthcare and financial

protection places equity at the heart of current policy debates of Universal Health Coverage

(UHC) and in the post 2015 Sustainable Development Goals (SDGs) agenda (WHO, 2015c).

UHC was founded on the principle of access to healthcare for all with financial risk

protection while SDGs are founded on the theme of inclusiveness.

2

The call for health for all dates back to 1948 when the World Health Organization (WHO)

constitution declared health a fundamental human right and the later reaffirmation in the

Alma Ata Declaration of 1978 (WHO, 1948; 1978). Since the fifty- eighth World Health

Assembly (WHA) endorsed the resolution of 2005 that called ‗for sustainable health

financing, universal health coverage and social health insurance‘ (WHO, 2005), the visibility

and importance of UHC has been increasing steadily. The entire World Health Report of

2010 dwells on UHC and acts as a guide on innovative and sustainable health financing

methods for countries at all levels of development (WHO, 2010a). More recently the United

Nation (UN) resolution of 2012 urged governments to ‗accelerate the transition towards

universal access to affordable and quality health care services‘. This not only proves the wide

consensus on the urgency to act but also the increasing concern about the large differences in

healthcare access and financial risk protection particularly in LIMCs (WHO, 2015c).

UHC is an aspiration that all people have access to quality and effective promotive,

preventive, rehabilitative, palliative and curative health services at the time of need without

suffering financial ruin (WHO, 2010a; 2015a). Realization of UHC and SDGs will require

commitment of more resources to healthcare (WHO, 2010b; Tangcharoensathien, Mills &

Palu, 2015). The desire to enhance financial risk protection and improve access to quality

healthcare services therefore lies at the core health financing. An important challenge

therefore is developing health financing mechanisms that guarantee access to quality

healthcare and offer financial protection for all. Health financing encompasses three

functions; revenue collection (includes enrolment rates and mix of contributions), risk

pooling and strategic purchasing functions. Revenue collection involves raising of funds; risk

pooling encompasses accumulation and use of the funds in equalization of financial risks

associated with ill health which strategic purchasing involves sourcing for cost effective and

quality health services from healthcare providers and paying for them (WHO, 2010a).

Government stewardship is critical for steering the implementation of these functions since

the government bears the ultimate responsibility for the health of its people (WHO, 2000).

Availability of sufficient financial resources for health remains a fundamental question for all

countries. Many high income countries experience limited fiscal space as their health

3

demands evolve driven by a large ageing population and declining payroll taxes as a result of

shrinking workforce (WHO, 2010a). On the other hand, many of the poorest countries

struggle to ensure availability of basic health services (WHO, 2010a; 2014a). In 2012, low

income countries spent an average of US$ 32 per capita which is only a small fraction of

US$ 4625 spent by Organization for Economic Co-operation and Development (OECD)

countries (WHO, 2015b). This is way below the minimum recommended per capita

expenditure of US$44 in 2009 to more than US$60 in 2015 for provision of essential health

services and attainment of health related Millennium Development Goals (MDGs) (WHO

High-level Taskforce on Innovative International Financing for Health Systems (HLTIIFHS),

2009). This highlights the absolute need for more resources in low income countries for

them to achieve equity as envisioned by UHC and SDGs.

The financing gap is exacerbated by the rising costs of health systems. In 2013 global health

expenditure is estimated to have increased by 2.8% from US$ 7.2 trillion in 2012. Further,

the spending is projected to increase sharply by 5.2% annually between 2014-2018 to US$

9.3 trillion (WHO, 2013; 2012; 2010). This escalation is driven by aging and growing

populations, rising prevalence of chronic diseases and expensive medicines, procedures and

technologies (WHO, 2015d). High income countries continually seek more financial

resources to pay for rapidly expanding technologies and options for improving health while

low and middle income countries bear a staggering double burden of stubbornly high

communicable diseases and an escalating prevalence of non-communicable diseases (WHO,

2010a). This accentuates the need to prioritize health in government budgets while

diversifying sources of domestic funding through innovative financing mechanisms.

A significant number of countries at various levels of development have embraced UHC.

Most of these countries are in the WHO regions of America, Europe and Western Pacific. In

these countries healthcare is largely financed through general taxation and health insurance

(WHO, 2010a). As result their total health expenditure (THE) as percentage of Gross

Domestic Product (GDP) is well above 5–6%, a level recommended for realization of UHC

(WHO, 2010a; 2015e). In contrast, OOP still accounts for almost half of healthcare funds

(WHO 2016). In 2012, OOP as a percent of THE was more than 20% in 37 out of 45

4

countries in African region (WHO, 2014a). In addition, key health financing indicators are

below the target recommended. In 17 out of 45 countries the THE per capita is less than

US$44; health expenditure as a % of government expenditure is less than 15% and THE as a

percent of GDP is less than 5%. General taxation (government financed system) and health

insurance (market based) approaches have been recognized as progressive methods of

achieving UHC (WHO, 2010a; 2015a).

Government spending is seen as a stable and sustainable source of healthcare funds, making

it an important source of financing healthcare for UHC. Government spending is critical for

ensuring access to essential health services and cautions the poor and vulnerable against

financial risk associated with healthcare costs (Durairaj & Evans, 2010). This fact was

recognized by African heads of states when they committed to spend at least 15% of

government budgets on health in 2001 (Organization of African Unity (OAU, 2001). Thirty

nine out of 45 countries, Kenya being one of them have not achieved the target (WHO,

2014a). The percentage of government spending on health in Kenya have fluctuated from a

base of 7% in 2001/02, rising to 8.6 % in 2002/03, then falling to 4.6% in 2009/10 and then

rising to 6.1% in 2012/13 (MoH, 2015). There is a general consensus that the levels of

government expenditure are determined by the revenue generated and the macro-economic

policy (McIntyre & Meheus, 2014; Doherty, Kirigia, Ichoku, McIntyre, Hanson & Chuma,

2014). Like in many African countries, increased government revenue and economic growth

in Kenya have not translated into expanded fiscal space for health. This highlights the need

for tradeoffs between improving health particularly for the poor and vulnerable and growing

the economy. In essence, UHC and long-term economic development are inextricably linked

(WHO, 2001).

The World Health Report of 2010 emphasizes on the absolute need for donor support in

LIMCs in the short term for realization of UHC (WHO, 2010a). Efforts to rally global

solidarity have however remained unsuccessful. Donor funding is still unpredictable and fails

to meet the set targets in most cases. For instance, only 5 out of 22 donors met the set

requirements in 2009 while the targets set for 2015 were not achieved (WHO, 2013; 2016).

Progress in coordination and harmonization of donor aid through sector wide approach

5

(SWAp) is still slow in many recipients‘ countries (WHO, 2013). This approach is aimed at

giving recipient countries control in allocating the aid to priority interventions that are critical

for reducing the systemic access discrepancies and financial risks.

At independence Kenya‘s health system was predominantly tax- funded. As part of broader

structural reforms the government liberalized its economy in the late 1980s. As a result, a

user fee was introduced in the health facilities to cover part of services costs in government

facilities. As a consequence there was challenge of affordability and decline in utilization of

healthcare services especially among the poorest population (Chuma and Okungu, 2011).

The user fee was modified following the introduction of free health services in dispensaries

and health centers in 2004 (Carrin et al, 2007) and free maternity services in all public

hospitals in 2013. In 2010, the Health Sector Services Fund (HSSF) was established. The

fund represents a systematic way of channeling pooled government and donor funds to level

2 and 3 facilities with an aim of cautioning them against decline in revenue associated with

abolishment of user fees (Chuma and Okungu, 2011). Today Kenya operates a pluralistic

health financing system with major contributors being government, households and donors.

Recent analysis of health expenditure and financing flows in the health sector in 2012/13

National Health Accounts (NHA) shows that the private sector is major financier

contributing 40% of THE, while government and donors contributed 34% and 26% of THE

respectively. Donor funding declined from 35% in 2009/2010 to 26%. The percentage of

THE mobilized through OOP (excluding cost sharing) was 27% up from 19% in 2009/10

whereas Non-Profit Institutions Serving Households (NPISH) financing schemes declined by

45% compared to 2009/10 estimates (MoH, 2015). Estimates based on Ministry of Health

(2014) indicate that OOP spending on outpatient and inpatient accounted for 78% (48.4

billion) and 22% (13.7 billion) of total household health expenditure respectively (MoH,

2014). Various studies estimate that OOP push about 1.48 million Kenyans below the

poverty line (Xu, Evans, Kawabata, Zeremdini, Klavus & Murray, 2003; Chuma and Maina,

2012). Although this is a strong predictor of catastrophic health expenditure especially

among the poor (WHO, 2010a), it implies that equity in healthcare for poor and vulnerable

6

groups can be realized by channeling a fraction of OOP payments to CBHIs that are

incentivized by government and donor funds (WHO, 2001; 2010).

Risk pooling mechanisms are poor with only 4% of all health funds are pooled through

insurance (Chuma & Okungu, 2011). The estimated health insurance coverage in 2014 was

about 17.1% (44% of the population). Out of this coverage, the National Insurance Hospital

Fund (NHIF) covered 88.4%, while private insurance, Community Based Health Insurance

Schemes (CBHI) and others forms of insurance covered 9.4%, 1.3 and 1.0% respectively. On

the basis on wealth status, only 2.9% of the poorest quintile is covered (MoH, 2014). Out of

the 18 million poor, 10 million people are extremely poor can only be covered through

dedicated taxes and donor support while 8 million can access care through a government

subsidized cover (Ministry for Medical Services (MOMS), 2011). This evidences a potential

of increasing access to healthcare with adequate financial protection for all through

innovative health financing mechanisms that guarantees rapid coverage of the informal sector

and inclusion the poor.

Despite the general agreement that government spending is critical for realization of equity

given its stability and sustainability as observed by Durairaj & Evans (2010), the country

faces immense hurdles in finding adequate fiscal space for health. Like many African

countries, Kenya is composed of a large informal sector and a comparatively small and

stagnant formal sector (Kenya National Bureau of Statistics (KNBS), 2016). This presents

practical difficulties in collecting tax and health insurance contributions particularly from the

informal sector due to lack of institutional capacity to collect taxes (WHO, 2010a). For

instance, the small and stagnant formal sector account for 61.1% of NHIF membership

(KNBS, 2016). This implies that the contributions from the salaried sector are not adequate

to cross-subsidize the poor and vulnerable group (Carrin et al., 2007). According to Kenya

Integrated Household Budget Survey (KIHBS) 2005/06) almost half of the Kenyan

population lives below the poverty line (KNBS, 2007).

Based on the 2009 population estimates, this translates to 18 million, 10 million of which are

estimated to be extremely poor (MOMS, 2011). Inclusion of this category is urgent and

7

vexing task that is dependent on expansion of fiscal space for health coupled with

diversifying domestic sources through innovative health financing mechanisms. Government

contribution through trusted institutions is imperative for subsidization of this category

(WHO, 2010a). The phased expansion of NHIF has been disputed due to concerns of poor

governance and lack of capacity among other reasons (Munguti, 2010). Furthermore, current

NHIF contribution rates for the informal sector are flat. The flat rates are regressive mainly

for the poor and vulnerable groups as they do not match payments with ability to pay. In the

meantime, the poor and vulnerable are expected to receive financial protection through the

existing waiver system. Research findings by Collins, Quick, Musau, Kraushaar & Hussein

(1996) & Bitrán & Giedion (2003) show that the system is fraught with weakness. Deciding

on who is eligible is difficult; lack of awareness on the waiving mechanisms and the waiving

process is complicated and time consuming are some of its shortcomings. This system has

therefore failed to offer financial protection to the poor and vulnerable.

Given the limited fiscal space for health and the urgency of achieving equity goals, it would

therefore be imperative to explore other financing alternatives that can circumvent the current

political and organizational challenges being experienced at the national level. In this context

community involvement in healthcare financing presents an option for improved access to

healthcare by the poor and financial protection against catastrophic health expenditures

(Jakab & Chrishnan, 2001; Carrin, 2003). Community financing have emerged in the

backdrop of economic constraints, lack of good governance and lack of government

stewardship in the informal health sector (Preker, 2002). Community financing schemes

continues to attract to widespread attention due to its potential to achieve a large degree of

penetration through community involvements compared government and market based health

insurers (Jakab & Chrishnan, 2001; Preker, 2002).

Various forms of community financing exist. First, community managed user fees mainly

involves payment of user fee at the point of use. An example is the Bamako Initiative, that is

involves the community in setting the user fee levels, apportioning funds, developing and

management of waiver criteria and carrying out general administration and oversight (Gilson,

Kalyalya, Kuchler, Lake, Oranga & Ouendo, 2001; Preker, 2002; Carrin, Waelkens & Criel,

8

2005). Second, is the community prepayment or mutual organizations which are typified by

voluntary membership, single annual prepayment and community involvement in designing

and management of the scheme. Third, the government or social insurance supported

community driven schemes enlist the community in reaching the rural and excluded groups

on behalf of formal government or a social health insurer (Preker, 2002). Fourth, the

provider based health insurance which is localized around a provider unit usually a town, city

or regional hospital. The schemes membership is voluntary and is designed by local

healthcare providers to promote utilization of health services. The schemes are managed by

the healthcare providers who collect premiums. They often cater for expensive inpatient

services (Melbratie, Sparrow, Alemu & Bedi, 2013).

This study focuses on community prepayments and mutual organizations; a form of

Community Based Health Insurance (CBHIs) that emerged in late 1980s. In Anglophone

literature, the schemes are mostly referred to as Community Health Insurance or Community

Based Health Insurance while in Francophone countries they are labeled as Mutuelle de

Santé (Soors et al., 2010) depicting the underlying social drive and solidarity as one their

major principle. The schemes are characterized by voluntary membership, nonprofit health

financing mechanism whereby the households in the community finance or co-finance a set

of health interventions as well other needs outside healthcare (McPake, 1993; Carrin et al.,

2005, Soors, Devadasan, Durairaj & Criel, 2010).

The governance of the scheme is entrusted on a committee which is selected by the

community members. The committee is responsible for revenue collection, management of

the fund and enlisting of new members. In addition to member contributions, some receive

donor funding. To cope with risks and vulnerability, the schemes exploit social capital

namely solidarity and reciprocity as a primary resource making it easier identify the

contributing population and collect the contributions (Carrin et al., 2005). These values are

inherent in the self-help strategies that have been employed by the poor for a long time

(DeRoeck, 1996; Musau 1999).

9

All over the world, community financing have been used as a strategy for mobilizing

resources to finance and deliver healthcare for the informal sector and the poor. Some

schemes have been successful in pooling resources and transforming money into effective

services efficiently (Preker, 2002). Germany, Japan, China, Korea and Taiwan for example

transformed their health insurance by enlisting the informal sector through small groups

which eventually merged to larger schemes (Criel & Van Dormael, 1999; WHO, 2010).

Thailand and Indonesia presents examples of countries that have made remarkable strides in

increasing service coverage and financial protection through gradual expansion and

integration of voluntary CBHIs into the broader healthcare system (Poletti, Balabanova,

Ghazaryan, Kamal-Yanni, Kocharyan, Arakelyan & Hakobyan, 2007). Ghana and Rwanda

have in the recent past successfully increased health insurance coverage to 60% and 91%

respectively by reorienting their health financing towards pro-poor prepaid schemes

(Fernandes et al., 2009 ; Durairaj, D‘Almeida & Kirigia, 2010; MOMS, 2011; Schieber,

Cashin, Saleh & Lavado, 2012). In Kenya CBHI schemes have evolved over time to take

care of healthcare financing requirements of the low income households who have been left

out of mainstream prepaid schemes (MoH, 2014).

In Kenya, the Schemes are registered by Ministry of Labour Social Security and Services

(MLSSS). They have an umbrella association called Kenya Community Based Health

Financing Association (KCBHFA) that assist the organizations and other key stakeholders in

promoting community based health financing initiatives. The number of CBHIs that were

registered with KCBHFA in 2015 was 115 (KCBHFA, 2015) with a membership of 94,000

(Munge, Mulupi & Chuma, 2016). They rely on social capital particularly social solidarity,

trust and social accountability to enhance community and ownership. They offer platform

for community participation through annual general meetings and other periodic meetings

where members voice their complaints, concerns and present their views on varied issues.

During these meeting, the management team gives feedback in form of reports on financial

performance (Munge et al., 2016). Despite the recognition of Community based health

financing by MoH National Health Policy (NHP) 2012-2030, the initiatives have not been

applied as a vehicle of identifying and subsidizing the poorest as well as strengthening

inclusion of the poor and the informal sector (MoH, 2012).

10

1.2 Statement of the Problem

In Kenya the poverty levels are high resulting to exclusion of almost half the country‘s

population from private and public prepaid health insurance schemes (MOMS, 2011; KNBS,

2010). Only 4% of all health resources are pooled through health insurance and risk pooling

mechanisms account for only 17.1% of the population. On the basis of wealth status, only

2.9% of the poorest quintile is covered (Chuma & Okungu, 2011; MoH, 2014). As a result

OOP pushes about 1.48 million Kenyans below poverty line while millions lack access to

essential healthcare services and many more are deterred from seeking healthcare services

(Chuma & Maina, 2012, MoH, 2015).

Limited capacity in raising healthcare funds through general and payroll taxes, declining

donor and NPISH funding (KNBS, 2016; MoH, 2015) are some of the challenges that

continue to undermine efforts of improving access to segments of population that are

excluded from mainstream prepaid schemes. Additionally, NHIF monthly contribution rate

for the informal sector does not reflect the ability to pay particularly for the poor and

vulnerable groups. The increasing pressure to financing healthcare from domestic resources

calls for employment of financing mechanisms that can circumvent the ensuing organization

and political challenges. CBHIs have emerged as an innovative healthcare financing

mechanism with a potential of addressing the existing health inequities particularly for the

poor and vulnerable segments of the population (Fernandes et al., 2009; Durairaj et al., 2010

& Schieber et al., 2012). Currently, CBHIs covers 1.3% of the Kenyan population (MoH,

2014).

Given, the high levels of exclusion that results in huge disparities (MoH, 2014) and high

levels of financial risk associated with healthcare costs (Chuma & Maina, 2012) coupled with

increasing pressure to finance healthcare from domestic sources (MoH, 2015) and the

urgency of moving closer to UHC (WHO, 2015a) there is need of examine innovative

healthcare financing and equity through CBHIs in Kenya within the health financing

functions.

11

Various empirical studies focusing on CBHI from the perspective of potential in inclusion

and sustainability have been conducted by Dror & Jacquier (1999); Musau (1999). Jutting

(2000), Preker (2002); Chuma & Okungu (2011); Schieber et al., (2012); Okech (2013) have

extensively studied CBHI as feasible financing options for the informal sector and poor

households. The issue of how healthcare financing functions are modeled within CBHIs for

realization of equity in healthcare has however not been expressly studied. This study seeks

to examine innovative healthcare financing and equity through CBHIs in Kenya within the

health financing functions of revenue collection, fund pooling and purchasing. The study also

looks at the moderating effects of government stewardship in healthcare.

1.3 General Objective of the study

The purpose of this study was to examine innovative healthcare financing and equity through

CBHIs in Kenya.

1.4 Specific Objectives

This study was guided by the following specific objectives:

1.4.1 To establish the effect of enrolment on equity in health care in CBHIs in Kenya.

1.4.2 To establish the effect of mix of contributions on equity in health care in CBHIs in

Kenya.

1.4.3 To establish the effect of risk pooling on equity in health care in CBHIs in Kenya.

1.4.4 To determine the effect strategic purchasing of health services on equity in health

care in CBHIs in Kenya.

1.4.5 To establish the moderating role of government stewardship on equity in health care in

CBHIs in Kenya.

1.5 Hypotheses: Null and alternative hypotheses

1.5.1 H0: Enrolment is not related to equity in health care in CBHIs in Kenya.

H1: Enrolment is related to equity in health care in CBHIs in Kenya.

1.5.2 H0: Mix of contributions is not related to equity in health care in CBHIs in Kenya.

H1: Mix of contributions is related to equity in health care in CBHIs in Kenya.

1.5.3 H0 Risk pooling is not related to equity in health care in CBHIs in Kenya.

H1: Risk pooling is related equity in health care in CBHIs in Kenya.

12

1.5.4 H0 Strategic purchasing is not related to equity in health care in CBHIs in Kenya.

H1: Strategic purchasing is related to equity in health care in CBHIs in Kenya.

1.5.5 H0 Government stewardship does not have a moderating effect on equity in health

care in CBHIs in Kenya.

H1: Government stewardship has a moderating effect on equity in health care in

CBHIs in Kenya.

1.6 Justification of the Study

1.6.1 Sectoral Policy Makers

This study will contribute towards policy debate and dialogue on the appropriate timings of

premium collection in CBHIs, the types and mix of contributions for greater risk equalization

and the suitable mechanisms of achieving equity in contributions in CBHIs as a vehicle of

reducing disparities in healthcare access and providing financial protection for uncovered

segment of the population. The study will therefore provide focal point on a new domain for

action on health financing for UHC.

1.6.2 The Ministries of Health both at the National and County levels, related

Government Ministries and Departments

This study will act as a focal point of the areas that require specific legal and regulatory

framework defining the role of CBHIs within the health financing framework including

enhancing the stewardship role. Additionally, be instrumental in guiding decisions on

resource allocation particularly from the ministries of finance and health and county

government with an aim of creating the right mix contributions in CBHIs.

1.6.3 Researchers and Academicians

The study will contribute to the body of knowledge on innovative health financing and equity

through CBHIs with particular focus on the informal sector. Hence the findings of this study

will be of interest to other researchers who seek to explore factors that influence how health

financing functions are executed in CBHIs. In particular researchers would want to on factors

that prevent CBHIs from achieving their targeted enrolment rates and the cause of

fluctuations in premiums collected and reimbursements with an aim of cushioning CBHIs

13

from financial deficits.

1.7 Scope of the Study

The study focused on innovative healthcare financing and equity through CBHIs in Kenya

within the health financing functions. The researcher observes that there are other community

financing schemes that offer other forms of community financing such payment of user fees

for healthcare at the point and time of use but are not the focus in this study. The target

population for the study was 82 CBHIs registered by the umbrella body (KCBHFA) in Kenya

that had complete and coherent data. The researcher collected pilot data from three CBHIs

under Bidii network, making the number of CBHIs from which data was collected from in

the main study to be 79 CBHIs. The researcher noted that most of the 79 CBHIs had an

average of four members of management team with vary roles. To make inference about the

CBHIs, responses were sought from four members of each CBHIs management team which

translated to 316 members. The sample size was adjusted to 306 using Yamane (1967)

formula. Data was collected between 23rd

March, 2016 and 14th

April, 2016.

1.8 Definition of Terms

1.8.1 Health Financing

Health financing involves mobilization of funds for healthcare, accumulation and allocation

of pooled funds in purchasing of health services efficiently and equitably (WHO, 2010a).

1.8.2 Equity in Healthcare

Equity in health refers to absence of systemic and possibly solvable disparities in healthcare

access, financial risk protection on health related costs and quality of care between social

groups emanating from social, economic and geographical differences (Starfield, 2011;

Mills, Ally, Goudge, Gyapong & Mtei, 2012; WHO, 2015a).

1.8.3 Innovative Financing

Innovative financing refers to non-traditional methods of financing that embrace public-

private partnerships including other financing mechanisms that tap new resources, deliver

14

value or ensure effectiveness in solving development problems on the ground (Taskforce on

Innovative International Financing for Health Systems Working Group 2, 2009).

1.8.4 Universal Health Coverage

UHC is an aspiration that all people have access to quality and effective promotive,

preventive, rehabilitative, palliative and curative health services at the time of need without

suffering financial ruin (WHO, 2010a; 2015a).

1.8.5 Revenue collection refers to the process employed by a health system in determining

and obtaining money to pay for health system costs from households, organizations and

donors (WHO, 2010a).

1.8.6 Enrolment

Refers to the uptake of pre-paid health insurance in CBHIs (WHO, 2010a).

1.8.7 Mix of Contributions

This is a desired mix of public and private healthcare funds for financing UHC (WHO,

2010a).

1.8.6 Risk Pooling

This involves the accumulation and management of financial resources in a way that permits

spreading of risk of payment for health among all members of a pool and not by individual

members of the pool who fall ill (WHO, 2010a).

1.8.7 Strategic Purchasing

Strategic Purchasing entails the search for most cost effective interventions for increased

healthcare access and financial risk protection (WHO, 2010a).

1.8.8 Government Stewardship

Government stewardship refers to the broader overarching accountability over the

performance of the entire health system and eventually over the health of the whole

population (WHO, 2000; Alvarez-Rosete, Hawkins & Parkhurst, 2013).

15

1.8.9 Community Based Health Insurance Schemes (CBHIs)

This is a form of nonprofit community health financing initiative that is characterized by

voluntary membership community involvement in designing and managing of the scheme

(Carrin et al. 2005, Soors et al., 2010).

1.9 Chapter Summary

The chapter articulates on the inequities in LIMCs and the systemic challenges in healthcare

financing that impede reduction of disparities in healthcare access and financial risks. It

describes the current financing systems in developed countries, Sub-Saharan Africa and most

importantly in Kenya. Its demonstrates the need of reducing out-of–pocket payments by

building a health financing system that enables people to pre-pay for healthcare based on

their capacity to pay while exempting the poorest groups. Further, it discusses that emerging

role of CBHI as a vehicle of inclusion of the informal sector and the poor. In addition, it

highlights achievements of other countries in expanding coverage for universal health

coverage that can be replicated in Kenya. This study seeks to examine innovative healthcare

financing and equity through CBHIs in Kenya within the health financing functions. Specific

objectives of the study are derived from the health financing and the stewardship functions.

The findings of this study will be of interest to policy makers, implementers, academicians

and researchers in different ways.

In the next chapter, literature related to health financing for UHC particularly the health

financing and the stewardship functions in CBHIs and their effect on equity in healthcare in

Kenya is reviewed. Chapter three details the research methodology employed in the current

study, detailing the methods used in data collection and analysis in the study. Chapter four

presents the results and findings. Discussions of the findings, conclusions and

recommendations are presented in chapter five.

16

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This section presents theoretical review, the conceptual framework and empirical review of

literature. Theoretical review describes theories relevant to this study while the conceptual

framework illustrates the relationship between the independent and the dependent variable as

mediated by the intervening variables. Literature review entails a review of studies that have

been carried out on performance of CBHIs.

2.2 Theoretical Review

The importance of a theoretical foundation in research cannot be understated. A theoretical

framework forms the foundation of any research since it is the basis on which knowledge is

construed (Grant & Osanloo, 2014). Lysaght (2011) postulates that a theoretical framework

acts as an anchor on which empirical literature is reviewed, research procedures are chosen

and employed. It also consists of selected theories that guide the researcher‘s thoughts and

understanding of the research topic. Whetten (1989) summarizes the critical components and

conditions that make a sound theory as ‗what‘, ‗how‘ and ‗why‘; and ‗who‘, ‗where‘ and

‗when‘ that give the study a well-rounded logical flow. In the first set constitutes of

elements, what specifies the factors and constructs that have been identified in a study, how

describes their causal relationship while why explains the plausibility of theorized

relationship. The second set constitutes of conditions that sets the range for the theory. In

essence they limit the applicability of a specific theory.

Saunders, Lewis and Thornhill (2016) categorizes theory development in research into three

approaches; deductive, inductive and abductive approach. In deductive approach, the theory

provides framework which research questions or hypotheses and data collection procedure is

carried out. The researcher therefore develops a theoretical framework by deriving theories

from existing literature in the beginning of the proposed study and then subjects it to rigorous

test with the objective of confirming or recommending its modification (Creswell, 2014).

Conversely, in inductive approach the researcher builds the theoretical framework in data

analysis phase. In abductive approach, the research uses cognitive and numerical reasoning to

explain surprising facts that cannot be explicated by existing theories. This process generates

17

new theories or modifies the existing ones (Saunders et al., 2016). This study employed a

deductive approach in developing a structured analysis for examining and understanding

innovative healthcare financing and equity through CBHIs in Kenya.

2.2.1 Social Capital Theory

Vast sources of literature posit that social capital which manifests as trust, cooperation and

reciprocity facilitates collective action and lowers opportunistic behaviour (Field, 2008;

Andriani, 2013). The contemporary implication of social capital was advanced by social

scientists including Bourdieu (1986), Coleman (1988) & Putnam (2000). Putnam defines

social capital as the connections among individuals that originate from social networks and

the norm of reciprocity, trustworthiness, and civic engagement that arise from these

networks. This definition underlines the significance of social capital as an essential catalyst

of coordination and achieving better social and economic outcomes. Operationally, Lin

(2001) view social capital as resources embedded in social ties that are accessed and utilized

by its members.

In the broader view of social capital theory, connections and higher associational activities

inside a community fosters a sense of civic engagement where cooperation, the norm of

reciprocity and mutual trust develops over time which is used to institute collective action,

reduce information asymmetry, reduce transaction costs and establish efficient transactions in

the community (Alesina & La Ferrara, 2002; Andriani, 2013). Recent research on the role

social capital on health outcomes have found that individuals behaviour and health outcomes

are to a large extent determined by social capital attributes rather than rational choices made

by the individuals (Eriksson, 2011). In particular, social capital influences positively on the

value people attach to their health (Donfouet & Mahieu, 2012). It can also promote sharing

and transmission of positive health messages that discourage negative health behaviour such

as alcohol abuse and smoking (Takakura, 2011). Communities that high levels of social

capital are easily persuaded to support health policies aimed at achieving equity in healthcare

(Donfouet & Mahieu, 2012).

18

Social capital is one of the key contextual factors that have greatly contributed formation and

success of CBHIs of extending equity in health care (Catherine & Salmen, 2000). Structural

elements of social capital facilitates formation of CBHIs, influences their composition and

practices. On the other hand cognitive elements of social capital whose key principles are

solidarity, reciprocity and trust informs the community‘s values, attitude, behaviour and

norms resulting to collective action (Krishna & Shrader, 1999). The two elements of social

capital are therefore critical for enrolment, pre-payments, risk pooling and purchasing of

health services in CBHIs (Chen et al., 2012; Mladovsky, 2014). They stir up members of a

community faced by uninsured health risks to pool their resources together for common use.

Mutual trust enhances transmission and assimilation of information hence reducing

information asymmetry (Donfouet & Mahieu, 2012; Tundui & Macha, 2014). Mladovsky &

Mossialos (2006) underline the significance of direct involvement of government through its

official in organizing its citizen and sustaining their involvement.

2.2.2 Stewardship Theory

The stewardship theory was advanced by Donaldson & Davis (1991) in search of a new

perspective of managerial motivation to agency theory. Traditionally the theory of

stewardship in literature is grounded in the principal- agent dichotomy. In contrast to the

agency theory, a steward of an organization is seen as the one who acts in the best interest of

an organization (Hill & Jones, 1992). More recent definition encompasses the welfare of all

stakeholders‘ interests as opposed to only the stockholders benefit. According to Donaldson

& Preston (1995) a steward transcends above an agent and goes beyond the stockholders

interest. He demonstrates unfaltering commitment to upholding the fiduciary and non-

fiduciary obligations of the organization as well as the moral duty to other stakeholders

impacted by organizations actions.

The stewardship theory holds that variations in executives‘ performance depend on the extent

to which the structural situation facilitates the executives‘ action. The question therefore is

whether or not the organizational structure aids the executive in formulation and executing

strategies for high organizational performance. A structure that delineates roles and assigns

authority is deemed to be facilitative (Donaldson, 1985). The stewardship theory shifts its

19

focus from the management to the facilitative and empowering structures of an organization

that deliver superior returns to all shareholders through enhanced effectiveness. According to

Donaldson & Davis (1991) & Donaldson (2008) some situational factors that incline an

individual towards becoming stewards include an involvement oriented environment, a

communalist and low-power distance culture. The theory presupposes that the stewardship

relationship is based on trust and reciprocity between the principal and the steward built

through long-term interaction. Additionally, the steward is presumed to be motivated by a

rational process (Van Slyke, 2006). Given its potential in furthering the interest of all

stakeholders‘ stewardship had a greater potential of achieving and maintaining long –term

benefits for all stakeholders in an organization, the theory of stewardship has been

successfully applied in corporate and social entities in reconciling economic and moral

aspects of the management (Nahapiet, Gratton & Rocha, 2005)

The concept of stewardship in health was introduced by the WHO report of 2000 (WHO,

2000). In health care, stewardship is defined as a broader and overarching accountability

over the performance of the entire health system and eventually over the health of the whole

population (WHO, 2000; Alvarez-Rosete et al., 2013). According to Alvarez-Rosete et al.

(2013) the distinguishing and conceptually useful facet of stewardship lies in its ability to

allocate ultimate responsibility for the health of the entire population. WHO (2000) puts

forward three distinct aspects of stewardship including policy formulation, regulation and

gathering and use of health information for decision making. Despite highlighting the

important role of the stewardship function in healthcare, this description fails to capture the

evolving role of the government as a steward and application of stewardship in different

contexts such as in developing countries. It also fails to identify the specific constructs of

stewardship within which different countries can strengthen stewardship (Alvarez-Rosete et

al., 2013).

WHO (2001) suggests a more expanded view of stewardship in the health sector. The report

from the WHO experts describes stewardship as a function that ‗fosters a culture self-

determination and self-direction among individuals and organizations in the system within an

overall framework of agreed norms and values‘ (WHO, 2001). This view lays emphasizes on

20

operational aspects of stewardship such as ethical, inclusive and proactive that were not

captured in the earlier WHO definition (Alvarez-Rosete et al., 2013). In line with the

expanded definition of stewardship, Veillard, Brown, Baris, Permanand & Klazinga (2011)

suggest an expanded list of domains within which the government can exercise the

stewardship function. They include defining health‘s vision and policy making, influencing

better health through advocacy, ensuring good governance in health systems, ensuring

alignment of health systems design with health system goals, directing health systems

through legal, regulatory and policy instruments and collection, dissemination and use of

health information and research.

While different aspects of this responsibility may be delegated to stakeholders in the health

sector, a country‘s government through its ministry of health remains the steward of stewards

(WHO, 2000). The stewardship theory is particularly important in healthcare because it

entails oversight that is exercised ethically, proactively and with inclusiveness (Alvarez-

Rosete et al., 2013). In effect, it has direct and indirect effect on the outcome of actions of

health system players in their advancement of UHC and its permanence from a financing

point of view; namely improving access healthcare services and financial risk protection

(WHO, 2000; 2010, Alvarez-Rosete, et al., 2013). CBHIs play a complementary role in the

health system particularly in health financing by extending health coverage to segments of

the population that are excluded from mainstream health insurance. Government stewardship

therefore a critical determinant of successful and sustainable health financing in community

based structures such as CBHIs (Preker & Carrin, 2004).

2.2.3 Diffusion of Innovations Theory

Healthcare systems had experienced an increase of innovations that are meant to enhance life

expectancy, quality of care, diagnostic and treatment options, delivery of care as well as

health financing options. Diffusion of innovation remains major challenge in all fields

including in healthcare. Healthcare is posited as one of the fields that have numerous

evidenced based innovations. Despite successful implementation of these innovations in

other areas, their adoption has been slow in other areas (Berwick, 2004). Diffusions of

Innovations theory seeks explain how, why and at what rate new ideas and technology

21

spreads through a social system. The theory was popularized by Rogers (1962). Rogers

(2003) defines diffusion as the process in which a new idea, product, practice, philosophy or

an object is disseminated through structures over a period of time among participants of a

societal system.

Rogers (2003) proposes four main elements that influence dispersion of innovation;

innovation, communication channels, time and social systems. Innovation refers to new idea,

practice or product to an individual or a unit of adoption. Communication channels refer to

the process of generating and sharing information with an aim of reaching a consensus, the

time dimension relates to the rate of adoption while the social systems represent interrelated

social units that are engaged in joint problem to achieve a common objective. Further, Rogers

(2003) identifies two types of innovative decisions that organizations use to adopt an

innovation; collective and authority innovations decisions. Collective innovation decisions

occur through a generally accepted mechanisms consensus while authority decision occurs

when an innovation is adopted by few individuals who possess position power in an

organization. Beyond its original domain, diffusion of innovation theory is applied in policy

transfer where administrative structures and ideas in an institution or a country influence the

development of policies in another (Marsh & Sharman, 2009).

CBHIs have emerged as innovation in health financing as result of poor government

spending in health, political instability and poor governance in the healthcare system (Preker,

2002). The rate at which various countries adopt CBHIs as a strategy for mobilizing

resources to finance and deliver healthcare for the informal sector and the poor varies. Many

countries have progressed towards UHC through gradual expansion and integration of

voluntary CBHIs into the broader healthcare system. Their adoption in some countries has

been slow, a situation that hampers their efforts of addressing the challenge of health

inequities (Criel & Van Dormael, 1999; Poletti et al., 2007; Durairaj et al., 2010; WHO,

2010a).

22

2.3 Conceptual Framework

Miles & Huberman (1994) describes a conceptual framework as a logical system of concepts,

assumptions, and beliefs that support and guide the research plan. It outlines the key factors

and constructs and the theorized relationships among them (Miles & Huberman, 1994, p. 32).

Camp (2001) views a conceptual framework as a configuration that best explains the

researcher‘s conceptualization of the natural progression of the observable facts that are

being studied. It presents the researcher‘s understanding of how the research problem will be

explored, the direction that the research problem is expected to take and how different

constructs in the study relate to one another (Grant & Onsoloo, 2014). Luse, Mennecke &

Townsend (2012) aptly posit that a conceptual framework provides the researcher an

opportunity to specify and delineate concepts in a problem.

This study is based on conceptualized relationship between health financing and equity in

healthcare and the moderating effect of government stewardship. This study proposes a

framework that combines a conceptual and an analytical framework. The framework depicts

the perceived relationships between the inextricably linked health financing functions as the

independent variables; enrolment, mix of contributions, risk pooling and strategic purchasing

and the dependent variable equity in healthcare, with the moderating variable being

government stewardship. The framework also offers a platform for analyzing the

performance of the CBHIs financing system along the financing functions. Achieving equity

depends on how the CBHIs combine the functions under the stewardship role of the

government as shown in figure 2.1.

23

Adapted from Kutzin (2001; 2008)

Adapted from Kutzin (2001; 2008); Mathauer & Carrin (2010)

Figure 2.1 Conceptual Framework

2.3.1 Equity in Healthcare

Equity in healthcare implies that all segments of the population should have a fair

opportunity to attain their full health potential and, more pragmatically, no one should be

disadvantaged from achieving this potential, if it can be avoided (WHO, 2008). Equity is

therefore concerned with bringing health differentials down to the lowest level possible.

UHC is a practical expression for equity in healthcare and the right to health (WHO, 2015a).

Moving towards UHC presents the best possible option of attaining the overall objective of

WHO, namely achieving the highest possible attainable standard of health for all as a

fundamental human right (WHO, 2010a); an indication of the basic principle of the

Independent Variables Moderating Variable Dependent Variable

Government Stewardship Design

Training

Monitoring Co-financing

H5

H1

H2

H3

H4

Risk Pooling -Social solidarity -Mechanisms for enhanced risk

pooling . Size of pools .

Purchasing - Contracting

- Provider payment mechanism

- Referrals - Waiting period

Mix of Prepaid

Contributions

Enrolment

- Affordability of contributions

- Unit of membership

- Timing of collection

- Trust

Equity in Health Care

- Increased access

- Equity in

Contributions

- Sustainability

- Quality of Care

24

Declaration of Alma Ata and the WHO Global Strategy for Health for All by the Year 2000

(WHO, 1978; 1981).

Gilson (2003) observes that health systems are intrinsicly relational. The systemic challenges

of access and financial risks associated with healthcare cost can therefore be construed to be

of relational and behavioural nature. The call for universality with particular concerns on the

health of the poorest and vulnerable segment of the population (Mills et al., 2012)

necessitates exploration of relationships and behaviour that influence equity in healthcare

(Glison, 2003). This study assessed equity based on the equity in contributions, increased

access to health care, quality of care and sustainability of CBHIs as explained in sections

2.3.1.1, 2.3.1.2, 2.3.1.3 and 2.3.1.4. Subsequently, the influence of enrolment, mix of

contributions, risk pooling and strategic purchasing on equity in healthcare in CBHIs and the

the moderating effect of government stewardship is discusses in sections 2.3.2, 2.3.3, 2.3.4,

2.3.5 and 2.3.6

2.3.1.1 Healthcare Access

Access to healthcare has featured prominently in global health policy literature (Xu et al.,

2010; WHO, 2010a). Guaranteed access to healthcare is one of the ways of reducing health

inequities as it is one of the two prongs of UHC. Despite its heighted level of importance and

attention, access to healthcare still remains a complex concept as demonstrated by the

divergent definitions of the notion by various authors. Levesque, Harris & Russell (2013)

conceptualizes five definitions based on the five dimensions in literature of access to health

namely; approachability, acceptability, availability and accommodation, affordability, and

appropriateness. The author further proposes five corresponding abilities of populations that

interact with these dimensions to generate access. These abilities include; ability to perceive

health needs, ability to seek health services, ability to reach health care, ability to pay

without suffering financial constraint and ability to engage through participation and

involvement in management and decision making on issues touching population healthcare

needs.

25

Looking at the dimensions, approachability implies that the sick are aware that some form of

services is offered, the services are within reach and seeking treatment will subsequently

have an impact on the individual‘s health. CBHIs membership rates are determined by the

distance of the household‘s home from the nearest health facility where insured services are

provided. Long distance to health facilities has been cited as a key barrier to equity in

healthcare particularly healthcare access. This barrier arises when patients cannot reach a

health facility due to long distance to health facility, huge transportation charges and lost

wages. The poor and vulnerable segment of the population is affected most (Parmar, Allegri,

Savadogo, Sauerborn, 2013; ILO, 2012). Geographical access can be measured using time

and distance required to access care. The results of these measurements are then compared to

benchmarks. Such standards need to be tailored to urban and rural settings, and should reflect

the existing standards in the community (Carrin, 2003).

In Kenya utilization of healthcare services was found to be negatively related to distance to

health facility (MoH, 2014). Franco, Diop, Burgert, Kelley, Makinen & Siampara (2008)

found that distance to the health facility was a significant negative predictor for healthcare

seeking, particularly regarding assisted deliveries. In Rwanda, Schneider & Diop (2004) also

established that members of 54 community schemes visited providers directly according to

their geographical ease of access. While limited in number, these results suggest that the

policies under consideration have not effectively addressed distance-based discrepancies in

access to insurance and service utilization. The CBHIs management team of Bamwanda in

the Democratic republic of Congo went a step further and implemented a differential fee

based on distance from households to hospital. This approach was however not effectual in

stimulating utilization of health services by members living furthest from the hospital (Criel,

1998). Studies in Nigeria and Burkina Faso established distance to be one of the factors that

influenced enrolment. Households far from health facilities were found to be more willing to

enroll than those near the health facilities (De allegri, Sanon & sauerborn, 2006; Ataguba,

2008). Conversely, earlier studies in Burkina Faso and India reported lower enrolment rates

among households travelling longer distance to health facilities (Mathiyazaghan, 1998;

Dong, Kouyate, Cairns & sauerborn, 2004; Dror, Radermacher & Koren, 2007; Panda,

Chakraborty, Dror, & Bedi, 2013). Distance was however not qualified in all these studies.

26

The barrier to access arises from both direct and indirect costs. Transport costs, a cost that is

rarely included in the benefit package forms part of direct cost. Evidence by McIntyre,

Thiede, Dahlgren & Whitehead (2006 p. 862) suggests that transport costs accounts for one

fifth of all direct cost of seeking healthcare costs. Direct costs arise from poor transport

infrastructure in rural settings which increases the time spent to and from the health facilities.

This results to high opportunity costs in form of foregone wages or income. Combined, the

direct and indirect costs present a high barrier to equity in health care. Evidence from

Burkina Faso shows that coverage by CBHI did not increase health services utilization if the

insured households are located more than 5 kilometers away from a health facility (Parmar et

al., 2013, p.5)

Secondly, acceptability relates to judgment on appropriateness of services based on the way

they are organized. For instance services that are equitable are acceptable to poor and

vulnerable. Thirdly, availability refers to the physical existence of the services that are

offered by qualified health personnel. Fourth, affordability reflects the economic capacity to

pay for health services, have time to travel to the health facility and the opportunity costs

associated with lost income, perceived quality of care and the health providers‘ attitude and

behaviour. Lastly, appropriateness denotes fit between the services provided and patient‘s

needs. It indicates the services provided and the manner in which they are provided. This

process should be integrated, consistence and timely (Levesque et al., 2013).

The concept which is frequently used as a proxy of utilization of health services or coverage

refers to ability to utilize health services when they are needed (Xu at al., 2010; Levesque et

al., 2013). Ability to utilize health services qualified by need is influenced by availability of

services and the ability of the sick to seek health services (Levesque et al., 2013). The

predisposing factors from supply side include location, availability, cost and effectiveness of

the services. On the other hand, ability of a client to use health services is influenced by

many factors. Firstly, people have differing perceptions of the health and as a consequence

have different expectations of their health. Secondly, ability to cater for direct and indirect

costs associated with seeking healthcare and thirdly, non-financial factors such as physical

27

acceptability and other forms of social exclusion and discrimination (Xu at al., 2010;

Levesque et al., 2013).

The foregoing intricacies present challenges in measuring access. In practice, many

researchers measure access in terms of health services utilization or coverage. This approach

fails to capture an important concept that qualifies access; the fact if the members receive the

services they actually need (Xu at al., 2010). Although, adjustments for need can be made,

self-reported data is fraught with a shortcoming of not reflecting the actual medical need.

This is especially true in communities that display heterogeneity in economic capacity. A

study by Salomon et al (2004) focusing on comparability of self-rated health in various

countries showed that the rich are likely to report greater need compared with the poor. This

is despite that fact the poor are in generally worse health since they are exposed to more

health risks in their work places and areas of residence (Smit & Mpedi, 2010; ILO, 2012 &

WDI, 2013).

This study employed reported information on utilization to measure access. This is because

the target population of the study is CBHIs which are composed of predominately the poor

segment of the population (Bennett et al., 1998; Atim, 1998; Jutting, 2000 & Hsiao, 2001).

Additionally, previous studies agree on the active role of community in mobilizing, pooling

and allocation of resources as well as strong solidarity mechanisms in CBHIs (Jakab &

Krishnan, 2001). The documented high level of involvement address the shortcomings on the

demand side by increasing ability of the community to perceive, seek, reach, pay and actively

participate in management of health services. This implies that they are empowered to report

on the ease with which they access health services. Regression analysis was used to

standardize for differences in factors thought to be influenced by the objective need as

recommended by Xu et al. (2010).

28

2.3.1.2 Quality of Care

Quality of care is expressed in terms of disappointment with the outcomes and through a

series of comparisons which they make between their original expectations and the reality,

between the care offered before and after they subscribed (Criel & Waelkens, 2003). From

the perspective of patient rights, patients from all socio-economic levels who seek healthcare

deserve correct and courteous treatment, safe medical conditions, and sufficient information

on their health status and treatment options (WHO, 2010b). It has also been argued that

providing high quality services can lead to increased equity in health care, service utilization

and, in turn, reduce unsupervised and often risky self-treatment (Tipke, Diallo, Coulibaly,

Storzinger, Hoppe-Tichy & Muller, 2008; Robyn, Sauerborn & Bärnighausen, 2013)

CBHIs have been seen as an attractive solution to the challenge of generating financial

resources for the formal health sector in developing countries (Fernandes et al., 2009;

Durairaj et al., 2010; MOMS, 2011; Schieber et al., 2012; Robyn et al, 2013). In particular, it

is a potential instrument to improve access to healthcare by reducing financial barriers to

health services, empowering enrollees through fostering dialogue between communities and

healthcare providers, and improving quality of care by introducing contractual arrangements

contingent on quality standards (Criel & Waelkens, 2003).

Research on quality of care in developing countries has continued to increase over the past

two decades (Baltussen & Ye, 2006). Evidence on the relationship between health insurance

and quality of care in sub-Saharan Africa is scarce. A recent systematic review conducted in

Asia and Africa using randomized controlled trials, quasi-experimental and observational

studies concluded that there was a weak positive effect of both social health insurance and

CBHI on quality of care (Spaan, Mathijssen, Tromp, McBain, Ten Have & Baltussen, 2012).

An earlier study of the Maliando scheme in Guinea-Conakry revealed that participants

viewed rapid recovery, good health personnel, good drugs and a nice welcome at the

participating health facilities as the most important features of quality. When membership

was discussed specifically, lack of quality of care was cited as the most important cause of

non-enrolment (Criel & Waelkens, 2003).

29

Despite the anticipated enrolment rates of approximately 50% in Nouna CBHIs in Burkina

Faso, membership remained low in spite of an upward trend over time. The enrolment rate in

the first year of operation was 5%, increasing marginally to 9% in 2010. The enrollee drop-

out rate declined substantially from 32% in 2004 over time but remains considerable at 16%

in 2010. In 2006, the most common reasons for dropping out, after affordability of the

insurance premium was patient dissatisfaction with the quality of care provided to CBI

enrollees. Patients judged the quality of care to be poor based on the health services they

received, such as drugs and medical staff behavior (Dong, De Allegri, Gnawali, Souares &

Sauerborn, 2009; Robyn et al., 2013). A recent mixed methods study on the relationship

between CBHIs provider payment and health worker satisfaction in Nouna found that

payment attributes such as insufficient level of capitation payments, infrequent schedule of

capitation payment and lack of a payment mechanism for reimbursing service fees strongly

affected service provider‘s satisfaction. It is possible that CBHIs provider payment

mechanisms result to dissatisfaction of health workers and in turn translates into a quality of

care differential between CBHIs enrollees and non-enrollees (Robyn et al, 2013).

This study uses patient experience measures to assess the quality of care offered by the

contracted service providers. Although this measure is relatively new, it is increasingly being

considered important measure of quality of care by experts as efforts to improve the quality

of care continues to evolve. Patient experience measures provide feedback on experiences of

patients‘ seeking health care including interpersonal aspects of care. Additionally, these

measures looks at various aspects of care ranging from clarity to existence of mechanisms

that check on patient perceived quality of care in contracted health facilities on issues

concerning waiting time, availability of staff, services, drugs and supplies. These measures

represent patients‘ needs, values, expectations and preferences and as result reveal critical

information on the extent to which the care offered by the contracted service providers is

truly patient centered. They therefore provide a robust and validated alternative to subjective

reviews (Robert, Berenson, Pronovost & Harlan, 2013).

30

2.3.1.3 Equity in Contributions

Catastrophic health payments can occur irrespective of the amount of the money paid for

healthcare services. Although catastrophic health expenditure occurs in both rich and poor

countries, poor countries contribute to over 90% of the affected (Xu et al., 2003). Shielding

households from catastrophic heath payments remains a principal objective in all nations at

various levels of development (WHO, 2010a; Chuma & Maina, 2012). Previous literature

posits that poorer households are subjected to higher financial risks due to ill health in the

face of competing needs. The case is worse for vast majority of vulnerable households who

require more financial protection to access healthcare (Xu et al., 2003; Su, KouyatЙ &

Flessa, 2006; Ahmed & Mesbah, 2015; Buigut, Ettarh & Amendah, 2015). These aspects of

vulnerabilities and inequities have to be taken into account when designing national and

global health financing policies (Saksena, Xu & Duraira, 2010).

Poor households are exposed to more risks since they work in small workplaces, live and

work in unsafe and unhealthy conditions often not under the purview of labor and health

regulations, are ill equipped in terms of skills and education, have limited access to health

education and prevention programmes and are not aware of their social entitlements (ILO,

2012). They earn low and unpredictable incomes, lack of access to assets, credit, finance,

training, information and technology (Smit & Mpedi, 2010; World Development Index

(WDI), 2013).

Health systems in low- income countries are financed predominantly from OOP (WHO,

2014c). This implies that everyone pays the same amount of money for health services

regardless of their income. When a member of the poor household is sick, the household

have to choose between paying for health services and paying for other basic needs such as

food, rent and children‘s education. Other coping strategies that employed by the poor when

such shocks arise include borrowing from relatives and friends, substituting household labour

supply by sending engaging children in workforce, selling assets and deferring seeking health

services (Asadul & Pushkar, 2012). When there is a compelling need to seek health services,

they risk impoverishment and in worse cases destitution. In addition, indirect health costs

such as transport cost in case of a chronic illness have been found to result to be more

31

prohibitive than direct cost of health services. Besides, the financial shocks arising from

direct cost of health care, such households also face income loss when the sick member is the

working adult(WHO, 2010a).

Equity in contribution relates to the principle of similar contributions for similar ability to

pay (horizontal equity) and the progressivity of a health financing system (vertical equity)

and financial protection. Vertical equity implies that everyone contributes the same

proportion of their income to the healthcare system. Various countries have re-oriented their

health financing system towards attaining progressivity, cross-subsidization and financial

protection. For instance, healthcare contributions in Ghana are based on people‘s ability to

pay. The large cross-sectional risk pools and high enrolment rates enhances cross-

subsidization (Durairaj et al., 2010 and WHO, 2010a). A systematic review of equity in

contribution and distribution of healthcare benefits in low and middle income countries

reveal mixed results. In Asia Pacific countries demonstrated high progressivity across all

financing sources (Asante et al., 2016).

On the contrary, Sub-Saharan Africa OOP and voluntary health insurance contributions were

regressive. Similarly, a study by Chuma & Maina (2012) revealed that Kenyan households

spend over one tenth of their household budget on health expenditure annually signaling high

reliance of OOP expenses among the poor. The poorest households spent a third of their

resources on healthcare payments each year compared to only 8% spent by the richest

households. About 1.48 million Kenyans are pushed below the national poverty line due to

healthcare payments. This implies that healthcare expenditure is not based on ability to pay.

This study measures equity in contribution based on the level horizontal equity in CBHIs.

The measure was appropriate for this study given its ability to measure the extent to which

payments are based on ability to pay and allocation of resources based on health needs (Tao,

Kizito, Qinpei & Xiaoni, 2014).

Additionally, the extent of cross-subsidization based on allocation of claim budget was

employed as a measure of equity in contribution in the studied CBHIs. This method is

preferred due to availability of information on horizontal equity and cross-subsidization.

32

2.3.1.4 Sustainability

CBHIs face myriad constraints attributed to their small size, limited management and

technical skills in insurance and inaccessibility of local service providers due to long

distances and poor medical services. Various studies have documented that CBHIs fail due to

limited managerial competence, financing or a combination of both (Tabor, 2005).

Consistence with other studies focusing on sustainability of CBHIs (Atim, 1998; Carrin,

2003), this study looks at sustainability in terms of financial and managerial sustainability.

Sustenance of equity in healthcare depends on long-term financial sustainability of schemes.

Availability of appropriate managerial skills in accounting, determination of packages,

setting of contributions and management information systems is critical for viability of the

schemes (Carrin, 2003).

The institutional arrangement of CBHIs has significant influence on performance of health

financing functions and more importantly the sustainability of the schemes. Sustainability of

the schemes is critical for scaling up and maintaining the achievements of CBHIs. The

management team usually negotiates with health service providers on behalf of the

beneficiaries. The team also manages the collection of revenue and ensures enhanced risk

pooling (Mebratie, Sparrow, Alemu, & Bedi, 2013). The strong community involvement in

management of schemes predisposes them to risks of sustainability due to limited

management skills available in the community (Preker & Carrin, 2004; De Allegri,

Sauerborn, Kouyaté, & Flessa, 2009).

Particularly, there is low utilization of management information system in CBHIs (Preker &

Carrin, 2004), while the management team lacks premium calculation skills, booking keeping

and accounting skills (Mebratie et al., 2013). Proper book keeping is necessary for effective

monitoring and control. According to LeRoy & Holtz (2012) control systems are necessary

for reduction moral hazard and fraud. They are critical in scrutinizing claims in fee-for-

service payment method where the scheme‘s management needs to verify suitability of

services provided and whether the provider charges reflect the services that were actually

provided. In addition, schemes need to monitor under servicing and provision of low quality

services under capitation payments system.

33

Besides increasing vulnerability of the schemes, these shortcomings hinder their growth. The

small pools are inefficient making it hard for them to achieve equity in health care. To

survive they adapt various techniques. These techniques include; first limiting total payouts

to reduce financial vulnerability related to common shock covariant risks. Second, they

introduce waiting periods to curb adverse selection. Third, they introduce gate keeping

mechanisms, ceilings and co-payments to reduce supplier induced moral hazard. All these

survival tactics reduce attractiveness of the schemes hence impacting negatively on

enrolment. Further, the limited risk pooling hinders cross- subsidization of the poorest and

vulnerable groups (Jacobs et al., 2008, p. 141).

Studies have shown that few management teams negotiate with service providers on price

and quality of health services. As a result this impact negatively on the quality of health

services delivered, which further reduces the attractiveness of the schemes. Small pools

worsen the negotiating position of the management with healthcare providers (Jacobs et al.,

2008). The management‘s capacity to improve members‘ awareness, knowledge, skills and

attitude toward pre-payment and build an insurance culture is also critical in increasing

enrolment (Aggarwal, 2010, p. 28; Donfouet, Makaudze, Mahieu, & Malin, 2011).

Expansive literature on coverage of CBHIs documents that CBHIs fail to cover the target

population. A study by Musau (1999) in Kenya, Uganda and Tanzania found coverage of

1%-7%. According to Tabor (2005) CBHIs cover less than 10% of the targeted population. A

study conducted by Ndiaye et al. (2007) revealed that 95% of the 580 schemes that were

studied had less 1000 members.

Limited managerial and administrative skills critical for viability of schemes was found in 22

CBHIs drawn nine West and Central African countries. The CBHIs management teams were

deficient in pricing, premium collection, and contribution enforcement, determination of

benefit package, contracting of providers, marketing, communication, management

information systems and accounting skills. Even though the schemes has been in existence

for over two decades promotion and marketing of the schemes is still largely driven by

external organizations despite (Tabor (2005). The schemes are faced with challenges of late

34

policy renewals (Carrin, 2003) making long-term planning difficult (De Allegri et al., 2009,

p. 591).

The capacity of the management team in achieving the goals they have committed to has also

been found to influence enrolment rates of CBHIs. Various studies indicate member CBHIs

hardly ever question the integrity and competence of the CBHIs management team (Criel &

Waelkens, 2003; Criel et al., 2002). On the contrary, in a study conducted by De Allegri et

al. (2006) in Burkina Faso majority of the complaints against the schemes management was

linked to failure in achieving commitments particularly in quality of healthcare and failure to

advocate for favourable relations with healthcare providers.

The meaning of financial sustainability varies widely depending on the organizational

structure, revenue structure and the encompassing goal of the organization (Bowman, 2011).

The overarching goal of CBHIs is to achieve their social mission of offering social protection

to the poor and vulnerable by reducing poverty and vulnerability through provision of

community based mechanisms to the low-income households in their efforts to manage risks

in exchange for regular premium payments proportionate to the likelihood and cost of the

risk involved (Bowman, 2011; ILO, 2012).

Broadly, financial sustainability refers to the ability to sustain financial performance over

time relative to risk and capital commitments (Bowman, 2011). One of the distinctive

characteristic of CHBIs is the community base. The community members covered by the

CBHIs determine the benefit package that need. The package is based on the premiums that

they are ready to commit during the policy period. On the contrary, commercial insurance

determines the premiums and benefits offered by the insurance while in social health

insurance the rates and benefit package is decided by the government (Tabor, 2005).

Financial sustainability in the context of CBHIs does not necessarily mean self-financing.

Expansive literature is in agreement that besides the members‘ contributions, contributions

from other partners namely; the government both national and county, donors and other

insurers form a significant source of revenue for the CBHIs (Carrin, 2003; Mebratie et al.,

35

2013). Hence, it is important to look at financial viability within the broader context of the

combined sources of funds. Financial viability of CBHIs can also be evaluated by comparing

the range of benefits offered across CBHIs. Some may not cover chronic cases that attract

repeated and sometimes high payouts. Such schemes report a high percentage of cost

recovery (Carrin, 2003). This undermines the spirit of UHC of access to need health services

for all without exposure to financial ruin (WHO, 2010a).

In addition, financial sustainability of CBHIs can be improved through subsidies and

reinsurance. The two are inextricability linked since subsidies encourage enrolment and

increase the size of the risk pool through increased membership. Besides, subsidies enhance

inclusion of poorer and vulnerable households, thereby reinforcing the social network and

social solidarity; increasing the impetus for UHC. On the other hand, risk transfer

mechanisms through reinsurance enhance viability of CBHIs particularly small pools typical

of CBHIs (Carrin, 2003; Carrin, 2011; Mebratie et al., 2013). Carrin (2003) observes that the

risk pool can be solidified by encouraging the small pools to merge into bigger one by

creation of a network or a federation.

Various studies agree that CBHIs are often unable to mobilize sufficient resources because of

the limited income from the target population. In addition, the pools often small undermining

chances of broader risk spreading and financial risk protection function; one of the key

components of UHC. A study by Private Sector Innovation Programme for Health (PSP4H)

(2014) in Kenya revealed that the limited resource base from the community impacts CBHIs

ability to raise adequate resources. For that reason the pools are intrinsically small hampering

broader risk spreading across the population. The small size of schemes and resource

constraints puts the viability of the CBHIs at risks (Chen et al, 2012; Mebratie et al., 2013).

Government, and its development partners, can support the growth of CBHIs by ensuring

that there is a satisfactory supply of appropriate health services, by subsidizing start-up costs

and the premium costs of the poor, by assisting CBHIs build technical and managerial

competence, by helping to foster development of CBHI networks, and by assisting CBHIs

establish and strengthen links with formal financial institutions and healthcare providers to

36

better manage covariate shocks and catastrophic health risks (Tabor , 2005, Mebratie et al.,

2013).

2.3.2 Effect of Enrolment on Equity in Helathcare

This study explored the relationship between enrolment and equity in healthcare by looking

at the affordability of contributions, unit of membership, timing of collections, and trust

influence access to affordable healthcare through uptake of health insurance in CBHIs. The

sub- constructs were adopted as a measure of enrolment since they are key drivers of

decisions to enrol and not to enroll in community based health insurance schemes (Carrin,

2003).

2.3.2.1 Affodability of Contributions

Wealth quintile is one of the major demographic factors that influence access to affordable

healthcare and increases finacial risk protection from healthcare cost. The higher quantile is

more willing and able to pay more for a health cover than the lower quintile (De allegri et al.,

2006; Basaza et al., 2008; MoH, 2014; Adebayo, Uthman, Wiysonge, Stern, Lamont &

Ataguba, 2015). Dror et al. (2006); Ahmed et al. (2016) & Babatunde et al. (2016) observe

that low income households are willing to pay for healthcare insurance. According to

Dercon, Gunning, Zeithlin, Cerrone & Lombardin (2012) low income households‘ display

high price elasticity due to low and irregular income, a factor that influences demand for

health insurance. The premium set by CBHIs is therefore a critical determinant of

households‘ decisions to enroll. Uptake of health insurance in CBHIs is influenced by pricing

strategies that responds to low income ability to pay including; affordable premium and

flexible premiums (Carrin et al., 2005), payment of premiums in kind (Jakab & Krishnan,

2001), savings linked premium payments (Tabor, 2005), subsidized and exemption of

premiums (Gustafsson-Wright & Schellekens, 2013).

McCord, Steinmann, Tatin-Jaleran, Ingram & Mateo (2012) suggest that premium should be

proportionate to income level low income households for it to stimulate willingness to pay.

Setting premium as a percentage of annual household income encourages purchase of health

insurance (Tabor, 2005). Varied premiums for different family sizes premium structure is

37

also used to encourage signing up. Large families pay less per person compared to smaller

families (Atim, 1999). On the other hand, sliding scale premium is employed among poor

households (Tabor, 2005). Mobile money transfer platform have been used to save money for

premium payments for health cover (Matul, Tatin-Jaleran & Kelly, 2011).

The poorest and vulnerable groups are unlikely to sign up for health insurance in CBHIs due

to lack of financial means to pay for premiums (Tabor, 2005). Basaza et al. (2008); Atagabu

et al. (2008) alluded that lack of financial capacity was the primary reason for non-enrolment

in CBHIs. Premium subsidies and exemptions given to lower quintile influences inclusive

health coverage and equitable healthcare access (Chriatian Aid, 2015). Governement

premium subsidies targeting the lower quintile in CBHIs aids in reducing health inequities

and ensures that the benefits packages address the needs of the poor (Tabor, 2005). The

extent of progressivity of premiums in CBHIs is a key determinant of affordability. Akazili,

Gyapong & McIntyre (2011) observe that flat rate premiums charged by CBHIs do not take

into consideration the disparities in ability to pay among the rich and poor in the community.

In effect, both the rich and the poor pay the same amount of premium. Plausibly, the poor are

not disposed to enroll in CBHIs (Adebayo et al., 2015).

2.3.2.2 Unit of Membership

The unit of membership targeted by CBHIs in critical for increasesing uptake of health

insurance and for controlling adverse selection (Atim, 1998). Most CBHIs have adopted

famiy as a unit of membership. The rational of defining housholds as the unit of membership

is to dissuade hosueholds from enrolling most vulnerable or sick family members. This

requirement creates risk subsidies that are essential for sustainabe risk pooling where the

health subsidizes the sick (Atim, 1998; Criel & Waelkens, 2003; Wodtke et al., 2012).

According to Donfouet, Mahieu, & Malin (2013) recruiting from pre-existing mutual benefit

associations in a target popualtion influences uptake of health insurance in CBHIs It allows

CBHIs to extend membership beyond those who can join voluntarily by exploiting the pre-

existing social networks. Donfouet et al. (2013) & Adebayo et al., (2015) postulate that

households which are part of communal associations such villages; cooperative societies and

38

development projects are more willing to enroll in CBHIs. Defining the percentage of

households in a village would be required to enroll before providing insurance is highly

associated with inclusiveness and sustainability of CBHIs Bennett et al. (1998) & Carrin et

al. (1999) observe that establishing a minimum requirement of enrollees from the poor and

vulnerable members of the community enhances access to healthcare and financial

protection. Desmet et al, (1999); Musau (1999) & Carrin et al. (2001) also put forward that

drawing a defined or allowing an automatic percentage of membership from mutual

associations creates risk subsidies besides increasing membership rates.

2.3.2.3 Timing of Collections

Peridocity of enrolement fee and premium paymnet is a major determinant of enrolment in

CBHIs. According to Tabor (2005) & De Allegri et al. (2006) the main hindrance of

enrolment in CBHIs is the requirement to pay enrolment and or annual premium as a single

payment. High enrolment rates have been registered in CBHIs that a have a flexible policy on

collection of premium, periodicity of premium payment and enrolment procedures (De

Allegri et al., 2006; Defourny & Failon, 2008). To obtain a CBHIs membership, one is

required to pay enrolment fee and premium as a single payment. This obligation acts as a

negative driver of enrolment particularly for large families that may not be able to pay

premium for all members of the family (De Allegri et al., 2006; Defourny & Failon, 2008;

Adebayo et al., 2015).

The period of the year when the CBHIs require households to pay enrolment fees and

premiums can be a driver or a barrier to enrolment. This is in view of the fact that cash

inflows vary from one time of the year to the other. For instance, households are likely to

have more capacity to pay during harvest or livestock sales, making it a more appropriate

time to collect fees (Criel and Waelkens, 2003; De Allegri et al., 2006; Matul et al., 2013).

According to Chen, Liu, Hill, Xiao & Liu (2012) deferring premiums payments to periods of

increased household liquidity increased uptake of health insurance by 6% in China. Mathauer

et al. (2007) observe that a lump sum premium payment influences the modest renewal rates

of poor households in Kenya. The preffered time of premium collections varies between

urban and rural. While spread payments constitute a driver to enrolment in urban areas;

39

annual payments at harvest time are preffered in rural areas (Bennett et al. 1998). Events

such communal meetings have proved to be perfect time for enrolment fee and premium

collection in Uganda (Carrin et al., 2001).

2.3.2.4 Trust

Given the relational nature of health systems the success of interventions put in place to

address challenges in healthcare access and financial risk protection is dependent on the

extent to which the interventions harness the already existing social capital which manifest

into both structural and congnitive elements (Krishna & Shrader, 1999; Catherine & Salmen,

2000; Gilson, 2003). Structural elements manifest in form of institutions created in response

to challenges, their composition and the practices that guide the collective actions. On the

other hand, cognitive elements are composed of trust, cooperation and reciprocity that inform

the values, attitudes and social norms rallying people to collective action and lowers

opportunistic behaviour (Catherine & Salmen, 2000; Putnam, 2000; Gilson, 2003; Chen et

al., 2012; Mladovsky, 2014).

Trust is therefore vital for formation as well as for success of CBHIs in their quest of

reducing health inequities (Krishna & Shrader, 1999; Mladovsky, 2014). Human contact and

connections encourages formation of CBHIs which are high relational activities compared to

informal risk management strategies. Additionally, trust influences enlisting and renewal

decisions. Chen et al. (2012) posits that enrolment rates in CHBIs is affected by three

dimensions of trust, namely, trust among CBHIs members, trust between CBHIs members

and contracted service providers and trust in CBHIs management and in the scheme.

Trust among members of the community increases optimism making members more open to

behaviour change (Takakura, 2011; Chen et al. 2012). High levels of trust persuades

members to try new ideas together such as CBHIs in order to address challenges of uninsured

health risks and barriers of heathcare access. Trust provokes collective actions that speak to

values; attitude and behaviour of members of a community encouraging them to pool their

resources together to cross subsidize each other and improve healthcare access. Trust also

enhances sharing and assimilation of information (Chen et al., 2012)

40

Trust between CBHIs members and contracted healthacare providers is experiential in nature

(Chen et al., 2012). The relatioship is determined by past and current expeience with a

healthcare providers. Quality care and reliability of healthcare providers facilities formation

of strong trust relationships between CBHIs members and health service providers which in

turn enhances enrolment and renewal rates (Jutting, 2004). A health facility that has

adequate and properly trained healthcare personnel, offers a wide range of health services

and has a regular supply of presciption medication is more likely to encourage enrolment,

renewal and utilization health services due to its reputation of trustworthy and dependable

relationship (Jutting, 2004; Chen et al., 2012). Close proximity to contracted health facilities

results to greater interactions between CBHIs members and management of the health

facility‘s. Frequent interactions increases exposure to information and establishes efficient

transactions (Alesina & La Ferrara, 2002; Chen et al., 2012; Andriani, 2013).

According to De Allegri (2006) trust in CBHIs management team is a key determinant of

enrolment rates in CBHIs. Trust in CBHIs management team is dependent on the team‘s

technical competence and collective character. Provision of adequate information and

documentation when enroling and positive past experiences with scheme‘s management team

or mutual benefit associations reinforce community‘s trust in CBHIs management team and

in the scheme itself. Further, the ability of the scheme‘s management to negotiate favourable

contracts with providers, enforce the contracts and achieve set goals strengthen trust in the

scheme and its management. Such contracts relate to quality of care and the range of health

services offered by contracted healthcare providers (Criel & Waelkens, 2003; De Allegri,

2006 & Chen et al., 2012).

2.3.3 Effect of Mix of Prepaid Contributions on Equity in Healthcare

Levies paid at the point of use deter millions of people worldwide from using health services

and makes them to defer checkups when chances of cure are cheaper and high. This promotes

overuse by those who can pay leading to inequity and inefficiency in the way in which health

resources are allocated (Xu, Evans, Carrin, Agila-Rivera, Musgrove & Evans, 2007).

Prepayments and pooling mechanisms present a viable alternative that de-links healthcare

utilization from the need to pay. It requires people to pay for healthcare when they are

41

healthy and more productive and allocate the payments for the duration of their life. Indeed,

some form of prepayments and pooling approach is a critical determinant of the progress

made towards UHC. The level of prepayments and pooling in health system is a critical

determinant of how well a system meets the populations‘ health needs and desires. Countries

that have made quick progress towards UHC have health financing systems that require

individuals to contribute to healthcare through taxation and or insurance based on their

ability to pay. This implies that the rich should pay more while the poorest and vulnerable

groups need to be exempted (WHO, 2010a).

Countries with large informal sectors face difficulties of collecting payroll based health

insurance taxes due to underdeveloped tax systems. Many countries aspiring to achieve UHC

use a targeted approach that focuses on the formal sector. A common resultant of this

approach is a two tier system; a small formal sector that is covered and a large uncovered

informal sector (WHO, 2010a). Additionally, studies focusing on health insurance and equity

in healthcare show that voluntary and private health insurance payment in Sub- Saharan

Africa are moderately regressive (Asante et al., 2016). CBHIs represent one of the

prepayments approaches that have been employed in the journey towards UHC in many

countries. The limited resource base of their target population and their small size influences

their ability to raise adequate resources necessary for greater risk sharing (PSP4H, 2014).

Inefficient prepayments systems in low income countries are a major obstacle to raising

adequate resources through prepayments and risk pooling systems. Government spending

and or donor funding is critical for increased access and financial protection in these

countries (WHO, 2010a). According to Durairaj & Evans (2010) & WHO (2015a)

government spending in healthcare influences access to healthcare and the level of financial

risk protection since it‘s a steady and sustainable source of funds. Moreno-Serra & Smith

(2012) posit that health outcomes are greatly influenced by financing health from pooled

public resources. Globally, there was a marginal increase in overall government spending

from 10.9% in 2002 to 12% 2013. Government expenditure varies greatly; OECD countries

spent 15.6% while Sub Saharan Africa, Middle East and South Asia spent 11.1%, 8.7% and

8.3% respectively. The level of health inequities in the three WHO regions is reminiscent of

42

the low government expenditure in healthcare. Despite the dire need of progressivity in

government spending many LIMCs face challenges of increasing their fiscal space for health.

The amount of funds available from taxes is influenced by the capacity of tax system to

collect taxes. According to WHO (2010) majority of low income countries have a large

informal sector which makes it difficult to collect both payroll and general taxes. The narrow

tax base results to fiscal rigidity. According to Durairaj & Evans (2010) expansion of space

for health is a viable option in developing countries that can be achieved by collecting

additional revenue through tax measures. According to WHO (2015a) many countries have

not exploited available scope for expanding their fiscal space for health. Additional revenue

raised from taxes can be used to fully or partially subsidize the contributions of the poorest

and vulnerable segment of the population. In Gabon, a tax on money transfers raised

approximately US$ 30 million in 2009. The money raised is used to subsidize low income

households. Similarly, Pakistan finances part of its health expenditure from taxes levied on

profits of pharmaceutical companies (WHO, 2010a). Within the context of CBHIs, Rwanda

and Ghana offers examples of African countries that have been able achieve equity by

subsidizes the poorest segments of their population through CBHIs (Humuza, 2011; Atim,

2011).

Some countries have moved back and forth on the best strategies of achieving the right mix

of contributions to meet their population health needs and expectations. In itself, political

goodwill influences the decisions that are made to expand fiscal space for health. Plans of

expanding benefits package in Philippines require expansion of fiscal space for health

through payroll tax reforms. These reforms have proved to be politically unfeasible slowing

the country‘s progress to UHC (Honda et al., 2016). This highlights the importance of

political goodwill in pushing for a country‘s health agenda. The great strides that have been

made by many African in increasing their fiscal space for health is owed to the Abuja

declaration that rallied political support for increasing government spending as a percentage

of THE (WHO, 2013). Rwanda is one of the African countries that have met the funding

requirements for both the Abuja Declaration targets and the HLTIIFHS owing to a

43

combination of multiple sources of funding that are supplemented by donor funds (WHO,

2013).

Taxes on harmful products such as alcohol and tobacco have dual benefits of raising

additional funds and mitigating adverse health and economics effects of their consumption.

The sin taxes are more politically acceptable since they provide progressivity in tax

collections while expanding the fiscal space for health (Bird, 2015). Sin taxes on Tobacco are

more common and have been introduced by Thailand, Nepal, Mongolia and Bulgaria where

all or a percentage of the tax revenue is earmarked for funding healthcare. Thailand has also

earmarked a portion of sin tax from alcohol to health. Similarly, in 2012, Philippines enacted

a new tax law on tobacco and alcohol products that seeks to increase sin tax revenue by 60%

(World Bank, 2014). The sin tax is earmarked for subsiding massive insurance premiums for

poor households (Honda et al., 2016).

Donor funding play a critical catalytic role of supplementing government and prepaid funds.

In 2012 external aid accounted for a quarter of THE in low income countries (WHO, 2015f).

The 2014 CHATHAM House report accentuates the need for additional funding for

realization equity goals. Predictability and harmonization of donor funds with national

priorities and systems remains a major challenge. The International Health Partnership

represents some of the initiatives that seek to rally donor countries and development partners

to adopt SWAp in aid disbursement (WHO, 2010a). Such initiatives would direct donor

support to a country national health strategy that allows the recipient country to fund priority

policy areas in health. For instance, harmonization would enable developing countries to

strengthen their capacity in tax collection and expand their prepayment and risk pooling

system (WHO, 2013).

The mix of contribution varies across countries; majority of developed countries health

systems are tax funded with subsidies for the poor. France has created two types of funds that

are used to cushion the elderly, disabled and the poor against healthcare costs. A national

solidarity fund funded by the social health insurance funds and from proceeds of the

solidarity day is used to pay for elderly and disabled peoples‘ social and health services. The

44

poor are covered through a public supplementary insurance programme that that pays service

providers directly for the cost that they have incurred. The fund pays for co-payments, optical

and dental services. Canada continuous to maintain a stable mix of contributions since 1997,

with the government funding 70 percent of total healthcare cost and the private sector

spending 30 percent of the healthcare bill. In Germany, healthcare is predominantly tax

funded with majority of the population contributing through mandatory statutory insurance

system. The population is divided into three tiers; one of the groups represents the poor

people whose premiums are paid through government revenue (Bidgood, 2013).

Switzerland operates a compulsory social insurance system composed of multiple health

insurance schemes and a government approved plan. To reflect the ability to pay across

different socio-economic groups premiums are set at the community level. Subsidization

policy varies across schemes, the poor are exempted from paying premiums and members co-

pay for doctor‘s visits. On the other hand, health insurance in US is voluntary with employer

based tax subsidies. Majority of the country‘s population receives services funded through

private insurance. Low income children and the elderly benefit from subsidized care and

several safety nets funded through government and private insurance payments. The US

health system is however the most expensive and inequitable with a significant population

still not covered (Bidgood, 2013).

All African countries operate a pluralists financing system with varying mix of contributions.

Healthcare funds originate from government, donors, employers, households and non-

governmental organizations. Capacity to collect taxes remains a big challenge in most

African countries owing to the informal nature of their economies. Health continues to

receive low priority in government allocations (WHO, 2013). According to IMF (2012)

almost half of the countries in the WHO African region receive substantial amount of

revenue from natural resources; in some cases the revenues from natural resources surpass

other government revenues. Despite the substantial revenues collected public funding to

healthcare remains insufficient (Costa-Font, Gemmill & Rubert, 2009). The capacity of

raising additional tax revenue exists in many countries. Ghana funds part of its national

health insurance scheme through a 2.5% increase in value added taxes. Gabon raises funds to

45

subsidize healthcare costs for low income through new taxes imposed on remittances and

mobile phone operators. The funds are channeled through the national health insurance and

social security schemes (WHO, 2013). According to WHO (2010) a 50% increase in tobacco

taxes has a potential of raising US$ 1.42 in 22 low income countries.

Given the foregoing funding challenges donor funding plays a critical role of funding health

initiatives. The amount of donor funds received by African countries varies from less than

20% to more than 40%. For instance, donor funds in Malawi accounted for more than 40% of

the THE between 2001 and 2010. Additionally, the trend of flow of the donor funds varies

from one country to another. Some countries have experienced a decrease in donor funding

while others have registered a boost in donor funding. Burundi and Malawi registered a

substantial increase in donor funding between 2005 and 2010 (WHO, 2013). On the other

hand, Kenya registered a decline in donor funding from 35% in 2009/2010 to 26% in

2012/13 (MoH, 2014). Donor funding is fraught with challenges; it is unpredictable and fails

to meet set targets in most cases (WHO, 2013).

Various African countries have established financing mechanisms that improve access to

healthcare and offer protection against catastrophic health expenditure. Most of these

initiatives benefit children under five years and pregnant and lactating women. For instance,

Uganda operates voucher schemes for pregnant women while Kenya offers free services for

children under five years and pregnant women. Additionally, pro-poor financing mechanisms

entail offering free services in primary healthcare facilities. Kenya offers free health services

in level 2 and 3 health facilities which are funded through a combined government and donor

fund. The insufficient levels of government and donor funding combined with high poverty

levels exposes the poor and vulnerable groups to a disproportionate economic burden. As a

result, healthcare costs continuous to pose significant barriers to healthcare access while

millions are driven below poverty line (WHO, 2010a; Chuma & Maina, 2012).

Within the context of CBHIs, a financing pool composed of different mix of contributions

drawn from membership premiums, government and or donor funds provides subsidies and

exemptions to poorer and vulnerable households, a practice that is significant for achieving

46

an equitable and sustainable resources allocation. In Rwanda, has the mix of contributions in

CBHIs is composed of approximately 50% membership contributions while government,

donors and insurance transfers contribute the remaining amount. Revision of the proportion

contributed by each party is critical sustenance of equity goals. As donors exit, the

government and CBHIs members should take up responsibilities of generating more revenue

(Christian Aid, 2015).

Government and donor funding can be combined to form a health equity fund is used to

cover healthcare costs incurred by the poor and vulnerable segments of the population. The

fund provides cross subsidies across the population since more healthcare resources are used

to cater for the cost of the neediest segment of the population. In Cambodia and Laos health

equity fund is used to purchase CBHIs premiums for the poor. Introduction of the fund was

associated with increased utilization of health services and increased financial risk protection

(WHO, 2010a). Health equity fund beneficiaries recorded lower cases of borrowing money

for healthcare compared with families that pay for healthcare at the point of use. Health

equity funds can be use a basis of lobbying for increased government funding. In Cambodia

the fund has attracted more funding from the government over time (WHO, 2010a).

Illegal payments results to cash flow problems for the provider, which eventually leads

provision of low quality health services. This results to service rejection and client

dissatisfaction making it difficult for the CBHI scheme to attract and retain clients. For

example, perception of poor quality service at Kitovu Hospital, in Uganda, made it difficult

for the KPPS micro-insurance program to obtain new clients (Basaza et al., 2010). Successful

CBHIs build long-term, stable relations with trusted partners. Due-diligence is undertaken

before partnership relations are entered into, and clear written agreements summarize the

responsibilities and obligations of all the partners. Therefore, there is need for increased

support for CBHI schemes and for the establishment of a co-financing scheme that would

complement premiums paid by individuals toward their health insurance with government or

donor funding (WHO 2001; Tabor, 2005).

47

This study uses the mixture of financial sources in CBHIs as ameasure of mix in

contributions. This measure was deemed appropriate for the study since a mix of contribution

in CBHIs have been recommended by various authors given the limited corrections from

members contributions, the sustainability of governement revenue and importance of donor

funds in the formative stages of CBHIs which implies the important role played by each

source in furtherance of equity in healthcare (Preker, 2002; Carrin, 2003 & Durairaj &

Evans, 2010)

2.3.4 Effect of Risk Pooling on Equity in Healthcare

Risk pooling makes an individual‘s health expenditure more knowable and manageable

(Davies & Carrin, 2001; WHO, 2015a). According to Smith & Witter (2004) the rationale of

risk pooling in healthcare in relation to equity and efficiency can be viewed from two

perspectives. First, the equity aspect entails sharing of some or all individual‘s health risks

associated with healthcare expenditure across a risk pool. The equity objective is therefore a

strong determinant of healthcare access and financial risk protection for a risk pool

membership with similar health needs irrespective their individual circumstances. The level

of risk pooling in developing countries is still low due to predominantly high levels of

poverty that diminishes the ability of the poor to pay for health insurance (Smith & Winter,

2004, Parmar, et al., 2013). Smith & Witter (2004) argue that communicable disease

commonly associated with the poor have been predominant in developing countries. This

implies that poor have higher health needs and less ability to pay.

Secondly, risk pooling increases efficiency in the manner in which health funds are utilized.

It facilities transfer of health resources to the poor since their demand for healthcare is higher

given their exposure to more risks in their workplaces and areas of residence (Smith &

Witter, 2004; Smit & Mpedi, 2010). Additionally, risk pooling encourages timely access to

healthcare when treatment options are cheaper and reduces chances of loss of income due to

illness. Risk pooling therefore is a therefore a critical determinant of improved health status

of a population and more importantly in reducing health disparities (Davies & Carrin, 2001;

Parmar, et al., 2013).

48

The current double burden of disease in developing countries results the increase of

uncertainty of illnesses and healthcare costs driven by costly medicines, procedures and

technologies (WHO, 2015a). These costs present a greater burden to the poor in absence of

risk pooling mechanisms (Smith & Witter, 2004). Attitudes towards making choices that

ensure tradeoffs between equity and efficiency is a key determinant of impetus towards

UHC. The level of social solidarity in a society influences the readiness of the rich to cover

the poor and the health to cover the sick, a situation that ensures risk sharing (WHO, 2010a).

Such system allows individuals to spread healthcare costs over their life cycle by

encouraging them to make payments when they are young and healthy and drawing on them

in times of sicknesses. Such arrangements reduce barriers to access and lower the incidence

of catastrophic health expenditure (WHO, 2010a). According to James & Savedoff (2010)

most societies possess a degree of social solidarity from which they can draw on when

establishing redistributive systems. Existence of an aspect of social influences the level of

inequities that a population can tolerate (WHO, 2010a). Previous experience with mutual

organizations influences willingness to share risks and in turn the realization of equity goals

(Goudge et al., 2012).

The composition and size of contributions to a pool influences the amount of resources raised

and in turn the attainment of equity from the benefits drawn by members (WHO, 2015a).

For a pool to raise adequate resources it must draw its membership from large group of

population. This move should be meticulously planned to avoid preclusion of the poor and

vulnerable groups who are desperate for healthcare. According to WHO (2010a) compulsory

contributions are more favorable given their ability to draw resources from a wider social

economic background resulting to risk equalization (WHO, 2010a). Voluntary schemes such

as CBHIs play a critical role of extending the benefits of risk pooling in countries that

experience high levels of exclusion (Davies & Carrin, 2001).

Limited organizational capacity in establishing a social health insurer results in

fragmentation of risk pools. Fragmentation raises the questions of sustainability. Multiple

pools draw their membership from different population groups which limit their size. They

49

also attract tend to attract covariant risk, duplicate benefits and efforts, incur high transaction,

administration and information systems cost and display low negotiating power with

contracted healthcare providers. As consequence of such duplication and inefficiencies they

are not sustainable options of addressing equity goals (WHO, 2010a; Langenbrunner &

Somanathan, 2011; Kutzin, 2012).

Establishing larger risk pools should be part of a health financing strategy from the start. In

order to achieve higher coverage a risk pool can enroll a larger population by targeting a

larger geographical area. For instance, instead of targeting village populations, for example,

the district population could be targeted. Expansion of risk pools can be achieved through

establishment of a federation or network of CBHIs. In that case, there is also greater

likelihood of having cross-subsidies between rich and poor households. This has proved

successful in the case of the Bwamanda Scheme in Congo and in the Nkoranza Scheme in

Ghana where the enrolment was high. It was however not so in the case of the CHBIs

established at district level in Tanzania where the consequent of fragmentation was low rates

enrolment (Davies & Carrin 2001; Aryeetey, Jehu-Appiah, Spaan, Agyepong & Baltussen,

2012).

One of the common themes in recent publications on African and Asian countries that have

realized great impetus towards UHC is pool consolidation (Lagomarsino, Garabrant, Adyas,

Muga & Otoo, 2012). Rwanda started with multiple schemes before consolidating the

contribution from all insurers (Makaka, 2012). Similarly, Latvia, Estonia, Lithuania and

Poland began their compulsory health insurance systems with multiple regional funds before

embarking on progressive consolidation and transformation of the territorial funds into

branches of the national funds (Kutzin, 2010).

A country can decide to consolidate or not to consolidate pools into a national pool or to

maintain separate pools each reflecting distinct needs of a population segment or to

encourage competition through multiple separate pools is based on national priorities as spelt

out in the national health policy. For instance, the Republic of Korea chose to consolidate

over 300 separate pools into one national fund. On the contrary, in 2007, Swiss citizens chose

50

a system encourages market competition by maintaining multiple pools rather than

consolidating them into a single caisse unique. Rwandan tax system is still in nascent stages;

it operates three health insurance organizations. However, there is some degree of risk

equalization where money is transferred from pools that serve low risk groups to those that

serve high risk groups (WHO, 2010a).

Geographical location can have a profound impact on the choice of risk-pooling

arrangement. For example, if development of rural healthcare is a priority, it would seem

sensible to design a system which establishes separate the rural and urban risk pools, with a

robust revenue allocation mechanism that transfer more funds to the rural area. Such a

mechanism will ensure that rural areas have access to a secure stream of finance that is not at

risk from increased demands for healthcare services from their urban counterparts (Smith &

Witter, 2004; Shimeles, 2010; Dutta & Hongoro, 2013). Risk equalization can also be

achieved through allocation of funds from general tax revenues to health facilities in poorer

regions where health needs are higher. By doing so, the government allocates money for the

poor who have less ability pay while the wealthier people who contribute more and have

fewer health needs receive less (Smith, 2008).

The choice of risk-pooling arrangement may be influenced by levels and distribution of

income, and the nature and magnitude of potential revenue bases. Where large differences of

income and healthcare needs exist, inter- pool transfers shield equalizes risks where the high

income members‘ cross- subsidize the low income. This requirement is particularly important

in a system in which there has been substantial purchasing devolution to a large number of

small risk pools. On the other hand, risk pools that have almost similar per capita income and

needs, a less integrated system will be more pragmatic (WHO, 2010a).

Fragmentation of risk pools is more widespread in devolved health system where purchasing

is carried out by the devolved health unit. Fragmentation leads equity and efficient problems

which policy response is to establish an aspect of integrated risk pools for all the devolved

units. This arrangement involves some extent of risk transfer at the national level where

financial transfers between pools are used to offset the variations (Smith & Witter, 2004).

51

Two main approaches have been employed in risk equalization arrangements among

devolved units risk pools. First, capitation payment is used as base of funding of the

combined risk pools. Capitation payment can be defined as actual contribution to a risk

pool‘s revenue that can be associated to a specific member of a pool at a point in time. One

of the advantages of the capitation method is its ability to correct variation emanating from

differences in size of pool while its inability bridge per capita needs variations between pools

is one of its downside (Smith, 2008).

To address the problem inherent in the capitation method, many countries have developed a

second, which adjust the capitation that is affiliated to an individual, based on the

individual‘s characteristics, health status and social economic ranking. The risk adjusted

capitation takes into consideration the variation in risk exposure among the risk pools. Such

arrangement may involve a central system that collects revenue and distribute it to risk pools

depending on their approximated expenditure needs. In an alternative system, risk pools

collect revenue by themselves and make financial inter-pools transfers from low expenditure

to high expenditure pools based on needs of each pools. When the variations cannot not

satisfactory offset among the pools extended transfers can be implemented. Differences in

when the revenues base may result to large discrepancies that will require further adjustments

for adequate risk transfers. Such circumstances arise where considerable differences in

income level exist requiring an additional transfer. The two sets of transfers match the risk

pooling and desired income reallocation needs. In its first round of reforms, Estonia faced

equity and efficiency challenges as a result of failed risk transfer from wealthier to poor

regions. This necessitated creation of tax based equity fund dubbed as the central sickness

fund from which per capita transfers are made to local sickness funds (Smith, 2008).

The choices made at various steps along the path to UHC and at different levels influence the

level of social solidarity exhibited by a population and it in turn influences the equity goals.

Chile enacted a health reform that permits the wealthy people to opt out of the national social

health insurer and instead enroll with competing private risk- based insurers. This reform

resulted in creation of two separate systems distinguished by income and individual risk

characteristics of the people. The ‗opt out reform‘ eroded social solidarity between the rich

52

and the poor. The envisioned goal of reallocating of resources to the poor who are also high

risk was not achieved. Instead the national insurer, which was largely composed of poorer

people, ended up subsidizing the private insurers resulting to equity and efficiency challenges

(WHO, 2015a).

While CBHIs provide risk pooling benefits, their small reserve base makes them vulnerable

to insolvency when faced with large claims resulting from covariant risks. The extent to

which the risk of insolvency can be mitigated depends on the scheme‘s management

technical expertise in risk management right from the design to implementation of their

activities (Fairbank, 2003). Social reinsurance provides risk transfer mechanisms that

guarantee the survival of small risk pools. The viability of social reinsurance is determined

by the schemes efforts in reducing the risks. This is in the view of the fact that social

reinsurance cannot address all the risks facing CBHIs. The practical requirements of

reinsurance mechanism are extensive and involve a complex task of identifying the losses

that are transferrable (Smith & Witter, 2004; Dror & Armstrong, 2006).

Fairbank (2003) proposes two dimensions of financial risks; first, the frequency of risk

occurrence and second, the amount of the financial cost that could be incurred in an event of

risk incidence. Risk pooling in CBHIs involves a combination of the two dimensions usually

at varying degrees. Extreme cases would involve either the CBHIs covering only expensive

but rare medical occurrences or insuring members against inexpensive medical conditions

that happen reasonably frequently. Although some CBHIs are able to insure the two

extremes, it is rather common to find CBHIs that cannot covers the two dimensions without

risking their own sustainability (Fairbank, 2003; Wang and Pielemeier, 2012).

CBHIs income is influenced by the resources base where they draw their premiums from;

low income households. Based on the low and irregular cash inflows to the targeted

households, CBHIs are constrained on the funds they can raise from the premiums. For

CBHIs to spread risks equitably and efficiently, they must improve access and reduce

financial risks to their members. In essence the kinds of risks insured by CBHIs are

influenced by the members‘ needs. Whatever the combination degree and frequency of risks

53

chosen by CBHIs, the risks should be insurable for the schemes to remain viable in the long

run (Fairbank, 2003; Dror & Armstrong, 2006).

Policymakers are exploring alternative risk pooling mechanisms as part of their efforts to

expand the availability and affordability of health insurance cover. From proposals that

would create health insurance exchanges to those that would include an individual mandate,

these alternatives have the potential to significantly affect the composition of health

insurance risk pools and subsequently affect premiums (Scheiber, et al., 2012). In other

words, rules governing health insurance attempt to balance the tradeoffs between access to

coverage and premium affordability (Makaka, 2012). The primary purpose of any insurance

scheme is to share risk between individuals and hence extend financial protection to

members of the scheme (Mills, Ally, Goudge, Gyapong & Mtei, 2012). Different

stakeholders in a risk pooling scheme may have different perspectives on the objectives of a

scheme, and stakeholder objectives will also vary according to the type of CBHI scheme.

In the current study, risk pooling is measured by size, composition, distribution and risk

transfer mechanisms that have been put in place in CBHIs. These measures are enhances risk

equalization among members (WHO, 2010)

2.3.5 Effect of Strategic Purchasing on Equity in Healthcare

As countries transform their health systems towards reducing barriers to healthcare access

and removing financial risks associated with illness, their decisions are not only informed by

enrolment strategies for various social economic groups, revenue collection approaches, mix

of contributions and risk pooling mechanisms but also on critical decisions on purchasing

mechanisms (Carrin, Mathauer, Xu & Evans, 2008; WHO, 2010a). WHO (2000) identifies

two distinct approaches of purchasing health services; strategic and passive. Strategic

purchasing is the active and evidenced based process of identifying a package of healthcare

services that needs to be purchased, select healthcare providers and deciding on the agreeable

methods of purchasing healthcare services (WHO, 2010a). On the other hand, passive

purchasing involves following a pre-determined budget. The design and implementation of

purchasing of health services influences the quality of care, resources allocation, equity and

54

responsiveness in the way health services are delivered and in so doing, it provides an

impetus for UHC (Munge, Mulupi & Chuma, 2016).

In strategic purchasing, the purchaser involves three main parties namely; healthcare

providers, members insured by an insurers or a scheme and the government through the

ministry of health. The purchaser should source for the most cost effective purchasing

mechanisms that addresses the insured populations‘ needs, desires and values and expressly

specifies sanctions and actions agreed on by the purchaser and providers (Munge et al.,

2016). As the purchaser buys health services for people, it is important for the purchaser to

ensure there are effective mechanisms in place to determine and reflect people‘s needs,

preferences and values in purchasing, and hold health providers accountable to the people

(Honda, McIntyre, Hanson & Tangcharoensathien, 2016)

The government role through the ministry of health in strategic purchasing of healthcare

services can be viewed in the light of its role as a steward of health and well-being of a

country‘s population. This is because the purchasing arrangements are influenced by the

policy framework within the country. The ministry of health should therefore set clear policy

guidelines that guarantee redistribution of pooled funds through strategic purchasing with an

aim of addressing distinct population needs (Preker, Liu, Velenyi & Baris, 2007; Honda et

al., 2016). For instance, lack of an integrated regulatory framework undermines realization of

equity and efficiency benefits of strategic purchasing in Kenya (Munge et al., 2016).

Ambiguous policy and regulation on purchaser- provider by the government weakens

efficiency in strategic purchasing. For instance, while the New Rural Cooperative Medical

Scheme in China is entrusted with purchasing of health services and supervision of providers

the government makes the final decision on the providers and has the powers to penalize

poor performing providers; this weakens efficiency and accountability in strategic purchasing

(Honda et al., 2016).

Achieving equitable access to needed healthcare services is dependent on the purchasing

arrangements in a health system. An important task in an active purchasing arrangement is

determination of healthcare needs of the population, the best set of interventions and their

55

suitable mix to meet these needs and desires based on the available resources (Honda et al.,

2016). With respect to delivery of health services equitable access should be addressed from

three fronts; physical, economic and needs responsive services. Imbalances in geographical

distribution of health facilities and human resources are more pronounced in LIMCs (WHO,

2010a). To reduce these disparities, an efficient strategic purchasing system should consider

incentivizing service providers to locate in the underserved areas, compensate members from

underserved areas in order to ease the burden of indirect health cost that they incur as well as

utilize the country‘s disease database as a reference for determination a population health

needs (Honda et al., 2016).

Contracting delivery of health services is problematic since it is difficult to observe and

confirm the delivery of services. Efficient purchasing is therefore hard to achieve since the

outcome of care delivered is determined by circumstances that are not easy observe and

verify (Honda et al., 2016). Capping payments is used to induce providers to deliver

outcomes that are not observable. However, capitation payments leave the provider exposed

to uncertain caseloads. Providers can practice active selection in order earn high rents by

serving low cost patients or by offering low quality services (Preker et al., 2007).

The underlying costs of each provider also determine how efficiently the pooled funds are

allocated for the best outputs possible from the service providers. Paying the same rent to all

providers does not consider the opportunity cost of different providers. An insurer can price

discriminate potential providers by requiring them to commit to different contract based on

the quality of their services. Czech Republic pays a premium price per procedure to high

quality providers with a cap while low quality providers are paid a low price without limit

(Preker et al., 2007; Honda et al., 2016).

The organizational structure of pool may also act as a hindrance to efficient strategic

purchasing. Small pools limit the scale and scope of strategic purchasing function and the

package of health services that can be bought. Over time, the schemes acquires wide

networks with service providers creating a rigid hierarchical connections which weakness the

benefits of mutual a relationship between the purchaser and the provider. Additionally,

56

fragmentation of pooling systems is extended to the purchasing function where different

pools purchase healthcare services separately. This weakens resource allocation and in turn

undermines equity and efficiency in healthcare (Preker et al., 2007).

Contracting in CBHIs is determined by the desire to ensure uninterrupted supply of health

services by contracted service providers. This has seen a departure from the tradition of

contracting local, low cost, public and faith based service providers to contracting some high

cost private hospitals to avoid frequent interference of service provision occasioned by health

workers industrial action. Simplified and easy to understand contract document are

commonly used in CBHIs to enable the CBHIs management committee to comfortably steer

the negotiations and at the same time ensure that CBHIs members understand the contract

and the benefit package. As such it fails to capture the expectations of the purchaser and

provider such particularly specifics of remedial actions in case of breach of contract (Tabor,

2005; Munge et al., 2016). Honda et al. (2016) alludes that members‘ views are not only

important in ensuring that the benefit package reflects their needs and expectations but they

also increase their awareness on the quality of services that they should expect from the

providers.

The payment policy adopted by a purchaser is associated with improved healthcare access

and reduced exposure to financial risk. Some of the advantages associated with fee for

service method include; encouraging providers to be more productive since it rewards the

efforts of a provider and its flexibility in offering premium prices and below cost prices to

influence the quality of services offered (Preker et al., 2007). Fee for service methods

increases utilization of health services as demonstrated by empirical evidence from health

systems in Western Europe, Canada and United States (Bloomberg & Price, 1990; Honda et

al., 2016). The method of payment adopted should reflect the desired efficiency outcomes of

a specific health system. For instance in Philippines weak gate keeping and referral system

necessitated a shift from fee for service payment method to case based payment (Honda et

al., 2016).

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Capitation curbs adverse selection and hence aids in cost containment. It encourages the

provider to seek low cost health delivery methods such as low cost care, alternative

treatments and technologies. It also encourages providers to focus on preventive measures

which are cheaper than curative. Promotive and preventive health services are particularly

beneficial for low-income groups due to their cost-effectiveness (Preker et al., 2007; Honda

et al., 2016). Additionally, capitation offers flexibility in its administration. Besides its

administration as a flat fee, capitation fee can be tailored to individual demographic,

economic and risk characteristics. Such adjustment can be used in ensuring resource re-

allocation during purchasing of health services; as a consequence achievement of equity. In

Germany, capitation fee in adjusted based on age, gender, family size and disability

(Barnum, Kutzin & Saxenian, 1995).

The extent to which the ministry of health monitors the conduct of health workers influences

whether or not health services are actually delivered at the health facilities. According to

Preker et al. (2007) the policy actions on health workers abseentism and reward of

hardworking health professionals are some of the approaches that can be used to curb moral

hazard during healthcare delivery. In the same way, adverse selection can be addressed by

establishing the underlying cost incurred by cost providers in order to avoid overpaying those

whose cost are low. Close monitoring of the underlying cost is necessary to avoid

misrepresentation of cost information. Moreover the government can establish treatment

protocol as a guide to diagnosis and means of determining the needs of patients and the

course of treatment. Diagnosis based payments can be applied where such mechanisms exist

(Preker et al., 2007; Honda et al., 2016).

According to WHO (2010a) countries should always look at areas of improvement as they

progress towards UHC. Such improvement is sometimes influenced by presence of skills and

technical capability of the steward. Meng & Xu Ling (2014) notes that despite high

population coverage in China, gaps in service and cost coverage weakened healthcare access

and the extent of financial protection due to rising medical costs. Regressive premium

payment risks exclusion of the poor families while schemes covering many low income

families face financial instability. The schemes management were not fully aware of the

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potential benefits strategic purchasing, an arrangement that could enhance equity and

improve sustainability of the schemes (Honda et al., 2016).

The role played the ministry of health and regulatory bodies in providing direction on

strategic purchasing and in ensuring that strategic purchasing decisions in healthcare reflect

the national health priories and population needs. Equally important is clarification of roles,

uncertainty in delegation to avoid duplication of duties and resources and addressing of

capacity gaps that may exist in various players (Picazo, 2014). Recent evidence from

Indonesia and the Philippines shows that roles, authority and capacity between the steward

and health services purchasers should be clarified. Additionally, the stewardship function at

the national level should be strengthened to enhance the capacity of the government in

promoting efficiency and equity through strategic purchasing particularly with regard to

quality standards, payment methods and systems and regulation of prices (Honda et al.,

2016).

An integrated regulatory framework is a critical determinant of adoption of efficient

purchasing approaches in health care systems. Regulations involve replacement of traditional

systems characterized by hierarchical relationships where reimbursements are based on

services offered. The traditional systems are substituted with formal contracts, decentralized

management and wide variety of providers drawn from public and private providers. Policy

makers should strike a balance between stimulating entrepreneur behaviour and regulating

the conduct of entrepreneurs (Honda et al., 2016). For instance despite their growing role in

extension of coverage to the excluded groups, CBHIs in Kenya report to the department of

Social Services. Additionally, unlike other private insurers CBHIs have no capital

requirements. This arrangement undermines the accountability requirements in strategic

purchasing of health services whose responsibility lies within the ministry of health (Munge

et al., 2016).

Enacting of laws and regulations that guide the operations of players in the healthcare sector

is critical for enhancing their roles in promoting equity in healthcare through design and

implementation of strategic purchasing. Additionally, despite CBHIs accounting to 1.3% of

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the covered population and their long existence in the country, there no specific rules and

regulation that guides health purchasing arrangements in CBHIs in Kenya. Lack of

government stewardship particularly in controlling the conduct of providers limits the

bargaining power of CBHIs (Munge et al., 2016).

While formulation of policy and enacting rules and regulations is imperative, their

implementation is equally important. Clear role allocation is critical for guaranteed

realization equity priories set in place by the overall steward (Honda et al., 2016). Despite

existence a clear certification policy on identification and enrolment of members who should

benefit from exemption of premiums from the Philippines national government, many

members of the sponsored program were unaware of their right to benefits. To address this

shortcoming PhilHealth mandated health facilities to identify potential beneficiaries and

ensure that they benefit from the program (Picazo, 2014).

The political will and support to health reforms influences the manner in which the role of

the government as a steward is viewed. Support for reforms is often influenced by several

socioeconomic factors including cultural affiliations, historical background, political will and

ideological outlooks (Preker et al., 2007). Similarly, countries follow different paths as they

move towards UHC, some progress faster than others. Strategic purchasing is a critical

determinant of how quickly a country can achieve greater access to health services and

expand financial risk protection. Resources should be allocated based on population needs

and product benefits. Such decisions should be informed by accurate population surveys and

good information systems for timely information management and analysis (WHO, 2010).

A mechanism for receiving and responding to complaints and membership is an enabler of

efficiency in strategic purchasing of health services. The purchaser of health services should

establish a robust complaint and feedback mechanism through which members should air

their complaints on quality of care, availability of health workers, services and prescribed

medication in contracted health facilities. Information technology offers a vibrant platform

for channeling and responding to members concerns. In CBHIs, members express their views

and concerns during meetings (Munge et al., 2016). Although PhilHealth, the national health

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purchasing agent in Philippines has established a website through which members and

providers views should be registered and addressed, complaints are not addressed on time.

Generally, many insurers have not exploited the social media as a channel for enhancing

communication for providers and insured members (Honda et al., 2016).

Gatekeeping measures such as a referral system improves geographical access to health

services and cost effectiveness in utilization health services. According to Munge et al.

(2016) CBHIs in Kenya have a well-established referral system where members can only

access specialized services in higher level hospitals through referral from lower hospital.

Where referral systems are weak provider payments measures can be used in curbing self-

referral. In Philippines, case based payment method was used to improve efficiency through

rational provision and utilization of services (Honda et al., 2016). This study measured

strategic purchasing by assessing the extent to which CBHIs purchasing decisions influence

providers‘ behaviour and encourage contracted service providers to pursue equity, efficiency

and delivery of quality of care (Munge et al., 2016).

2.3.6 Moderating effect of Government Stewardship on Equity in Healthcare

According to WHO (2000) all health systems perform four functions irrespective of how they

are organized or where they are located. The functions include financing, resource

generation, service delivery and stewardship (WHO, 2000). The renewed focus on the

significance of health system in improving a population‘s health has put the role of state in

healthcare and in the governance of health systems while at the same time recognizing the

growth and diversity of players involved in healthcare provision of into limelight (Alvarez-

Rosete, 2008; Hafner & Shiffman, 2012). Stewardship in healthcare entail an overarching

obligation for guiding the health system, a role which has implications in reducing inequities

in access and financial risks (WHO, 2010a; Alvarez-Rosete et al., 2013).

According to Veillard et al. (2011) there six domains within which the government can

exercise the stewardship function. They include defining health‘s vision and policy making,

influencing better health through advocacy, ensuring good governance in health systems,

ensuring alignment of health systems design with health system goals, directing health

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systems through legal, regulatory and policy instruments and collection, dissemination and

use of health information and research. The manner in which each stewardship domain is

implemented in health systems influences the realization of equity agenda in a country

(Alvarez-Rosete et al., 2013, WHO, 2014b).

WHO (2014b) elucidates that the policy direction set by a country influences the equity

agenda in a country. Policy formulation entails identifying and clarifying priorities in

healthcare systems including health financing. UHC priorities include provision of essential

health services for all. This ensures fairness, an issue that is at the heart of the equity agenda

(WHO, 2010a). Primary healthcare was endorsed in 1978 by WHO members as a platform of

reducing persistent inequities in healthcare. According to WHO (2010a) various countries

have formulated strategic policy direction that have reduced financial access barriers.

Investment in primary healthcare was highly associated with thrust towards UHC in Thailand

where reforms in health financing focused largely on nationwide expansion of primary

healthcare (Prakongsai, Limwattananon & Tangcharoensathien, 2009). Mebratie et al. (2013)

point out that implementation of CBHIs is getting heighted policy attention in many

developing countries as an alternative financing mechanism for extending coverage to the

poor and vulnerable groups. In practice the use of evidence based information influences

policy formulation and regulation particularly in regard to progress towards UHC (WHO,

2010a).

The inclusion of generation and use of intelligence as feature of stewardship emanates from

its potential of influencing evidence based planning and decision making in a country‘s

health sector. As a mandated steward of health, the ministry of health should establish

institutional arrangement to harness health related information and utilize it in decision

making within the health sector and other related sectors (Mebratie et al., 2013). According

to WHO (2010a) national statistical agencies play a key role of channeling information to the

health and national planning sectors. Information gathered by the agencies on the proportion

of population that have access to needed health services and the level of financial risk

protection influence strategies put in place for realization of equity in healthcare.

Additionally, national health accounts and households health expenditure surveys provide

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information that is essential for assessing OOP and financial risk protection. Different

contextual situations in different countries require that country customize information

gathering based on their ability to gather, monitor, analyze and interpret information for

decision making and policy formulating.

With regard to provision and purchasing of health services, efficiency and quality of health

services can be improved by considering the set of services needed by each segment of the a

country‘s population, the interventions that can best meet the needs, the appropriate mix of

services, the best purchasing arrangement and the service providers that can best deliver

these services. In reality, such active purchasing influence access to healthcare. Additionally,

allows health consumer in encouraging and enforcing set standards of quality and efficiency

(WHO, 2010a).

The high cost of gathering health performance data from small groups makes its impractical

for CBHIs to gather intelligence (Tabor, 2005). The government can play a supplemental role

of collection and analysis of information. The information gathered can be used in

stimulating establishment of CBHIs, detect problems in existing CBHIs and recommend

practical solutions to the problems (Carrin et al., 2005). A review of CBHIs in Nepal shows

that intelligence gathering was non -existence in all the schemes despite having been initiated

by the government (Deutsche Gesellschaft fur Internationale Zusammenarbeit (GIZ), 2012).

For the ministry of health to ensure that the policies designed to attain equity goals are

implemented, it should possess the requisite tools for steering the entire health sector. These

tools include powers, incentives and sanctions that influence the behaviour of different

players in the health sector (Veillard et al., 2011). The mix of rules, incentives and sanctions

with a regulatory framework influences how successful the ministry of health is able to

influence the behaviours of actors towards achieving equity goals (WHO, 2000).

It is common for the ministry of health to delegate some stewardship responsibilities to other

actors in the healthcare system. Irrespective the stewardship arrangements that exist the

ministry of health remains the stewarded the stewards, in that it takes the ultimate

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responsibilities for its population‘s health. The powers given to each actor must be

commensurate to the delegated responsibilities (WHO, 2000; Alvarez-Rosete et al., 2013).

Where the ministry of health is expected to implement a national health policy funds should

be allocated for execution of the tasks. Similarly, the ministry of health should ensure that the

implementation of delegated is coordinated (WHO, 2012).

The ability to incentivize the actors in the health sector is another important tool that

influences the implementation of national health policies including addressing disparities in

healthcare access and financial risk protection. In order to secure interest active participation

of sectoral actors the ministry of health should address the discrepancies in resource and

power allocation (WHO, 2012). WHO (2000) advices governments to consider the barriers

that impede achievement of the health system goals and then align the actors‘ behaviour

towards achievement of those goals. Evidence shows that incentives and regulation aid in

influencing the interest of the actors‘. For instance, inequities in access and lack of a

responsive contribution system are some of the problems that impede achievement of UHC.

In recognition of the critical role played CBHIs in extending health coverage to the informal

sector, the Rwandan government uses performance based financing as an incentive to

improve the quality of care offered by healthcare providers. In this case performance based

financing in health facilities encourages health workers to ‗offer quality care to CBHIs

members (Humuza, 2011).

According to WHO (2012) the extent to which the ministry of health deals with the challenge

of actors‘ hidden agendas and possible conflict of interest influences how successfully the

health policies are implemented. As a steward mandated with coordination of partners, the

ministry of health should take a lead role in managing dissonance and mediate divergences

among actors by maintaining continuous dialogue and collaboration with the actors. It

imperative that the steward to clarify and define the role and responsibilities of each actor to

mitigate unnecessary conflicts. Actors who deviate from the norms and standards that are set

by the steward should be sanctioned. Brinkerhoff (2003) identifies self-policing measures

such a professional code of conduct among healthcare providers and incentives to service

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users that allow them to switch from low quality healthcare providers to high quality

providers.

According to WHO (2008) health equity is affected by wide range of forces including social,

economic and political factors. These forces have both direct and indirect impact on health

outcomes, some over which ministry of health and other sectoral actors have no direct

control. For instance some social determinants that influence equity in healthcare include

water and sanitation, infrastructure, food security, working and living conditions, social

economics status as well as power and resources. This illustrates that the ministry of health

needs to build wider of relationships for effectiveness of its stewardship role (WHO, 2000).

The ministry of health should build different types of relationships ranging from simple

affiliations to formal associations. Similarly the amount of resources and time involved in

establishment and maintaining such relationships vary considerably based on their nature.

As an effective steward, the ministry of health should be flexible and practical in creating and

maintaining partnership with an understanding of the broader determinants of equity in

healthcare. The decision on whom to build partnerships with is influenced by their inspiration

and connections among other factors (WHO, 2000; 2008).

Various countries through their healthcare steward have established formal partnerships with

community financing schemes as an alternative of extending healthcare to the informal

sectors. Germany, Japan, China, Korea, Taiwan, Thailand, Indonesia, Ghana and Rwanda

have moved closer to realization of equity in healthcare by enlisting the excluded segments

of the population through community driven schemes. More importantly, they have defined

the place of community financing initiatives within the context of the national health

financing policy (Preker, 2002; WHO, 2010a; Fernandes et al., 2009; Durairaj et al., 2010;

Schieber et al., 2012).

Accountability in healthcare delivery is critical for improving the performance of health

systems and is so doing contributes to achievement of equity (WHO, 2000). Accountability is

critical given the huge access, information and expertise asymmetries that intrinsically exist

65

among oversight bodies, users and providers. It is therefore important to first, clarify roles on

which that each player will be held accountable. Actors in health sector include from the

users, providers, professional bodies, donors and government. They represent diverse groups

which reflect the complex interdependences among the players. The government as a steward

bears the responsibility of establishing institutions and mechanisms that ensure that actors

adhere to acceptable conduct (Alvarez-Rosete et al., 2013).

Such mechanisms include reduction of corruption, fraud and misuse by requiring

transparency in execution of social security policies and openness to public scrutiny in health

system players‘ activities; ensuring compliance with laid down processes, procedures and

standards and sanctioning unacceptable conduct through the professional licensing, judicial

and legal framework .The accomplishment of these activities depends on the extent to which

line ministries implement governance principles in sectors under their ministries (WHO,

2000; Alvarez-Rosete et al., 2013). For instance, despite the patients being the focus of

service delivery, their role in ensuring accountability is not clear and enforceable. Huge

information and expertise asymmetries and the power of health services provider makes it

hard for the users to obtain and interpret health information. These constraints make it hard

for them to demand for accountability (Brinkerhoff, 2003).

Secondly, clarity of specific areas in which the actors should demonstrate and account for

performance based on the agreed targets. Some of the determinants of performance include

services, outputs and results of programs and government agencies. Performance

accountability focuses on outcomes such as increased access, quality of care, patient

satisfaction and equity. For instance, the ministry of health is responsible for setting priority

actions, regulations and overall performance of health systems in realization equity goals

(Travis et al., 2002). Various appraisal tools are used measure performance at various levels;

from service delivery points to the top management. At service delivery point customer

satisfaction surveys and complaints and feedback mechanisms are more appropriate and

valid. A strategic plan which clearly defines expected outcomes and performance

measurements is a suitable tool for top management (WHO, 2000).

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The extent to which equity in healthcare is achieved depends on the level of congruence

between organizational structure of the actors and national health policy (WHO, 2000). As

the main actor, the government should ensure that the linkages among the actors are seamless

with clear roles and efficient communications. A fit between the organization structure and

policy objectives should enhance equitable and efficient utilization of resources and engender

a supportive management culture. Organizational misfit may arise from gaps that occur when

separation of functions are not complemented with the necessary organizational changes. It

may also arise when the structures created are not backed by law or when there duplication of

tasks (WHO, 2000; Alvarez-Rosete et al., 2013).

Communication facilitates exchange of information critical for planning and implementation

of actions that key for achievement of equity (WHO, 2000). For instance communication

between information generating agencies and the purchasers ensures that the purchaser

focuses on the populations‘ health care needs and allocates adequate resources for the

neediest segment of the population. Similarly, the communication between the donors and

government facilitates clarification of policy priorities for funding (WHO, 2010a).

Interministerial communication particularly between the ministry of finance and the ministry

of health helps reorienting governments spending towards healthcare. Closer collaboration

between the two ministries is critical for making a case for increased allocation to healthcare.

According to WHO (2013) interministerial committees helps in demystifying the myth of

health being an unproductive sector. Involvement of the ministry of finance from planning

through implementation and monitoring aids in setting the stage for evidence based dialogue

about health and its contributions to national development. Continued exchange of ideas and

information between bilateral and multilateral donors and the ministries of finance and health

help in making a case funding priority areas in health (WHO, 2001; 2013).

The desire for change at the community is evident in the periodic population surveys (WHO,

2010b). At the grass root level service providers and CBHIs have be used as channels of

change for achieving universality. Chen et al. (2012) point out that success in execution of

CBHIs products is dependent on effective communication among stakeholders particularly

service providers and the beneficiaries. Advocacy and information campaigns create

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awareness on the importance of health insurance. Additionally, trust is critical determinant

for enrolment and renewal of policies at the community level. Various studies have shown

that trust is built and maintained through frequent communication. Meetings and field visits

increases helps in nurturing trust since they reduce physical proximity. Civil society groups

helps in eliciting political support needed for enacting requisite laws for expanding fiscal

space for health needed for achieving universality (WHO, 2010a). This study adopted

schemes design of CBHIs, monitoring CBHIs related activities, as a trainer and as co-

financier as measures of stewardship in CBHIs. The four tasks were deemed critical for

steering CBHIs in the direction of equity in healthcare (Carrin, 2003).

2.4 Empirical Review

A review of the past literature has identified a variety of equity in healthcare and health

financing resources. Policy makers and researchers recommend that countries aspiring to

achieve equity in healthcare to embrace innovative health financing mechanisms that

diversify domestic sources of funding for heath for a guaranteed rapid coverage of the

informal sector and inclusion the poor. CBHIs have been used as a strategy for mobilizing

resources to finance healthcare for the informal sector and the poor. This section presents

empirical literature on equity in healthcare within the health financing functions and related

concepts.

2.4.1 Effects of Enrolment on Equity in Healthcare

Equity in healthcare can only be achieved when the excluded segment of the population is

enrolled in a health insurance scheme. The percentage of population that has signed up for

CBHI compared to the target is critical given the voluntary nature of CBHIs. A low

membership rate is an indication of adverse selection while broader membership is critical

for long-term viability of CBHIs (Jütting, 2004, Chen et al., 2012). The presence of

willingness to pay has been documented by various authors (Dror et al., 2006; Ahmed et al.,

2016; Babatunde et al., 2016). The enrolment strategies employed by CBHIs should

therefore respond to willingness to pay. Uptake of CBHIs health insurance is influenced by

affordable premiums, flexible and varied options of payments, trust and unit of enrolment

(Carrin et al., 2005).

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Generally, premium should be proportionate to income level low income households

(McCord et al., 2012). However, establishing an affordable price for low-income households

is an intricate task (Churchill, 2006). This is in the view of the fact that low income

households‘ exhibits high price elasticity for demand as a consequence of low and irregular

income (Dercon et al., 2012). Correspondingly, given voluntary enrolment of CBHIs,

affordability of premium is often cited as the main determinant of membership (Carrin,

2003). Leftley (2005) rule of thumb recommends that insurers targeting the poor should work

with members to establish the cash they can spare on an average day before making cost

adjustment. Low income holders‘ purchase decision is influenced by their perception of

products cost and benefits (ILO, 2012). Furthermore, pricing products for this market

requires that the CBHIs achieve a delicate balance of equitable, affordable premium, benefits

and sustainability. Choosing Healthplan All Together (CHAT), an innovative model offers a

quick and practical tradeoff between costs and benefits since it assists low-income

households in choosing benefits based on their ability to pay (Dror, 2007).

Various studies have focused on the issue of premium affordability. An extensive study

conducted by WHO targeting 82 community health insurance schemes covering populations

outside formal employment in developing countries within the context of membership and

coverage found that the premiums in the Nkoranza scheme in Ghana varied from 5 to 10% of

annual household budgets. In the Rwandan Project Study, premium varied from 5.6% to

7.7% in the lowest and highest income category, respectively. Flat rates contributions are

regressive and disadvantage the poorest while sliding scale contributions are sometimes not

statistically significant like in Rwandan case. One indication though in this study is that

affordability matters, is that large households with more than five members had a greater

probability to enroll in the CHIs than others (Schneider and Diop, 2004). A Thiès Study in

Senegal that sampled four villages in which CBHIs operated through a two stage sampling

procedure found income to be a significant factor in explaining enrolment. Belonging to

lower and upper income quintile decreased and increased the probability of enrolment,

respectively. 31% of the wealthiest quintile was insured compared to 8% in the poorest

quintile (Jütting, 2004; Diop, 2005). A recent survey of CBHIs in Kenya focusing on

purchasing decisions in CBHIs shows that there is a high relationship between price and

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perceived product benefits. In effect members sometimes shun product that are low priced

due to the perception they many not deliver value (Munge et al., 2016).

CBHIs have modified their pricing policies to increase access of healthcare to the poor and

vulnerable segment of the population. In order to improve affordability, membership

enrolment periods are typically long and follow harvest times, thereby maximizing the

probability that households have cash available (ILO, 2013, n.p.). A multiple methods study

aimed at establishing the lessons learnt by CBHIs in Ghana and Nigeria within the context of

enrolment, partnership and policy framework found that some CBHIs in Ghana and Nigeria

sometimes allow installmental and in-kind premium payment (Christian Aid, 2015 &

Atagabu, 2008). Experience from a CBHI in Ghana suggests that allowing in-kind payment

has increased enrolment rates since households could afford to pay in-kind (Chankova,

Sulzbach & Diop, 2008, p. 268).

Non-affordability of the premiums by the poorest segment could be addressed by subsidizing

or exempting their premiums. Poor people are willing to pay a part of their premium if their

contributions are supplemented by a government subsidy. Payments based on the

households‘ ability to pay are deemed to be progressive. Lack of reliable data on households‘

income for the informal sector is cited as the main reason why CBHIs charge a flat premium

which is regressive for poorest and vulnerable households. An income dependent fee

redistributes income and is therefore a more equitable way of raising healthcare funds

(WHO, 2010a). Using a health demand model to estimate the price-related change for

various types of healthcare Dong et al. (2009) found that premium adjusted for income or

subsidies for poor increases enrolment in CBHIs and in effect equity in enrolment in Burkina

Faso. Data was collected from a household survey of 988 sampled using a two stage cluster

sampling approach.

In Rwanda, CBHIs were able to achieve progressivity with time; they started with a flat fee

but later changed to income based fee backed by donor subsidies. Descriptive analysis of

data obtained from CBHIs based on evaluation of experiences gained through years of

membership indicate that access of health care improved drastically when subsidies were

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used to ease financial access of the very poor, resulting to access of one in every six

Rwandans (Kalk, Groos, Karasi, & Girrbach, 2010). Further, an empirical study by Dhillon,

Bonds, Fraden, Ndahiro & Ruxin (2012) investigating the impact of subsidized enrolment

payment in Mayange CBHIs in rural Rwanda with approximately 25,000 people found that

100% coverage was realized through subsidies introduced between February 2007 and April

2007. Regression analysis was used to analyze the data from this survey research.

A wide range of experiences from China, Thailand, Vietnam, Korea and Indonesia shows

that incentives are critical for stimulating enrolment. These countries have successfully

scaled up CBHIs (Poletti et al., 2007). Government subsidies have longer lasting effect given

their higher potential of sustainability (Ridde, Haddad, Nikiema, Oudraogo, Kafando &

Bicaba, 2010). Adequate financial resources are necessary to cover the subsidized premiums.

Although, the law on national health insurance exempts the poorest from paying the premium

in Ghana although,, the proportion of the poorest among the insured decreased from 30% in

2005 to 1.8% in 2006 due to lack of enough resources to cover the subsidized premiums

(Oxfam International, 2008).

An empirical study by Desmet, Chowdhury & Islam (1999) on community‘s mobilization for

participation in organizing and managing health care delivery and financing using data

collected from two largest rural and non-governmental health insurance schemes in

Bangladesh in a survey research design that used descriptive analysis found that a pro-poor

policy that differentiates contributions according to socio-economic groups where the

contributions for the destitute were 1/10th

of the contribution proposed to the highest income

category increased membership rates among the two lowest socio-economic groups. Renewal

contributions and user fees for consultations and medicine, and caesarian section were also

differentiated: the poorest categories pay the smallest co-payment or face no charge as in the

case of medicine (Morestin & Ridde, 2009).

Another option would be to exempt the indigent from all co-payments. This is theoretically

the case in Rwanda, but in reality recent observations in the field shows that this exemption

is rarely respected (Durairaj et al., 2010). In the WHO study only 13 of the 44 schemes

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surveyed in the WHO Study had exemption policies to allow the poor households to join

(Carrin, 2003). For instance in one of the three districts in the Rwandan Project, the local

church paid for the contributions of about 3,000 orphans and widows with their family

members. However, after 15 years of operation of the GK scheme, 20% of the ‗destitute‘

group and more than half of the ‗poor‘ group had still not been reached. The contribution

levels and other payments are still said to be too excessive especially for the poor and

vulnerable (Desmet, Chowdhury & Islam, 1999; Morestin & Ridde, 2009). Similarly,

exemption mechanisms were found to be ineffective in Tanzania in an empirical study

conducted by Msuya, Jütting & Asfaw (2007). The study evaluated the impact of community

health funds in lowering barriers to health care access. In effect, like loans, installment

payments mostly benefit the moderately poor.

Achieving adequate membership rates is likely to be easier when households or even

villages, cooperatives or mutual benefit societies are taken as the basis of membership.

Adopting an appropriate unit of membership is critical for increased uptake of CBHI.

Targeting households as opposed to individuals extends schemes membership beyond

voluntary membership. A study analyzing the potential of 50 Mutual Health Organizations in

contributing to healthcare access and extending social protection to disadvantaged population

in six countries in West and Central Africa conducted by Atim (1998) using an inventory

survey and case study methodology found that using family as an obligatory unit of

membership boosted enrolment rates. The study used both qualitative and quantitative

methods of data analysis. This mitigates the problem of adverse selection. Given the high

association of poverty and higher family size (Wodtke, Elwert & Harding, 2012), flat rates

have been found to increase the probability of enrolment of poorer and vulnerable families.

Various researches focusing on unit membership have shown the majority of CBHIs had the

family as the unit of membership (Carrin, 2003; Carrin et al., 2005). Carrin (2003) found that

a number of schemes had actually switched to this type of membership, after experiencing

problems of adverse selection, as a result of families signing up ill family members or family

members most prone to consume health care. Also, most of the case studies (14) reviewed in

the WCA study had an automatic family coverage (Atim, 1998).

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Some schemes have gone beyond adapting family as a unit membership and set a minimum

percentage of households in a village would be required to enroll before providing insurance.

For instance Kasturba Hospital scheme in India set at minimum enrolment of 75% of poor

households in a village while the Vietnam Health Insurance programme recommended

insuring adequate numbers of children by establishing a minimum of 50% per class (Bennett

et al., 1998, Carrin et al., 1999). Correspondingly, some CBHIs in Uganda have defined a

minimum of 60% membership from mutual benefits societies (Carrin et al., 2001). The same

is true for Grameen Health plan in Bangladesh and Mburahati scheme in Tanzania. In

Bangladesh, participating in Grameen Bank credit programme guarantees automatic

membership of the scheme while Mburahati scheme targets the entire membership of co-

operative societies (Desmet et al., 1999; Musau, 1999).

The requirement to pay one fixed annual installment is a common practice in many CBHIs.

The periodicity of the payment of premiums seems to influence the decision to enroll

especially for the poor and vulnerable groups. Indeed, it appears that the obligation to pay the

enrolment fee and/or the yearly membership premiums in one payment constitutes an

important obstacle, in particular for the poor and vulnerable. De Allergri et al. (2006)

demonstrates that enrolment in CBHIs is related to higher social economic status using data

collected from a population based case control among 15 community offered insurance in

2004 in rural Burkina Faso. The former used conditional logistic regression to explore the

relationship between the variable. Adoption of approaches that make premium payments

more flexible is critical for enrolment and renewal of policies in CBHIs given the low and

irregular income earned by the poor households (Smit & Mpedi, 2010; ILO, 2012).

Spreading enrolment fee and renewal fees are some of payment policies that encourage

enrolment (Criel, 1998; Criel, 2002; De Allegri et al., 2006).

Flexible payments which correspond with cash inflows from harvest and livestock sale are

likely to significantly boost insurance enrolment and renewal rates (De Allegri et al., 2006;

Chen et al. (2012). An empirical study by Chen et al. (2012) focusing on the effect of

deferred insurance premiums offered through credit vouchers established that enrollment

rates rose by 11 percentage points. Using self-administered questionnaires to collect data

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Mathauer, Schmidt & Wenyaa (2007) evaluated 23 focus group discussions. Data analyzed

using content analysis and descriptive statistics revealed that inability to pay lump sum

premiums when they fall due influenced the poor not to enroll according to a study

conducted in Kenya.

From the WHO Study, it was observed that schemes in urban areas were more inclined to

establish monthly or quarterly contributions so as to match the income patterns of urban

informal sector workers. Annual contributions, collected at the time of harvest of cash crops,

seem to be prevalent among schemes in rural areas (Bennett et al. 1998). However, empirical

study by Ron (1999) on the role played by CBHIs in Philippines and Guatemala in reducing

financial barriers to seeking care with data collected from 3000 families, analyzed using

descriptive statistics found that the ORT Health Plus Scheme (OHPS) in the Philippines

flexible premium payment plan increased enrolment. The payments are made monthly,

quarterly or semi-annually. Other schemes link the time of payment of the contribution with

a suitable event in the community. For instance, burial societies in Uganda use their monthly

meetings for the collection of premiums, for both first-time members or for those who renew

their membership (Carrin et al., 2001).

An empirical study by Basaza, Criel, Van der Stuyft & Basaza (2007) on reasons for low

enrolment in two CBHIs using a case study design with data collected through key informant

interviews, exit polls on both insured and non-insured and review of schemes records found

that revealed that spreading their premium payments over the year greatly increased their

membership. The study employed a framework method for data analysis. A study

investigating the determinants of mutual health organization in Ghana, Mali and Senegal by

Chankova, Sulzbach & Diop (2008) found that the level of household wealth had more

influence on insurance membership in Ghana, where the CBHIs being studied collected the

premium in a lump sum, and less influence in Senegal and Mali, where payments were

spread over the year. The household data was analyzed using multiple regression analysis.

The period of the year when enrolment fees and/or membership premiums are collected can

also favour enrolment or, on the contrary, constitute an obstacle, according to whether it

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takes into account or not the seasonal fluctuations of income (Criel, 1998; Atim, 2000; De

Allegri et al., 2006). An empirical study by Criel & Waelkens (2003) evaluating the reasons

for declining subscriptions to Maliando mutual health organization on Guinea-Conakry using

data collected from current and past enrollees and non-enrollees validated through focus

group discussions found that collecting fees during the harvest period, which seems more

appropriate in some contexts, does not guarantee individuals' capacity to pay the due amount.

Due to their local presence CBHIs can offer innovative approaches of premium collection

such as paying premiums in kind makes insurance accessible to everyone, including

chronically poor. This increases inclusion of the poor, boost renewal rates and increases

willingness to pay; and as a result promotes equity in health care. Another possibility is to

pay the premium by giving work time to the insurance (for example, in a field from which

the harvest is then sold). This alternative should however not be used to exploit the already

vulnerable households (Jakab & Krishnan, 2001). A survey conducted by Asfaw & von

Braun (2004) on the role of CBHIs in protecting against the downside health effects of

economic reforms in rural Ethiopia using household data and double-bounded dichotomous

choice contingent valuation method of data analysis revealed that the poorer households

preferred to pay their premium with work. The households are nonetheless subject to

exploitation in view of the fact that the premium in kind they give as work is higher than the

amount they would have paid in cash.

Trust is an important factor when considering CBHI enrollment, given the amount of risk

that is inherent in the nature of insurance schemes. Catherine & Salmen (2000), refer to trust

and social cohesion as ―the most important single factor determining the success of any

external intervention was the degree of trust already existing in a particular community‖.

CBHI enrollment rates are likely affected by three manifestations of trust relationships—trust

in others within the community, trust in health providers covered by the scheme, and trust in

the CBHI scheme and management team (Chen, Daukste, Przybyl & Fechter, 2012).

Trust among individuals in a community can effectively be assessed through examining

social networks. Woolcock (1998) & Fay (2005) postulate that the poor in both urban and

75

rural settings rely on existing social networks to manage risk, although the formation of these

networks and the outcomes they are designed to fulfill can vary greatly. In rural

environments, trust is based primarily on the relationships created by traditional customs,

ethnic groups, and common occupations, rather than other social arrangements (Fay, 2005).

Conversely, in urban settings, individuals look to build trust relationships with one another

according to the degree of reciprocity and/or mutually beneficial support that can be derived

from those relationships, rather than through kinship ties as often found in rural settings

(Jellenik in Fay 224, 2005). Generally, urban networks tend to form with greater diversity

and are generally larger in size; however urban networks are usually more susceptible to

instability as a result of the transient and impermanent nature of urban dwellers (Jellenik qtd

Fay, 2005).

In terms of trust in health providers covered by the CBHI scheme, factors such as the

availability, quality, and reliability of health providers have been found to be significant

determinants of enrollment. Trust relationships between health providers and individuals are

generally influenced by experiential lessons which are mainly composed of the past and

current experiences. The trust relationship between individuals and health providers covered

by the CBHI scheme is based largely on experiential knowledge comprised of past and

current experiences. An analysis of qualitative data from 13 rural and urban CBHIs drawn

from multiple regions across the world established that there exist high level of trust between

the members and the CBHIs management team. Further, the perception of fairness and

transparency in schemes is better positioned to nature trust relationships with the community

(Chen et al., 2012).

The main challenges associated with the formation and facilitation of trust relationships

between individuals and organizations in urban and rural settings are similar to the

challenges faced in building trust relationships among individuals, namely the role of

geographic proximity and residential transience. For trust to arise there must be consistent

and sustained behavior. Transience impedes the formation of trust, as it undermines the time

and effort individuals and/or health service providers are able to invest to build the

foundation upon which trust can develop (Chen et al., 2012). The high levels of transience in

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urban settings make it difficult to build trust between urban residents and contracted health

service providers despite their closer proximity to health providers. Empirical study by

Tibandebage & Mackintosh (2005) on the constitution and destruction of trust within

Tanzanian healthcare transactions using data from patients interviews and secondary data on

their social economic status; charges paid and payment methods within the context of service

providers revealed that transience among health providers particularly private health

providers in Dar es Salaam, Tanzania, notably dispensaries, opened and closed regularly in

an attempted to remain financially sustainably in a fiercely competitive low-income market.

While physical proximity proves to be a relatively insignificant impediment to the formation

of trust between urban residents and health providers, there is evidence that indicates the

reverse may be the case in rural settings. In a study of the Vimo Self Employed Women‘s

Association (SEWA) CBHI scheme in Gujarat, India using a prospective cluster randomized

controlled trial study design and data collected from a baseline of 713 claimants and 1440

claimants after two years from CBHIs drawn from 16 rural areas found that non-financial

barriers, primarily the distance to health service provider, were found to exclude the poorest

of the poor when left unaddressed. The survey data was analyzed using principal component

analysis and the impact distance had on preventing trust from forming between rural resident

and micro health organizations is reported in terms of those excluded. It was found to have a

disproportionate effect on rural SEWA membership (Ranson et al., 2007).

The trust that potential CBHI members have in the health insurance scheme is a significant

determinant of whether or not community members will decide to enroll in the scheme.

Ozawa & Walker (2009) suggest that that respondents who were newly enrolled or had

renewed their membership in the local CBHI scheme expressed higher levels of trust towards

insurers they had good past experience with than those that had never enrolled or had

dropped out. In addition, the research showed that despite the high levels of trust in

Cambodian villages, individuals with poor previous experiences with other organizations

were less willing to trust CBHI insurers. The analysis was based on data collected using a

questionnaire that was administered on 560 households and focus groups selected by

stratified and population proportion to size sample. Factor analysis, test retest and

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multinomial logistic regression model were used to identify dimensions of trust, measure

trust levels and explore association between enrolment in CBHIs and trust in insurers.

Schneider (2005) evaluates trust in micro health insurance in Rwanda with data collected

using a questionnaire from a survey of 24 focus groups in three districts. The study used an

exploratory research design and descriptive statistics to analyze the data. Findings reveal that

trust influences enrolment decisions in micro health insurance. Given the importance of

client trust in the insurer and the CBHI scheme, it is crucial to build an understanding of how

to best to foster individual and community trust in a CBHI scheme. One way in which trust is

fostered relates to the management team and the enabling processes found to repair, create,

and develop trust among potential CBHI members. CBHI managers have at their disposal a

number of tools that can enhance the likelihood of enrollment among targeted communities.

These tools incorporate the stakeholder feedback to determine schemes benefits package and

pre-payment amounts, the acceptable payment mechanisms and the schedule of payment

collections (Chen, Liu, Hill, Xiao & Liu, 2012).

The following hypothesis was proposed from empirical literature:

H0: Enrolment is not related to equity in health care in CBHIs in Kenya.

2.4.2 Effect of Mix of Contributions on Equity in Healthcare

In most low-income countries, a large proportion of the population pay for health services at

the point of use from its own pockets. The high level of out of pockets presents myriad

harmful effects. The poor and vulnerable are deterred from using health services or from

continuing with treatment because they cannot afford to pay. Utilization of health services for

poorer people means cutting spending on the basic need such as food, clothing, shelter and

education in order to pay for health costs (WHO, 2010a). Empirical study by Xu et al. (2007)

on protecting the poor from catastrophic health expenditure by reducing reliance on OOP

with survey data analyzed using regression analysis from 89 countries covering eighty nine

percent of the world‘s population found that each year, approximately 150 million people

suffer financial catastrophe, indicating that they are forced to spend more than 40 % of the

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income available to them on healthcare while 100 million of those people are pushed below

the poverty line.

WHO report of 2010 postulates prepayments as the most efficient and equitable method of

raising funds for healthcare. Hypothecated, general and payroll taxes, insurance or a

combination of two have been hailed as the most progressive ways of funding healthcare for

universal coverage (Doetinchem, 2010; Durairaj & Evans, 2010). It allows people to

contribute when they are health then draw on services funded by these sources when they

need them rather than paying out of pocket for them. It also spreads contributions through

one‘s life time and enhances sharing of financial costs of ill health across the population

(WHO, 2010a). In general, the greater the proportions of prepayment in overall health

financing, the more households are protected from financial catastrophe and impoverishment.

Although Xu et al. (2007) found no difference between the protection offered by prepayment

system and tax based system.

The WHO constitution asserts the right of entire population in all countries to access all

ranges of needed health services (WHO, 1948). This fundamental right can only be

guaranteed when all segments of the population are guarded against severe financial risks

associated with OOP payments. UHC evokes equity in healthcare through guaranteed

protection against financial risk associated with ill health and is therefore closely associated

with equity in financing (Soors et al., 2010). The best approach is to develop a health

financing system which facilitates contributions before healthcare is needed (WHO, 2010a).

Countries world over continue to face the challenge of raising adequate resources for health

care. Healthcare costs continue to rise amidst the financing shortfall. The uptick is driven by

communicable diseases that are predominant in low income countries and the global upsurge

of prevalence of non-communicable diseases. The trend is aggravated by technological

advancement, infrastructure and procedures improvement and development of sophisticated

medicine (WHO, 2010a; 2013). Advancement towards UHC is therefore dependent on

raising adequate funds from a sufficient number of individuals.

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The prepaid funds from the population should be supplemented with general government

revenue and or donor support where necessary. The WHO report of 2010 urges countries at

all levels development to adapt innovative financing mechanism that exploit novel resources

particularly from domestic sources. Different countries have taken different paths to UHC

depending on the starting point and the choices they make along the way. Similarly, various

countries are at different points on the path to UHC and at different stages of developing

responsive financing systems. For instance countries that have come closer to attaining

universal health coverage employ tax revenue; payroll and general government taxes or both

to cover the needs health needs of its population, hence guaranteeing access to all at the time

of need (WHO, 2010a; HLTIIFHS, 2009).

This has proved problematic especially for low income countries where a large population

works in the informal sector, making it hard to collect income taxes and wage-based health

insurance contributions. Majority of those who work in the informal sector have no form of

financial protection against health related costs. They have to pay for health services at the

point of use hence they risk facing financial hardship and even impoverishment when faced

with sickness (WHO, 2010a). Policy makers across the world recommend that countries

aspiring to move away from OOP payments at the point of use can adopt three

interconnected options; first is to replace OOP payments with forms of prepayments; second,

is to consolidate the funds into large pools and third is to ensure that the funds are employed

efficiently to purchase needed health services. Where the economic context and fiscal space

is constrained, voluntary schemes have proved to be valuable starting point. Besides

extending financial protection against costs of ill health to the excluded, they are instrumental

in familiarizing people on the benefits of pre-payments and risk pooling (WHO, 2010a).

In Kenya health insurance coverage is limited; only at 17.1% (MoH, 2014). This can be

attributed to large informal sector and a comparatively small and stagnant formal sector. The

informal sector in Kenya accounts for approximately 82.8% of jobs in Kenya (KNBS, 2016).

This presents practical difficulties in collecting tax and health insurance contributions

particularly from the informal sector due to lack of institutional capacity to collect taxes

(WHO, 2010a). In addition, the health financing system is highly disintegrated with OOPs

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being the main form of fragmentation. Other forms of fragmentation include NHIF, CBHIs,

private insurers and donor funding (Chuma & Okungu, 2011). This fragmentation

jeopardizes cross-subsidization necessary in pooling system (WHO, 2010a).

Like other countries that have espoused UHC, Kenya faces continuous challenges of trading

off and balancing competing demands as it moves along stages towards its realization and

sustenance (Carrin et al., 2007). Faced by increasing demand for healthcare services and

limited fiscal space for health, Kenya began by targeting the formal sector through the

national insurer, NHIF. This strategy has resulted into a two tier system making the

conditions worse for the uncovered. The formal sector enjoys financial protection against

health cost while majority in the informal sectors are not covered. Chuma & Maina (2012)

found that each year Kenyan households spend one tenth of their household budget on health

expenses with the burden of OOP being highest among the poor. As a consequence the

incidence of catastrophic health expenditure is approximately 1.48 Kenyans each are driven

below the national poverty line as a result of catastrophic health expenditure. This analysis is

based on data from 8414 households in a national household and expenditure survey. The

incidence of catastrophic health expenditure was estimated from a sample with the health

care costs as a share of the cost. The existence of such a broad based system calls for a

targeted approach (WHO, 2010a).

CBHIs have emerged in the backdrop of poor government spending in health, political

instability and poor governance in the healthcare system. One of the distinguishing

characteristic of CBHIs is the enlisting community‘s participation that allows community‘s

involvement in setting the premium. This is quite the opposite in commercial or social health

insurance where the premium is decided by the insurer and the government respectively.

CBHIs face a challenge of mobilizing sufficient resources attributable to their small size and

the contributory capacity of the target population (Tabor, 2005; Schieber et al., 2012).

Another distinctive feature of CBHIs is the social solidarity. CBHIs employ traditional self-

help and social mobilization strategies that have been embraced by the poor in low income

(Jakab & Krishnan, 2001; Preker et al., 2002). They exploit willingness and ability to pay for

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healthcare and try to build local risk-sharing arrangements based on solidarity. They can

therefore be employed innovatively in identifying pockets of exclusion and subsequent

subsidization of the poor and exemption of the poorest and socially excluded groups.

According to Fuchs (1996) one of the requisite conditions for universality is subsidization of

the poor. Subsidies drawn from general revenues and donor funding can channeled through

CBHIs to further enhance equity in health care. In addition cash transfers and vouchers can

be used to lessen access barriers associated with indirect health costs such as transport,

accommodation and lost work time (WHO, 2010a).

The pledge to advance towards UHC requires reprioritizing of budgetary allocations with

health in mind (WHO, 2010a; Kutzin, 2012). Given the history of volatility of aid flows, the

level of total government expenditure on healthcare is crucial for guaranteed equity in

healthcare to essential health services and financial risk protection for all particularly for the

poor and socially excluded groups. This is in view of the fact that government spending is a

stable and sustainable source of financing. There are numerous estimates on how much

financing is needed for realization of UHC. The 2014 CHATHAM House report, ―Shared

responsibilities for health, a coherent global framework for health financing‖, recommends

that all countries should spend at least US$86 on health per capita, and strive to spend 5% of

GDP on health (WHO, 2010a; Chatham House, 2014). To ensure increased level of

government spending, countries in the WHO African region have realized the pressing need

of reprioritizing their government expenditure in line with the 2001 Abuja declaration that

requires countries to allocate at least 15% of its TGE to health (OAU, 2001). The WHO

High-level Taskforce on Innovative International Financing for Health Systems (HLTIIFHS),

2009) recommends that countries should allocate at least $44 per capita for delivery of an

essential package of healthcare services. WHO (2013) report on the state of financing in the

WHO African region identifies Botswana as one the countries that have met the Abuja and

HLTIIFHS target through ring-fencing of state budget. In Kenya, the percent of government

spending on health have fluctuated from a base of 7% in 2001/02, rising to 8.6 % in 2002/03,

then falling to 4.6% in 2009/10 and then rising to 6.1% in 2012/13 (NHA, 2012/13).

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Although discussions on innovative financing have in the past focused on donor support,

many middle income countries have successfully employed innovative financing in raising

domestic resources. By so doing they have proved that innovative financing is not a preserve

for high income countries. For example, Gabon was able to raise US$ 30 million for health in

2009 after imposing a 1.5% levy on the post-tax profits of companies that money transfers

and a 10% tax on mobile phone operators. Similarly, Pakistan government has for many

years imposed a tax on profits of pharmaceutical companies to fund healthcare expenditure

(WHO, 2010a). In the earlier Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) /

MOH missions, the government of Kenya had committed to earmark 11% of Value Added

Taxes (VAT) for the proposed National Social Health Insurance Fund (NSHIF). The

commitment was later reduced to a general commitment for financial support (sixth Deutsche

Gesellschaft für Technische Zusammenarbeit (GTZ) / MOH mission report, 2004).

Additionally, sin-taxes on products that are harmful to health have a potential of raising

additional funds. They have dual advantages of improving health by reducing consumption

while raising additional funds. In Kenya, Tobacco and Alcohol have always been targeted for

raising additional government revenue. Despite the perennial tax increases, no taxes have so

far been hypothecated for health. An increase in health budget is seen as a more pragmatic

approach of raising additional funds (WHO, 2010a). The WHO High-level Taskforce on

Innovative International Financing for Health Systems (HLTIIFHS), 2009) recommends that

countries should implement options that suit their economies and are like have political

backing (HLTIIFHS, 2009).

Many low income countries are characterized by underdeveloped pre-payments and pooling

structures. According to the Organization for Economic Co-operation and Development-

Development Assistance Committee (OECD- DAC, 2010), these countries will require

substantial financial support from external partners in the short to medium term for them to

achieve UHC. The 2014 CHATHAM House report highlights the absolute need for donor

funding in low income countries. The report estimates that the provision of essential

preventive and curative package focusing mainly on communicable diseases would require

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approximately $86 per person. In 2012, the low-income countries spent only $21, $6 of

which was financed through external aid (WHO, 2015f)

External aid can have a key catalytic role in strengthening of prepayment and pooling

system; a financing method that would propel the countries towards UHC. Although Kenya

registered a decrease in donor funding from 35% in 2009/10 to 26% in 2012/13, the first

decline of donor funding in health financing history, the donor funds continue to play a

critical role in health financing particularly in HIV/ AIDs, Malaria and Non-communicable

diseases programmes (WHO, 2010a; Stenberg et al., 2010; MoH, 2015f).

Within the context of CBHIs, donor support is critical in subsidizing and exempting those

who are unable to pay the CBHI premiums particularly the poorest and vulnerable.

Expansive literature on the potential of CBHIs in offering social inclusion and financial

protection through community financing documents that CBHIs extend coverage to rural and

low-income segments of the population excluded from other forms of healthcare pre-

payments. However, the poorest and socially excluded groups are excluded as a result of

extreme poverty (Atim, 1998, 1999; Bennett et al., 1998; Jakab & Krishnan, 2001; Musau,

1999; Chuma & Okungu, 2011;; Okech, 2013). The Ghanaian National health program

which was upgraded from smaller CBHIs has achieved inclusion of the poor and vulnerable

from earmarked funds (Schieber et al., 2012).

To achieve equity in healthcare, the poor will need to be subsidized, while the poorest and

socially excluded will require to be exempted from paying the premiums. Donor funding

channeled through government pre-payment and pooling structures reduces fragmentation

and duplication of Official Development Assistance (ODA) and other forms of international

aid efforts. To ensure effectiveness of donor support in realization of UHC, it is imperative

that the support is given in spirit of 2005 Paris Declaration on Aid Effectiveness. The

declaration urges donor countries to honour their pledges and ensure sustained mobilization

of resources. It also requires donors to adapt a sector wide approach (SWAp) in disbursement

of funding to avoid fragmentation. This approach gives the government a leeway to fund

priority interventions including subsiding and exempting the poorest and vulnerable

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segments of the population. For instance, the poorest category of population in Rwanda is

exempted through a national risk pool which is funded by the government, development

partners and other health insurers (WHO, 2010a).

The small size of the CBHI pool however makes many CBHIs vulnerable to failure. Indeed,

the realization of one single large risk might lead them to bankruptcy. Moreover, most

schemes are especially subject to covariant risks, because in a limited geographical area, an

individual‘s health is not independent from the health of his or her neighbours, especially

when an epidemic or a natural disaster occurs (Tabor, 2005). Several alternative strategies

exist for greater risk-pooling aiming at protecting schemes from bankruptcy and sustaining

the financial protection of insured households. Using data from national socioeconomic

survey and health and welfare survey data from Thailand Limwattananon et al., (2011) found

that general taxation is the most sustainable and progressive method of financing health

services for the poor and the informal sector equitably.

The infancy stage of CBHIs is costly making it difficult for new CBHIs to consider

reinsurance in their formative stages. Their success from the beginning is therefore

influenced by both the technical and financial assistance they receive from the government or

and donors in mitigating the preventable and the unavoidable risks. In the infancy stage,

assistance on managing avoidable risks is more desirable while support on addressing

unavoidable risks should be reflected in long term plan for social reinsurance (Fairbank,

2003).


H0: Mix of contributions is not related to equity in health care in CBHIs in Kenya.

2.4.3 Effects of Risk Pooling on Equity in Healthcare

Risk management have always been a concern to individuals and wider society. This has

given rise to informal risk coping strategies which are based on the rule of balance

reciprocity and trust in the social circles. According to FinAccess report of 2009, the

informal sector relies heavily on informal risk coping strategies. The report documents that

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25% of the respondents relies on family members, 12.7% on savings, 10% on loan and only

1% on insurance claims. While these strategies are flexible and plausible for small loss

events that occur less frequently and predictably, and affect only a few members of the

community at a time, these mechanisms break down and wipe out years of progress in an

event of covariate shocks (Maleika and Kuriakose, 2008). Industrialization and urbanization

presents an additional challenge: a gradual collapse of the informal risk sharing strategies

(Chen et al., 2012).

The cost of health care relative to individual‘s income remains an impendement to UHC

(James and Savedoff, 2010). Individuals from low-income households earn low and irregular

income and often lack access productive assets (Smit and Mpedi, 2010). In addition, the poor

are exposed to more risks, have limited access to health education and prevention

programmes and are often not aware of their social entitlements (ILO/STEP – GTZ, 2006;

ILO, 2012). This makes the notion for equity in health care and by extension UHC uncertain

particularly for the poor. Worldwide, risk pooling has been lauded as the most effectual

mechanism of protecting people from financial barrier to health care (WHO, 2010a). Risk

poling de-links utilization of health services from direct payments as a result protecting poor

households from relying on coping strategies that push households into poverty (Carrin et al.,

2005). Rashad and Sharaf (2015) examines the incidence and intensity of catastrophic health

payments and the poverty impact of OOP using data from national surveys from Egypt,

Jordan and Palestine. The concentration index revealed in 2011 more than one fifth of the

population is pushed into financial catastrophe by OOP while 3% is driven into extreme

poverty in Egypt. Catastrophic health expenditure was found to be more profound in

wealthier households in the three countries.

Health insurance schemes are supposed to reduce unforeseeable or unaffordable healthcare

costs through calculable and regularly paid premiums. Sparse monetary/financial resources,

minimal economic development, public sector restrictions and low organizational capacity

explain the reasons for unavailability of health financing systems in less developed countries

(WHO, 2010b). In particular, the low income countries remain significantly needing hence

the significance of the debate regarding CBHIs in them (Chen et al., 2012). In response to

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restrictions on government expenditure, most developing countries introduced user fees as a

form of cost recovery. Many researchers have documents the negative effects on equity in

healthcare as result of user fees. For instance, demand for health particularly among the

poorest households. Jütting (2004) evaluates the effect of health insurance in rural Senegal

using data collected through hosuehold interviews in four villages. Descriptive statistics

reveal a decrease in out of pockets payments for members (Jütting, 2004).

Proponents of equity in healthcare encourage governments to establish alternative health

financing methods that can de-link healthcare utilization from payments at the point of use.

Direct payments including user fees are identified as the major obstacle to UHC.

Prepayments and pooling mechanisms is regarded as the most efficient and equitable

approach of expanding health coverage. Abundant evidence shows that progress towards

UHC is largely dependent on raising sufficient funds from large pool, supplemented with

general government revenues and where necessary donor funding (WHO, 2010a).

For a risk pool to remain viable, it must be of sufficient size and comprised of a broad cross

section of risks. Health insurance risk pools are large groups of individual entities (either

individuals or employers) whose medical costs are combined in order to calculate premiums

(Preker et al., 2007). The pooling of risk is fundamental to insurance since it allows the costs

of those at higher risk of high medical costs to be subsidized by those at lower risk. Large

pools of similar risks exhibit stable and measurable characteristics that enable actuaries to

estimate future costs with an acceptable degree of accuracy. This, in turn, enables actuaries to

determine premium levels that will be stable over time, relative to overall trends (WHO,

2010a).

The most viable option of financing healthcare is through general tax revenues and

contributions to health insurance. As such, risk-pooling becomes a core characteristic of such

health insurance systems necessary for enabling provision of health services according to

people‘s as opposed to their individual capability to pay for the services (Bitran & Giedion,

2003; WHO, 2010a). These are based on payments contributed prior to illness; individual

contributions are one pool and used to purchase health services for all members based on

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their needs. In tax-funded systems, the population contributes indirectly via taxes, whereas in

social health insurance systems, households and enterprises generally pay in via contributions

based on salaries or income (WHO, 2010a). One of the perquisites of establishing a tax

funded health system is a robust tax base. In addition to institutional capacity to collect tax,

strong tax compliance is also critical for developing a robust tax base. A tax funded system

responds to the fairness in financing healthcare in that it ensures equity in contributions. The

beneficiaries pay according to their means while guaranteeing them the right to health

services according to need (Gilson et al., 2001; Fronstin, 2008).

When a CBHI scheme proposes a health insurance contribution based on average healthcare

costs of the target population, a number of households usually the healthier ones may not be

interested in signing up. They find the proposed premiums too high in view of low expected

healthcare costs. The less healthy may be interested in signing up for the opposite reason. In

a voluntary scheme, adverse selection and its impact on healthcare costs and contributions

may lead to the discontinuation of a CBHI scheme (Preker et al., 2007). In addition,

voluntary schemes tend to attract enrollees from similar socio-economic background, lack the

necessary regulatory framework relating to contribution and health insurance

reimbursements. For example, funds may be organized along professional lines, for instance

farmers versus formally employed population (Preker et al., 2007; WHO, 2010a).

The practice of risk pooling is an indicator of fairness of contribution and of equity in

healthcare to health services. A solid risk pool capable of insuring its members adequately

should also consist of a sufficient number of members. Creating a large risk pool, however,

does not necessarily translate into lower premiums. Just as a pool with more low-risk

individuals can result in lower premiums, a large pool with a disproportionate share of high-

risk individuals will have higher premiums. When healthier individuals perceive no

economic benefit to purchasing an insurance cover, the membership becomes increasingly

skewed high risk members (Preker et al., 2007). Countries and societies must choose the

extent to which individual financial contributions depend on financial means, healthcare

utilization, or other factors. Whatever system is chosen, a crucial constraint is that the

revenues received must be sufficient to provide the desired system of healthcare (World

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Bank, 2014). The failure of social and private insurance programs to achieve coverage

beyond 20% of the population has renewed concern in smaller scale risk pooling for instance

through community financing initiatives. Schemes like the Bamako Initiative, which was

widely promoted in the early 1990s, tended to pool finances regionally (WHO, 2000).

A pertinent issue at the core of universal health coverage is inclusion of the precluded groups

particularly the poorest and vulnerable segments of the population (WHO, 2010a). To

adequate resources to finance healthcare services, some form of contribution must be levied

on the population. The contributions may exclude the poorest and vulnerable groups if they

are not based on the ability to pay. This highlights the need for health financing policies that

support risk pooling mechanisms that protect people from financial barrier that inhibit access

to health and catastrophic health expenditure especially among the poor (James & Savedoff,

2010). The inference of such risk pooling mechanism is that it enables financial risks of ill

health to be spread across the population. They engender solidarity between the risk and the

poor and the healthy and the sick. The contributions from the rich are reallocated to the poor

who pay less for more services. Similarly, the healthy cater for all or some healthcare costs

incurred by the sick. Additionally, pooling and prepayments systems ease the financial

burden associated with contribution since they enable individuals to distribute the payments

throughout their life time (WHO, 2010a).

The design of risk-pooling arrangements may be heavily influenced by various aspects the

existing health insurance providers in a given setting. CBHIs are based on informal risk

sharing mechanisms which are built and guided by the rule of balanced trust and reciprocity

where members of risk pooling groups or community expect a return on their contributions

when their time comes. The risks are decentralized in that each individual‘s risk is borne by

others through a direct risk exchange mechanisms which is bound by the strong mutual ties

(Debebe, et al., 2012). Most of the CBHI schemes are small and seem to cover relatively

homogenous populations within a single pool (Jacobs et al., 2008, p. 141).

In some situations the existing public institutions which form a natural basis for community-

financed risk pools defined by geographical demarcations. In some other situations, providers

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are predominantly unregulated and focused on making profit where more formal system of

purchaser-provider contracts need to be put in place (Mclntyre, et al., 2006; Durairaj, et al.,

2010). Pooling does not necessarily imply a single fund; different funds with different

financial capacities may exist at a lower level with a consolidation at a higher level for risk

transfer. Consolidation at a higher level involves creation of risk equalization fund funded by

multiple pools. Pools that suffer deficits are cushioned by pools that have financial surplus.

For instance, while Rwanda maintains three distinct pooling systems at the micro-level, they

are consolidated at the national level (WHO, 2010a). In Kenya, there is minimal risk pooling

in Kenya and hence very little cross-subsidization. Apart from tax funding, other forms of

pooling include NHIF, private health insurance, CBHIs and donor funding where the funds

are channeled through the general budget support. Only 4% of all health funds are pooled

through health insurance with NHIF operating the largest risk pool in the country (Chuma &

Okungu, 2011).

The Kenyan health financing system is fragmented. OOP payments present the main form of

fragmentation in the Kenyan health system. Other forms of fragmentation exist in the form of

NHIF, CBHIs, private insurance and donor funding. The NHIF mainly covers people

working in the formal sector; private health insurance companies cover the high income

groups, while most CBHI members are small scale farmers (Carrin, et al., 2007; Muiya &

Kamau, 2013). There is an extremely narrow revenue cross-subsidization in CBHIs and other

privately owned health insurance schemes since they draw their membership from members

whose socio-economic backgrounds. The poorest segment of the population which accounts

for a considerable percentage at bottom of the economic pyramid is left out. Even though the

NHIF enjoys large number of members, its funds are not pooled together with the

contributions from the CBHIs and with tax funding (Chuma & Okungu, 2011).

This situation is seen to be changing as CBHIs customer base increases by mainly drawing

membership from the NHIF scheme. Donor funds are also very fragmented where most

projects operate independently. It is therefore common to find two different donors funding

similar health projects within the same area with little cooperation in terms of financing,

operations and service delivery (Chuma & Okungu, 2011). Funding specific health

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programmes independently undermines efficiency and equity goals envisioned by an

effective health financing system. According to WHO (2010; 2013) SWAp provides a

coordinated and harmonized structure that aligns donor funds to countries priorities and

within the broader objective of UHC (WHO 2010). Failure to pool donor resources in Kenya

results to inefficiency in allocation of resources since resource allocation does not reflect the

region‘s needs; this promotes inequities in access to care and financial risk protection (Wang

& Pielemeier, 2012; Muiya & Kamau, 2013).

Previous attempts to establish a social national health insurance scheme that offers financial

protection to all Kenyans have not been fruitful. Provision of health services to the informal

sector remains a major challenge for UHC in Kenya (Carrin et al., 2007). As a part of the

preparation towards implementing the new financing strategy for universal coverage, WHO

(2010) proposes a financing mechanism that has an equity outlook particularly for the

precluded segments of the population. A tradeoff between economic growth and reducing

health inequities should be evaluated carefully given that increased healthcare access and

financial protection for the poor is well documented means of achieving sustainable

economic development (Carrin et al., 2007; WHO, 2010a).

Efforts to increase NHIF coverage among those working outside the formal sector have

achieved limited success. Consequently, there is very limited income cross-subsidization in

NHIF. The proposed national health insurance scheme is regarded as the main mechanism

towards universal coverage (Chuma & Okungu, 2011; Muiya and Kamau, 2013). Large co-

payments undermine the financial risk protection provided through health insurance. It is

important to design an affordable and sustainable benefit package with minimal or no

copayments (Kamau & Holst, 2008; Kimani et al., 2012). A major limitation to most of the

past and present policy developments in Kenya is the failure to involve the public in the

identification and implementation of policy interventions. The government should engage

with the public when designing policies to promote universal coverage in order to ensure that

their preferences are adequately considered (Chuma & Okungu, 2011).

The amount of political goodwill and community commitment towards redistributive

mechanisms is critical for UHC particularly in the initial stages of UHC journey. Despite the

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distinct routes taken by different countries based on the contextual and ideological

differences the prevailing attitudes towards solidarity and self-reliance remains a common

feature propels them towards UHC (WHO, 2010a). James & Savedoff (2010) defines social

solidarity as the willingness and readiness of the rich to subsidize the poor and the health to

subsidize the sick in health risk pooling systems (James & Savedoff, 2010; WHO, 2010a).

Goudge et al (2012) takes a broader view at the definition of social solidarity. They define

social solidarity as a communal property of a social- political culture that is acquired through

the process of collective historical learning. A degree of social solidarity is therefore

necessary for achieving equity in healthcare given that any effective system of financial

protection for the entire population relies on people‘s willingness to share healthcare costs

(James & Savedoff, 2010).

Current thinking among health policy makers‘ construes CBHIs as a transitional mechanism

of attaining UHC in low –income countries. This policy link between CBHIs and UHC is

informed by historical experiences in countries such as Germany, Japan and Thailand. Ghana

and Rwanda presents more recent examples (Mladovsky & Mossialos, 2006). The

emergence, scaling up and success of these schemes provides a critical learning process on

plausible strategies that low and middle income countries can embrace in their efforts to

achieve equity in health care. The health financing initiatives has emerged as a result of

governments‘ failure in meeting healthcare needs of the poor population (Jutting, 2004).

James & Savedoff (2010) hypothesizes that individuals without a health insurance cover are

likely to be worried about possible financial losses that may arise from future illness. The

individuals are therefore likely to exhibit social solidarity. CBHIs leverage on social

solidarity, reciprocity norms and cohesion to overcome problems of small risks, exclusion,

moral hazard, fraud and cost escalation (Preker et al., 2002; 2004; ILO, 2014).

Given the emphasis on health financing for equity, variation in social solidarity has been

viewed as the main explanation of variation on health insurance coverage in societies

worldwide (Jost, 2008; van Leeuwen, 2008; James & Savedoff, 2010). Empirical study by

Goudge et al. (2012) on social solidary and willingness to share health risks in Ghana,

Tanzania and South Africa found differences in willingness of cross-subsidization across the

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countries driven mainly by past experience with health insurance, mutual ties and degree of

disparities in healthcare. With regard to subsidizing the poor, majority respondents indicated

that such subsidies should cover half of the cost or less. Data was collected from household

surveys using questionnaires from households sampled using different sampling methods in

each country. Similar, empirical study by James & Savedoff (2010) on attitude towards

solidarity conducted in 24 countries demonstrates that 31% of Kenyans favour greater cross-

subsidization of the poor by the entire population. With regard to solidarity with the sick the

average response in Kenya was 3.1, implying existence significant support for risk sharing

with the sick. Analysis was conducted using descriptive statistics on data collected from

randomly selected households in 2002-2003. Greater solidarity was demonstrated in poorer

countries than in the richest one. Correspondingly individuals with lower educations levels,

the sick and households‘ heads expressed greater solidarity with the poor (James & Savedoff,

2010). The nature and the extent to which communities are willing to take part in

redistributive schemes act a limit of acceptable inequities (WHO, 2010a).

Diverse studies have documented the importance of social solidarity in risk pooling. A study

in Guinea-Conakry shows that members exhibited a higher degree of social solidarity.

Consequently, the schemes members endorsed the redistributive mechanisms of the CBHIs.

A qualitative study by Criel & Waelkens (2003) conducted in Guinea- Conakry on the

reasons for low subscriptions in Mutual Health Organizations found that the enrollees value

redistributive effects of insurance which surpasses social circles including households, next

of kin and villages. The conclusions were drawn from analysis data collected from 147

villages through focus group discussions in March 2000. The responses were translated

before the analysis of the transcripts. Similarly, Hsiao (2004) found that members who

express high levels of social solidarity are more likely to accept cross-subsidization study in

China found that members that express high levels of solidarity are more likely to accept

cross-subsidization (Hsiao, 2004). Mladovsky & Mossialos (2006) argue that social solidarity

may reduce problems associated with insurance such as moral hazard and adverse selection.

Benefits of social solidarity in risk pooling have also been documented by Desmet et al

(1999) in Bangladesh and Schneider (2005) in Rwanda.

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Social reinsurance offers risk transfer to CBHIs for social reasons rather than for commercial

motives. This implies that social reinsurers are concerned about the survival and possible

thriving of CBHIs (Fairbank, 2003). The goal of reinsurance is to offload risk and reward to

the re-insurer in return for more stable operating results, but the provider's additional costs

make this impractical (Wang & Pielemeier, 2012). Reinsurance is thus attractive because it

expands the size of the risk pool (Giedion, et al., 2013). Multiple insurance companies share

risk by purchasing insurance policies from other insurers to limit the total loss the CBHI

scheme might experience should a disaster occur. By spreading risk, a CBHI scheme can take

on clients whose coverage would be too great of a burden for the single insurance company

to handle alone (Gnawali et al., 2009). Reinsurance would pool the risks of several schemes,

thus granting them greater financial stability. More often however, there is very limited

experience with and capacity to undertake reinsurance (Mladovsky & Mossialos, 2006; Soors

et al., 2010; Debebe, 2012).

A partnership with local and or central government may be established so as to adequately

finance the health service benefits from the agreed upon benefit package. The average CBHI

scheme involves partnerships with health providers, pharmaceutical suppliers, financial

institutions, NGOs, local governments, donors, and in some instances, licensed insurance

companies. Lack of high quality service provision on the part of any partner has a negative

impact on all of the others (Tabor, 2005). In practice, the implications of multiple risk pools

will depend considerably on the extent to which there is clear market segmentation between

them, and the extent to which behaviour of CBHI schemes is regulated (Giedion et al., 2013).

In Tanzania, multiple risk pools already exist, with the Community Health Fund operating in

parallel with a social security scheme, and with different Community Health Funds in

different districts. However, there is fairly clear market segmentation between these risk

pools, and limited or no competition (Bennett, 2004).

Progressive scaling up of CBHI schemes and eventual mergers leads to larger risk

pools. Real merging of small-scale groups was achieved in hospital-based schemes in

Uganda where pre-existing groups such as dairy co-operatives, rather than individual

households, constituted the basis for enrolment (McCord & Osinde, 2003). To be viable

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however, the introduction of professional management may well require external subsidies.

Merging of CBHIs in the same may take time (Usoroh, 2012). CBHI schemes can be unified

through risk-adjustment or equalization mechanisms. Equalization mechanisms would bring

about monetary support for those CBHI plans that face more than average risks. This support

would be financed via transfers from those CBHI schemes that face lower than average risks.

Thus, CBHI schemes in relatively poor areas with high health risks would be able to set

contributions at an affordable level, in view of subsidies received via equalization

mechanisms (Mathauer & Carrin, 2010; WHO, 2010a). Mechanisms are also required to

ensure the equitable allocation of funds pooled via tax revenue. Both mechanisms for risk-

equalization between insurance schemes and for the allocation of general tax resources

ensure that the relative risk of ill-health or likely health-care needs of the population served

are taken into account (Mclntyre et al., 2006; Durairaj et al., 2010).

Government may subsidize the cost of social health insurance where tri-partite contributions

to the Social Security Scheme are made by employer, employee and government. In

Tanzania, the government matching grant was introduced when it was realized that estimated

premium costs would be too high for the average household to enroll. A quasi experimental

study targeting enrollees of Hygeia Community Health care program by Gustafsson-Wright

& Schellekens (2013) on tri-partite contributions found that matching grants reduces

premiums, therefore increasing enrolment. Besides making scheme membership more

affordable, subsidies may be used to offset risk differences between schemes or compensate

for regional income inequities. However, in practice these other rationales for government

subsidy to schemes have not been observed in developing countries where CBHI schemes

receive some external donor support. Sometimes this has supported technical assistance to

the scheme, or has covered certain operating costs and on some occasions this has been used

to bail out failing schemes (Bennett, 2004; Makaka, 2012).

Macro-level risk is the risk associated with medical expenses generally. Micro-level risk is

the risk associated with incurring losses associated with particular medical treatments or

services (Usoroh, 2012). Individuals with health insurance pool their macro-level risk, while

those who lack health insurance of any kind retain the risk of loss associated with medical

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expenses. The only way to increase macro-level risk sharing is to increase the number of

individuals with health insurance coverage (Carrin, 2003; Mills et al., 2012).While

individuals with health insurance pool their macro-level risks, the particular scope of their

insurance contracts determines which micro level risks are pooled. By enacting mandated

benefit laws, federal and state governments regulate micro-level risk pooling by requiring

coverage for certain benefits in all contracts of health insurance (Gnawali et al., 2009; Soors

et al., 2010.

The risk of loss associated with any service or treatment not covered by a standard contract is

retained at the individual level. In order to increase micro-level risk pooling, the scope of

health insurance coverage would need to be broadened (Fronstin, 2008; Gustafsson-Wright

and Schellekens, 2013). Some schemes cover in-patient but not outpatient drugs. This

disparity can have adverse consequences for other policies such as reducing length of stay

and reducing hospital utilization for minor ailments. Using data from household expenditure

and insurance enrolment surveys from seven states in Mexico, Galarraga et al. (2010) studied

the effect of insurance on catastrophic health expenditure among 36000 insured and

uninsured households in mid-2006. Using econometric method, sensitivity analysis and bi

variate probit model found that an insurance cover that incorporates an inpatient and

medicine cover is more effective in reducing catastrophic health expenditure.

Managing demand is often more difficult for drugs than for other healthcare technologies, so

some element of copayments is probably necessary in most health systems, in conjunction

with exemptions for the poor and an active regulatory policy that reduces the use of

medicines that are not cost-effective (Smith & Witter, 2004). A case study of New Rural

Cooperative Medical Scheme, an insurance scheme for the rural poulation by Honda et al.

(2016) on strategic purchasing in China, found that China has a standard an essential drug list

to check overuse of medical treatment. Data was gathered through documents review, actors

interviews and focus group dicussions after which both deductive and inductive methods of

data analysis were used to analyse the data.

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The optimal size of risk pools is a central design consideration. The choice will to some

extent be dependent on the purposes of the risk-pooling scheme. For example, the nature of

the healthcare package under consideration has important implications for risk pooling. If it

is confined to relatively routine care of common conditions, expenditure is predictable, and

care can be delivered at a local level, so small risk pools may be satisfactory. However,

coverage of less common, more expensive care may require pooling at a higher level to

ensure that random expenditure variation can be managed and providers can be properly

regulated (Carrin, 2003; Fronstin, 2008; Debebe et al., 2012).

According to Aryeetey et al. in 2012, small risk pools introduce important additional

managerial incentives that may adversely affect system performance in terms of both equity

and efficiency, particularly if the pools are subject to very hard budget constraints. These

arise because the importance of the unpredictable random element of expenditure grows as

the size of the risk pool contracts. Small risk pools that perceive that their expenditure will

fall below their budget may spend up to protect their budgetary position in future years. Risk

pools that perceive that their expenditure will exceed their budget may be thrown into crisis,

leading perhaps to serious unplanned rationing, as they seek to conform to the budget

(Drechsler & Jutting, 2010). Different small risk pools will be under different budgetary

pressures, and so may adopt different treatment practices. Moreover, within a risk pool,

choice of treatment may vary over the course of a year if the risk pool‘s perception of its

budgetary position changes (Dong et al., 2009). A small CBHI scheme not only implies poor

financial viability and danger of bankruptcy, it also has implications on the managerial

capacity.

Small schemes may be unable to set aside the financial resources needed to hire professional

management. Managers are often voluntary members and may lack the skills as well as the

time to improve the performance of the scheme. Given the limitations to the size of the

association due to voluntary management, assisting voluntary managers to carry out certain

administrative tasks may promote expansion of the schemes (Smith & Witter, 2004;

Drechsler & Jutting, 2010) Risk pools may adopt a variety of defensive stratagem such as

cream skimming or insuring with a third party against overspending their budget. The nature

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and magnitude of these managerial responses will depend heavily on the nature of ownership

and governance arrangements in place. They impose implicit or explicit costs on the system

which need to be weighed against any benefits brought about by devolving responsibility to

small risk pools (Soors et al., 2010).


H0 Risk pooling is not related to equity in health care in CBHIs in Kenya.

2.4.4 Effects of Strategic Purchasing on Equity in Healthcare

Purchasing is defined as the process by which pooled contributions are used to pay providers

to deliver a set of health interventions. According to WHO (2010) strategic purchasing

involves an active search for the most cost effective interventions that best serves the

healthcare needs of the meet the target populations healthcare. The healthcare financing

function of purchasing encompasses a set of decisions. First, active identification of the

targets population health needs their preferences and values by assessing their health needs

and spending patterns. Secondly, searching the best health services based on the target

population‘s needs, preferences, available resources and health sector‘s priorities. Thirdly,

searching for services providers taking into consideration the quality, efficiency and equity as

well as determining the best payment methods and contractual arrangements (WHO 2000;

2010; Munge et al., 2016). The mandate may comprise the right of the CBHIs to purchase a

set of health services at the best price from pre- selected providers (WHO 2000; 2010;

Munge et al., 2016).

In many countries, lack of geographical access to inpatient facilities remains a major barrier

to health care access. A case study of New Rural Cooperative Medical Scheme in China,

Jaminan Kesehatan Nasional in Indonesia and PhilHealth in Philippines by Honda et al.

(2016) on strategic purchasing in revealed that there are a limited number of service

providers particularly those offering inpatient services in areas that are geographically hard to

access and the ensuing costs of transportation can also be a major impediment to inpatient

care in all the three countries. Data was gathered through actors interviews and focus group

dicussions after which both deductive and inductive methods of data analysis were used to

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analyse the data. There is a case then for considering transportation as a possible benefit so as

to help avoid or reduce catastrophic expenditure associated with ambulance transport.

Incorporating ambulatory care in the benefit package also has a financial advantage. In cases

where ambulatory care would not be fully accessible, lack of effective ambulatory treatment

may result in urgent needs for more expensive inpatient care (Carrin et al., 2005). Where

ambulance services and ambulatory care are not cost effective, a compensation policy for

members from remote areas can be used as an alternative (Honda et al., 2016).

Empirical study by Munge et al. (2016) on strategic purchasing practices of 96 CBHIs in

Kenya found strategic purchasing to be a strong indicator of access to health services and

financial risk protection. The data was collected through documents review and actors

interviews and analyzed using descriptive statistics. Purchasing of health services can be

done in three main ways; first, an integrated purchaser provider approach where the

government allocates funds to government health service providers. The funds are usually

raised from general government revenue and or form insurance contributions. The second set

up involves a separate purchasing institution which purchases health services on behalf of a

country‘s population. The purchaser provider split is usually a national health insurance

agency or a government authority. Thirdly, individuals pay service providers for health

services (WHO, 2010a). The motivation behind such separation has been the desire to

develop a market for providers, which can lead to all the putative benefits associated with

market competition.

In practice, the merits of creating markets in healthcare provision remain a subject of debate.

Service providers can operate in two types of markets; a vertical or a horizontal integrated

market. Vertical integration occurs when a provider acquires or develops competences that

allow it to reduce its dependence on other providers. An examination of contractual

relationships by Baker, Bundorf & Kessler (2014) using hospital claims from Truven

Analytics MarketScan for the nonelderly privately insured in America in the period 2001–

2007 revealed that vertical integrated enables the provider to cut cost and have a direct access

to customers. The results were derived from regression analysis models that were used to

calculate the probable changes in prices, volume or spending index associated with standard

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deviation change of hospitals market share holding other variables constant. On the other

hand, horizontal integration involves a merger of two or more service providers that provide

similar services to different members of the population. By consolidating their customers the

providers increase their market share, have more negotiation power and gain economies of

scale. Vertical integration may result in some loss of incentives for provider efficiency. If

open access to a provider is guaranteed for all in the locality, vertical integration is an

implicit way of creating a local risk pool (Smith & Witter, 2004).

Like other countries, Kenya has employed a combination of health purchasing methods.

Health services are purchased by different organizations with Ministry of Health, which

operate 191 government hospitals, 465 health centers and 2122 dispensaries being the main

purchaser. Other purchasing organizations include NHIF, CBHIs, private health insurers and

employers. Government owned health facilities get budgetary allocations based on a

historical incremental approach while staffs are paid salaries using general government

pooled tax funds. Lack of a coordinated and harmonized policy through SWAp has resulted

to disintegration of health funds. Only a small percentage of donor funds is channeled

through the Kenyan government to support the employment of health workers in remote rural

areas. A consolidation of purchaser provider is also used to provide free services in level 2

and 3 government owned facilities through a HSSF composed of pooled general government

revenue and donor funds (Chuma and Okungu, 2011).

Benefits packages under private health insurance are premium rated and vary from basic

packages tailored for middle income groups to sophisticated packages that are mainly

designed to meet the needs of the richest populations. Benefits packages for CBHI members

mainly involve inpatient care and are often linked to specific healthcare providers, usually

private-not-for profit and public health facilities. A study of purchasing practices in 96

CBHIs in Kenya by Munge et al. (2016) revealed that the services purchased by CBHIs are

dependent on premiums contributed by each member and in most cases high cost services,

specialist services and chronic care services are not covered. These findings were derived

from descriptive statistics analysis of data collected through documents review and actors

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interviews. In addition, Private-for-profit services are rarely provide services to CBHIs due to

high cost.

Macro- level risk pooling influences access to health services; a realization of which requires

mandated benefits laws. An increase in micro-level risk pooling results to increased access to

health services through an expanded benefit package by health insurance contracts. The

downside of an expanded benefit package through macro-level risk pooling is the potential of

increase health services prices that decreases coverage rates. As a result, there is significant

interest in eliminating or decreasing mandated benefit laws. The purchaser might negotiate a

block contract with an independent provider, which implies that the provider will give all

necessary care to pool members for a fixed sum, regardless of the volume or severity of

demands. This arrangement effectively shifts the relevant part of the risk pool from the

purchaser to the provider (Honda et al., 2016).

In strategic purchasing, provider payment mechanism is an important element. One of the

most prominent methods of provider payment in most CBHI schemes is the use of salaries

and budgets. The payment mechanisms are expected to be beneficial for cost containment but

they may also lead to rationing, as a result of the enforcement of hard budget (Bennett et al.,

1998). Fee for service is also used to induce the performance of providers, certainly in a

situation of under-provision of health services (WHO, 2010a). A case study of Pereang

District in Cambodia by Soeters & Griffiths (2003) found that fee for service was part of an

incentive system that was geared towards increasing the quantity and quality of care. To

some extent, fee-for-service method of payment resulted to reduced OOP expenditure over a

period of time. Data was collected from health services providers in Pereang District. These

controls can be costly to implement since they require skilled human resource and

infrastructure to measure and monitor the use and possible overuse of services (WHO,

2010b).

Capitation involves payment of a fixed sum per person enrolled with a provider or facility in

each in each time period regardless of the services provided. To cushion the patients against

sub-optimal care, reports are made available to the public as a measure of health quality and

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can be sued as a basis of financial rewards. Capitation rates are developed using local costs

and average utilization of services and therefore vary from one region of the country to

another. In many plans, a risk pool is established as a percentage of the capitation payment.

Money in this risk pool is with-held from the service providers until the end of payment or in

some cases contract period. When the primary care provider signs a capitation agreement, a

list of specific services that must be provided to the patients is included in the contract

(Preker et al., 2007). A case study of the Organizing for Educational Resources and Training

(ORT) Health Plus Scheme (OHPS) by Ron (1999) in the Philippines concluded that

capitation payment service providers was financially viable for risk reduction in CBHIs since

it reduces provider generated demand for inpatient care.

The amount of capitation is determined in part by the number of services provided and varies

from health plan (Preker et al., 2007). In cases where the purchaser compensates the

remotely located primary provider for referral or ambulance services, the primary service

provider uses this additional money to pay for the referrals. Obviously, this predisposes the

primary care provider to greater financial risk if the overall cost of referrals exceeds the

capitation payment. However, the potential financial rewards are also greater if diagnostic

referrals and sub-specialty services are controlled. Alternatively, some plans pay for test and

sub-specialty referrals through fee for service arrangements based on contractually agreed

upon fee schedules. Empirical study by Munge et al. (2016) found that some CBHIs in

Kenya do not negotiate for lower prices in to avoid compromising the quality of care

provided to its members.

A clear referral system is one of the methods used in controlling cost in strategic purchasing

of health services. A referral system influences the flow of patients to the right health facility.

In absence of a clear referral system, enrolled clients suffering from minor ailments will by-

pass the nearest health facilities and go direct to high level hospitals. This clogs higher level

health facilities with primary healthcare patients (Honda et al., 2016). This is a common

occurrence where an insurance scheme offers coverage only for higher hospital levels and

omits primary health care facilities. Gatekeeping through a referral system remains a

challenge since many patients prefer to go directly to high level hospitals where referral

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system is weak because they expect the quality of care to be superior at that level (Creese &

Bennett, 1997; Galarraga et al., 2010). Though mandatory referral can be required by the

insurance scheme, the controls have to be implemented at the hospital. This can eventually

lead to the problem of adverse selection where some schemes exclude high risk population

groups such as the elderly and patients with pre-existing conditions. A more pragmatic

approach of containing costs through gate keeping involves introduction a broad benefit

package (Preker et al., 2007). Empirical study of Bwamanda Hospital Insurance Scheme in

D. R. Congo by Criel, Van Dormael, Lefevre, Menase & Van Lerberghe (1998) found that

the scheme uses broad benefit package as a method of gate keeping. The results were derived

from cross case analysis of data that was collected from ten focus groups discussions held in

Bwamanda District in March –April, 1996.

A referral system can adopt a one way or a two way referral approach. A one way referral

policy involves referring of patients to higher level facilities from lower level facilities. On

the other hand, two way referral systems involve referral of patients to both high and high

level facilities. For instance, a patient will be referred to a higher facility for specialized care

while a referral to a lower health facility is done where less specialized care such as

rehabilitation is required. A two way referral system leads to efficient utilization of health

resources and reduces patients‘ health care costs. The mutual system requires a well-

coordinated regulatory and communication structures all levels. A case study of New Rural

Cooperative Medical Scheme in China‘s Qinghai and Henan provinces by Honda et al.

(2016) found that adoption of the two referral system in Qinghai achieved greater efficiency.

The results were derived from inductive and deductive methods of data analysis that was

carried out on data gathered through documents review, actors interviews and focus group

dicussions.


H0 Strategic purchasing is not related to equity in health care in CBHIs in Kenya.

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2.4.5 Moderating Effects of Government stewardship on Equity in Healthcare

A moderating variable is a variable that influences the strength and direction of relationship

between a predictor or independent variables and a dependent variable. In the current study

the moderating variable was government stewardship. The concept of stewardship in health

was introduced by the WHO report of 2000 (WHO, 2000). Since then, the function has

received considerable attention with many authors debating its varying technical and political

perspectives ranging from the role of government in policy formulation to implementation,

from innovation to regulation, from performance and social equity to public-private

partnerships in healthcare (Travis, Egger, Davies & Mechbal, 2002; Alvarez-Rosete,

Hawkins & Parkhurst, 2013; Mladovsky, 2014). Despite significant disciplinary and

ideological differences, a common thread of interest has emerged, ‗the ultimate responsibility

for the performance of a country‘s health system lies with government‘.

Although responsibilities for different aspects of stewardship may be delegated to

stakeholders in the health sector such as county or national governments‘ parliamentarians,

professional associations, insurance funds and other purchasing agents, some providers, and

ministries such as finance and planning, a country‘s government through its ministry of

health remains the steward of stewards. As envisaged by WHO, the government role as a

steward is particularly focused on taking responsibility for the health and well-being of the

entire population as well as guiding the entire system (Travis et al., 2002). Governments

therefore has a responsibility of ensuring continuous progress towards UHC and permanence

of the achievements (WHO, 2000; 2010, Alvarez-Rosete et al., 2013).

The term stewardship as it relates to the state has been defined in numerous ways. The WHO

report of 2000 views stewardship as the careful and accountable management of well-being

the population (WHO, 2000). Armstrong views stewardship in an equally ethical and

efficiency- oriented manner. He defines it broadly and comprehensively as ―the willingness

to be accountable for well-being of the larger organization by operating in service rather than

in control of those around us‖ (Armstrong, 1997). Alvarez-Rosete, Hawkins & Parkhurst

(2013) take a nuanced view of stewardship that delineates stewardship from the related yet

distinctive concept governance. They define stewardship as a broader overarching

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accountability over the performance of the entire health system and eventually over the

health of the whole population. They posit that the distinguishing and conceptually useful

facet of stewardship lies in its ability to allocate ultimate responsibility for the health of the

entire population. In healthcare debates, stewardship therefore occupies a special place

because it entails oversight of all the functions and in effect it has direct and indirect effects

on their outcomes.

Governments world over have the ultimate responsibility for ensuring all segments of the

population obtain services they need without suffering financial ruin associated with their

utilization. Stewardship entails maintaining a delicate balance between competing influences

and demands. It encompasses the task of defining and maintaining a strategic vision and

direction of the health policy, exerting influence through legislation, regulation the behaviour

of players and advocacy, and collecting and using intelligence (WHO, 2000; 2010; Alvarez-

Rosete et al., 2013). In countries that receives substantial amount of ODA, stewardship will

be concerned with leadership in channeling the donor funds to national health plans that are

informed by national priorities.

Beyond the formal health structures government stewardship is often hypothesized as a

critical determinant of successful and sustainable health financing in community based

structures such as CBHIs (Preker & Carrin, 2004). Various authors have different views how

appropriately the state can play the role of stewardship in CBHIs. Bennett et al. (1998)

construes that even where a clear government policy does not exist, the schemes are still

likely to play a critical role of increased equity in healthcare. Empirical study by Criel et al.

(1999) found that Bwamanda Scheme in the Democratic Republic of Congo has managed to

generate stable revenue for purchasing of health services for its members in a context where

government stewardship and external support was practically absent. Data was collected

from ten focus group discussions in Bwamanda District in March- April 1996. Cross case

analysis was employed in data analysis. The role of the scheme in the broader health system

remains largely undefined.

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Carrin et al. (2005) view stewardship as critical to encouraging enrolment across different

income categories. He points out that in absence government stewardship the schemes will be

associated with certain population groups. Mladovsky & Mossialos (2006) views government

stewardship as critical to the success of schemes on condition that the government adapts

CBHIs as a strategy for achieving its equity and UHC objectives. On the contrary, Pauly et al

(2006) advocates for minimal government regulation citing an increase of cream skimming

and adverse selection in present of government subsidies. This study proposes four

government mechanisms for supporting the health financing functions in CBHIs namely

stewardship in schemes design of CBHIs, monitoring CBHIs related activities, as a trainer

and as co-financier.

Majority of CBHIs in Africa were created in response to and survived regardless of a vacuum

in government stewardship (Criel & Van Dormael, 1999). Most of these schemes are owned,

designed and managed by the community that they serve (Diop et al., 2006). Although most

studies on the performance the schemes are in agreement that they increase access to

healthcare and reduce catastrophic health expenditure, numerous studies have also

documents that the schemes are far from perfect. Their penetration levels are low raising the

question of their viability in the long-term. Additionally, abundant literature indicates that the

poorest and socially excluded segments of the community are not automatically covered by

these schemes (Jakab & Krishnan, 2001; Carrin et al., 2005; Rashad & Sharaf, 2015).

Empirical study dubbed as the WHO study carried out in 1998 focusing on nonprofit health

insurance schemes for populations outside formal sector in developing countries observed

that only few schemes were able to cover the targeted population. Based out on results

derived from descriptive statistics 44 covered 24.9% of eligible population, 13 schemes

covered below 15% while 12 schemes registered coverage above 50%. These challenges are

related to the context in which they are designed such as absence of formal insurance culture,

poverty and regressive health insurance premiums which lead to low revenue levels that can

be mobilized by the CBHIs (Jakab & Krishnan, 2001; Chen et al., 2012). Given the

aforementioned challenges and pitfalls, CBHIs requires government support for them to

expand their coverage to the excluded segments. It is here that government‘s stewardship in

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the design of CBHIs becomes critical since it is ultimately responsible for the overall health

system performance of the country health system.

Limited technical capability and managerial skills in the community is well documented in

literature (Preker & Carrin, 2004; De Allegri et al., 2009, p. 591). Tabor (2005) put forward

that during the design, particularity at the start up and early operational phases it is

imperative for government and development partners to provide technical support and

management capacity development. Technical support in form of feasibility studies,

assistance in setting premiums, determining benefit packages, provider payment methods,

providing tools and skills that are essential for designing insurance related policies and

procedures in CBHIs such dealing with and mitigating moral hazard and adverse selection. In

addition, the government through legislation can recommend the minimum number of target

population that should enlist before starting up as well as enrolment of households as

opposed to individual. Further, the government can encourage integration into a regional or

national federation to ease provision of technical and managerial support on design and

management. Such networks can also facilitate re-insurance and political advocacy (Wang &

Pielemeier, 2012). CBHI schemes perform a complementary role of extending equity in

healthcare to those not covered by formal health insurance. In line will this, it is the duty of

the government to define their place within the context of the national health financing policy

(Soors et al., 2010; WHO, 2010a).

Despite different paths taken by countries to progress towards UHC, there is point of

congruence that health is an entitlement that should be based on citizenship and or residency

rather than financial capability. Use of government general revenue and or donor funding to

cover people who cannot afford to contribute has been put forward as one of the key priority

action of financing healthcare equitably (WHO, 2010a; Oxfam International, 2013).

Subsidies increase CBHIs capacity to reach the poorest of poor, thereby decreasing health

inequities. Using two national households datasets from Thailand, Prakongsai,

Limwattananon & Tangcharoensathien (2009) studied the equity impact of universal

coverage policy in Thailand. Findings derived from measures of equity in financial

contribution, healthcare utilization and public subsidies, and in assessing the incidence of

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catastrophic health expenditure and impoverishment revealed that low income households

contribute half of the premiums while the government subsidizes the other half by general tax

revenue through the Ministry of Public Health (Prakongsai et al., 2009). Similarly, Honda et

al., 2016 observed that the New Rural Co-operative Medical Care System in China provides

a government subsidy for smaller rural communities. These results were derived from

deductive and inductive analysis of data collected from actors interviews and focus group

dicussions.

Financial support from the government can also be in form of a re-insurance or solidarity

fund that ensures financial sustainability of schemes. Such fund can serve a dual purpose of

cautioning schemes against expenditure fluctuations in cases of local epidemics and bridging

deficits in small CBHIs (Wang & Pielemeier, 2012). Such solidarity mechanisms have to be

understood and normally agreed upon by CBHIs. Targeted exemption is another avenue for

government financing. Cambodia offers a good case in time where this option has worked at

the community level. Empirical study by Jacobs, Price & Oeun (2007) in Cambodia focusing

on exemptions of user fee and access to healthcare found that community leaders are

effective in determining who should benefit from the exemptions from the government equity

fund. Their assessment was rated as accurate to the extent that those selected were poorer

than those not selected. Data was collected from 199 pairs of patients using a pre-coded

structured questionnaire. Unstructured in- depth interviews were used to authenticate the data

collected using the questionnaires.

In addition, the government could counteract, to some extent, the regressive character of flat

contributions by households in many CBHIs. Of course the latter presupposes that the

taxation system itself is progressive, which is not necessarily guaranteed (WHO, 2010a). No

blueprints exist on how best CBHIs can be integrated in a national policy towards UHC. The

options at hand are path dependent and subject to the specificities of the national context. The

most frequent picture however seems to be that of a fragmented approach, with CBHI as one

of the strategies in a pluralistic environment where the CBHI model coexists with and

hopefully complements other financing modalities targeting specific population groups

(Carrin, 2003; Soors et al., 2010).

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Effective implementation of health policies that are meant to promote equity in healthcare

requires not only improved design but also monitoring and evaluation. Monitoring generates

results that are useful in modifying ineffective government policies as well as reinforcing the

effective ones. In essence monitoring implementation of health policies enables that

government to move toward results-oriented policies that maximizes the potential of players

in health to achieving the policy objectives. Empirical study of CBHIs in Nepal by Deutsche

Gesellschaft fur Internationale Zusammenarbeit (GIZ) (2012) reveals that supervision and

monitoring mechanisms were non-existence in all the schemes despite having been initiated

by the government. These results were derived from analysis of data collected from a survey

of CBHIs in Nepal using descriptive statistics.

Tabor (2005) argues that it may be impractical for CBHIs to measure that actual impact

particularly on health outcomes due to the high cost of gathering health performance data

from a small group of beneficiaries. Carrin et al (2005) suggests that the government can

monitor each CBHIs basic performance, track progress across various CBHIs over time as

well carrying out comparative analysis along the health financing functions. Monitoring

enables the government to proactively stimulate establishment of CBHIs, detect problems in

existing CBHIs and offer practical solutions to the problems.

In relation to training, the government and or donors can build the capacity of CBHIs

management team through provision of basic skills in accounting, management information

systems, setting up of insurance development plans and negotiating and contracting of

providers and other third parties, preparation of organization structures, statues and

regulations as well as monitoring and evaluation (Tabor, 2005). Moreover outcomes from

monitoring should be used as a natural input into the training activities (Carrin et al., 2005).


H0 Government stewardship does not have a moderating effect on equity in healthcare in

Kenya.

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2.5 Chapter Summary

This chapter presents a diagrammatic lay out of the proposed conceptual framework

stipulating the health financing functions as the independent variables – enrolment, mix of

contributions, risk pooling, strategic purchasing and the dependent variable equity in

healthcare. The function of stewardship in healthcare is presented as the moderating variable.

The chapter also reviews the theoretical framework where the theories that provide a

structured analysis for examining and understanding innovative healthcare financing and

equity through CBHIs in Kenya are discussed. Additionally, empirical review of studies

carried out by other researchers on the subject of CBHIs and equity in healthcare within the

health financing functions and other related concepts are presented. The hypotheses that were

to be tested in the study as suggested in chapter one are also highlighted at the end of

subtopic. The next chapter discusses the methodology that was used to examine innovative

healthcare financing and equity through CBHIs in Kenya.

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CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1 Introduction

This chapter covers the research methodology that was used in the study to establish examine

innovative healthcare financing and equity through CBHIs in Kenya. It discusses the research

philosophy, paradigm and design especially with respect to the choice of the design. It also

discusses the population of study, sample and sampling techniques, data collection methods

as well as data analysis and data presentation methods that was employed in the study. The

detailed description of the research procedure is important so that if another researcher

follows it, he or she will be able to reach similar conclusions without difficulty.

3.2 Research Philosophy and Research Paradigm

Saunders, Lewis & Thornhill (2007) defines research philosophy as the way data of a certain

phenomenon should be collected and analyzed. Kalof, Dan & Dietz (2008), Saunders et al.

(2016) identified two main philosophical dimensions to distinguish existing research

paradigms, namely ontology and epistemology. Ontology is concerned with the view of how

one perceives reality. Ontologically, existence of reality can be viewed independent of social

factors and their interpretations, termed which can either be objectivist (Saunders et al.,

2016; Neuman 2011).

Conversely, subjectivist or nominalist believes that reality is dependent on social actors and

assumes that individuals contribute to social phenomena or subjective (Saunders et al., 2016;

Wahyuni, 2012). On the other hand epistemology thinking is concerned with the way to

generate, understand and use the knowledge that is deemed to be acceptable and valid

(Saunders et al., 2016). The present study seeks to examine innovative healthcare financing

and equity through CBHIs in Kenya within the health financing functions which an

observable phenomenon based on credible data, facts. An epistemological philosophical

research was therefore adopted in this study.

This study adopted a positivism paradigm. Taylor et al. (2007); Jonker & Pennink (2010)

defined research paradigm as a broad view of phenomena or a worldview of a set of

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assumptions regarding how things work which serves as a thinking framework that directs

the behaviour of the researcher. The authors further argued that research paradigm can be

categorized as positivist and interpretive view. Where positivist view is objective and beliefs

that in the existence of one truth while interpretive view is based on the fact that there are

many different realities and truths due to the fact that different individuals have different

perceptions, needs and experiences (Kalof et al., 2008; Saunders et al., 2016).

Positivist holds that if different researchers were to study the same factual problem using

similar statistical methods then they will generate similar results (Saunders, Lewis &

Thornhill, 2012; 2016). This implies that there exists a universal generalisation of

phenomena. Interpretivists view contends that though generalization is possible as in the case

of positivist view, this generalisation may result from social conditioning thus understanding

phenomena need to be framed in the context of relevant law or dynamic of social structures

which creates observable phenomena within the social world (Saunders et al., 2016). In order

to gain knowledge on innovative healthcare financing and equity through CBHIs in Kenya

the researcher used positivism paradigm since it allows the researcher generate knowledge by

testing proposed hypothesis and generation of the phenomena taking into account the social

conditioning (Blaikie, 1993; Chia, 2002).

Saunders et al. (2016) identifies three research approaches, namely deductive, inductive and

abductive. Deductive approach involves development of theory and hypothesis that are

subjected to rigorous research while inductive approach is based on the principle of

developing theories from observed empirical data. In abductive research, the researcher

develops new theories or modifies existing ones through numerical and cognitive reasoning.

In the present study the researcher developed a theoretical and conceptual framework

deductively which were subsequently tested using qualitative and quantitative data.

Additionally the variables were operationalized in a way that enabled facts to be measured

quantitatively and the researcher was independent of variables that were being studied.

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3.3 Research Design

Cooper & Schindler (2014) defined research design as a general framework that outlines how

the researcher will go about answering the research questions. The author categorizes three

types of research design namely exploratory, descriptive and causal research designs.

According to Robson (2002) exploratory research design seeks new insights, questions and

assesses phenomenon in new light. Under this design the researcher is not compelled to

following a structured process thus the findings of this design are tentative.

Descriptive research design is based on the fact that the researcher narrates how various

events regarding a certain phenomenon occur without interfering with the subjects

understudy. Descriptive studies describe characteristics associated with the subject

population and explain the variables that exist between these variables in order to provide a

picture of a particular phenomenon (Cooper & Schindler, 2014). According to Gill &

Johnson (2002) descriptive surveys are concerned primarily with addressing the particular

characteristics of a specific population of subjects, either at a fixed point in time or at varying

times for comparative purposes. Causal research design is used to establish cause and effect

links between variables. It is used to establish whether X leads to occurrence or influences

variable Y (Creswell, 2014). Creswell (2014) posits that explanatory research design aims at

finding the causal relationship or to establish relationships between variables. Under this

design, the researcher maybe interested in finding the relationship between a particular

dependent variable and one or more independent variables. Based on this research design,

predictions on certain outcomes can be made.

Based on the objectives and specified hypothesis of this study the researcher employed

descriptive and causal research designs. Descriptive research design was used to describe

certain variables of interests such as enrolment, mix of contributions, risk pooling, strategic

purchasing, government stewardship and equity in healthcare. Causal or explanatory design

was used in establishing the causal effects relationships between the independent variables

and the dependent variables. Causal research design was also used to explain the magnitude

of the relationship between equity in healthcare and the independent and variables

(enrolment, mix of pre-payment contributions, risk pooling and government stewardship).

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3.4 Population

A population is the total collection of elements about which we may wish to make some

inferences (Cooper & Schindler, 2014). It is a group of individuals or people, or items under

consideration for statistical purposes (Collis & Hussey, 2009). Mugenda & Mugenda (2003)

define a target population as a group of individuals to which the researcher would like to

generalize his/her results from. Accordingly, a target population is defined as a universal set

of the study of all members of real or hypothetical set of people, events or objects to which

an investigator wishes to generalize the result. The population of this study was composed of

the 115 CBHIs registered in Kenya by the year 2015. The choice of the 115 CBHIs (see

appendix 3) is based on the data provided by KCBHFA, the umbrella association for CBHIs

in Kenya. The 115 CBHIs had been operation for period of between 1-15 years and were

drawn from four networks; Afya Yetu Initiative, ADS Western, ADS Nyanza and STIPPA.

3.5 Sampling Design

Sampling is a statistical method of selecting adequate units or elements from the study

population. Saunders et al (2016) postulate that a sample should be representative of the

study population for the results to be extrapolated back. The sampling design lay out the

process of drawing the study‘s sample.

3.5.1 Sampling Frame

According to Sanders et al., (2009) a sampling frame is a complete list of all the cases in the

population from which a sample is drawn from. It is a physical representation of the target

population and comprises all the units that are potential members of a sample (Kothari &

Garg, 2014). Lavrakas (2008) defines a sampling frame as a list of the target population from

which the sample is selected and that for descriptive survey designs a sampling frame usually

consists of a finite population. Cases listed in a sampling frame should enable the researcher

to answer the research questions and meet the study objectives. To this end a sampling frame

should be accurate, complete and up to date (Saunders, Lewis & Thornhill, 2009). The

sampling frame for this study was 115 CBHIs registered in Kenya under the umbrella

association of KCBHFA in 2015. Thirty three CBHIs were omitted from the study due to

lack of coherent data and intermittent periods of activity. The ensuing situation reduced the

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population of interest to 82 CBHIs. Pilot data was collected from three CBHIs, making the

number of CBHIs eligible for data collection in the main study to be 79 CBHIs.

3.5.2 Sampling Technique

In the current research data was collected from all possible cases. A census ensures

representation and unbiased selection of elements especially in a small population (Saunders

et al., 2016). The target population was therefore 79 CBHIs registered by the umbrella body

(KCBHFA) in Kenya that had complete and coherent data.

3.5.3 Sample Size

As mentioned above, the target population for this study was 79 CBHIs that had coherent

data. From the information obtained from the CBHIs, the researcher noted that the CBHIs

had an average of four members of management team. For researcher to make inference

about the CBHIs, responses were sought from four members of each CBHIs management

team. This translated to 316 members of CBHIs management team. The study estimated a

sample based on this target population using Yamane (1967) formula. This formula is

preferred over other formulae due to its simplicity and it is scientific. This formula is

specified as shown:

Where n is the sample size, N is the population and denotes the precision error. Based on

this formula and a sample size of 316 and precision error of 1 percent (0.01) the sample size

was calculated as shown:

The sample size of this study was 306 members of management teams from 79 CBHIs

registered under KCBHFA in 2015.

3.6 Data Collection Methods

To provide both the descriptive and causal picture of innovative healthcare financing and

equity through CBHIs in Kenya within the health financing functions study collected both

primary and secondary data. A structured questionnaire used in collection of primary data on

n=316 / [1+316 (0.012)]= 306 ………………………………………………………………..3.1

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all constructs in the study- enrolment, mix of contributions, risk pooling, strategic

purchasing, government stewardship and equity in healthcare. Additionally, a secondary data

sheet was used to collected data on three constructs- enrolment, mix of prepaid contributions

and equity in healthcare. The table below shows the type of data and data collection

instrument that was used for each study constructs.

Table 3.1 Type of data and data collection tools

No Variables Indicator variables Type of data Data collection Instruments

1 Enrolment Affordability of

contributions

Unit of membership

Timing of collection

Trust

Primary and

Secondary

Questionnaire

Secondary data sheet

2 Mix of

prepaid

contributions

Mix of prepaid

contributions

Primary and

Secondary

Questionnaire


3 Risk Pooling Social solidarity

Mechanisms for

enhanced risk pooling

Size of pools

Primary Questionnaire

4 Strategic

Purchasing

Contracting

Provider payment

mechanism

Referrals

Waiting period


5 Government

Stewardship

Design

Training

Monitoring

Co-financing


6 Equity in

Healthcare

Increased access

Equity in Contributions

Sustainability

Quality of Care

Primary and

Secondary

Questionnaire


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3.6.1 Questionnaire

A questionnaire was developed by the researcher on the basis of the research questions.

According to Best & Kahn (2007) a questionnaire is used to collect factual information from

a large number of the respondents. They also allow respondents time to think about

questions (Cooper & Schindler, 2014). The questionnaire was mostly structured and the

respondents were provided with instructions to ensure that they understand the questions.

Structured questions reduce data collection time while unstructured questions encourage the

respondent to give in depth responses thereby enhancing the quality of data collected

(Cooper & Schindler, 2014).

Both closed and open ended questions were used to capture the quantitative and qualitative

data. Open-ended questions are easy to construct, permit free responses from the

respondents, stimulates respondents‘ feelings allowing them to present a clearer picture of

the subject at hand. Shortcomings of open-ended questions include; collecting responses that

are difficult to classify and capturing information that is irrelevant to the study objectives.

Open-ended questions were used to capture any information that may have been omitted.

Closed-ended questions enable the respondents to select answers among the stated

alternatives. They require minimal writing and hence they save on time and money (Saunders

et al., 2016). According to Kothari & Garg (2014), closed-ended questions are also easy to

administer, compare and analyze. Closed-ended pose a challenge of limiting the respondent

to researcher‘s choices (Saunders et al., 2016). The closed-ended questions sought responses

in a five point likert-type scale. The responses ranged from (1) Strongly disagree (2) disagree

(3) Neutral (4) Agree (5) Strongly Agree. To measure the degree of social solidarity in risk

pooling the researcher sought responses that ranged from (1) None of the cost (2) Some of

the cost (3) Half of the cost (4) Most of the cost (5) All of the cost 1 = None of the cost, 2 =

Some of the cost, 3= Half of the cost 4 = Most of the cost, 5 = All of the cost. In addition

dichotomous scales questions were used. The questionnaire tested with a few members of the

population for further improvements. This was done in order to enhance its validity and

accuracy of data collected for the study (Dillman, 2007; Sauder et al., 2016).

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3.6.2 Secondary Data Sheet

Like other organizations, CBHIs store a lot of information. Practically, not all the

information was relevant for the current study. The researcher developed a secondary data

sheet that captured only the information that was deemed relevant for this study. For the

purpose of this study, secondary data was collected on the number of households targeted,

number of households enrolled, type of product, premiums per product and product uptake,

utilization of healthcare, longitudinal data on total premiums collected (from households,

government and donors), healthcare costs reimbursements and administration cost.

3.7 Research Procedures

The researcher developed a questionnaire on the basis of the research questions. The

questionnaire was first piloted on a part of the study population before embarking on the full-

scale research (Welman, Kruger & Mitchell, 2007:12; Kumar, 2011). A pilot study or pilot

test is a small-scale study undertaken to explore areas that need more development and

refinement. Litwin (1995:60) posits that pilot testing also predicts difficulties that may arise

during subsequent data collection, which might otherwise have gone unnoticed. Litwin

(1995), Anderson (2004) & Welman et al. (2007) indicate that a pilot study aids in detecting

possible flaws or errors in the measurement procedures and to identify unclear or

ambiguously formulated questions early enough.

This gives the researcher a chance to correct errors and to redesign problematic parts before

the data collection tool is distributed to the respondents. Anderson (2004:218) posits piloting

as process that ensures that a survey generates valuable data. Saunders et al (2016) propose

that a pilot should be carried out on a minimum number of 10 persons, who are of similar

ability and background to the target population. This is done to obtain an assessment of the

validity of the questions, as well as the likely reliability of the data that was collected.

Saunders et al. (2009) suggest that a pilot should be carried out on a minimum number of 10

persons, who are of similar ability and background to the survey target population. This is

done to obtain an assessment of the validity of the questions, as well as the likely reliability

of the data that was collected.

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In the current study, the following three steps were undertaken: First, the questionnaire was

circulated to three experts in finance, two of which was expert‘s health financing experts in

health financing. Comments were sought on the questionnaire‘s representativeness and

appropriateness. The researcher also worked closely with her supervisors in improving

reliability and structure of the questionnaire. Their recommendations were used to make the

necessary amendments before pilot testing. Their recommendations were used to amend the

layout contents and instructions. Secondly, the questionnaire was piloted and thirdly, the pilot

test data was coded before preliminary analyses were carried out. Content validity was

achieved in two ways. First, through administering the instrument in English, the language

that was familiar and well understood by all respondents who participated in the study.

Secondly, as mentioned earlier, the pilot study was carried out prior to the main study to test

the content of data collection instrument.

3.7.1 Pilot Study

Reliability and validity of measurements are important for the interpretation and

generalization of research findings. Valid, reliable and comparable measures performance of

CBHIs within the health financing and stewardship functions are critical components of the

evidence base for health policy. In this study, a draft questionnaire was pilot tested on 10

respondents drawn from Ithaeni, Kilome and Enzai CBHIs under the network of BIDII. The

respondents of the pilot study were however not included in the study sample.

The respondents were encouraged to comment on the clarity and relevance of the questions

as well as to make suggestions on the instructions. Ambiguous and vague questions were

identified from difference in understating of individual questions by the respondents while

various insufficiencies in the questionnaire were discovered. The comments and suggestions

put forth by the respondent were also used to improve various aspects of the questionnaire.

Questions that did not pass the validity and reliability tests were dropped from the final

questionnaire. The pilot test also used in testing the adequacy of the analysis tools. The pilot

test data was later coded before conducting preliminary analysis to test for reliability using

Cronbach‘s alpha.

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3.7.1.1 Pilot Test Results

The study used the Test-Retest/Stability Reliability which compares results from an initial

test with repeated measures later on, the assumption being that if the instrument is reliable

there will be close agreement over repeated tests if the variables being measured remain

unchanged. The Kappa score, specificity, and positive predictive values (PPVs) were also

used to measure reliability and validity, respectively. Reliability was tested using Cronbach‘s

alpha. Cronbach‘s alpha is known as a good measure of reliability. The values of Cronbach‘s

alpha ranges between 0 and 1 where the Cronbach‘s alpha values between 0.8 and 1.00

indicate a considerable reliability, values between 0.70 and 0.80 indicate an acceptable

reliability while values below 0.70 are considered less reliable and unacceptable (Nunnally,

1978). In this study, Cronbach‘s alpha coefficient which is a measure of internal consistency

was used to assess reliability. Reliability indices for the pilot study ranged from 729 to 0.941.

This suggested acceptable levels of internal consistency. This implies that the items included

in measuring different constructs were indicative of the same underlying disposition; equity.

Reliability of the constructs is shown below in table 3.2.

Table 3.2: Reliability Test of Constructs

Endogenous and Exogenous

Constructs

Reliability Cronbach’s

Alpha

Numbe

r of

Items

Commen

ts

Enrolment 0.941 12 Accepted

Pre-payment mix 0.743 5 Accepted

Risk pooling 0.838 15 Accepted

Strategic purchasing 0.729 5 Accepted

Government stewardship 0.874 20 Accepted

Equity in health care 0.922 26 Accepted

Findings indicate that enrolment in CBHIs had a coefficient of 0.941, equity in healthcare

had a coefficient of 0.922, government stewardship had 0.874, risk pooling had 0 0.838, pre-

payment mix had a coefficient of 0.743 and strategic purchasing had an alpha value of

0.729.

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Table 3.3: Kaiser-Meyer-Olkin and Bartlett's test

Test Value

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.654

Bartlett's Test of Sphericity Approx. Chi-Square 327.307

Df. 9

Sign. 0

In addition, this study tested for both convergent and discriminant validity. The Bartlett‘s test

for sphericity and Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was ran.

Results in table 2.3 show that KMO test had a score of 0.654, which was well above 0.50

levels, indicating acceptable degrees of sampling adequacy for the variables (Tabachnick &

Fidell, 2014; Brett, Ted & Andrys, 2010). The results also showed that the Bartlett‘s test of

Sphericity had a Chi-Square value of 327.307 with a significant value of 0.000.

3.7.2 Reliability of the Instruments

According to Mugenda & Mugenda (2003), the accuracy of the data collected largely

depends on the data collection instrument in terms of validity and reliability. A scale or test is

reliable if measurements made under constant conditions are likely to give the same results,

assuming that no changes in the basic characteristics being measured occur. Reliability refers

to the consistency of the scores obtained. Reliability of the scale for the constructs describing

the variables of the study was found to be sufficient because all the items and composite

reliability coefficients were equal to or above 0.6 which is set as the acceptable minimum

(Nunnaly, 1978; Cronbach, 1951). Reliability is a measure of how stable, dependable,

trustworthy and consistent a test is in measuring the same thing each time. In research

measurement scores normally constitute the true component and the error component

(Sekaran & Bougie, 2016).

Accordingly, the reliability is higher when the degree of error in an instrument is lower

(Kumar, 2007). The analysis of reliability is specifically important when there are several

items that measure the same concept or phenomenon (before constructing an index or scale)

so as to minimize errors of single items. Reliability may be measured in terms of stability or

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consistency. The stability aspect of reliability refers to a comparison of the same measure for

the same sample at two or several points in time, i.e., test-retest whereas internal consistency

reflects homogeneity of the several items comprising a scale (Cooper & Schindler, 2014).

3.7.3 Validity of the Instruments

In the simplest terms, a test can be judged valid if it measures what it is intended to measure.

According to Mugenda & Mugenda (2003) validity is the accuracy and meaningfulness of

inferences based on research results. It comprises of the degree to which results obtained

from the data analysis represent the subject of the study. Content validity that refers to

whether the items measure the substance or subject matter they were intended to measure

was another important aspect addressed (Williams, 2007). It is the extent to which evidence

and theory are in congruence, thus supporting the interpretations of test scores entailed by

proposed uses of tests (Saunders et al., 2016). Construct validity therefore, seeks to ensure

that the test is actually measuring the intended attribute and not any other extraneous

attributes. Validity takes different forms including content, criterion-related and constructs

validity (Creswell, 2014).

In this study, assessment of content validity was accomplished by determining of the degree

to which data collected using a particular instrument represents a specific domain or content

of a particular concept. Mugenda & Mugenda (2003) contend that the usual procedure in

assessing the content validity of a measure is to use a professional or expert in a particular

field. To establish the validity of the research instrument, the researcher sought opinions of

management staffs and experts including the study supervisors. The questionnaire was

validated by discussing it with randomly selected management officials of the CBHIS. Their

views were evaluated and incorporated to enhance content and construct validity of the

questionnaire. Additionally, the researcher employed face validity method to evaluate the

extent to which the researcher believes that the data collection tools are appropriate. The

researcher requested experts in research to review items in the tools that were used. Invalid

items were removed before the research is conducted. Furthermore, the researcher assessed

the responses and non-responses per question to determine if there was any technical

dexterity with the questions asked. After piloting testing and refining the questionnaire, a

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research expert with the help of two research assistant, administered the refined questionnaire

to the sampled respondents.

3.7.4 Administration of the Instruments

After piloting testing and refining the questionnaire, a research expert with the help of two

research assistant, administered the refined questionnaire to the sampled respondents. A total

of 318 questionnaires were distributed to the respondents. The questionnaires were

accompanied by two letters; an introductory letter from United States International

University detailing the purpose of the study and a letter from the researcher addressed to the

respondents explaining the scope and purpose of the study in addition to introducing the

research assistants. To minimize non-sampling errors emanating from data collection

procedures the research assistants were trained on propriety and correct research procedures.

Kothari & Garg (2014) points out that non-sampling error stem from incorrect data collection

procedures such as ineffective interviewers, bias from interviewers and non-response errors.

Knowledge gained by the researcher during pilot testing enabled the researcher to train the

research assistants. Further, the researcher coordinated the entire data collection process.

Data was collected between 23rd

March, 2016 and 14th

April, 2016. In total, 224 complete

and usable questionnaires were received from the respondents. In addition 79 secondary data

sheets were received from each CBHI. Prior to data analysis, the data was cleaned, grouped

on the constructs before responses of each question were coded. The data was then entered

and analyzed in the software package SPSS Version 20.

3.7.5 Ethical Issues

According to Cooper & Schindler (2014) ethics is standard behaviour that guides our

behaviour and relationship with others. In research, ethics refers to the suitability of a

researcher‘s behaviour in relation to the study subject and those affected by the study. Ethics

therefore touches on every aspect of any research from formulating the research topic,

research questions, literature review, research methodology, analysis and interpretation of the

findings. In effect, the entire research process should be meticulously and morally defensible

to those affected by it (Saunders et al, 2016).

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Before commencing on the field data collection exercise, the researcher sought approval

through a letter of recognition from USIU, and subsequently obtained a research permit from

NACOSTI. The data collection instrument was developed in such a way that the study

procedures do not cause any harm or emotional distress to the respondents. Due to sensitivity

of some information to be collected, the researcher holds a moral obligation to treat the

information with utmost propriety. The research was based on voluntary participation;

participants were reassured of confidentiality and were not under duress in any way to

answer any questions they feel uncomfortable about.

Participants were fully informed about the procedures involved in the research and their

consent was sought before commencing. The research assistants were required to explain to

the respondent the scope and purpose of the study, and further assured them of

confidentiality. There was no infringement of the respondents and his/her human rights were

also observed. The consent of the respondents was required before the questionnaire was

distributed to them, and they were assured of confidentiality of the information under the

study. Honesty and integrity was maintained throughout the study period. Another factor that

was taken into consideration was that the research process entailed a number of steps which

included the research questions, literature review, research design, data collection procedure

and data analysis and interpretation of the findings. Items in the instruments for data

collection were clear, simple and did not have leading to answers.

3.8 Data Analysis Methods

Ordinarily, the amount of data collected in a study is rather extensive and research questions

and hypotheses cannot be answered by a simple perusal of numeric information. According

to Chadran (2004) the data should be processed and analyzed in an orderly and coherent

manner. Quantitative information is usually analyzed through statistical procedures.

Statistical analyses cover a broad range of techniques including decretive and inferential

statistics. Before processing the responses, the completed questionnaires were checked for

completeness and consistence upon which any additional information needed for clarity was

sought from the respondents. The data was then coded in SPSS ready for analysis. The raw

primary data collected was coded prior to being input into SPSS statistical analysis software.

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Once coded, the data was then cleaned to ensure accuracy and completeness of the

information obtained. In analyzing the data collected, both descriptive and inferential

statistics was utilized. The quantitative data that was obtained from the questionnaires was

coded and keyed into statistical package of social science (SPSS) analysis software. The

current study employed various data analysis methods to answer the research questions and

meet the objectives of the study. In this sub-section the data analysis method that was used to

analyze the secondary and primary data is discussed.

Analysis of Secondary Data

The quantitative data was first coded before applying descriptive statistics in grouping the

responses into various categories including percentages and frequency distribution to indicate

the variable values and the number of occurrences in terms of frequencies and averages.

Patterns and trends were also derived from the data.

3.8.2 Analysis of Primary Data

The primary data was analyzed using both descriptive and inferential analysis.

Descriptive analysis

The data was analyzed using descriptive techniques descriptive statistical tools (SPSS

Version 20 and MS. Excel 2010) were employed. Descriptive statistics enables a researcher

to compare the values numerically (Saunders et al., 2016). A likert scale was used to rate the

extent of agreement on the given constructs of enrollment strategy for the CBHIs in a scale of

1 to 5 where 1 is the least extent whereas 5 is the maximum indicating the level of

agreement. The results from the collected responses were analyzed based on means and their

standard deviations to show the variability of the individual responses from the overall mean

of the responses per each aspect of enrollment strategy. The mean results are therefore given

on a scale interval where a mean value of up to 1 is an indication of a strong extent of

disagreement; 1.1 – 2.0 is disagree; 2.1 – 3.0 is a moderate extent of agreement (neither agree

nor disagree), 3.1 – 4.0 is agree and a mean value of 4.2 and above is an indication of a

strong extent of agreement. The responses were also analysed in terms of frequencies.

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3.8.2.2 Correlation Analysis

The relationship between independent variable and the constructs under dependent variable

was established using Karl Pearson‘s coefficient of correlation. Correlation analysis is a

statistical measure of strength of linear relationship between paired variables. Commonly

referred to as coefficient correlation, it is one of the methods used measure correlation

between latent variables. In the current study Pearson correlation coefficient (r) was used to

establish the degree of relationship between sub-constructs of the independent variable -

enrolment (affordability of contributions, unit of membership, timing of collections and trust)

and independent variable construct; mix of contributions, risk pooling and strategic

purchasing and the sub-constructs under the dependent variable - equity in health care

(healthcare access, equity in contributions, quality of care and sustainability). Pearson

correlation coefficient was deemed suitable for this study since the data collected for

independent variables and the constructs under the dependent variable was numerical.

Additionally, one of advantage of SEM PLS is its ability to calculate a predicted score Ŷ (for

each sub-construct or construct) from predictors of a sub-construct or construct. Ŷ is a

composite or a weighted linear combination of the predictors. The predicted score were used

in Pearson correlation coefficient to establish degree of relationship between the dependent

variables sub-constructs or construct and the sub-constructs from dependent variable. The

coefficient denoted by ‘r’ ranges from -1 to +1. Pearson correlation coefficients between -1

and +1 signify a weaker positive and negative relationship while a value of 0 implies a

weaker positive and negative relationship while a value of 0 implies independent relationship

between variables in question (Creswell, 2014 ; Saunders et al., 2016).

Correlation coefficient with a value of +1 means a perfect positive correlation. This shows

that the two variables have a precise relationship where an increase in one variable results to

an increase of the other variable. On the other hand, a value of -1 represents a negative

correlation, signifying a precise relationship between variables, where an increase in one

variable results to decrease of the other variable (Saunders et al., 2016).

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4.8.2.3 Multicollinearity Test

Multicollinearity is the undesirable situation where the correlations among the independent

variables are strong. In other words, multicollinearity misleadingly bloats the standard errors.

Thus, it makes some variables statistically insignificant while they should be else significant

(Martz, 2013). Tolerance of a respective independent variable is calculated from 1 - R2. A

tolerance with a value close to 1 means there is little multicollinearity, whereas a value close

to 0 suggests that multicollinearity may be a threat (Belsley, Kuh & Welsch, 2004). The

reciprocal of the tolerance is known as Variance Inflation Factor (VIF). Equally, the VIF

measures multicollinearity in the model in such a way that if no two independent variables

are correlated, then all the VIF values will be 1, that is, there is no multicollinearity among

factors. But if VIF value for one of the variables is around or greater than 5, then there is

multicollinearity associated with that variable (Martz, 2013).

3.8.2.4 Structural Equation Modeling

Thirdly, multivariate data analysis was conducted using structural equation modelling (SEM)

partial least square (PLS) approach. In this research, SmartPLS(R)

software, a SEM PLS

software Version 3 was employed to develop the measurement and structural model under

study, test hypothesized relationships between variables and bootstrap (Ringle, Sarstedt,

Schlittgen & Taylor, 2013). SEM uses variates in both the measurement and structural

models, referred to as outer and inner models. Each indicators of a construct acts collectively

to define the construct in a measurement models whereas in the structural model the variates

are related to one another in both interdependence and correlational relationships (Hair et al.,

2010).

SEM belongs to a family of multivariate techniques such as multiple regression analysis,

MANOVA and interdependence techniques such as factor analysis. Unlike other dependence

techniques, SEM has the capability of testing for relationships simultaneously. Additionally,

SEM uses multiple measures for each construct an aspect that allows the estimation

procedure to directly correct for the measurement error. This principal difference between

SEM and interdependence techniques is that it allows the relationships between constructs to

be estimated more accurately in SEM (Hair, Black, Babin & Anderson, 2010; Babin &

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Svensson, 2012). PLS maximizes the variance explained for all endogenous variables by

working with a block of variables as opposed to latent variables in model estimation. The

variances are generated through a series of ordinary least squares regression. PLS based

equation modeling assumes that the sample distribution reasonably represents the distribution

of the population of interest. Since this method does not assume normality it uses boot

strapping to obtain standard errors during hypothesis testing. Bootstrapping is a non-

parametric statistical technique that is used to drawn statistical inference by estimating the

properties of an estimator. The strength of bootstrapping lies in its ability to draw

conclusions about the attributes of the study population strictly from the sample at hand

instead of relying on unrealistic assumptions about the population being studied (Hair, Ringle

& Sarstedt, 2011).

SEM was appropriate for this study since the researcher had multiple exogenous constructs

which were represented by several measurable variables. Additionally, it permitted automatic

correction of measurement errors among constructs as the simultaneous estimation of

measurement and structural models were being executed (Hair et al., 2010). The

development and analysis of the model was carried out in a six stages decision process as

follows;

Stage 1 Defining individual constructs

Stage 2 Developing and specifying the overall measurement model

Stage 3 Designing a study to produce empirical results

Stage 4 Assessing the measurement model validity

Stage 5 Specifying the structural model

Stage 6 Assessing the structural validity

Stage 1: Defining individual constructs

Good theoretical and structural grounding is critical for establishing causality relationships in

SEM. This is especially important in cross-sectional studies (Hair et al., 2010). A pretest on a

section of the target population was carried out to determine the suitability of the items as

well as reliability and validity of the constructs.

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Stage 2: Developing the overall measurement model

In this stage, each latent constructs to be used in the model were identified and individual

variable were assigned to the latent constructs. This process was represented in a path

diagram for ease of labeling notation for indicators, constructs and relationships between

them. The error item for each indicator was also specified at this stage. Partial Least Square

Path Modeling (PLS) was used to develop the measurement model. PLS belong to the family

of multivariate data analysis techniques that enables the researcher to simultaneously assess

the measurement of latent constructs and test the hypothesized relationships among the

constructs within the same analysis. The technique achieves this by performing iterative set

of factor analysis and ordinary least squares regressions until the difference in the average r2

of the constructs become non-significant (Gefen, Rigdon & Straub, 2011). Advantages of

PLS technique include its ability to can accommodate both reflective and formative

measurement models as well as its capability of holding many constructs and indicators

without leading to estimation problems (Henseler et al. 2009, p.279-281). According to

Fricker, Kreisler, and Tan (2012) reflective indicators are presupposed to be affected by same

underlying concept; the latent construct. Therefore, a change in the underlying latent

construct manifests in all its indicators. As a result, the reflective indicators should be

correlated. On the contrary, the formative indicators are not assumed to cause variation in

latent constructs and thus they are not presumed to be correlated. The current study used

partial least squares estimation to examine the causal relationship among latent variables

(Hair, Anderson, Tatham & Black, 1998).

Stage 3: Designing a study to produce empirical results

Having specified the basic model, the researcher focused on research design and estimation.

With regard to design the researcher collected ordinal data. The data was analyzed through

correlations due to the required interpretive and statistical issues. Unlike other multivariate

methods of data analysis, SEM is more sensitive to the sample size. In deed some of the

alogarithms applied in SEM are unreliable in small samples. As in any other statistical

method, sample size forms the basis of estimation of the sampling error. Generally, larger

samples generate stable and more likely replicable solutions (Hair et al., 2010). Kline (2011)

advocates for a sample size of 200 or larger. Kenny (2012) posits that the SEM model

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performs optimally in sample sizes ranging from 200-400 since the main test of model fit is

sample size dependent. The sample size for the study was 318 members of CBHIs

management team. The sample size was thus adequate enough to allow the model to run and

was representative of the population of interest. Bootstrapping determines the distribution of

the empirical sample by resampling form a sample with replacement (Kenny, 2012). In the

current study bootstrapping was used to draw 500 samples with replacement from the

original sample 318.

Stage 4: Assessing the measurement model validity

Having specified the measurement model, collected sufficient data and made critical and

having determined the estimation technique, the next step assessing the measurement model

validity. Hair et al. (2010) suggest four criteria of assessing model fit in PLS, namely;

construct unidimensionality, and construct reliability, convergent validity and discriminant

validity. Construct unidimensionality is used to confirm that the indicators used to measure a

particular latent construct only measures that specific construct. Further the authors put

forward that exploratory factor analysis and or CFA can be used to evaluate this criterion.

In consecutive steps, SmartPLS was used to measure the construct, composite and

convergent reliability and discriminant validity. Construct reliability assess consistency of

the scales in measuring a particular latent construct measures. Construct reliability was

assessed by computing the composite reliability and the Cronbach alpha of the constructs.

The Cronbach alphas were all above the 0.6 threshold as specified for PLS analysis (Hair et

al., 2006). The values ranged from 0.74 and 0.99 which indicated good reliability. Composite

reliability measures were evaluated by using SmartPLS (Hensler et al., 2009). Convergent

validity measures that ability of indicators relevant latent constructs to actually measure a

particular construct. The current research used CFA assess convergent analysis at a statistical

significant level of above 0.5 (Nunnally, 1978). Additionally, average variance extracted

(AVE) was used to measure convergent validity. A 0.5 threshold was adapted indicating that

the latent constructs should account for at least fifty percent of the variance in the items

(Hair, Black, Babin, Anderson & Tatham, 2006). Only items significance levels of each test

were retained for further analysis. Discriminant validity measures the inter-constructs

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covariances. Forenell Lacker Measure was used to assess the discriminant validity of the

outer model. The Fornell Larker measure compares the AVE to the highest squared

correlation of each construct (Fornell & Bookstein, 1982; Hair et al., 2010). Items that did

not discriminate themselves well were deleted as shown in appendix v.

Stage 5: Specifying the structural model

After development and evaluation of the measurement model as well as validation of the

study measures, the next step involves the specification of the structural to test the

hypothesized relationships between the constructs based on the proposed theoretical model

(Fricker et al., 2012). This task was carried out in two steps. First, the path diagrams

indicating the structural relationships among the latent constructs were constructed. In the

second step involved inclusion of the measurement specifications. The two steps approach

was preferred since it ensures accurate representation of reliability of the indicators, hence

avoiding the interaction of the two models in the integrative model (Hensler et al., 2009).

Stage 6: Assessing the structural validity

In this final stage, the validity of the theoretical measurement model was measured against

the sample data collected. This was achieved by evaluating the path coefficients, t-values,

overall model fit and significance levels for the structural paths to determine the causal

relationships among research constructs as hypothesized in the integrative model.

Bootstrapping was used to measure the strength and direction of the hypothesized

relationship. Initially, the significance testing of the independent variables was conducted

without the mediator. The mediating variable (government stewardship) was then included in

the model and the resultant t- values were generated. The second assessment of model fit

allowed an evaluation of the integrative model fit and individual parameters estimates for the

structural path in the structural regression model. The statistical objective of PLS is to show

high r2 and significant t-values, thus rejecting the null hypothesis of no effect. Parameters

with an absolute t-value greater than 1.65 indicate a significance level of 0.1 (i.e. p<0.1), 1.96

indicate a significance level of 0.05 (i.e. p<0.05), those with an absolute t-value over 2.58

present a significance level of 0.01 (i.e. p <0.01), and those with an absolute t-value over

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3.26 present a significance level of 0.001 (i.e. p<0.001) (Hair et al., 2010; Fricker, Kreisler &

Tan, 2012). Model measure fit criterion is presented in Table 3.4.

Table 3.4: Measures to Fit PLS Model

Measures Procedure Statistical Criterion

Construct

Unidimensionality

Confirmatory Factor Analysis Factor Loading > .70

Construct Reliability Reliability Analysis Cronbach Alpha >0.6

Convergent Validity Factor Analysis

Composite Reliability

Variance

Factor Loadings > .50

Composite Reliability > .70

Average Variance Extracted > .50

Fornell-Larker Measure AVE> (Highest correlation for factor)2

Discriminant Validity Coefficient of Determination R2 > .19 (weak)

R2 > .33 (moderate)

R2 > .57 (substantial)

Sources: Hair et al., 2006; Hair et al., 2010; Vinzi, Trinchera & Amato, 2010; Fricker et al.,

2012).

3.8.2.5 Path Model representation for Equity in Healthcare

The overall model shown in figure 3.1 was constructed to test the hypothesized relationships.

The latent variable are depicted by the elliptic shapes (e.g., ) while the indicators

measuring the latent constructs are represented inside rectangular frames (e.g. ).

Hypothesized directional relationships of one variable on another are depicted with a line

with a single arrow (e.g., →). For example, the indicators scale items for enrolment include

AFF1, TM3, and TRU2 among others.

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3.8.1.3.7 Path Model representation for Equity in Healthcare

Figure 3.1 Path model for Equity in Healthcare

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Table 3.5 Items of measure for study constructs

Exogenous Variable Variable Codes Wording

Indicators

1. Enrolment Affordability AFF 1 We give members a chance to allocate premium among preferred products

AFF 2 Members can pay premium in kind e.g. through farm produce or labour

AFF 3 We give subsidies and exemption of premiums for extremely poor and

vulnerable

AFF4 We encourage members to use savings-linked premium payment mechanisms

such as rotating saving groups (Chamas), MPESA

AFF5 Members can make irregular payments of premium

Unit of UM1 The CBHIs membership is based on coffee, tea, villages or mutual benefit

Membership societies or administrative areas

UM2 The CBHIs have adapted households as unit of membership

UM3 CBHIs encourages members to join when they are healthy

UM4 CBHIs membership is open to poor and vulnerable groups

Timing of TM1 Members pay in a single annual premium /contribution

Collections TM2 Members can pay their premiums in installments

TM3 Premium payments correspond with income from e.g. harvest, sale of

livestock or salary payment

TM4 Premium payments are linked to loans from SACCOs and banks

TM5 Mobile premiums payments are allowed

Trust TRU1 Members interact with the scheme‘s administrative / management team about

their needs, concerns and make suggestions for improvements

TRU2 Members participate in setting of benefit package

TRU3 Members participate in setting the premiums

TRU4 The CBHIs uses existing Chamas, community development projects and

credit schemes as entry points for CBHIs membership

TRU5 Members of the scheme are willing to cover the poorest and vulnerable groups

in the community such as orphans and disabled

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Table 3.5 Items of measure for study constructs (Continued)


Indicators

Pre-payments Mix of MC1 Members‘ contributions are adequate in meeting the cost of the set benefit

Contributions package

MC2 NHIF covers costs of services not covered by the CBHIs

MC3 The CBHIs receives financial support from donor(s)

MC4 The poor and vulnerable members of the CBHIs are covered through

government and or donor subsidies

Risk Pooling Enhanced ERP1 Members of CBHIs comes from a wide range of social economic background

Risk Pooling ERP2 CBHIs targets a large geographical /administrative area

and Social ERP3 Community members are encouraged to join CBHIs when they are healthy

Solidarity ERP4 We have a waiting period before one can benefit from CBHI

ERP5 CBHIs has other branches in other geographical or administrative areas

ERP6 CBHIs reinsures its risks

ERP7 CBHIs has a partnered with the county / national government and or NHIF

ERP8 CBHIs merged with other CBHIs to form a network or a federation

ERP9 Members of the CBHIs have expressed the opinion that if they would not need

healthcare themselves, at least they had done something good for the

community by contributing to the insurance fund

ERP9 Members of the CBHIs are willing to contribute to pay for health care services

used by the sick

ERP10 Members of the CBHIs are willing to contribute to pay for health care services

used by the poor

Strategic Strategic SP1 We have signed a contract with all our health services providers

Purchasing Purchasing SP2 We only select providers who are accredited by NHIF

SP3 The providers must provide health services according to conditions put

forward by the CBHIs

SP4 We allocate resources based on population needs

SP5 CBHIs has been successful in negotiating agreeable terms and contract with

\service providers – in terms of service quality, fee, and reduction in

unnecessary services/prescription (moral hazard)

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Indicators

Government Design The Government recommends

Stewardship AD1 Startup of CBHIs when a minimum percentage of population in enrolled

AD2 A waiting period

AD3 Enrollment of households as opposed to individuals

AD4 A flexible premium collection system

AD5 A benefit package that reflect the needs of the target population

AD6 A standard treatment protocols for members of CBHIs

AD7 A standard referral procedure

AD8 Consolidation of CBHIs through a federation or a network

AD9 Creation of a risk equalization fund or a reinsurance mechanism

AD10 Community participation in management and decision making

Monitoring The government

MO1 Tracks the progress of CBHIs through time

MO2 Monitors the basic performance of CBHIs

Training The government organizes trainings on

TR1 Determination of benefit packages

TR2 Determination of contributions

TR3 Collection of contributions

TR4 Claims processing

TR5 Use of Management Information Systems

TR6 Establishment of Health Insurance Development Plans

TR7 Exchange visits

Co- The government and/ or donors

Financing COF1 Partially or fully subsidizes the poorest and vulnerable members of the

community

COF2 Has set a solidarity fund for financing epidemics and deficits of the CBHIs

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Endogenous Variable Codes Wording

Variable Indicators

Equity in Healthcare ACC1 There is distribution of enrolment across income categories

Healthcare Access ACC2 The contracted providers are within the proximity of covered population

ACC3 We cater for transport / accommodation cost related to healthcare utilization

ACC4 The covered population is entitled to similar benefits

ACC5 The number of members seeking health services has increased in the past 12

months

Equity in AMC1 Everyone pays the same amount

Contributions AMC2 Everyone pays an equal amount of their income

AMC3 We allow flexible premium payments

AMC4 We offer allow members to match premium or products to their income

AMC5 We offer premium subsidies

AMC6 We allocate a larger claim budget for low cost products

Quality of QO1 The CBHIs has a standard client compliant management mechanism

Care QO2 Members have complained about long queues before being seen

QO3 Members have complained on availability of health services

QO4 Members have complained about lack of key prescribed medicines

QO5 Members have raised concerns relate to cleanliness

QO6 Members have raised concerns on availability of trained staff in the contracted

health facilities

QO7 The CBHIs have put in place mechanisms to check on patient perceived

quality of care in contracted health facilities on issues concerning waiting

time, availability of staff, services, drugs and supplies

QO8 There are other organization(s) that conduct quality checks in the contracted

health facilities

QO9 These organizations share their findings with the CBHIs

137


Endogenous Variable Codes Wording

Variable Indicators

Sustainability The administrative committee has basic skills in

i) Administrative FS1 Setting of contributions

and Managerial FS2 Collection of contributions and compliance

Capability FS3 Determination of the benefit package

FS4 Claim management

FS5 Marketing and communication

FS6 Contracting with providers

FS7 Use of management information systems

FS8 Accounting

ii) Financial FS9 CBHIs have partnered with organizations that assist in collection of premiums

Sustainability FS10 Premiums are not paid on time

FS11 The CBHIs is funded through a mix of contributions from county / national

government /donors and members contributions

FS12 Government and or donors‘ covers health cost for those who cannot afford to

pay premiums

FS13 Chronic conditions are covered by the CBHIs

FS14 The CBHI have put in place mechanisms to check whether the invoices sent

from the health facilities are correct

FS15 There are instances which health facilities tried to overstate the reimbursement

request amount

FS16 CBHIs is part of a network of CBHIs

FS17 We have merged with other CBHIs

FS18 We are in partnership with NHIF

FS19 Besides treatment we finance community prevention, promotion and

rehabilitation activities

138

3.8.3 Hypotheses Testing

The table below provides the study hypothesis and how they were tested.

Table 3.6: Summary of Hypotheses Testing

Hypothesis Analysis Accept/Reject Criteria

H1 H1: Enrolment is related to

equity in healthcare in CBHIs.

Partial Least Squares Analysis

Path coefficient and T values

Degree of Correlation is

Positive or Negative

Accept hypothesis when level of significance, indicated

by T values t values > 1.65 - 0.1 Sig. level

> 1.96 – 0.05

> 2.5 – 0.001

(two tailed)

H2 H1: Mix of contributions is

related to equity in healthcare

in CBHIs







> 1.96 – 0.05

> 2.5 – 0.001

(two tailed)

H3 H1: Risk pooling is related to

equity in healthcare in CBHIs







> 1.96 – 0.05

> 2.5 – 0.001

(two tailed)

H4 H1: Strategic purchasing is


in CBHIs







> 1.96 – 0.05

> 2.5 – 0.001

(two tailed)

H5 H1: Government stewardship is


in CBHIs







> 1.96 – 0.05

> 2.5 – 0.001

(two tailed)

139

3.9 Chapter Summary

This chapter gave a detailed description of the methodology that was used to examine

innovative healthcare financing and equity through CBHIs in Kenya. The chapter starts with

a discussion of various research philosophies and paradigm before underlining the one that

was employed in the current research. The chapter then discusses diverse research designs

before highlighting the research design that was used to empirically examine the

hypothesized relationships between the constructs based on the proposed theoretical model.

The chapter then describes the population and the sampling method that was used to derive

the sample. The chapter also focused on the research procedures that were employed in the

current study from development, piloting and the refinement of the research instruments. In

addition, administration of the research instrument in the main survey, ethical considerations

and hypothesis testing are discussed. The next chapter reports on results and findings of the

data collected and analysis based on the methodology.

140

CHAPTER FOUR

4.0 FINDINGS

4.1 Introduction

This chapter presents an analysis of the data that was collected through the use of structured

questionnaires, interpretation and discussion of the findings. The mode of presentation

employed for the results depends on the appropriateness of the used mode providing ease of

interpretation and understanding of the results to any interested person. Both descriptive and

inferential analysis methods have been employed in the analysis. The results are presented

according to the research objectives and the chapter is organized according to the themes

derived from the research questions.

4.2 General Information

Under the general information, the study examined the response rate, the duration within

which the CBHIs, the targeted number of households to the CBHIs, the number of

households covered by the CBHIs, average enrolment, average increase in healthcare

utilization (hospital and pharmacy visits) and the average mix of contributions in CBHIs. The

results under this section are based on the frequencies and percentages giving the patterns of

the characteristics of the CBHIs studied.

4.2.1.1 Response Rate for the primary data

Figure 4.1 Response Rate

141

The research targeted to collect secondary data from 79 CBHIs. The study achieved a 100%

response rate. For the primary data, a sample of 306 members of 79 CBHIs management

teams was targeted. The study however achieved a response rate of 71% of the targeted

responses.

4.2.1.2 Responses based on Years of Operation

0%

10%

20%

30%

40%

50%

60%

70%

80%

1-5 years 6-10 years 11- 15 years

70.10%

22.10%

7.80%

Figure 4.2 Responses based on Years of Operation

Figure 4.2 shows that respondents came from CBHIs that were in existence for 1-5 years

(70.1% of CBHIs), 6-10 years (22.1% of CBHIs) and 11-15 years (7.8%).

142

4.2.2 Descriptive Statistics – Secondary Data

4.2.2.1 Targeted Households

20, 8.9%

103, 46.0%

86, 38.4%

15, 6.7%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

0-500 501-1000 1001-2000 More than 2000

Figure 4.3 Households targeted by CBHIs

According to the findings, majority of CBHIs targeted between 501 – 1000 households, 38%

targeted between 1001 – 2000 and 9% targeted between 0 – 500 households whereas 7%

targeted more than 2000 households in their operations.

4.2.2.2 Households Covered by the CBHIs and Average Enrolment

0

50

100

150

200

250

0-500 501-1000 1001-2000 AverageEnrolment

205, 91.5%

5, 2.2%14, 6.3%

169

0-500

501-1000

1001-2000

Average enrolment

Figure 4.4 Households Covered by CBHIs and Average Enrolment

143

With regard to the number of households covered by the CBHIs, majority (91.5%) of these

CBHIs covered up to 500 households, 6% covered between 1001 – 2000 households whereas

2% covered between 501 – 1000 households in their operations.

4.2.2.3 Number of Households Targeted and Those Covered by CBHIs

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0-500 501-1000 1001-2000 More than 2000

8.9%

46.0%

38.4%

6.7%

91.5%

2.2%6.3%

0.0%

Targeted

Covered

Figure 4.5 Relationships between the Number of Households Targeted and Those

Covered by CBHIs

Findings show that majority CBHIs targeting 501-1000, 1001-2000 and more than 2000

households have not been able to enroll the targeted number of households.

144

4.2.2.4 Benefits Package, Premiums and Products Uptake in CBHIs

7336

288654

206

1561

482

1454

120 0 00

1000

2000

3000

4000

5000

6000

7000

8000

Figure 4.6 Benefits Package, Premiums and Products Uptake in CBHIs

The study established that CBHIs offer diverse products with different benefits package.

Afya Yetu Initiative offers six products; two CBHIs only cover and four CBHIs and NHIF

cover. CBHIs only cover offers two types of products; small households cover (Kes 700) and

an expanded households cover (Kes 968.5). The cover offers security for services accessed in

public hospital services. Through its partnership with NHIF, Afya Yetu Initiative offers a

basic NHIF cover (Kes 2300) and an expanded benefits NHIF cover through CBHIs (Kes

2570). Additionally, members of the CBHIs purchase a NHIF that offers security outside

CBHIs. A basic cover for CBHIs outside CBHIs cost Kes 380 and expanded NHIF cover cost

Kes 650. The composite cover allows members to access services in private not-for-profits

hospitals. ADS western, ADS Nyanza and STIPPA offers four types of covers; a general

outpatient cover (Kes 1200), a general inpatient and outpatient cover (Kes 2000), a general

145

inpatient and outpatient cover with ambulance services (Kes 2400) and a general inpatient

and outpatient cover with ambulance services and funeral expenses (Kes 2700).

Majority of households have purchased a small household cover (7336). The uptake for the

composite products is 1561 and 482 households at a cost of Kes 2300 and Kes 2570

respectively. 654 and 206 members of CBHIs have bought NHIF separately for product 9

and 10 respectively. Non-renewal rates are high among NHIF Product with a total of 1680

households failing to renew their covers in 2015. In total 12,101 households are covered

through these products.

4.2.2.5 Methods of Payment: Inpatient and Outpatient services

Figure 4.7 Methods of Payment: Inpatient and Outpatient services

Majority of CBHIs (94.2%) use fee for service method to pay services providers for both

inpatient and outpatient services. Six percent CBHIs employ mixed methods when

purchasing health services from contracted providers.

146

4.2.2.6 Distance to the Nearest Contracted Service Provider

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Within 5kilometers

5-10 kilometers More than 10kilometers

62.9%

27.7%

9.4%

Figure 4.8 Distance to the Nearest Contracted Service Provider

According to the findings, majority of the CBHIs (62.9%) members live within a proximity

of 5 kilometers to the nearest contracted service provider. Twenty eight percent of the

respondents reported that their members live 5-10 kilometers while 9% reported that that

their members live more than 10 kilometers from a contracted service providers.

4.2.2.7 Average Mix of Contributions in CBHIs

52669

10932

0

10000

20000

30000

40000

50000

60000

AVERAGE CBHIs CONTRIBUTION AVERAGE NHIF CONTRIBUTION

Figure 4.9 Average mix of contributions in CBHIs

147

Findings in figure 4.9 show that the mean NHIF contribution through CBHIs was 52,668.831

while the mean CBHIs contribution was 109,315.08.

4.2.2.8Trends of Total Premiums collected, healthcare cost reimbursements,

administration cost and deficit/surplus in CBHIs between 2010-2015

Figure 4.10 Trends of Total Premiums collected, healthcare cost reimbursements,

administration cost and deficit/surplus in CBHIs between 2010-2015

Findings on trends of total premiums collected, healthcare cost reimbursements,

administration cost and deficit/surplus in CBHIs between year 2010-2015 shows a sharp

decrease in total premiums collected and surplus between year 2010 and 2013 followed by a

slight increase in the next two years. The healthcare cost decrease gradually between year

2010 and 2014 followed by a slight increase in year 2015. However, the administration cost

remained constant throughout the entire period.

148

4.2.3 Descriptive Analysis - Extent Effect of Equity in Healthcare - Healthcare Access

Table 4.1 Descriptive Analysis - Extent Effect of Equity in Healthcare- Healthcare

Access

Healthcare Access

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std. Dev

Membership is

distributed across

income categories

0

0

2

27

71

4.70

.498

The contracted

providers are within

the proximity of

covered population

0

0

1

27

72

4.71

.472

We cater for

transport and or

accommodation cost

related to healthcare

utilization

49

45 6 0 0 2.58 .624

The covered

population is

entitled to similar

benefits

The number of

members seeking

services has

increased in the past

12 months

0

0

0

0

1

0

28

25

71

75

4.70

4.74

.478

.449

Findings in Table 4.1 shows that membership is distributed across income categories in the

CBHIs (mean = 4.70; std. dev. = 0. 498), the contracted providers of the healthcare services

are within the proximity of covered population in the CBHIs (mean = 4.71; std. dev. =

0.472), the covered population is entitled to similar benefits (mean = 4.70; std. dev. = 0.478).

Similarly, number of members seeking services has increased in the past 12 months (mean =

4.74; std. dev. = 0.449). Findings also indicated that some CBHIs cater for transport and or

accommodation cost related to healthcare utilization (mean = 2.58; std. dev. = 0.624).

149

Table 4.2 Descriptive Analysis - Extent Effect of Equity in Healthcare - Equity in

Contributions


SD

(%)

D

(%)

N

(%)

A

(%)

SA

(%) Mean Std. Dev

Everyone pays equal premium 2 51 41 6 0 2.52 .656

Everyone pays an equal amount of

their income 2 51 41 6 0 2.52 .656

Flexible payments are allowed 2 51 41 6 0 2.52 .656

We offer allow members to match

premium to their income 0 0 1 29 70 4.68 .494

We offer premium subsidies 2 51 41 6 0 2.52 .656

We allocate a larger claim budget for

low cost products 0 0 1 35 64 4.63 .511

With regard to equity in contribution, some CBHIs members pay the same amount (mean =

2.52; std. dev. = 0.656), some members pay an equal amount of their income (mean = 2.52;

std. dev. = 0.656), some CBHIs have a flexible payment system (mean = 2.52; std. dev. =

0.656) and some CBHIs subsidize of premium (mean = 2.52; std. dev. = 0.656). It is also

clear that CBHIs allow members to match products with their income (mean = 4.68; std. dev.

= 0.494), allocate a larger claim budget for low cost products (mean = 4.63; std. dev. =

0.511).

150

Table 4.3 Descriptive Analysis – Extent Effect of Equity in Healthcare - Quality of Care

Quality of Care

SD

(%)

D

(%)

N

(%)

A

(%)

SA

(%) M

Std.

Dev

CBHIs has a standard client compliant

management mechanism 0 0 1 34 64 4.63 .511

Members have complained about long queues

before being seen 2 51 41 6 0 2.52 .656

Members have complained on availability of

health services 2 51 41 6 0 2.52 .656

Members have complained about lack of key

prescribed medicines 2 51 41 6 0 2.52 .656

Members have raised concerns relate to

cleanliness 2 51 41 6 0 2.52 .656

Members have raised concerns on availability

of trained staff in the contracted health

facilities

2 51 41 6 0 2.52 .656

Members have raised concerns on referral

system 2 51 41 6 0 2.52 .656

CBHIs have put in place mechanisms to check

on patient perceived quality of care in

contracted health facilities on issues

concerning waiting time, availability of staff,

services, drugs and supplies

0 0 1 32 67 4.65 .505

There are other organization(s) that conduct

quality checks in the contracted health

facilities

0 0 1 28 71 4.70 .489

These organizations share their findings with

the CBHIs 0 0 1 34 64 4.63 .511

Table 4.3 presents the study findings on the quality of the care services provided in the

CBHIs. According to the findings, the CBHIs have put in place a standard complaint

management mechanism as the respondents reported where the mean value indicated that the

respondents strongly agreed with a mean of 4.63 and a standard deviation of 0.511. However,

the respondents neither agreed nor disagreed indicating that in some CBHIs, members have

complained of long queues before being seen. This had a mean of 2.52 and a standard

deviation of 0.656 indicating that the responses given had mixed reactions where some of the

respondents agreed and some disagreed whereas a significant others had a neutral response.

151

From the table also, the respondents indicated that some members to the CBHIs have

complained on lack of healthcare services (mean = 2.52; std. dev. = 0.656), lack of key

prescribed medicines (mean = 2.52; std. dev. = 0.656), members have raised concerns

relating to cleanliness contracted health facilities (mean = 2.52; std. dev. = 0.656), members

have raised concerns on availability of trained staff in the contracted health facilities (mean =

2.52, std dev. = 0.656), members have raised concerns on referral system (mean = 2.52, std

dev. = 0. 656). The respondents however agreed strongly indicating that the CBHIs have put

in place mechanisms to check on patient perceived quality of care in contracted health

facilities on issues concerning waiting time availability of staff, services, drugs and supplies

(Mean = 4.65, std. dev. = 0.505). There are other organization(s) that conduct quality checks

in the contracted health facilities. This is according to the responses given where the

respondents strongly agreed to this aspect with a mean of 4.70 and a standard deviation of

0.489. Further, findings illustrate that, the organizations share their findings with the CBHIs

as indicated by a mean of 4.63 and a standard deviation of 0.511 showing that the

respondents agreed to this aspect.

Table 4.4 Descriptive Analysis - Extent Effect of Equity in Healthcare - Sustainability

(Administrative and Managerial Capability)

Administrative and

Managerial Capability

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

Setting of contributions 0 0 1 32 67 4.66 .503

Collection of

contributions and

compliance

0 0 1 34 65 4.63 .510

Determination of the

benefit package 0 0 1 29 70 4.68 .494

Claim management 0 13 24 63 0 3.48 .745

Marketing and

communication 0 17 23 60 0 3.41 .798

Contracting providers 0 0 40 57 0 4.54 .559

MIS 3 54 38 5 0 2.45 .661

Accounting 0 0 25 25 50 4.25 .835

152

Table 4.4 shows that the administration committee has basic skills in setting contributions

(mean = 4.66; std. dev. = 0.503) , collection of contributions (mean = 4.63; std. dev. = 0.510).

The study also established the existence of other skills in CBHIs including collection of

contributions (mean = 4.63; std. dev. = 0.510), determination of benefits package (mean =

4.68; std. dev. = 0.494), basic skills in Claim management (mean = 3.48; std. dev. = 0.745),

basic skills in Marketing and communication (mean = 3.41; std. dev. = 0.798), basic skills in

contracting health services providers (mean = 4.54; std. dev. = 0.559), basic skills in

recruiting and retaining core staff (mean = 4.25; std. dev. = 0.835). Some CBHIs‘

management team however lacked basic skills in Accounting as indicated by the mean

response of 2.45 and a standard deviation of 0.661.

153

Table 4.5 Descriptive Analysis - Extent Effect of Equity in Healthcare – Sustainability (Financial Sustainability)

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std. Deviation

We have partnered with organizations that

assist in collection of premiums 0 0 1 29 70 4.68 .494

Premiums are not paid on time 0 0 3 40 57 4.54 .559

The CBHIs is funded through a mix of

contributions from county / national

government / donors and members

contributions

2 51 41 6 0 2.52 .656

Government and or donors‘ covers health

cost for those who cannot afford to pay

premiums

2 51 41 6 0 2.52 .656

Chronic conditions are covered by the

CBHIs 0 0 2 26 72 4.70 .496

The CBHI have put in place mechanisms to

check whether the invoices sent from the

health facilities are correct

0 0 17 24 59 4.42 .765

There are instances which health facilities

tried to overstate the reimbursement

request amount

2 47 46 4 0 2.55 .633

The CBHIs is part of a network of CBHIs 0 0 2 31 67 4.65 .514

We have merged with other CBHIs 13 53 25 4 6 2.38 .958

We are in partnership with NHIF 0 0 1 26 73 4.72 .468

Besides treatment we finance community

prevention, promotion and rehabilitation

activities

0 0 2 26 72 4.70 .505

154

According to the findings in Table 4.5 CBHIs worked in partnership with organizations that

assist them in collection of premiums (mean = 4.68; std. dev. = 0.494), CBHIs members do

not pay premiums on time (mean = 4.54; std. dev. = 0.559), CBHIs covers members with

chronic conditions (mean = 4.70; std. dev. = 0.496), CBHIs have put in place mechanisms to

check whether the invoices sent from the health facilities are correct (mean =4.42; std. dev. =

0.765), CBHIs studied are part of a network (mean = 4.65; std. dev. = 0.514), CBHIs were in

partnership with NHIF (mean = 4.72; std. dev. = 0.468), CBHIs finances preventive, promote

and rehabilitative health services (mean = 4.70; std. dev. = 0.505).

Findings also indicate that some CBHIs were funded through a mix of contributions from the

County or National government or donors and members contributions (mean = 2.52; std. dev.

= 0.656), government or donors covers health cost of those who cannot afford to premiums

through some CBHIs (mean = 2.52; std. dev. = 0.656), contracted health facilities has in

some instances tried to overstate the claims reimbursements (mean =2.55; std. dev. = 0.633),

not all the CBHIs had merged with other CBHIs as the mean response suggested (2.38).

155

Table 4.6 Cronbach’s Alpha Coefficients, AVE and KMO values for Equity in

Healthcare (Healthcare Access, Equity in Contributions, Quality of Care and

Sustainability)

Equity in

Health

Care

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Healthcare

access

0.833 ACC1

.551

0.786

.725

67.19% None

ACC2 .707

.848

ACC3 .693

.846

ACC4 .712

.852

QOC 0.961 QOC1 .956 0.722 .981 92.79%

QOC8 .930

.969

QOC9 .868

.939

AMC 0.953 AMC1 .947 0.812 .976 88.52%

AMC2 .945

.973

AMC3 .905

.952

AMC6 .767

.858

S 0.909 FS1 .848 0.784 .917 73.84%

FS2 .785

.862

FS5 .740

.838

FS8 .702

.795

FS10 .792 .880

The results presented in Table 4.6 showed Cronbach‘s alpha coefficients of above the 0.7

threshold for all first order constructs, total item correlations of above 0.3, AVE of above

65%, KMO values greater than 0.5 and satisfactory principal component loadings of above

0.50. These findings imply that the items of measure were measuring what they were initially

set out to measure, and therefore the data was maintained for further analysis.

4.3 Effect of Enrolment on Equity in Healthcare

This sub-section presents the results of enrolment based on the primary data. Firstly, the

descriptive statistics are discussed. Secondly, Pearson‘s coefficient correlations between the

indicators of enrolment and the indicators of equity in healthcare are presented. Thirdly, the

Cronbach‘s Alpha Coefficients, AVE and KMO values for enrolment and the test for

hypothesized relationship between enrolment and equity in healthcare are presented.

156

4.3.1 Descriptive Analysis - Extent Effect of Enrolment Indicators on Indicators of

Equity in Healthcare

Table 4.7 Extent Effect of Affordability on Equity in Healthcare

Affordability of

Premiums

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

We give members a

chance to allocate

premium among

preferred products

0 0 1 34 64 4.63 .511

Members can pay

premium in kind-

through farm produce or

labour

4 41 55 0 0 2.52 .568

We give subsidies and

exemption of premiums

for extremely poor and

vulnerable

0.4 7 43 34 16 1.99 .847

We encourage members

to use savings-linked

premium payment

mechanisms such as

rotating saving groups

(Chamas), mobile

money transfer

0 0 1 34 64 4.63 .511

Members can make

irregular payments of

premium

1 8 35 52 4 2.50 .752

Findings on premiums affordability in CBHIs indicate that members are given a chance to

allocate premium among preferred products. This is as indicated by a mean of 4.63 for

agreed and a standard deviation of 0.511. Further, CBHIs permit members to use savings-

linked premium payment mechanisms as indicated by a mean of 4.63 for agree and a

standard deviation of 0.511. Findings as well indicate that not all the CBHIs allowed

members can pay premium in kind or in work (Mean = 2.52; Std. Dev. = 0.568), allow

members to make irregular installments payments (Mean = 2.50; Std. Dev. = 0.752).

Findings further illustrated that CBHIs do not give subsidies and exemption of premiums for

extremely poor and vulnerable (Mean = 1.99; Std. Dev. = 0.784).

157

Table 4.8 Extent Effect of Unit of Membership on Equity in Healthcare

Unit of Membership

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

The CBHIs

membership is based

on coffee, tea,

villages or mutual

benefit societies or

administrative areas

0 0 0.4 24 76 4.75 .444

The CBHIs have

adapted households

as unit of membership

0 0 0.4 24 76 4.75 .444

CBHIs encourages

members to join when

they are healthy

0 0 1.3 35 64 4.63 .511

CBHIs membership is

open to poor and

vulnerable groups

0 0 25 25 50 4.26 .830

Findings as presented in Table 4.8 shows that, Coffee, tea, villages, cooperatives or mutual

benefit societies are basis of CBHIs membership. This is as indicated by a mean of 4.75

which is in the interval of 4.1 – 5.0. CBHIs have also adopted households as unit of

membership (mean =4.75; std. dev =0.444). Similarly, there evidence that CBHIs encourages

members to join when they are healthy (mean =4.63; std. dev =0.511). CBHIs membership

is open to poor and vulnerable groups (mean =4.26; std. dev =0.830).

158

Table 4.9 Extent Effect of Timing of Collections on Equity in Healthcare

Timing of collections

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

Members pay in a

single annual

premium

/contribution

0 0 0 29 70 4.70 .470

Members can pay

their premiums in

installments

4 54 38 4 0 2.45 .661

Premium payments

correspond with

income from e.g.

harvest, sale of

livestock or salary

payment

0 0 0 28 72 4.72 .451

Premium payments

are linked to loans

from SACCOs and

banks

4 54 38 4 0 2.45 .661

With regard to the timing of collection, the table illustrate that the members of the CBHIs

pay in a single annual premium or contribution. This is according to the mean response

obtained (4.70) which is in the interval of 3.0 – 3.9 for agreement and a standard deviation of

0.470. Premium payments in CBHIs correspond with cash inflows from harvest or livestock

sale or salary payment. This is according to the mean obtained of 4.72 and a standard

deviation of 0.451.

Further, some CBHIs allow members to pay their premiums in installments (Mean =2.45;

Std. Dev. = .661). Similarly, some of the CBHIs had their premium payments linked to loans

from SACCOs and banks (Mean =2.45).

159

Table 4.10 Extent Effect of Trust on Equity in Healthcare

Trust: Members or

Membership

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

Interact with scheme‘s

management team and

service providers

0 26 74 4.74 .439 .439 .439

Participate in setting

of benefit package 1 34 64 4.63 .511 .511 .511

Participate in setting

the premiums 0 23 77 4.77 .420 .420 .420

Is based on existing

Chamas, development

projects and credit

schemes

2 34 64 4.62 .522 .522 .522

Are willing to cover

the poorest and

vulnerable

0 16 84 4.83 .384 .384 .384

Findings on trust in CBHIs indicate existence of strong social networks in CBHIs. Members

interact with the scheme‘s administrative/ management team about their needs, concerns and

suggestions for improvements. This is as indicated by a mean of 4.74 for agreed and a

standard deviation of 0.439. Members participate in setting of benefit package (Mean = 4.63;

std. dev. = 0.511). Further, Members of the CBHIs participate in setting the premiums as

indicated by a mean of 4.77 and a standard deviation of 0.420. From the table also, the study

findings show that the CBHIs uses existing Chamas, community development projects and

credit schemes as entry points for CBHIs membership. This is evidenced by a mean of 4.62

and a standard deviation of 0.522. It is also evident that the members are willing to cover the

poorest and vulnerable in the community such as orphans and disabled as the findings

illustrate with a mean of 4.83 and a standard deviation of 0.384.

160

4.3.2 Correlation between Enrollment Indicators and Equity in HealthCare Indicators

for CBHIs

This section presents the correlations results between enrolment indicators and indicators of

equity in healthcare in CBHIs.

4.3.2.1 Correlations between Affordability and Indicators Equity in HealthCare

Table 4.11 Correlation between Affordability and Healthcare Access

Healthcare Access

Affordability Pearson Correlation .726**

Sig. (2-tailed) .000

N 224

The findings in table 4.11 show the output of the Pearson correlation coefficient indicates a

statistically significant strong positive relationship between affordability and healthcare

access. From the table, it is clear that the significance level is 0.000 (p = 0.000), therefore,

there is a statistically significant difference in the affordability and healthcare access.

Table 4.12 Correlation between Affordability and Equity in Contributions




N 224

The findings in table 4.12 show the output of the Pearson correlation coefficient demonstrate

a statistically significant strong positive relationship between affordability and equity in

contributions. From the table, it is clear that the significance level is 0.000 (p = .000),

therefore, there is a statistically significant difference in the affordability and equity in

contributions.

Table 4.13 Correlation between Affordability and Quality of Care

Quality of Care



N 224

The findings in table 4.13 show the output of the Pearson correlation coefficient demonstrate

a statistically significant strong positive relationship between affordability and quality of

161

care. From the table, it is clear that the significance level is 0.000 (p = .000), therefore, there

is a statistically significant difference in the affordability and quality of care.

Table 4.14 Correlation between Affordability and Sustainability

Sustainability



N 224

Table 4.14 shows that the Pearson correlation coefficient between affordability and

sustainability was 0.878 with a p value of 0.000, suggesting that Affordability has a positive

and significant influence on sustainability at 5%.

4.3.2.2 Correlations between Timing of Collections and Indicators of Equity in

HealthCare

Table 4.15 Correlation between Timing of Collections and Healthcare Access

Healthcare Access

Timing of Collections Pearson Correlation .749**


N 224

Table 4.15 presents the Pearson correlation coefficient results on the relationship between

timing of collections and healthcare access. Timing of collections and healthcare access has a

statistically significant strong relationship with a significance value of 0.000.

Table 4.16 Correlation between Timing of Collections and Equity in Contributions




N 224

162

The results shown in table 4.16 indicate that the Pearson correlation coefficient between the

timing of collections and equity in contributions was 0.677 with a p-value of 0.000. This

indicates a statistically significant strong (r = .677, p < .01) (Table 4.8).

Table 4.17 Correlation between Timing of Collections and Quality of Care

Quality of Care



N 224

Table 4.17 presents the correlation results between timing of collections and quality of care.

Pearson correlation coefficient demonstrates a statistically significant strong positive

relationship between timing of collections and quality of care with a significance level of

0.000.

Table 4.18 Correlation between Timings of Collections and Sustainability

Sustainability



N 224

The findings in table 4.18 show the output of the Pearson correlation coefficient show a

statistically significant strong positive relationship between timing of collections and

sustainability. The findings are illustrated by a significance value of 0.000.

4.3.2.3 Correlations between Trust and Indicators of Equity in HealthCare

Table 4.19 Correlation between Trust and Healthcare Access

Healthcare Access

Trust Pearson Correlation .771**


N 224

163

Pearson correlation coefficient between the trust and healthcare access shows there is

statistically significant difference (r = .771, p < .01) (Table 4.19). Therefore, trust in CBHIs

has a statistically significant difference effect on healthcare access.

Table 4.20 Correlation between Trust and Equity in Contributions




N 224

Table 4.20 presents the Pearson correlation coefficient results on the relationship between

trust and equity in contributions. The findings are illustrated by a significance value of 0.000.

Trust and equity in contributions has a statistically significant strong relationship.

Table 4.21 Correlation between Trust and Quality of Care

Quality of Care



N 224

The findings in table 4.21 show the output of the Pearson correlation coefficient shows a

statistically significant strong positive relationship between trust and quality of care. From

the findings, it is clear that the significance level is 0.000 (p = .000), therefore, there is a

statistically significant difference in the trust and quality of care in CBHIs.

Table 4.22 Correlation between Trust and Sustainability

Sustainability



N 224

The Pearson correlation coefficient in Table 4.22 shows insignificant relationships between

the trust and sustainability (r =.797, p<.01). Thus, trust in CBHIs has a statistically

significant influence on their sustainability.

164

4.3.3 SEM analysis on Enrolment and Equity in Healthcare

4.3.3.1 Cronbach’s Alpha Coefficients, AVE and KMO values for Enrolment

Table 4.23 Cronbach’s Alpha Coefficients, AVE and KMO values for Enrolment

2nd

order

constr

uct

First order

constructs

Cronbac

h’s

alpha

Item

Item

total

correlati

on

KM

O

PCA

compon

ent

loading

varian

ce

extract

ed

Items

deleted

En

rolm

ent

Affordabilit

y 0.964 AF1 0.931 0.763 0.983 96.57%

AF2,

AF3,

AF4

AF4 0.931

0.983

Membership 0.977 MT1 0.956 0.500 0.989 97.78% MT2 ,

MT3

MT4 0.956

0.989

Timing of

collections

0.939 TM1 0.886 0.500 0.971 94.29% TM1,

TM2 ,

TM4 TM3 0.886

0.971

Trust 0.934 TRU

1 0.770 0.766 0.864 71.45% None

TRU

2 0.850

0.907

TRU

3 0.764

0.859

TRU

4 0.825

0.892

TRU

5 0.561 0.685




0.50. The factors with low standardized regression weights were subsequently deleted. These

findings imply that the items of measure were measuring what they were initially set out to

measure, and therefore the data was maintained for further analysis.

165

4.3.2.2 Hypothesized effect of enrolment in CBHIs on equity in healthcare

Figure 4.11 Path coefficients for effect of enrolment in CBHIs on equity in health care

Figure 4.12 t-values for effect of enrolment in CBHIs on equity in healthcare

166

Table 4.24 Path Coefficients (Mean, STDEV, t-value)

Original

Sample (O)

Sample

Mean (M)

Standard

Deviation

(STDEV)

T Statistics

(|O/STDEV|) P Values

Enrolment ->

Equity 0.908386 0.910345 0.017342 52.381868 0.0000

H0: Enrolment in CBHIs is not related to equity in healthcare.

H1: Enrolment in CBHIs is related to equity in health care.

Enrolment in CBHIs had a positive statistically and significant effect on equity in healthcare

at the 0.05 level of significance (β=0.908, t-value=52.382 >1.96, p<0.05) as indicated in

figure 4.11, figure 4.12 and table 4.24. The null hypothesis is therefore rejected and the

alternative Hypothesis H1 that stated that Enrolment in CBHIs is related to equity in health

care is supported. Results thus reveal that, when enrolment increases by 1 unit, Equity in

health care increases by 0.908 units. Figure 4.11 shows that enrolment had a coefficient r2

mean of 0.825 showing the proportion of variation in dependent variable explained by the

SEM model. r2 indicates that 82.5% of the variations in equity in health care can be

accounted for by enrolment in CBHIs.

4.4 Effect of Mix of Contributions on Equity in Healthcare

This section presents the results of mix of contributions based on the primary data. Firstly,

the descriptive statistics are discussed. Secondly, Pearson‘s coefficient correlations between

mix of contributions and the indicators of equity in healthcare constructs are presented.

Thirdly, the Cronbach‘s Alpha Coefficients, AVE and KMO values for mix of contributions

and the test for hypothesized relationship between mix of contributions and equity in

healthcare are presented.

167

4.4.1 Descriptive Analysis - Extent Effect of Mix of Contributions in CBHIs on Equity

in Healthcare

Table 4.25 Descriptive Analysis - Extent Effect of Mix of Contributions in CBHIs on

Equity in Healthcare

Mix of Contributions

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

Members‘ contributions

are adequate in meeting

the cost of the set benefit

package

4 54 38 4 0 2.45 .661

NHIF covers costs of

services not covered by

the CBHIs

0 0 3 30 67 4.64 .533

The CBHIs receives

financial support from

donor(s)

0 36 58 5 0 2.69 .592

The poor and vulnerable

members of the CBHIs

are covered through

government subsidies

0 38 56 5 0 2.67 .597

Table 4.25 gives the study findings on the mix of contributions. From the table, NHIF covers

costs of services not covered by the CBHIs for the members as indicated by the mean of 4.64

and a standard deviation of 0.533. However, some of the CBHIs receives financial support

from donor(s) whereas others had no such connections and donor support (mean = 2.69; std.

dev. = 0.592), Members‘ contributions are not adequate in meeting the cost of the set benefit

package (mean = 2.45; std. dev. = 0.661) and the poor and vulnerable members of the CBHIs

are covered through government subsidies (mean = 2.69; std. dev. = 0.597).

168

4.4.2 Correlation between Mix of Contributions in CBHIs and Equity in Healthcare

Indicators

This section presents the correlations results on the mix of contributions and indicators of

equity in healthcare.

4.4.1.1 Correlation between Mix of Contributions and Healthcare Access

Table 4.26 Correlation between Mix of Contributions and Healthcare Access

Healthcare Access

Mix of Contributions Pearson Correlation -.035


N 224

According to the findings in Table 4.26, Pearson correlation coefficient between the mix

of contributions and healthcare access did not show a statistically significant relationship

(r = -.035, p>.05). The values are insignificant suggesting that there is no relationship

between mix of contributions and healthcare access.

4.4.2.2 Correlation between Mix of Contributions and Equity in Contributions

Table 4.27 Correlation between Mix of Contributions and Equity in Contributions


Mix of Contributions Pearson Correlation .

Sig. (2-tailed) .

N 224

Mix of contributions was not able to converge to form equity in contributions (Table 4.27).

These findings indicate that the relationship between mix of contributions and equity of

contributions is not statistically different from zero.

169

4.4.1.3 Correlation between Mix of Contributions and Quality of Care

Table 4.28 Correlation between Mix of Contributions and Quality of Care

Quality of Care



N 224

The Pearson correlation coefficient results in Table 4.28 shows insignificant relationships

between the mix of contributions and the quality of healthcare (r = -.114, p>.05). The

values are insignificant suggesting that there is no relationship between mix of

contributions and quality of care.

4.4.1.4 Correlation between Mix of Contributions and Sustainability

Table 4.29 Correlation between Mix of Contributions and Sustainability

Sustainability



N 224

Pearson correlation coefficient in Table 4.29 shows insignificant relationships between the

mix of contributions and sustainability (r = -.127, p>.05). Thus, the mix of contributions

in CBHIs does not significantly influence sustainability.

4.4.2 SEM results for Mix of Contributions and Equity in Healthcare

4.4.2.1 Cronbach’s Alpha Coefficients, AVE and KMO values for Mix of Contributions

Table 4.30 Cronbach’s Alpha Coefficients, AVE and KMO values for Mix of

Contributions

First order

constructs

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Mix of

Contributions

0.714 MC2 0.523 0.500 0.715 51.11% MC1,MC3

MC4 0.523 0.715

170







4.4.2.2 Hypothesized effect of Mix of Contributions in CBHIs on equity in healthcare

Figure 4.13 Path coefficients for effect of mix of contributions on equity in healthcare in

CBHIs

171

Figure 4.14 t-values for effect of mix of contributions on equity in healthcare in CBHIs

Table 4.31 Path Coefficients (Mean, STDEV, t-values)

Original

Sample

(O)

Sample

Mean (M)

Standard

Deviation

(STDEV)

T Statistics


Mix of

Contributions ->

Equity

-0.117995 -0.098878 0.112944 1.044718 0.29666

H0: Mix of contributions in CBHIs is not related to equity in health care.

H1: Mix of contributions in CBHIs is related to equity in health care.

Mix of contributions in CBHIs had a negative and insignificant effect on equity in healthcare

at the 0.05 level of significance (β=-0.118, t-value=1.045<1.96, p>0.05) as indicated in figure

4.13 and 4.14 and table 4.31. The null hypothesis is therefore not rejected.

4.5 Effect of Risk Pooling in CBHIs on Equity in Healthcare Indicators

This sub-section presents the results of risk pooling based on the primary data. Firstly, the

descriptive statistics are discussed. Secondly, Pearson‘s coefficient correlations between risk

pooling and the indicators of equity in healthcare constructs are presented. Thirdly, the

Cronbach‘s Alpha Coefficients, AVE and KMO values for risk pooling and the test for

hypothesized relationship between risk pooling and equity in healthcare are presented.

172

4.5.1 Descriptive Analysis - Extent Effect of Risk Pooling on Equity in Healthcare

Table 4.32 Extent Effect of Risk Pooling in CBHIs on Equity in Healthcare

Enhanced Risk Pooling

SD (%) D (%) N

(%)

A

(%)

SA

(%) Mean Std. D

Members:

Interact with the scheme‘s

management team about their

needs, concerns and make

suggestions for improvements

0 0 2 21 76 4.74 .487

Participate in setting of benefit

package 0 0 17 16 67 4.49 .775

Participate in setting the

premiums 0 0 1 24 75 4.74 .471

Are willing to cover the

poorest and vulnerable in the

community

CBHIs:

Uses existing Chamas,

community development

projects and credit schemes as

entry points for CBHIs

membership

1

0

38

0

56

0

5

7

0

92

2.67

4.92

.597

.288

Has a partnered with the

county / national government

and or NHIF

Have merged with other

CBHIs to form a network or a

federation

Social Solidarity

Members of the scheme have

expressed the opinion that if

they would not need healthcare

themselves, at least they had

done something good for the

community by contributing to

the insurance fund

0

0

0

0

0

0

0

2

0

0

0

8

9

23

19

38

91

77

81

52

4.90

4.76

4.81

4.41

.313

4.36

.395

.710

173

Table 4.32 Extent Effect of Risk Pooling in CBHIs on Equity in Healthcare (continued)

How much do you think

members of the CBHIs are

willing to contribute to pay

for health care services used

by the

None

of the

cost

(%)

Some

of the

cost

(%)

Half

of the

cost

(%)

Most

of the

cost

(%)

All of

the

cost

(%)

Mean Std.

Dev

Sick 0 0 1 35 64 4.63 .511

Poor 1 30 56 13 0 2.82 .659

According to the findings as illustrated in Table 4.32, the respondents agreed and reported

that the members of CBHIs comes from a wide range of social economic background (Mean

= 4.74; Std. Dev. = 0.487), CBHIs targets a large geographical / administrative area (mean =

4.49; std. dev. = 0.775), community members are encouraged to join CBHIs when they are

healthy (mean = 4.74; std. dev. = 0.471), CBHIs‘ have a waiting period before one can

benefit from insurance (mean = 4.74; std. dev. = 0.471), CBHIs has a risk transfer

mechanism (mean = 4.92; std. dev. = 0.288), CBHIs has partnered with the county/ national

and or NHIF (mean = 4.76; std. dev. = 0.436), CBHIs merged with other CBHIs to form a

network or a federation (mean = 4.81; std. dev. = 0.395). Responses on the aspect that CBHIs

has other branches in other geographical or administrative areas were neutral (mean = 2.67;

std. dev. = 0.597) indicating that some of the CBHIs from different geographical or

administrative regions have merged to form one CBHIs.

With regard to the social solidarity, the study findings showed that the members of the

scheme have expressed the opinion that if they would not need healthcare themselves, at least

they had done something good for the community by contributing to the insurance fund

(mean = 4.41; std. dev. = 0.709). Also, the findings shows that, the respondents agreed that

members of the CBHIs are willing to contribute to pay for healthcare services used by the

sick (mean = 4.63; std. dev. = 0. .511). It is also clear that the respondents neither agreed nor

disagreed that members of the CBHIs are willing to contribute to pay for healthcare services

used by the poor (mean = 2.82; std. dev. = 0.659). The findings show that the respondents

had mixed reactions with some displaying willingness to contribute for health services used

by the poor, other were unwilling while a significant number were neutral on this aspect.

174

4.5.2 Correlation between Risk Pooling in CBHIs and Equity in HealthCare Indicators

This section presents the Pearson correlation and SEM results on the risk pooling and its

effect on indicators of equity in health care.

4.5.2.1 Correlation between Risk Pooling and Healthcare Access

Table 4.33 Correlation between Risk pooling in CBHIs and Healthcare Access

Healthcare Access

Risk Pooling Pearson Correlation .494**


N 224

According to the findings in Table 4.33, Pearson correlation coefficient between risk pooling

in CBHIs and healthcare access show a statistically significant but weaker relationship (r

=.494, p < .000).

4.5.2.2 Correlation between Risk Pooling and Equity in Contributions

Table 4.34 Correlation between Risk Pooling and Equity in Contributions


Risk Pooling Pearson Correlation .

Sig. (2-tailed) .

N 224

Risk pooling in CBHIs was not able to converge to form equity in contributions (Table 4.34).

These findings indicate that the relationship between risk pooling in CBHIs and equity of

contributions is not statistically different from zero.

175

4.5.2.3 Correlation between Risk Pooling and Quality of Care

Table 4.35 Correlation between Risk Pooling and Quality of Care

Quality of Care



N 224

The Pearson correlation coefficient in Table 4.35 shows a statistically significant but weaker

relationships between the risk pooling and the quality of healthcare (r =.490, p < .000)

4.5.2.4 Correlation between Risk Pooling and Sustainability

Table 4.36 Correlation between Risk Pooling and Sustainability

Sustainability



N 224

Findings of Pearson correlation coefficient shows a statistically significant but weaker

relationships between risk pooling in CBHIs and sustainability (r =.497, p < .000). Thus, the

risk pooling in CBHIs fairly influences sustainability.

4.5.3 SEM analysis for Risk Pooling and Equity in Healthcare

4.5.3.1 Cronbach’s Alpha Coefficients, AVE and KMO values for Risk Pooling

Table 4.37 Cronbach’s Alpha Coefficients, AVE and KMO values for Risk Pooling

First

order

constructs

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Enhanced

risk

pooling

0.816 ERP1 0.669 0.602 0.79 83.73%

ERP2,

ERP5,

ERP7

ERP3 0.761

0.969

ERP4 0.525

0.993

ERP6 0.586

0.957

ERP8 0.559 0.834

176







4.5.3.2 Hypothesized effect of risk pooling in CBHIs on equity in healthcare

Figure 4.15 Path coefficients for effect of Risk pooling in CBHIs on equity in healthcare

Figure 4.16 t-values for effect of Risk pooling in CBHIs on equity in healthcare

177


Original

Sample (O)

Sample

Mean (M)

Standard

Deviation

(STDEV)

T Statistics


Risk Pooling -

> Equity 0.552729 0.561240 0.050984 10.841140 0.0000

H0 Risk pooling in CBHIs is not related to equity in healthcare.

H1: Risk pooling in CBHIs is related equity in healthcare.

Risk pooling in CBHIs had a positive statistically and significant equity in healthcare at the

0.05 level of significance (β=0.553, t-value=10.81 >1.96, p<0.05) as indicated in figures 4.15

and 4.16 and table 4.38. The null hypothesis is therefore rejected and the alternative

Hypothesis H1 that stated that risk pooling in CBHIs is related to equity in health care is

supported. Results thus reveal that, when Risk pooling in CBHIs increases by 1 unit, Equity

in healthcare increases by 0.553 units. Figure 4.15 shows that risk pooling had a coefficient r2

mean of 0.306 showing the proportion of variation in dependent variable explained by the

SEM model. r2 indicates that 30.6% of the variations in equity in healthcare can be accounted

for by Risk pooling in CBHIs.

4.6 Effect of Strategic Purchasing in CBHIs on Equity in Healthcare Indicators

This sub-section presents the results of strategic purchasing based on the primary data.

Firstly, the descriptive statistics are discussed. Secondly, Pearson‘s coefficient correlations

between strategic purchasing and the indicators of equity in healthcare constructs are

presented. Thirdly, the Cronbach‘s Alpha Coefficients, AVE and KMO values for strategic

purchasing and the test for hypothesized relationship between strategic purchasing and equity

in healthcare are presented.

178

4.6.1 Descriptive Analysis - Extent Effect of Strategic Purchasing on Equity in

Healthcare

Table 4.39 Descriptive Analysis - Extent Effect of Strategic Purchasing on Equity in

Healthcare

Strategic

Purchasing: CBHIs

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

%)

Mean Std.

Dev

Signed a contract

with health services

providers

0 0 0 20 80 4.79

.419

Select accredited

providers 0 0 1 31 68 4.67

.501

Providers must

provide services

according to

conditions put


0 0 1 25 74 4.73

.475

Allocate resources

based on population

needs

Has been successful

in negotiating

agreeable terms and

contract with service

providers

0

0

0

0

0

0

20

20

80 4.79

80 4.79

.419

.420

According to the study findings CBHIs have put in place strategic purchasing mechanisms as

indicated in Table 4.39. The respondents strongly agreed indicating that CBHIs only select

providers who are accredited (mean = 4.79; std. dev. = 0.419), allocation of resources are

done based on population needs (mean = 4.67; std. dev. = 0.501), CBHIs allocate resources

based on population needs (mean = 4.73; std. dev. = 0.475), CBHIs has been successful in

negotiating agreeable terms and contract with service providers in terms of service quality,

fee, and reduction in unnecessary services/prescription (moral hazard) (mean = 4.79; std. dev.

= 0.419).

179

4.6.2 Correlation between Enrollment in CBHIs and Equity in HealthCare Indicators

This section presents the Pearson correlation and SEM results on the strategic purchasing and

its effect on indicators of equity in health care.

4.6.2.1 Correlation between Strategic Purchasing and Healthcare Access

Table 4.40 Correlation between Strategic Purchasing and Healthcare Access

Healthcare Access

Strategic Purchasing Pearson Correlation .785**


N 224

Findings in Table 4.40 indicates that Pearson correlation coefficient between strategic

purchasing in CBHIs and healthcare access has statistically significant strong positive

relationship (r =.785, p < .000).

4.6.2.2 Correlation between Strategic Purchasing and Equity in Contributions

Table 4.41 Correlation between Strategic Purchasing and Equity in Contributions


Strategic Purchasing Pearson Correlation .

Sig. (2-tailed) .

N 224

Strategic purchasing in CBHIs was not able to converge to form equity in contributions

(Table 4.41). These findings indicate that the relationship between strategic purchasing in

CBHIs and equity of contributions is not statistically different from zero.

180

4.6.2.3 Correlation between Strategic Purchasing and Quality of Care

Table 4.42 Correlation between Strategic Purchasing and Quality of Care

Quality of Care



N 224

The Pearson correlation coefficient results in Table 4.42 indicates that strategic purchasing

and quality of care has a statistically significant relationships (r = .903, p<.01). This shows

that strategic purchasing influences quality of care.

4.6.2.4 Correlation between Strategic Purchasing and Sustainability

Table 4.43 Correlation between Strategic Purchasing and Sustainability

Sustainability



N 224

The Pearson correlation coefficient in Table 4.43 shows a statistically significant strong

positive relationships between the strategic purchasing and the sustainability (r =.840, p <

.01). Thus, strategic purchasing in CBHIs influences sustainability.

181

4.6.3 SEM analysis for Strategic Purchasing and Equity in Healthcare

4.6.3.1 Cronbach’s Alpha Coefficients, AVE and KMO values for Strategic Purchasing

Table 4.44 Cronbach’s Alpha Coefficients, AVE and KMO values for Strategic

Purchasing

First order

constructs

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Strategic

purchasing

0.876 SP1 0.702 0.802 0.832 73.95%

SP5 SP2 0.603

0.777

SP3 0.821

0.91

SP4 0.793 0.892







4.6.3.2 Hypothesized effect of strategic purchasing in CBHIs on equity in health care

Figure 4.17 Path coefficients for effect of Strategic purchasing in CBHIs on equity in

healthcare

182

Figure 4.18 t-values for effect of Strategic purchasing in CBHIs on equity in health care


Original

Sample (O)

Sample

Mean (M)

Standard

Deviation

(STDEV)

T Statistics


Purchasing ->

Equity 0.552729 0.561240 0.050984 10.841140 0.0000

H0 Strategic purchasing in CBHIs is not related to equity in healthcare.

H1: Strategic purchasing in CBHIs is related to equity in healthcare.

Strategic purchasing in CBHIs had a positive statistically and significant equity in healthcare

at the 0.05 level of significance (β=0.911, t-value=54.416 >1.96, p<0.05) as indicated in

figures 4.17 and 4.18 and table 4.39. The null hypothesis is therefore rejected and the

alternative Hypothesis H1 that stated that Strategic purchasing in CBHIs is related to equity in

healthcare is supported. Results thus reveal that, when Strategic purchasing in CBHIs

increases by 1 unit, Equity in healthcare increases by 0.830 units. Figure 4.17 shows that

Strategic purchasing had a coefficient r2 mean of 0.830 showing the proportion of variation

in dependent variable explained by the SEM model. r2 indicates that 83% of the variations in

equity in healthcare can be accounted for by strategic purchasing.

183

4.7 Moderating effect of Government Stewardship on Equity in Healthcare

This section presents the descriptive statistics, Cronbach‘s Alpha Coefficients, AVE and

KMO values for government stewardship and the test for hypothesized relationship of

moderating effect of government stewardship and equity in healthcare.

4.7.1 Descriptive Analysis - Respondents on Extent Effect of Government Stewardship

Indicators on Equity in Healthcare

Table 4.46 Descriptive Analysis - Extent Effect of Government Stewardship on Equity

in Healthcare - Advisory Role on Design

Advisory Role on Design

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

Startup of CBHIs on

minimum of population

enrolled

0

47 48 6 0 2.58 .601

A waiting period 0 47 47 6 0 2.59 .629

Enrollment of households

as opposed to individuals

0

47

47

6

0

2.59

.621

A flexible premium

collection system

0 44 51 5 0 2.62 .603

A benefit package based on

target population needs

A standard treatment

protocols

A standard referral

procedure

Consolidation of CBHIs

Creation of a risk

equalization fund

Community participation in

management and decision

making

0

0

0

0

0

0

48

49

44

43

44

45

46

45

50

52

50

49

6

6

6

6

6

6

0

0

0

0

0

0

2.29

2.58

2.62

2.64

2.63

2.62

.622

.623

.616

.612

.615

.616

As illustrated in Table 4.46, there is some extent of government influence on the aspect of

minimum percentage of population required to enroll before a CBHIs begins its operations

(mean = 2.58; std. dev. = 0.601). Similarly, there is some extent of government influence on

the awaiting period set in CBHIs (mean = 2.59; std. dev. = 0.629). Further, findings show

that to some extent, the government influences the enrolment of households as opposed to

individuals (mean = 2.59; std. dev. = 0. 621), a flexible premium collection system (mean =

184

2.62; std. dev. = 0.603), a benefit package that reflect the needs of the target population

(mean = 2.59; std. dev. = 0.622), a standard treatment protocols for the members of the

CBHIs (mean = 2.58; std. dev. = 0.623), a standard referral procedure (mean = 2.62; std.

dev. = 0.616), consolidation of CBHIs through a federation or network (mean = 2.64; std.

dev. = 0.612), creation of a risk equalization fund (mean = 2.63; std. dev. = 0.615) as well

that the government encourages community participation in management and decision

making (mean = 2.62; std. dev. = 0.616).


in Healthcare- Monitoring

Monitoring of

CBHIs Activities

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std. Dev

Tracks the

progress of CBHIs

through time

0 47 47 6 0 2.59 .628

Monitors the basic

performance of

CBHIs

0 44 50 6 0 2.62 .609

As illustrated in Table 4.47, the government does not does not track the performance of

CBHIs through time (mean = 1.72; std. dev. = 0.713) nor does it monitor the performance of

the CBHIs (mean = 1.54; std. dev. = 0.774).

185


in Healthcare- Training

Training

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

Determination of

benefit packages 0 44 50 5 0 2.62 .609

Determination of

contributions 0 47 47 5 0 2.59 .615

Collection of

contributions 0 49 45 6 0 2.58 .624

Claims processing 0 48 45 6 0 2.58 .631

Use of MIS 0 47 46 6 0 2.58 .630

Establishment of

Health Insurance

Development Plans

0 47 47 6 0 2.60 .620

Educational visits 0 41 53 6 0 2.66 .608

The findings shows that the government does not organize trainings on determination of

benefit packages (mean = 1.73; std. dev. = 0.852), setting of contributions (mean = 1.79; std.

dev. = 0.807), collection of premiums (1.79; std. dev. = 0.862), claims processing (1.57; std.

dev. = 0.590), use of management information system (1.71; std. dev. = 0.842), establishment

of Health Insurance Development Plans (1.75; std. dev. = 0.842). Findings from the table

also illustrate that the government does not organize exchange visits to other CBHIs as

indicated by a mean of 1.73 and a standard deviation of 0.852.

186


in Healthcare- Co-financing

Co- Financing: The

government

Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Std.

Dev

Partially or fully

subsidizes the poorest

and vulnerable

members of the

community through

CBHIs

0 41 54 5 0 2.66 .601

Set a solidarity fund

for financing

epidemics and

deficits of the CBHIs

0 42 52 6 0 2.65 .609

With regard to the co-financing role, the findings show that there is lack of financial support

from government in form of general revenue and or donors support to cover the poorest and

vulnerable through the CBHIs. In addition, government initiated re-insurance and solidarity

funds that reinforce CBHIs financial sustainability are non-existence. The government and

or donors do not partially or fully subsidize the poor and vulnerable members of the

community through the CBHIs. This is illustrated by a mean of 2.66 which is in the interval

for moderate disagreement with a standard deviation of 0.601. The findings also indicated

that the government has not set aside a solidarity fund to caution CBHIs against epidemics

and deficits. This evidenced by a mean of 2.65 and a standard deviation of 0.609. Generally,

there is no much variance of the individual responses from the calculated mean. This implies

that there is absence of government stewardship in co-financing healthcare with CBHIs in

Kenya.

187

4.7.2 Cronbach’s Alpha Coefficients, AVE and KMO values for Government

Stewardship

4.7.2.1 Cronbach’s Alpha Coefficients, AVE and KMO values for Government

Stewardship – Design

Table 4.50 Cronbach’s Alpha Coefficients, AVE and KMO values for Government

Stewardship - Design

First order

constructs

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Design

0.876 AD1 0.902 0.956 0.920 90.23% None

AD2 0.948

0.958

AD3 0.954

0.963

AD4 0.920

0.935

AD5 0.960

0.968

AD6 0.966

0.973

AD7 0.938

0.950

AD8 0.922

0.937

AD9 0.932

0.945

AD10 0.936 0.949






188


Stewardship - Monitoring

First order

constructs

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Monitoring 0.958 MO1 0.919 0.5 0.980 95.97% None

MO2 0.919 0.980







Stewardship - Training

First order

constructs

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Training

0.986 TR1 0.934 0.913 0.952 92.19% None

TR2 0.959

0.970

TR3 0.962

0.973

TR4 0.945

0.959

TR5 0.951

0.964

TR6 0.967

0.976

TR7 0.901 0.926






189


Stewardship - Co-financing

First

order

constructs

Cronbach’s

alpha Item

Item total

correlation KMO

PCA

component

loading

variance

extracted

Items

deleted

Co-

financing

0.903 COF1 0.823 0.5 0.955 91.13% None

COF2 0.823 0.955






4.7.3 Multicollinearity Test

Table 4.54 Multicollinearity Test

Collinearity Statistics

Tolerance VIF

Enrolment .630 1.588

Government .763 1.311

Mix of contributions .551 1.815

Purchasing .814 1.228

Risk pooling .715 1.399

The table above indicates the test results for multicollinearity, using both the VIF and

tolerance. With VIF values being less than 5, it was concluded that there was no presence of

multicollinearity in this study. The VIF shows us how much the variance of the coefficient

estimate is being inflated by multicollinearity.

190

4.8 Overall Model

4.8.1 Reliability

Details of construct reliability are presented in Table 4.54.

Table 4.55 Construct reliability

Construct Composite

Reliability

Cronbach's

Alpha

Enrolment 0.961604 0.954142

Equity 0.966859 0.960305

Government 0.993834 0.993534

Mix of Contributions 0.750234 0.746829

Purchasing 0.914737 0.874918

Risk Pooling 0.870987 0.823401

4.8.2 Convergent Validity

The CFA results of item loadings and their respective t-values are reported in Table 4.56.

The items were significantly loaded on the proposed factors with loading higher than 0.5.

The AVE of all the constructs were above the 0.5 threshold indicating that the measurement

scales exhibited adequate measurement validity.

Table 4.56 Convergent Validity of outer model

Construct

Original

Sample

(O)

Sample

Mean

(M)

Standard

Deviation

(STDEV)

Standard

Error

(STERR)

t-

value AVE

Equity

0.786

ACC4 <- Equity 0.806 0.809 0.076 0.076 10.669

AMC1 <- Equity 0.956 0.955 0.015 0.015 61.957

AMC6 <- Equity 0.815 0.815 0.056 0.056 14.529

FS1 <- Equity 0.935 0.932 0.023 0.023 39.835

FS10 <- Equity 0.878 0.875 0.055 0.055 16.042

FS5 <- Equity 0.839 0.837 0.060 0.060 13.879

QOC8 <- Equity 0.950 0.947 0.017 0.017 56.184

QOC9 <- Equity 0.896 0.893 0.037 0.037 24.267

Government

0.885

AD1 <- Government 0.913 0.884 0.132 0.132 6.900

AD10 <-

Government 0.952 0.911 0.170 0.170 5.598

AD2 <- Government

191

0.944

0.910

0.147

0.147

6.410

AD3 <- Government 0.958 0.922 0.129 0.129 7.408

AD4 <- Government 0.927 0.889 0.137 0.137 6.763

AD5 <- Government 0.964 0.927 0.161 0.161 6.007

AD6 <- Government 0.968 0.931 0.161 0.161 6.002

AD7 <- Government 0.936 0.899 0.156 0.156 6.012

AD8 <- Government 0.929 0.887 0.164 0.164 5.666

AD9 <- Government 0.941 0.904 0.139 0.139 6.759

COF1 <-

Government 0.892 0.858 0.107 0.107 8.304

COF2 <-

Government 0.911 0.874 0.129 0.129 7.078

Risk pooling

0.578

ERP1 <- Risk

pooling 0.804 0.801 0.056 0.056 14.312

ERP3 <- Risk

Pooling 0.881 0.878 0.041 0.041 21.255

ERP4 <- Risk

Pooling 0.625 0.596 0.127 0.127 4.935

ERP6 <- Risk

Pooling 0.684 0.663 0.112 0.112 6.121

ERP8 <- Risk

Pooling 0.779 0.787 0.052 0.052 14.961

Mix of

Contributions 0.552

MC1 <- Mix of Cont 0.913 0.860 0.186 0.186 4.912

MC2 <- Mix of Cont 0.881 0.844 0.191 0.196 4.618

MO1 <- Government 0.937 0.901 0.146 0.146 6.418

MO2 <- Government 0.938 0.897 0.152 0.152 6.173

TR1 <- Government 0.938 0.897 0.166 0.166 5.662

TR2 <- Government 0.961 0.921 0.162 0.162 5.948

TR3 <- Government 0.973 0.935 0.160 0.160 6.079

TR4 <- Government 0.950 0.914 0.143 0.143 6.660

TR5 <- Government 0.946 0.910 0.147 0.147 6.442

TR6 <- Government 0.968 0.931 0.154 0.154 6.286

TR7 <- Government 0.902 0.863 0.175 0.175 5.154

Purchasing

0.729

SP1 <- Purchasing 0.826 0.822 0.062 0.062 13.380

SP2 <- Purchasing 0.772 0.773 0.069 0.069 11.186

SP3 <- Purchasing 0.916 0.916 0.024 0.024 37.914

SP4 <- Purchasing

Table 4.56 Convergent Validity of the outer Model

192

0.894

0.897

0.033

0.033

26.847

Enrolment

0.738

AFF4 <- Enrolment 0.916 0.920 0.036 0.036 25.181

TM1 <- Enrolment 0.859 0.860 0.042 0.042 20.347

TM3 <- Enrolment 0.869 0.865 0.045 0.045 19.301

TRU1 <- Enrolment 0.853 0.852 0.048 0.048 17.894

TRU2 <- Enrolment 0.918 0.920 0.027 0.027 34.393

TRU3 <- Enrolment 0.834 0.831 0.046 0.046 18.041

TRU4 <- Enrolment 0.919 0.922 0.023 0.023 40.455

TRU5 <- Enrolment 0.581 0.573 0.086 0.086 6.754

AFF1 <- Enrolment 0.931 0.936 0.017 0.017 56.313

4.8.3 Discriminant validity

As indicated in Table 4.57 all the constructs in the model met this criteria indicating that

discriminant validity is supported.

Table 4.57 Measures of Discriminant Validity

Construct Fornell Larker Measure (AVE >

highest correlation2)

Enrolment 0.7383>0.6685

Equity 0.78558>0.66851

Government 0.88479>0.003921

Mix of Contributions 0.552417>0.091431

Purchasing 0.729302>0.658589

Risk Pooling 0.577872>0.369683

Table 4.58 Latent Variable

Correlations / Correlation matrix

of constructs

Enrolment Equity Government

Mix of

Contributio

ns

Purchasing Risk

Pooling

Enrolment 1.00000

Equity 0.81762 1.00000

Government -0.06262 -0.06052 1.00000

Mix of

Contributions -0.07215 -0.11802 0.30238 1.00000

Purchasing 0.88472 0.81154 -0.06741 -0.08386 1.00000

Risk Pooling 0.60802 0.55225 -0.03748 -0.08032 0.52640 1.0000

0

Table 4.56 Convergent Validity of the outer Model

193

4.8.4 Structural Model Estimation and Hypothesis Testing

Figure 4.19, 4.20, 4.21 and 4.22 presents the paths coefficients and t-statistics for the overall

model without moderation and the moderated overall model.

194

4.8.5 Optimum model without Moderation

Figure 4.19 Path coefficients for the optimum model without moderation

195

Figure 4.20 t- values for the optimum model without moderation

196

Table 4.59 Path Coefficients (Mean, STDEV, t-Values)

Original

Sample

(O)

Sample

Mean

(M)

Standard

Deviation

(STDEV)

T Statistics


Enrolment -> Equity 0.473319 0.476809 0.147603 3.206705 0.001429

Purchasing -> Equity 0.495651 0.493281 0.148596 3.335560 0.000915

Figure 4.19 shows that the endogenous latent variable Equity in Healthcare had a coefficient

r2 mean of 0.882 implying that the out the four exogenous variables only two exogenous

variables; Enrolment and Strategic Purchasing explain 88.2% of variation in Equity in

Healthcare. Enrolment account for 47.3% of variation in Equity in Healthcare while Strategic

Purchasing account for 49.5% of variation Equity in Healthcare. Figure 4.20 suggests that

the hypothesized paths between Enrolment and Equity in Healthcare (β=3.207) and Strategic

Purchasing and Equity in Healthcare (β=3.336 are significant at 0.05 level of significance.

197

4.8.10 Optimum model with Moderation

Figure 4.21 Path coefficients for the optimum moderated model

198

Figure 4.22 t-values for the optimum moderated model

199


Original

Sample (O)

Sample

Mean

(M)

Standard

Deviation

(STDEV)

T Statistics


Enrolment*Gov ->

Equity -0.054218 -0.05422 0.105034 0.516191 0.60595

purchasing*Gov ->

Equity -0.139900 -0.01541 0.135515 1.032357 0.302405

Figure 4.21 shows that the endogenous latent variable Equity in Healthcare had a coefficient

r2 mean of 0.898 implying that the two exogenous variables, Enrolment and Strategic

Purchasing explain 89.8% of variation in Equity in Healthcare. This represents an

improvement in the variation explained compared to when the moderating variable,

government stewardship was excluded (r2

increases by 7%).

Under moderation, Enrolment account for 53.2% of variation in Equity in Healthcare while

Strategic Purchasing account for 32.5% of variation Equity in Healthcare. This presents a

slight improvement in variation explained by Enrolment (r2

increases by 5.9%) while the

variation of Equity in Healthcare accounted by Strategic Purchasing decreases by 17%.

Figure 4.22 suggests that the hypothesized paths between Enrolment and Equity in

Healthcare (β=4.596) and Strategic Purchasing and Equity in Healthcare (β=2.591) are

significant at 0.05 level of significance.

4.9 Chapter Summary

This chapter reported the descriptive and inferential analysis of the results from the main

study. First, the chapter presents the descriptive analysis, followed by the diagnostic test and

inferential statistics. Correlation analysis was used to establish the degree of relationship

between sub-constructs of the independent variables and the sub-constructs of the dependent

variable. A six step approach to SEM was applied using SmartPLS software version 3.0.

SmartPLS was employed to develop the measurement and structural model under study, test

hypothesized relationships between variables and bootstrap. The results shows enrolment in

CBHIs influences and equity in healthcare positively, mix of contributions in CBHIs

influences equity in healthcare negatively, risk pooling in CBHIs influences equity in

healthcare positively, strategic purchasing in CBHIs influences equity in healthcare

200

positively at the 0.05 level of significance. The optimum model shows that out of the four

exogenous variables only two of them; enrolment and strategic purchasing influences equity

in healthcare. When the optimum model was moderated by government stewardship,

enrolment in CBHIs improves while strategic purchasing decreases significantly. The next

chapter provides a discussion of the findings, conclusions and recommendations.

201

CHAPTER FIVE

5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This chapter provides a discussion of the findings reported in chapter four. The findings are

presented into four sections. It also presents the conclusions drawn from the study as well as

the suggestions on how to improve the performance of CBHIs as an alternative mechanism of

extending equity in healthcare and UHC for excluded groups. The chapter concludes with

suggestions of further research.

5.2 Summary of Findings

The purpose of the study was to examine innovative healthcare financing and equity through

CBHIs in Kenya. To accomplish this, the study investigated the effect of the health financing

functions enrolment, mix of contributions, risk pooling, strategic purchasing and the

moderating effects of government stewardship in CBHIs on equity in healthcare and drew

inferences from 82 CBHIs where responses were sought from four members of each CBHIs

management team. A sample size of 318 was determined using Yamane (1967) formula. The

study used survey sampling to select the CBHIs that offer pre-paid community health

financing. A structured questionnaire was used to collect primary data while a secondary data

sheet was used to capture longitudinal data on enrolment, mix of contributions and equity in

healthcare (number of members accessing healthcare and financial sustainability). The

questionnaire was structured and mostly had closed-ended questions that sought responses in

a five point likert-type scale. Comments and recommendations of health financing experts

and supervisors on representativeness and appropriateness of the survey questionnaire were

sought before the pilot study was carried out. A draft questionnaire was pilot tested on 10

respondents drawn from three CBHIs under the network of BIDII. Ethical research

procedures were observed throughout the entire research process. The data was analyzed

using SPSS version 20, MS. Excel, Karl Pearson‘s coefficient of correlation and SmartPLS

version 3.

202

Findings based on the secondary data show that 91.5% of CBHIs covered up to 500

households; average enrolment across the studied CBHIs was 169 households; majority of

CBHIs have not been able to enroll the targeted households; most of the enrolled households

(7336) had purchased the cheapest cover which cost Kes.700 per year which targets small

households while 1680 dropped from NHIF covers. Further, the study found that majority of

CBHIs uses fee for service method to pay services providers for both inpatient and outpatient

service whilst 62.9% of the covered population lives with a proximity of 5 kilometers to the

nearest contracted service provider. With regard to the mix of contribution, the study

established that contributions from members through CBHIs only product and CBHIs and

NHIF products are the only sources of CBHIs funds. Finding on the trends of total premiums

collected, healthcare cost reimbursements, administration cost and deficit or surplus in

CBHIs between 2010-2015 shows an up and down fluctuation of the total premiums

collected, healthcare cost reimbursements and deficit or surplus while the administration cost

remained constant throughout the entire period.

Findings based on primary data show that respondents came from CBHIs that have been in

operation shows that respondents came from CBHIs that were in existence for 1-5 years

(70.1% of CBHIs), 6-10 years (22.1% of CBHIs) and 11-15 years (7.8%). The study also

established that enrolment and strategic purchasing in CBHIs significantly influent equity in

healthcare in Kenya. The two independent variables had a coefficient of 0.882, indicating

that 88.2% of the variations in equity in healthcare can be accounted for by two independent

variables (enrolment and strategic purchasing in CBHIs). When moderated by government

stewardship, the variation of equity in healthcare that is accounted for by enrolment and

strategic purchasing increases to 89.8%. There was a positive statistically and significant

relationship between enrolment in CBHIs and equity in healthcare (β=0.908, t-value=52,382

>1.96, p>0.05). Pearson correlation coefficient demonstrate a statistically significant strong

positive relationship between the sub-constructs of enrollment and the sub-constructs under

equity in healthcare including; affordability and healthcare access (r=0.726, p=0.000),

affordability and equity in contributions (r=0.933, p=0.000); affordability and quality of care

(r=0.936, p=0.000); affordability and sustainability (r=0.878, p=0.000); timing of collections

and healthcare access (r=0.749, p=0.000), timing of collections and equity in contributions

203

(r=0.677, p=0.000); timing of collections and quality of care (r=0.749, p=0.000) and timing

of collections and sustainability (r=0.664, p=0.000); trust and healthcare access (r=0.779,

p=0.000), trust and equity in contributions (r=0.839, p=0.000); trust and quality of care

(r=0.872, p=0.000) and trust and sustainability (r=0.797, p=0.000).

Mix of contributions in CBHIs had a negative and insignificant effect on equity in healthcare

(β=-0.118, t-value=1.045<1.96, p>0.05). Similarly, Pearson correlation coefficient shows an

insignificant relationships between the mix of contributions and three of the sub-constructs

under equity in healthcare including healthcare access (r=-.035, p>.05), quality of care (r=-

.114, p>.05) and sustainability (r=-.127, p>.05). Mix of contributions and equity in

contributions did not show any relationship. Risk pooling in CBHIs had a positive

statistically and significant effect on equity in healthcare (β=0.553, t-value=10.841>1.96,

p<0.05). Pearson correlation coefficient shows a statistically significant but weaker

relationships between risk pooling in CBHIs and three sub-constructs under equity in

healthcare including healthcare access (r=.494, p<.000), quality of care (r=.490, p<.000) and

sustainability (r=.497, p<.000). Risk pooling and equity in contributions did not show any

relationship. Strategic purchasing in CBHIs had a positive statistically and significant effect

on equity in healthcare at the 0.05 level of significance (β=0.830, t-value=54.416 >1.96,

p<0.05). Pearson correlation coefficient shows a statistically significant strong positive

relationship strategic purchasing in CBHIs and three of the sub-constructs under equity in

healthcare including healthcare access (r=.785, p<.000), quality of care (r=.903, p<.000) and

sustainability (r=.840, p<.000). Strategic purchasing and equity in contributions did not show

any relationship.

5.3 Discussion of Results

This sub-section presents an in-depth discussion of the finding from the data analysis and a

comparison with previous empirical studies related to the research objectives.

5.3.1 Enrolment and Equity in Healthcare

The study established that there was a positive and significant relationship between

enrolment in CBHIs and equity in healthcare at 5% level of confidence. Enrolment was a

204

second order latent construct whose antecedents were affordability of contributions, unit of

membership, timing of collections and trust. Carrin et al. (2005) used these antecedents to

study the contribution of CBHIs to performance of health systems in developing countries.

These precedents were also applied in an analytical framework developed by Mathauer &

Carrin (2010). The framework uses the health financing functions to analyze the performance

of a health financing system.

This finding is consistent with the findings of Carrin et al. (2005) & McCord et al. (2012)

that established that high membership rates increase equity in healthcare in CBHIs. Findings

from this study show that the average enrolment in the studied CBHIs was 169 households

with majority of CBHIs enrolling up to 500 households. This finding is in congruence with

WHO (2010) that voluntary schemes attract fewer members. As a result of changes in NHIF

premiums prices in 2015, 1680 households dropped the cover. Dercon et al. (2012) concur

that low income household‘s display high price elasticity due to low and irregular income, a

factor that influences demand for health insurance. CBHIs can make premiums affordable in

various ways including allowing members to allocate their spare income among preferred

products, allowing premium payment in kind, installmental payments, saving linked

payments system and premium subsidies for the poorest segment (Carrin et al., 2005; Dror,

2007; Aggarwal, 2010; ILO, 2013). The study found that together the 82 CBHIs that were

studied offers 10 different products. Members are allowed to allocate their income among the

preferred products. Majority of members have purchased a small household at Kes 700; the

cover offers services in public hospitals only. The high uptake levels shows that the cover

responds to the households‘ ability to pay since it is the cheapest CBHIs only cover. The

CBHIs also encourage members to use savings-linked premium payment mechanisms. The

study however found that the schemes do not allow installmental payments, in-kind

payments for premium are not allowed and subsidies for poor people through CBHIs are non-

existent.

Recruiting members from villages, pre-existing mutual and development groups makes it

easy for CBHIs to enroll more members. Similarly, adequate membership rates are easily

achieved when CBHIs use households as a unit of membership. Such enrolment strategies

205

allow CBHIs to extend membership beyond those who would join the scheme voluntarily

(Atim, 1998), and thus mitigate adverse selection problem. The finding of this study supports

earlier findings of various researchers by Atim (1998), Desmet at al., (1999), Carrin (2003) &

Carrin et al. (2005) that found that most CBHIs recruit their members from mutual benefits

societies besides using family as a unit of membership. To enhance enrolment through

inclusion of all members of the excluded segment, schemes need to ensure that members of

the uncovered segments are included. This study found that CBHIs membership is open to

the poorest and vulnerable groups while majority of members (63%) live with proximity of 5

kilometers from contracted healthcare providers. Result on the influence of geographical

proximity on enrolment is supported by findings of Carrin et al. (2005). Carrin et al. (2005)

found that physical proximity in China Rural Co-operative was suggestive of the low

enrolment rates and small risk pools given that enrolment was reliant on trust among

community members.

Finding of this study also show that CBHIs allow members to pay premium during cash

inflows. This finding is consistent with the findings of Criel (1998); Criel (2002); De Allegri

et al. (2006); ILO (2012); Chen et al. (2012) & Matul et al., (2013) that found that

households express a will to be allowed match cash inflows with premium payment periods.

This study also found that members are required to pay premium in a single payment. This

finding is inconsistent with the findings by Basaza et al. (2007) that found that a Ugandan

mutual greatly increased its membership by spreading its premium payments over the year.

Trust is a critical determinant of uptake in CBHIs given their voluntary nature of enrolment.

Chen et al. (2012) puts forward that trust in communities can manifest in three ways, trust

among community members, trust in health providers contracted by the scheme, and trust in

the management team and CBHI scheme. This trust is fostered through involvement of

members in setting of premiums and benefits package. Frequent interactions of members,

management team and service providers also offer forums for members to raise concerns and

make suggestions for improvements on the services offered by the providers. The study also

established members interact with the scheme management team during annual general

meetings and other scheduled meeting where they air their views, concerns and give

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feedback on issues concerning CBHIs. Additionally, that there exist high levels trusts among

members since CBHIs draw their membership from mutual benefits societies which already

enjoy high degree of reciprocity and/or mutually beneficial support. Members are actively

involved in determination of benefits packages and setting of premiums. These results is

supported by finding by Chen et al. (2012) that found that trust in CBHI management team

and in the scheme itself has a positive impact on enrolment decisions. Further, the perception

of fairness and transparency in schemes is better positioned to nature trust relationships with

the community (Chen et al., 2012).

5.3.2 Mix of Contributions and Equity in Healthcare

The WHO report of 2010 postulates prepayments as the most efficient and equitable method

of raising funds for healthcare. Hypothecated, general and payroll taxes, insurance or a

combination of two have been hailed as the most progressive ways of funding healthcare for

universal coverage (Doetinchem, 2010; Durairaj & Evans, 2010). Like other countries that

have espoused UHC, Kenya faces immense challenges of trading off and balancing

competing demands as it moves along stages towards realization and sustenance of equity in

healthcare (Carrin et al., 2007). Majority of its population works is the informal sector. This

presents practical difficulties in collecting tax and health insurance contributions due to lack

of institutional capacity to collect taxes (WHO, 2010a; KNBS, 2016). As a result, only

17.1% of Kenyans are covered while only 2.9% of the poorest quintile is covered (MoH,

2014).

Lack of institutional capacity to collect payroll taxes in LIMCs means that majority of those

who work in the informal sector have to pay for health services at the point of use (WHO,

2010a). As a result, high levels OOP push about 1.48 million Kenyans below the poverty line

(Xu et al., 2003; Chuma & Maina, 2012). In Kenya, cross-subsidization necessary for

effective risk pooling is weakened by high levels of disintegration in health financing (WHO,

2010a; Chuma & Okungu, 2011; MoH, 2015).

The study finding revealed that membership contributions are the only source prepayments in

CBHIs. Members pre-pay through both CBHIs and NHIF. In addition, the current study

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found that majority of CBHIs (88.3%) covered up to 500 households. This hampers their

ability to mobilize adequate resources required for achieving equity in healthcare. This

finding agree with Tabor (2005) & Schieber et al. (2012) that found that CBHIs face a

challenge of mobilizing sufficient resources attributable to their small size and the

contributory capacity of the target population (Tabor, 2005; Schieber et al., 2012). Finding

on the trends of total premiums collected, healthcare cost reimbursements, administration

cost and deficit or surplus in CBHIs between 2010-2015 shows an up and down fluctuation

of the total premiums collected, healthcare cost reimbursements and deficit or surplus while

the administration cost remained constant throughout the entire period. The low premiums

collected in 2015 compared to 2010 collection can be attributed to low renewal rates for the

composite CBHIs and NHIF products following the increase of NHIF rates for the informal

sector.

Subsidization of the poor and exemption of the poorest and socially excluded groups is

critical for reduction of disparities healthcare access and financial risk protection particularly

among these segments of the population. Donor funding channeled through pre-payment and

pooling structures such as CBHIs reduces fragmentation and duplication of Official

Development Assistance (ODA) and other forms of international aid efforts (WHO, 2010a).

The study found that the poor and vulnerable are covered through government and or donor

funding. The funding is however not channeled through CBHIs which means that the finding

is at odds with the recommendations of WHO (2010) on creation of health equity funds that

exempt the poorest and vulnerable group and subsidize the poor. Further, the finding also

disagrees with recent practices from Rwanda and Ghana where health equity funds have

ensured rapid inclusion of the poorest and vulnerable groups (Durairaj et al., 2010; WHO,

2010a).

5.3.3 Risk Pooling and Equity in Healthcare

Risk pooling is one of the fundamental principles of insurance. Long-term viability of a risk

pool is an important factor to attaining equity in healthcare. Financial sustainability is

contingent on the size and cross section of insured risks (WHO, 2010a). The study finding

established that risk pooling in CBHIs had a positive statistically and significant equity in

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healthcare at the 0.05 level of significance. This implies that there exists higher risk

equalization in CBHIs in Kenya that enable them to spread risks across the members. This

finding agree with the recommendations of Wang & Pielemeier (2012) that larger risk pools

have higher risk equalization mechanisms that enable them to spread risk, lower transaction

cost and offer accurate and stable premiums. The study also found that all the CBHIs studied

are part of network. Carrin et al. (2005) recommends creation of larger risk pools by

encouraging merging of the small pools.

Results from the current study indicate risk pooling in CBHIs and healthcare access have a

statistically significant and weak relationship. This finding concurs with recommendations by

James & Savedoff (2010) that establishment of risk pooling measures enhances healthcare

access particularly to the poor. This result is also in congruence with Ndiaye et al. (2007) &

Chuma et al. (2013) on existence of predominantly small size of risk pool in individual

CBHIs which explains the weak relationship between risk pooling and healthcare access. The

empirical results also revealed that risk pooling had statistically significant but weaker

relationships between the risk pooling and the quality of healthcare. This result concurs with

Spaan et al. (2012) findings on the relationship between health insurance and quality of care

in sub-Saharan Africa. A systematic review by Spaan et al. (2012) found that there was a

weak positive effect of both social health insurance and CBHI on quality of care.

As far as risk pooling in CBHIs and their sustainability is concerned, the study established

that there is a weak positive relationship between the two constructs. Carrin (2011) &

Mebratie et al. (2013) concur that risk transfer mechanisms through reinsurance enhance

viability of CBHIs particularly small pools typical of CBHIs. A study by Private Sector

Innovation Programme for Health (PSP4H) (2014) in Kenya revealed that the limited

resource base from the community impacts CBHIs ability to raise adequate resources. For

that reason the pools are intrinsically small hampering broader risk spreading across the

population. The small size of schemes and resource constraints puts the viability of the

CBHIs at risks (Chen et al, 2012; Mebratie et al., 2013). This explains the significant but

weak relationship between risk pooling and sustainability of CBHIs in Kenya. With regard to

mix of contributions and equity in healthcare, empirical results revealed there is no

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relationship between the two constructs. The results are contrary to Doetinchem (2010) and

Durairaj & Evans (2010) that posit that hypothecated, general and payroll taxes, insurance or

a combination of two have been hailed as the most progressive way of funding an equitable

healthcare system.

5.3.4 Strategic Purchasing and Equity in Healthcare

The healthcare financing function of purchasing involves a series of decisions which

includes; the active identification of the members health needs, their preferences and values;

the search for the best health services taking into consideration the members needs and

priorities in the health sector; searching for service providers to purchase from taking into

consideration the quality, efficiency and equity as well as determining the best payment

methods and contractual arrangements (WHO 2000; Basaza, et al., 2010). The study found

that strategic purchasing in CBHIs had a positive statistically and significant equity in

healthcare in Kenya. This implies that CBHIs identify members‘ health needs and search for

the most cost effective interventions in realization equity in healthcare. This result is

supported by finding of Baeza et al. (2002) in the ILO study. Baeza et al. (2002) found that

67 CBHIs practiced some form of strategic purchasing with some schemes embracing even

greater roles in strategic purchasing. Lagomarsino & Kundra (2008) and Christian Aid

(2015) recommend that from inception CBHIs should define a benefit package before

employing strategic models in purchasing the services.

The results also indicated that strategic purchasing in CBHIs had a statistically significant

and positive relationship with healthcare access. Hendricks et al. (2011) & Christian Aid

(2015) found that CBHIs in Kwara and Lagos state increase access to a set of health services

through contracts with public and private hospitals. Various previous researches reported

distance as a negative predictor of healthcare access. Geographical access measured using

time and distance required to access care was found to be a barrier to healthcare access in

Rwanda (Schneider & Diop, 2004), Kenya (Chuma & Okungu, 2011), ILO (2012), Ghana,

Burkina Faso, and Mali, Burkina Faso (Parmar et al., 2013). This barrier arises when

patients cannot reach a health facility due to long distance to health facility, huge

transportation charges and lost wages.

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Quality of care offered to members of an insurance scheme is a critical determinant of health

outcomes and overall patient satisfaction; and in turn it influences access to care.

Disappointments with health services outcomes resulting from comparison between the

original expectations and the reality between the services offered reflect poor quality of care

(Criel & Waelkens, 2003). Result from current research also suggests that strategic

purchasing in CBHIs has a strong positive relationship quality of care. This implies that

strategic purchasing In CBHIs improves the quality of care accessed by CBHIs members.

Previous studies reported that CBHIs improve quality of care by empowering enrollees

through fostering dialogue between communities and health care providers on patient

perceived quality of care (Criel & Waelkens, 2003). Introduction of contractual arrangements

contingent on quality standards was also found to improve quality of care accessed by CBHIs

members (Tipke et al., 2008 & Robyn et al., 2013). A recent systematic review concluded

that there was a weak positive effect of both social health insurance and CBHI on quality of

care in Sub Saharan Africa (Spaan et al., 2012).

The finding from current study also indicates that strategic purchasing in CBHIs and equity

of contributions are not related. The result is at odds with the finding by Munge et al. (2016)

that when strategic purchasing is well designed and executed promotes equity. Further,

research finding established that strategic purchasing in CBHIs has strong positive

relationship sustainability. This result disagrees with the finding of Jacobs et al. (2008) that

most of CBHIs management teams do not negotiate with service providers on price and

quality of health services. As a result this reduces the attractiveness of the schemes,

predisposing them to failure.


Alvarez-Rosete et al. (2013) describes stewardship as a broader overarching accountability

over the performance of the entire health system and eventually over the health of the whole

population. The distinguishing and conceptually useful facet of stewardship lies in its ability

to allocate ultimate responsibility for the health of the entire population. Beyond the formal

health structures government stewardship is often hypothesized as a critical determinant of

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successful and sustainable health financing in community based structures such as CBHIs

(Preker & Carrin, 2004).The current study used four measures to estimate government

stewardship. These measures include the government‘s role in design of CBHIs, monitoring

CBHIs related activities, as a trainer and as co-financier in CBHIs.

The results of the present study established that government‘s role in design of CBHIs,

monitoring CBHIs related activities, as a trainer and as co-financier in CBHIs in Kenya in

non-existence. This implies that like most of CBHIs in Africa, CBHIs in Kenya were created

in response to extensive exclusion of certain population categories and have survived

regardless of a vacuum of government stewardship. They continue reducing differentials in

health access and financial risk protection for the excluded groups in Kenya, therefore

playing a critical role of extending equity in healthcare in Kenya. Their penetration level

however remains low with majority of them covering up to 500 households. Lack a right mix

of contributions and sufficient funds to subsidize and exempt the precluded segment of

population results to exclusion of the poorest and vulnerable segment of the community.

This finding is in congruence with Carrin et al. (2005) that views stewardship as critical to

encouraging enrolment across different income categories. Similarly, Mladovsky &

Mossialos (2006) views government stewardship as critical to the success of schemes as a

strategy for achieving its equity and UHC objectives. Pauly et al (2006) who advocates for

minimal government regulation citing an increase of cream skimming and adverse selection

in present of government subsidies.

With regard to the role of the government in design in CBHIs there lacks government

guidance on design issues ranging from minimum enrolment, risk management, community

participation and operating procedures. The finding is contrary to recommendation by Tabor

(2005) and Wang & Pielemeier (2012) on the critical role of government in design of CBHIs,

particularity at the start up and early operational phases. Soors (2010) & WHO (2010)

postulates that it is the duty of the government to define the role of CBHIs in realization of

equity goals within the context of the national health financing policy.

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Effective implementation of health policies that are meant to promote equity in healthcare

requires not only improvement in design but also monitoring and evaluation. Monitoring

generates results that are useful in modifying ineffective government policies as well as

reinforcing the effective ones. Finding from current research shows that the government does

not track the performance of CBHIs through time. This finding agrees with GIZ (2012)

finding in Nepal. A review of CBHIs in Nepal by GIZ established that supervision and

monitoring mechanisms were non-existence in all the schemes despite having been initiated

by the government. Tabor (2005) argues that it may be impractical for CBHIs to measure that

actual impact particularly on health outcomes due to the high cost of gathering health

performance data from a small group of beneficiaries. This finding however disagrees with

Carrin et al. (2005) views that monitoring enables the government to proactively stimulate

establishment of CBHIs, detect problems in existing CBHIs and offer practical solutions to

the problems. The authors recommend that the government should monitor each CBHIs basic

performance, track progress across various CBHIs over time in addition to carrying out

comparative analysis along the health financing functions.

As far as training is concerned the empirical results do not support Tabor (2005) & Carrin et

al. (2005) recommendation that the government and or donors should build the capacity of

CBHIs management team through provision of basic skills in accounting, management

information systems, setting up of insurance development plans and negotiating and

contracting of providers and other third parties, preparation of organization structures, statues

and regulations as well as monitoring and evaluation. Further, Carrin et al. (2005)

recommend that the outcome from monitoring should be used as an input into the training

activities. Finding on the co-financing role contrasts with WHO report of 2010

recommendation on co-financing being a key priority action for financing healthcare

equitably. Oxfam International (2013) put forward that subsidies increase CBHIs capacity to

reach the poorest of poor, thereby decreasing health inequities.

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5.4 Conclusions

5.4.1 Enrolment and Equity in Healthcare

The results show that there was a positive and significant relationship between enrolment in

CBHIs and equity in healthcare in Kenya. This implies that the enrolment strategies put in

place by CBHIs stimulate willingness to pay among those excluded were effective in

achievement of equity in healthcare. An effective enrolment strategy in CBHIs should focus

on affordability of premiums, draw their membership from existing mutual groups, allow

members to pay premium when household liquidity is high and exploit social capital that is

intrinsic in target communities. The amount of premium set should be proportionate the

households ability to pay while increase in premium rates results to decrease demand for an

insurance cover. Majority of CBHIs have not met their enrolment targets; this can be

attributed to the voluntary nature of these schemes and lack of subsidies for the poor and

vulnerable groups.

The CBHIs management team also gives members a chance to allocate their income among

the range of product offering. To increase enrolment CBHIs recruit their members from

existing structures in the community namely; households, villages, cooperatives or mutual

benefit societies. Such pre-existing communal networks are rich in social capital whose one

of the components is trust. Social capital is a critical factor when considering CBHIs since its

constituents namely; trust, cooperation and reciprocity facilitate collective action thus

encouraging enrolment. This enables the CBHIs to extend their membership beyond those

who can join the scheme voluntarily. Having a membership that is open to the poor is a

reflection reciprocity norms and social solidarity that have long existed in informal risk

sharing mechanisms. The reciprocity norms inform the values and attitudes of CBHIs

members with regard collective action of address barriers to access to healthcare and sharing

of health care costs. Trust in CBHIs is reinforced by taking into consideration members‘

preferences in setting of benefits packages and premiums. Annual general meetings and other

scheduled meeting offers a platform for members to air their views, complaints and give

feedback. In conclusion, the strengths of CBHIs in stimulating enrolment of precluded

groups‘ lies in their focus on pre-existing social capital and in their ability to stimulate

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willingness to pre-pay for healthcare through community involvement. By so doing, the

schemes play a critical complementary role of propelling the country towards UHC.

5.4.2 Mix of Contributions and Equity in Healthcare

The negative relationship between mix of contributions and equity in healthcare in CBHIs in

Kenya suggest presence of insufficient amount prepayments and lack of the right

combination of prepayments necessary for realization and sustenance of equity in healthcare

through CBHIs particularly for the excluded segments of the population. The current mix of

contributions in CBHIs is composed of CBHIs and NHIF premium contributions only. The

composite CBHIs and NHIF products covers services that are not covered by the CBHIs

cover. Although the study did not establish existence of any form of direct government and

or donor subsidies through CBHIs, findings shows that poor and vulnerable members of

CBHIs benefit from some form of subsidies from government or donor funding. This can be

explained by the fact that government offers free healthcare at level 2 and 3 facilities that is

funded by HSSF. Although it is fraught with shortcomings, the waiver system also caters for

all or part of healthcare costs in level 4 and 5 facilities. Absence of contributions from

government and or donors to subsidize or exempt the poor and vulnerable through CBHIs

hampers extension of equity in healthcare to these groups. The study then concludes that

current mix of contributions composed of only CBHIs and NHIF the members‘ contributions

is not adequate enough to allow for reallocation of resources for subsidization or exemption

of poor and vulnerable groups. The current mix does not offer an optimal mix of funds

necessary for increased access to care and financial risk protection for precluded groups.

5.4.3 Risk Pooling and Equity in Healthcare

The positive and significant relationship between risk pooling and equity in healthcare

implies that the risk pools in CBHIs in Kenya are significant and come from a cross section

of risks. Despite the existence of small risk pools in each scheme, CBHIs have solidified the

risk pools by encouraging the small pools to merge into bigger ones under a network. The 79

CBHIs that were studied were under 4 networks with the largest network having 67 CBHIs.

The larger risk pools have higher risk equalization mechanisms that enable them to cross-

subsidize, spread risk, lower transaction cost and offer accurate and stable premiums. The

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varied socioeconomic backgrounds from which the members are drawn from permits

reallocation of pooled funds from wealthier households to poorer households. Encouraging

members to enroll when they are healthy and having a waiting period reduces adverse

selection by ensuring that the scheme does not attract a disproportionate high number of

riskier members. Risk spreading mechanisms within CBHIs in a network offsets risk

variations between CBHIs. Similarly, risk transfer measures through reinsurance enhance

viability of CBHIs particularly small pools atypical of CBHIs. The study concludes that

CBHIs have embraced enhanced risk pooling mechanisms that enhance reallocation of

resources for efficiency and equity gains. The extent of risk pooling is however hampered by

the limited resource base from the community that dictates the amount of premiums.

5.4.4 Strategic Purchasing and Equity in Healthcare

The positive and significant relationship between strategic purchasing and equity in

healthcare implies that CBHIs is responsive to members‘ healthcare needs and searches for

the most cost effective interventions in realization equity in healthcare. CBHI have signed

formal contracts with service providers which imply that the benefits package, quality of care

and payments methods are well spelt out. Contracting accredited service providers acts a gate

keeping measure on issues related to quality of care delivered to CBHIs members. Allocation

of resources based population needs ensures that the neediest members of the CBHIs access

the services they need without exposure to financial ruin. This study concludes that CBHIs

takes an actives role in searching and implementation of purchasing of health services, a

practice that ensures that members access quality care, reallocates resources based on

members needs and responds to the way health services are delivered and in effect it results

to equity in healthcare.


The finding shows that government stewardship in CBHIs had positive effect on the

relationship between enrolment and strategic purchasing and equity in healthcare in Kenya.

This implies that the government despite the absence of governments role as a steward in the

design, training, monitoring and co-financing of CBHIs other existing regal and regulatory

framework in the insurance industry (particularly in micro-insurance) may be influencing

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how the health financing functions are executed in CBHIs. This is evidenced by the role

complementary role that CBHIs play in extension of coverage, the networks that they have

established among themselves and with NHIF. The studied CBHIs have enrolled 12,101

households; they have partnered with NHIF to diversify the mix of contributions; they have

employed cost effective methods of purchasing health services and have merged to form

networks for enhanced risk pooling. These practices have increased access to healthcare;

quality of care and have ensured equity in contributions. Absence of government

stewardship has however hampered the size and role played by CBHIs in extending equity in

healthcare. For instance, the sizes of risk pool are small while absence of subsidies and or

exemptions have resulted to exclusion of the poorest and socially excluded segments of the

community. This hampers their efforts of CBHIs in extending the equity goals. Lack of

specific legal and regulatory framework means that their place within the context of the

national health financing policy is not well defined.

5.5 Recommendations

Recommendations are divided into sections; suggestions for improvement and suggestions

for further research. Suggestions for improvement are based study findings. Adoption of

these findings will contribute towards policy debate and dialogue by providing a nuanced

view of CBHIs with an aim clarifying the role played by CBHIs in realization of UHC and

how the interactions between them and the broader health financing system. Suggestions for

further research propose gaps that have not been addressed by the current research and can be

taken up for further research.

5.5.1 Suggestions for Improvements

5.5.1.1 Enrolment and Equity in Healthcare

Based on the number of household enrolled in CBHIs, it is clear that CBHIs contribute

towards reducing differences in healthcare access and enhancing financial risk protection.

This study also found that members are required to pay premium in a single annual payment.

Although single annual payments ease collection premiums, they may reduce enrolment

particularly for households that does not receive lump sum incomes; suggesting that CBHIs

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management team in consultation with members explore the possibility of spreading

premium payments on a need basis.

5.5.1.2 Mix of Contributions and Equity in Healthcare

Presence of the right mix of contributions for financing healthcare is the responsibility of

government. Mix of contribution in CBHIs had a negative and insignificant influence on

equity in healthcare in Kenya. This means that the proportion and mix of contributions in

CBHIs does not favour their efforts of extending equity in healthcare to excluded segment of

the Kenyan population. Based on the mix of contributions, government and or donors

contributions are conspicuously absent based on the mix of contributions. To increase the

amount of pooled funds in CBHIs, government and policy makers should ensure that

necessary legislation is put in place to allow channeling of some general government

revenue, sin taxes and or donor funding through CBHIs for greater risk equalization. The

funds can be consolidated into an equity fund for subsidizing CBHIs premium for the poor

and exemption of the poorest and vulnerable households, a measure that that will further

enhance equity.

5.5.1.3 Risk Pooling and Equity in Healthcare

The small risk pools in individual CBHIs weaken cross subsidization. To strengthen risk

equalization CBHIs have consolidated into networks. Risk pooling in CBHIs therefore had a

positive statistically and significant influence on equity in healthcare. Government and policy

makers should however enhance broader risk pooling among various health insurers.

Additionally, risk pooling can be improved through promotion of sector wide approach

(SWAp) where all the resources are combined.

5.5.1.4 Strategic Purchasing and Equity in Healthcare

Strategic purchasing in CBHIs had a positive statistically and significant influence on equity

in healthcare in Kenya. However, strategic purchasing in CBHIs and equity of contributions

are not related. This means the optimal performance of this function is hampered by lack of

reallocation of resources and lack greater subsidization in the country due to the vacuum in

government stewardship.

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5.5.1.5 Moderating influence of Government Stewardship on Equity in Healthcare

Government stewardship in CBHIs had a negative and insignificant influence on equity in

healthcare in Kenya. Governments through the ministry of health have the ultimate

responsibility for ensuring all segments of the population obtain services they need without

suffering financial ruin associated with their utilization. This implies it‘s the government

responsibility to guide the operation of CBHIs as a complementary health financing

mechanism lies in inclusion of the poor and vulnerable groups. This study recommends that

the government should define the place of CBHIs within the context of the national health

financing policy. This will require enacting the necessary legal and regulatory framework to

guide CBHIs administrative and fiscal structures. A clear regulatory framework supporting

pro-poor financing mechanisms can help improve equity in contributions and improve

efficiency and equity through resources reallocation.

5.5.2 Suggestions for Further Research

This research draws attention to the innovative healthcare financing and equity through

CBHIs in Kenya. Inclusion of the precluded population is critical for realization of UHC and

equity in healthcare. An in depth research should be carried out to establish why CBHIs have

failed to meet their enrolment targets despite the existence of strong social capital.

The study found that there was a sharp decline of total premiums collected, healthcare cost

reimbursements, administration cost and deficit or surplus in 2013. Further research should

therefore aim at establishing the reason for the sharp decline in 2013. Efficiency in execution

of the health financing functions is critical for preserving the meagre resources for realization

of equity goals. A similar study using different data method such as data envelopment

analysis can be used to measure how efficiently each health financing function is carried for

realization of equity in healthcare. Further, a study on the progressive effect of NHIF

coverage on renewal rates in CBHIs would offer insight on the expected effects on CBHIs as

NHIF expands its benefits beyond those offered by CBHIs.

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REFERENCES

Adebayo, E. F., Uthman, O.A., Wiysonge, C.S., Stern, E, A., Lamont, K.T., & Ataguba, J.E.

(2015). A systematic review of factors that affect uptake of community-based health

insurance in low-income and middleincome countries. BMC Health Services Research ,

15:543.

Aggarwal, A. (2010). Impact evaluation of India‘s ‗Yeshasvini‘ community-based health

insurance programme. Health Economics , 19 (1), 5-35.

Ahmed, S. R., & Mesbah, F. S. (2015). Catastrophic and Impoverishing Effects of Out-of-

Pocket Health Expenditure: New Evidence from Egypt. American Journal of

Economics , 5(5): 526-533.

Ahmed, S., Hoque, M.E., Sarker, A.R., Sultana, M., Islam, Z., Gazi, R., & Khan, J.A.M.

(2016). Willingness-to-Pay for Community-Based Health Insurance among Informal

Workers in Urban Bangladesh. PLoS ONE , 11(2).

Akazili , J., Gyapong, J., & McIntyre, D. (2011). Who pays for health care in Ghana? . Int J

Equity Health , 10:1–13.

Alesina, A., & La Ferrara, E. (2002). Who trusts others? Journal of Public Economics ,

85(2), 207–234.

Alvarez-Rosete, A ., Hawkins, B., & Parkhurst, J. (2013). Health System Stewardship and

Evidence Informed Health Policy. London School of Hygiene and Tropical Medicine.

Alvarez-Rosete, A. (2008). The role of the state in public health. In: Heggenhougen K

(ed).International Encyclopedia of Public Health , 211-18.

Anderson, V. (2004). Research Methods in Human Resource Management . London: CIPD

Andriani, L. (2013). Social Capital: a Road Map of Theoretical Frameworks and

Empirical Limitations. London: Department of Management Birkbeck University of

London. ISSN 2052-0581.

Aradhna, A. (2011). Achieving Equity in Health through Community-Based Health

Insurance: India's Experience with a Large Cbhi Programme. The Journal of

Development Studies , 47.11: 1657.

Armstrong, J. L. (1997). Stewardship and public service. Ottawa: Canadian Public Service

Commission. Discussion paper.

220

Aryeetey, G., Jehu-Appiah, C., Agyepong, I., Spaan, E., & Baltussen, R. (2012). Household

perceptions and their implications for enrolment in the national health insurance

scheme in Ghana. Health Policy and Planning , 17(1), pp.43-51.

Asadul, I., & Pushkar, M. (2012). Health Shocks and Consumption Smoothing in Rural

Households: Does Microcredit Have a Role to Play? Journal of Development

Economics , 97.2: 232-43.

Asante, A., Price, J., Hayen, A., Jan, S., & Wiseman, V. (2016). Equity in Health Care

Financing in Low- and Middle-Income Countries: A Systematic Review of Evidence

from Studies Using Benefit and Financing Incidence Analyses. PLOS ONE , 10.1371.

Asfaw, A., & von Braun, J. (2004). Can community health insurance schemes shield the poor

against the downside health effects of economic reforms? The case of rural Ethiopia.

Health Policy , 70: p. 97-108.

Ataguba, J.E. (2008). Community Health Insurance Scheme as a viable option for rural

population in Nigeria. Oxford: University of Oxford. p. 1–11.

Ataguba, J.E., Ichoku, H.E., & Fonta, W. M. (2008). Estimating the willingness to pay for

community healthcare insurance in rural Nigeria. Laval: Poverty and Economic Policy

Research Network.

Atim, C. (2000). Contribution financière des mutuelles de santé au financement, à la

fourniture et à l’accès aux soins de santé : Synthèse de travaux de recherche menés

dans neuf pays d’Afrique de l’Ouest et du Centre. STEP: Bureau International du

Travail.

Atim, C. (1998). Contribution of Mutual Health Organizations to Financing, Delivery, and

Access to Health Care : Synthesis of Research in Nine West and Central African

Countries, PHR Technical Report No. 18.

Atim, C. (2011). Ghana NHIS Design and Experience Lessons for Nigeria with special

reference to funding sources of financing.

Babatunde, R. O., Oyedeji, O. A., Omoniwa, , A. E., & Adenuga, A. H . (2016). Willingness-

to-pay for community based health insurance by farming households: a case study of

Hygeia community health plan in Kwara state, Nigeria . Trakia Journal of Sciences ,

No 3, pp 281-286.

221

Babin, B., & Svensson, G. (2012). Structural equation modelling in social science research:

Issues of validity and reliability in the research process. European Business Review ,

24(4), 320-330.

Baeza, C., Montenegro, F., & Núñez, M. (2002). Extending Social Protection in Health

through Community Based Health Organizations: Evidence and Challenges.”

Discussion Paper. Universitas Program—Strategies and Tools against Social

Exclusion and Poverty (STEP). Geneva: International Labor Organization (ILO).

Baker, L.C., Bundorf, M. K., & Kessler, D. P. (2014). Vertical Intergration: Hospital

Owership of physician practices is associated with higher prices and spending. Health

Affairs , 33 (5), 756-763.

Baltussen, R., & Ye, Y. (2006). Quality of care of modern health services as perceived by

users and non-users in Burkina Faso. Int J Qual Health Care , 18:30-34.

Barnum, H., Kutzin, J., & Saxenian, H. (1995). Incentive and Provider Payment Methods.

International Journal of Health Planning and Management , 10: 23–45.

Basaza, R., Criel, B., Van der Stuyft, P., Basaza, R., Criel, B., & Van der Stuyft, P. (2007).

Low enrollment in Ugandan Community Health Insurance schemes: underlying causes

and policy implications. BMC Health Services Research , 2007. 7: p. 105.

Belsley, D. A. (2004). Regression Diagnostics: Identifying Influential Data and Sources of

Collinearity, 1st ed. New York: John Willey & Sons, Inc.

Bennett, S. (2004). The role of community-based health insurance within the health care

financing system. Health Policy and Planning , 19, 147-158.

Bennett, S., Creese, A., & Monash, R. (1998). Health Insurance Schemes for Poeple outside

Formal Sector Employment. ARA Paper No.16. Geneva: World Health Organization.

Berwick, D. M. (2004). Lessons from developing nations on improving healthcare. BMJ ,

328:1124-1129.

Best, J. W., & Khan, J. V. (2007). Research in Education. New Delhi: Prentice Hall.

Bidgood, E. (2013). Health Systems: Germany. Civitas.

Bidgood, E. (2013). Healthcare Systems: France. Civitas.

Bird, R. M. (2015). Tobacco and Alcohol Excise Taxes for Improving Public Health and

Revenue Outcomes: Marrying Sin and Virtue? Policy Research Working Paper 7500.

Washington DC: World Bank.

222

Bitrán, R., & Giedion, U. (2003). Waivers and exemptions for health services in developing

countries. World Bank: Social Protection Discussion Paper Series; No. 0308.

Blaikie, N. (1993). Approaches to Social Enquiry (1st ed.). Cambridge: Polity Press

.Bourdieu, P. (1986). The Forms of Capital pp. 241-258 in Handbook of Theory and

Research for the Sociology of Education, edited by J.G. Richardson. Westport, CT:

Greenwood Press.

Bowman, W. (2011 ). Financial Capacity and Sustainability of Ordinary Nonprofits.

Nonprofit Management and Leadership , Vol. 22, No. 1, Fall 2011, pp. 37–51.

Brett, W., Ted, B., & Andrys, O. (2010). Explatory Factor analysis: A five step guide for

novices. Journal of Emergency Primary Health Care , 8 (3).

Brinkerhoff, D. (2003). Accountability and Health Systems: Overview, Framework, and

Strategies. Bethesda, MD: The Partners for Health Reformplus Project, Abt Associates

Inc.

Broomberg, J., & Price, M. (1990). The Impact of the Fee-for-Service Reimbursement

System on the Utilization of Health Services. South Africa Medical Journal , 78(4):

130–32.

Buigut, S., Ettarh, R., & Amendah, D.D. (2015). Catastrophic health expenditure and its

determinants in Kenya slum communities. International Journal for Equity in Health 5,

14:46.

Camp, W. G. (2001). Formulating and evaluating theoretical frameworks for career and

technical education research. Journal of Vocational Educational Research , 26(1), 27-

39.

Carrin G., Desmet, M., & Basaza, R. (2001). Social health insurance development in low-

income developing countries: new roles for government and non-profit health

insurance organizations. Ch.10 in Scheil-Adlung X. Building Social Security: The

challenge of privatization . London: Transaction Publishers.

Carrin, G. (2003). Community based Health Insurance Schemes in Developing Countries:

facts, problems and perspectives. Geneva: WHO.

Carrin, G. (2011). Health Financing for the Developing World: Supporting Countries' Search

for Viable Systems. ASP Pub Press.

223

Carrin, G., et al. (2007). Health financing reform in Kenya – assessing the social health

insurance proposal. S Afr Med J 2007 , 97: 130-135.

Carrin, G., Mathauer, I., Xu, K., & Evans, D. B . (2008). Universal coverage of health

services: tailoring its implementation. Bull World Health Organ , 86:857-63.

Carrin, G., Ron, A., & Yang, H et al. (1999). The reform of the rural coooperative medical

system in the People's Republic of China: Interim experience in 14 pilot counties.

Social Science and Medicine , 48:961-972.

Carrin, G., Waelkens, M., & Criel, B. (2005). Community-based health insurance in

developing countries: a study of its contribution to the performance of health financing

syste. Tropical Medicine and International Health, volume 10 no 8 pp 799–811.

Catherine, R., & Salmen, L. (2000). Understanding Social Capital Agricultural Extension in

Mali: Trust and Social Cohesion. Washington, D.C: The World Bank.

Central Bank of Kenya & Financial Services Deepening Kenya. (2009). FinAccess National

Survey 2009: Dynamics of Kenya's changing financial landscape. Niarobi: FSD Kenya.

Chandran, E. (2004). Research Methods: A quantitaive Approach . Nairobi : Daystar

University.

Chankova, S., Sulzbach, S., & Diop, F. (2008). Impact of mutual health organizations:

evidence from West Africa. Health Policy Plan , 23(4): p. 264-276.

Chatham House. (2014). Shared Responsibilities for Health A Coherent Global Framework

for Health Financing. Final Report of the Centre on Global Health Security Working

Group on Health Financing: Chatham House Report.

Chen, K. Z., Liu, Y., Hill, R., Xiao, C., & Liu, L. (2012). Can we relax liquidity constraints

on microinsurance demand? Piloting an innovative design for agricultural insurance.

Technical report, Microinsurance Innovation Facility. Geneva: ILO.

Chen, K., Daukste, R., Przybyl, A., & Fechter, N. (2012). Trust, Community-Based Health

Insurance and Enrollment Rates. Financing Practicum, pp. 1-32.

Chia, R. (2002). The Production of Management Knowledge: Philosophical Underpinnings

of Research Design", in Partington, D. (ed.). Essential Skills for Management Research,

1st ed. SAGE Publications Ltd., London , pp. 1-45.

Christian Aid . (2015). A Review of Community-Based Health Insurance Schemes: Lessons

from Nigeria and Ghana. Abuja, Nigeria : Christian Aid.

224

Chuma, J., & Maina, T. (2012). Catastrophic health care spending and impoverishment in

Kenya . BMC Health Services Research , 12: 413.

Chuma, J., & Okungu, V. (2011). Viewing Kenyan health system through an equity lens:

Implications for Universal Health Coverage. International Journal for Equity in Health

, 10:22.

Chuma, J., Mulupi, S.,& McIntyre, D. (2013). Providing Financial Protection and Funding

Health Service Benefits for the Informal Sector: Evidence from sub-Saharan Africa.

Working Paper 2. RESYST.

Churchill, C. (2006). What is insurance for the poor? in C. Churchill (ed.), Protecting the

poor, A microinsurance compendium. Geneva: ILO.

Coleman, S. J. (1988). Social Capital in the Creation of Human Capital. American Journal of

Sociology , Vol. 94 pp. S95-S120.

Collins, D., Quick, J. D., Musau, S. N., Kraushaar, K., & Hussein, I. M. (1996). The fall and

rise of cost sharing in Kenya: the impact of phased implementation. Health Policy Plan

, 11:52–63.

Collis, J., & Hussey, R. (2009). Business Research: A Practical Guide for Undergraduate &

Postgraduate Students. 3rd ed. London: Palgrave Macmillan.

Cooper, D.R.,& Schindler, P.S. (2014). Business Research Methods (12th edn). Boston, MA:

McGraw-Hill Higher Education.

Costa-Font, J., Gemmill. M.,& Rubert, G. (2009). Revisiting the health care luxury goods

hypothesis: aggregation, precision, and publication biases? Health, Econometrics and

Data Group (HEDG) Working Papers 09/02. Department of economics, University of

York.

Creese, A., & Bennett, S. (1997). Rural Risk-Sharing Strategies. In: Schieber, G.(ed),

Innovations in Health Care Financing Proceedings of a World Bank Conference,

March 10-11. Wasnington, DC : World Bank.

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative and Mixed Methods. Los

Angeles, CA: SAGE Publications Inc.

Creswell, J.W. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods

Approaches, 3rd edn. Thousand Oaks, California: Sage Publications.

225

Criel B., Noumou Barry, A., & von Roenne, F. (2002). Le Projet PRIMA en Guinée

Conkary-une expérience d’organisation de mutuelles de santé en Afrique rurale.

Brussels: Medicus Mundi Belgium.

Criel, B. (1998). District-based health insurance in sub-Saharan Africa, Part II : Case-

studies. Anvers : Studies in Health Services Organisation & Policy.

Criel, B., & Van Dormael, M. (1999). Mutual health organizations in Africa and social health

insurance systems: Will European history repeat itself? Tropical Medicine and

International Health , 4, 155–159.

Criel, B., Van Dormael, M., Lefevre, P., Menase, U. & Van Lerberghe, W. (1998). Voluntary

Health Insurance in Bwamanda, Democratic Republic of Congo. An exploration of its

meaning to the community. Tropical Medicine and International Health, vol.3, nr.8,

pp.640-653.

Criel, B., & Waelkens, M.P. (2003). Declining subscriptions to the Maliando Mutual Health

Organisation in Guinea-Conakry (West Africa): what is wrong? Social Science

Medicine , 57 (7), 1205-1219.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests . Psychometrika,

16, 297-334.

Davies, P., & Carrin, G. (2001). Risk Pooling - Necessary but not sufficient . Bulletin of the

World Health Organization , 79 (7).

De Allegri, M., Sanon, M., & Sauerborn, R. (2006b). To enrol or not enrol?: A qualitative

investigation of demand for health insurance in rural West Africa. Social Science

Medicine , 62 (6), 1520-1527.

De Allegri, M., Sauerborn, R., Kouyaté, B., & Flessa, S. (2009). Community health

insurance in sub-Saharan Africa: what operational difficulties hamper its successful

development? . Tropical Medicine and International Health , 14(5), 586-596.

Debebe, Z. Y., Kempen, L. V., & Hoop, T. D. (2012). A perverse' net' effect? Health

insurance and ex-ante moral hazard in Ghana. The Hague: Institute of social studies.

Defourny, J., & Failon, J. (2008). Community-Based Health Insurance Schemesin Sub-

Saharan Africa: Which Factors Really Influence Enrolment? Working Paper. Centre

for Social Economy.

226

Dercon, S., Gunning, J. W., Zeitlin, A., Cerrone, C., & Lombardin, S. (2012). ‘Health

insurance participation: Experimental evidence from Kenya. ILO Microinsurance

Innovation Facility Research Paper No. 10. Geneva: ILO.

DeRock, D., Knowles, J., Wittenberg, T., Raney, L., & Cordova, P. (1996). Rural health

services at Seguridad Social Campesino facilities: Analyses of facility and households

surveys. Technical No. 13. Bethesda, MD. Health financning and Sustainability Project,

Abt Associates Inc.

Desmet, M., Chowdhury, A.Q., & Islam, M.K. (1999). The potential for social mobilisation

in Bangladesh: the organisation and functioning of two health insurance schemes.

Social Science & Medicine , 48(7): p. 925-938.

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIT). (2012). Review of

Community-based Health Insurance Initiatives in Nepal. Health Sector Support

Programme.

Dhillon, R.S., Bonds, M.H., Fraden, M., Ndahiro, D.,& Ruxin, J. (2012). The impact of

reducing financial barriers on utilization of primary health care facility in Rwanda.

Glob Public Health , 7(1): 71-86.

Dillman, D. (2007). Mail and internet surveys: The tailored design method (2nd ed.). New

York: Wiley.

Diop, F. (2005). Determinants of Financial Stability of Mutual Health Organizations in the

Thies Region of Senegal: Household Survey Component. Bethesda, MD: Abt

Associates Inc.

Diop, P.F., Sulzbach, S., & Chankova, S. (2006). The Impact of Mutual Health

Organizations on Social Inclusion, Access to Health Care, and Household Income

Protection: Evidence from Ghana,Senegal, and Mali. Bethesda, MD; The Partners for

Health Reform plus Project. Abt Associates,Inc.

Doetinchem, O. (2010). Hypothecation of tax revenue for health. World Health Report

(2010). Geneva: WHO.

Doherty, J., Kirigia, D., Ichoku, H., McIntyre, D., Hanson, K., & Chuma, J. (2014).

Increasing the fiscal space for health: The experience of Kenya, Nigeria and Lagos

State (Nigeria). RESYST.

227

Donaldson, L. (1985). In Defence of Organization Theory, A Reply to the Critics. Cambridge:

Cambridge University Press.

Donaldson, L., & Davis, J. H. (1991). Stewardship Theory or Agency Theory: CEO

governance and shareholder returns. Australian Journal of Management , 16: 49-65.

Donaldson, T., & Preston, L. E. (1995). The stakeholder theory of the corporation: Concepts,

evidence, and implications. Academy of Management Review , 20, 65–91.

Donfouet, H. P. P., Mahieu, P., & Malin, E. (2013). Using respondents‘ uncertainty scores to

mitigate hypothetical bias in community-based health insurance studies. Eur J Health

Econ , 14:277–85.

Donfouet, H. P. P., Makaudze, E. M., Mahieu P. A., & Malin, E. (2011). Willingness to pay

for a community-based prepayment scheme in rural Cameroon. Int J Health Care

Finance Econ , 11:209–20.

Donfouet, H.P., & Mahieu, P. (2012). Community-based health insurance and social capital:

a review. Health Economic Review , Volume 2, Number 1, Page 1.

Dong, H., De Allegri, M., Gnawali, D., Souares, A., & Sauerborn, R. (2009). Dropout

analysis of community-based health insurance membership at Nouna, Burkina Faso.

Health Policy , Vol. 92, pp. 174–179.

Dong, H., Kouyate, B., Cairns, J., & Sauerborn, R. (2004). Differential willingness of

household heads to pay community-based health insurance premia for themselves and

other household members. Health Policy Plan , 19:120–6.

Drechsler, D., & Jütting, J. P. (2010). Six Regions, one story. In Global marketplace for

private health insurance: strength in numbers. Edited by Preker, A. S., Sweifel, P., &

Schellekens, O. P. Washington DC: World Bank.

Dror, D., & Jacquier, C. (1999). Micro-insurance: extending health insurance to the

excluded. International Social Security Review , Vol. 52. 1/99.

Dror, D. M., Radermacher, R., & Koren, R. (2007). Willingness to Pay for Health Insurance

Among Rural and Poor Persons: Field Evidence from Seven Micro Health Insurance

Units in India. Health Policy , (82):1.

Dror, I. (2007). Demystifying Micro Health Insurance Package Design - Choosing

Healthplans All Together (Chat). Microfinance Insights , 4:17-19.

228

Dror, D. M., & Armstrong, J. (2006). Do micro health insurance units need capital or

reinsurance? A simulated exercise to examine different alternatives . Geneva : The

International Association for the Study of Insurance Economics.

Durairaj, V., & Evans, D. B. (2010). Fiscal space for health in resource-poor countries.

World Health Report (2010) Background Paper, 41. Geneva: WHO.

Durairaj, V., D‘Almeida, S., & Kirigia, J. (2010). Ghana’s Approach to Social Health

Protection. Background paper for the 2010 World Health Report. GENEVA : WHO.

Dutta, A., & Hongoro, C. (2013). Scaling Up National Health Insurance in Nigeria:

Learning from Case Studies of India, Colombia, and Thailand. Washington, D.C:

Futures Group, Health Policy Project.

Eriksson, M. (2011). Social capital and health—Implications for health promotion. Global

Health Action , 4: 5611.

Fairbank, A. (2003). Sources of Financial Instability of Community-Based Health Insurance

Schemes: How Could Social Reinsurance Help? Technical Report No. 024. Bethesda,

MD: The Partners for Health Reformplus Project, Abt Associates Inc.

Fay, M. (2005). The Urban Poor in Latin America. Washington. D.C : World Bank.

Fernandes, A et al. (2009). Health financing systems review of Rwanda- options for universal

coverage. World Health Organization and Ministry of Health, Republic of Rwanda.

Field, J. (2008). Social Capital. New York: Routledge.

Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS

applied to consumer exit-voice theory. Journal of Marketing Research , 19, 440-452.

Franco, L.M., Diop, F.P., Burgert, C., Kelley, A.G., Makinen, M., & Siampara, C.H.T.

(2008). Effects of mutual health organizations on use of priority health care services in

urban and rural Mali: A case-control study. in Bulletin of the World Health

Organization , Vol. 86, No.11, pp. 830–838.

Fricker, S., Kreisler, C., & Tan, L. (2012). An exploration of the application of PLS path

modeling approach to creating a summary index of respondent burden. JSM , Section

on Survey Research Methods.

Fronstin, P. (2008). Sources of Health Insurance and Characteristics of the Uninsured:

Analysis of the March 2008 Current Population Survey. ERBI issue Brief , No. 321.

229

Fuchs, V. R. (1996). What every philosopher should know about health economics.

Proceedings of the American Philosophical Society , 140(2): 186-96.

Galárraga, O., Saso-Rubí, S. G., Salinas- Rodríguez, A., & Sesma-Vázquez, S. (2010).

Health insurance for the poor: impact on catastrophic and out-of-pocket health

expenditures in Mexico. The European Journal of Health Economics , 11(5), 437-447.

Gefen, D., Rigdon, E. E., & Straub, D. W. (2011). Editor's Comment: An update and

Extension to SEM Guidelines for Administrative and Social Science Research . MIS

Quartely , 35 (2), PP. iii -xiv.

Giedion, U., Alfonso, E. A., & Díaz, Y. (2013). The impact of universal coverage schemes in

the developing world: A review of existing evidence . Washing DC: World Bank.

Gill, J., & Johnson, P . (2002). Research Methods for Managers (3rd edn). London: Sage.

Gilson, L. (2003). Trust and development of health care as a social institution. Social Science

and Medicine , 56:1453-1468.

Gilson, L., Kalyalya, D., Kuchler, F., Lake, S., Oranga, H., & Ouendo, M. (2001). Strategies

for promoting equity: experience with community financing in three African countries.

Health Policy, 58: 37-67.

Gnawali, D. P., Pokhrel, S., Sanon, M., De Allegri, M., Dong, H., Sauerborn, R. (2009). The

effect of community-based health insurance on the utilization of modern health

facilities: evidence from Burkina Faso. Health Policy , 90 (2-3): 214-22.

Goudge, J., Akazili, J., Ataguba, J., Kuwawenaruwa, A., Borghi, J., Harris, B., & Mills, A.

(2012). Social solidarity and willingness to tolerate risk- and income-related cross-

subsidies within health insurance: experiences from Ghana, Tanzania and South

Africa. Wits: Centre for Health Policy, School of Public Health, University of the

Witwatersrand.

Government of Kenya. (2010). 2009 Kenya Population and Housing Census, Volume II.

Nairobi: Ministry of State for Planning, National Development and Vision 2030; and

Kenya National Bureau of Statistics.

Grant, C., & Osanloo, A. (2014). Understanding, selecting, and integrating a theoretical

framework in dissertation research: Creating the blueprint for your ―house‖.

Administrative issues journal: connecting education, practice, and research, Volume 4,

issue 2.

230

Gustafsson-Wright, E., & Schellekens, O. (2013). Achieving universal health coverage in

Nigeria one state at a time: A public private partnership community-based health

insurance model. Working Paper 2. Brooke Shearer Working Paper Series.

Hafner, T., & Shiffman, J. (2012). The emergence of global attention to health systems

strengthening. Health Policy and Planning, 28: 41-50.

Hair, J. F. Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data

Analysis (5th Ed.). Upper Saddle River, NJ: Prentice Hall.

Hair, J. F., Black, W. C., Babin. B. J., & Anderson, R. E . (2010). Multivariate Data Analysis

(5th Ed.). New Jersey: Pearson Prentice Hall.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). Pls: indeed a silver bullet. Journal of

Marketing Theory and Practice , 19(2), 139-151.

Hair, Jr., J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006).

Multivariate Data Analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Heller, P. (2006). The Prospect of Creating ‗Fiscal Space‘ for the Health Sector. Health

Policy and Planning, 21(2): 75-79.

Hendricks, M., & et.al. (2011). Cardivascular disease prevention in rural Nigeria in the

context of a community based health insurance scheme: Quality improvement

Cardiovascular care Kwara-I(QUICK-I). BMC Public Health , 11(86), 1471-2458.

Henseler, J., Ringle, C., & Sinkovics, R. (2009). The Use of Partial Least Squares Path

Modeling in International Marketing. Advances in International Marketing , 20, 277-

319.

Hill, C. W. L,. & Jones, T. M. (1992). Stakeholder-agency theory. The Journal of

Management Studies , 29 (2), 131–155.

Honda, A., McIntyre, D., Hanson, K., & Tangcharoensathien, V . Eds. (2016). Strategic

Purchasing in China, Indonesia and the Philippines. Geneva: WHO.

Hsiao, W. C. (2004). Experience of community health financing in the Asian region. In A.

S.Preker & G. Carrin (Eds.), Health financing for poor people: resource mobilisation

and risk sharing (pp.119-155). Washington: World Bank.

Hsiao, W.C . (2001). Unmet health needs of two billion: Is community financing a solution?

Preliminary draft for the Commision on Macroeconomics and Health. WHO.

231

Hsiao, W.C., & Shaw, R. P. (2007). Social Health Insurance for developing countries.

Washing DC: World Bank.

Huzuma, J. (2011). Coexistence of performance based financing (PBF) and Community

Based Health Insurance (CBHI): Rwanda experience.

ILO & STEP - GTZ. (2006). The role of microinsurance as a tool to face risks in the

context of social protection. Geneva: ILO/STEP - GTZ.

ILO. (2012). Protecting the poor: A microinsurance compendium, Volume II. Craig, C &

Matul, M (eds). Geneva: Munich Re Foundation,Micro Insurance Network, ILO.

ILO. (2014). Universal Health Protection: Progress to date and the way forward. Paper 10.

geneva: ILO, Social Protection Department.

International Labour Organisation. (2013). Community-based health insurance/ health

microinsurance premiums: What are the advantages of CBHI schemes? Geneva: ILO.

International Monetary Fund. (2012). Regional Economic Outlook: Sub-Saharan Africa.

Sustaining growth amid global uncertainty . Washington DC.

Jacobs, B., Bigdeli, M., van Pelt, M., Ir, P., Salze, C., & Criel, B. (2008). Bridging

community-based health insurance and social protection for health care – a step in the

direction of universal coverage? Tropical Medicine and International Health , 13(2),

140-143.

Jacobs, B., Price, N. L., & Oeun, S. (2007). Do exemptions from user fees mean free access

to health services? A case study from a rural Cambodian hospital. Tropical medicine

& international health , 12:1391-1401.

Jakab, M., & Chrishnan, C. (2001). Community involvement in Health Care Financing: A

survey of literature on impacts strengths and weaknesses. Washington DC: World

Bank.

James, C., & Savedoff, W. (2010). Risk pooling and redistribution in health care: an

empirical analysis of attitudes towards solidarity. World health report 2010

background paper, no. 5. Geneva: WHO.

Jonker, J., & Pennink, B. (2010). The essence of research methodology: a concise guide for

master and phD students in management science. London: Springer Science and

Business Media.

232

Jost, T.S . (2008). Health care access in the United States: conflicting concepts of justice and

little solidiarity. Medical Law , 27:605-16.

Jütting, J. (2000). Do mutual health insurance schemes improve theh access to healthcare?

Preliminary results from household survey in rural Senegal. International conference:

Health systems financing in low-income African and Asian Countries.

Jütting, J. P. (2004). Do community based health insurance schemes improve poor people's

access to health care? Evidence from Rural Senegal. World Development , 3 (2); 273-

288.

Jütting, J.P. (2004). Do Community-based Health Insurance Schemes Improve Poor People‘s

Access to Health Care? Evidence From Rural Senegal. World Development , 32(2): p.

273-288.

Kalk, A., Groos, N., Karasi, J. C., & Girrbach, E . (2010). Health systems strengthening

through insurance subsidies: the GFATM experience in Rwanda. Tropical Medicine

and International Health , 15(1):94-7.

Kamau, A., & Holst, J. (2008). National Hospital Insurance Fund: An evealuation Report.

Nairobi: MoH/GTZ Health Sector Programme Kenya.

Kenny, D.A. (2012). Measuring Model Fit. Retrieved electronically November 5:

http://www.davidakenny.net/cm/fit.htm, 2016.

Kenya national bureau of statistics. (2010). 2009 Census Report.

Kimani, J. K., Ettarh, R., Kyobutun, C., Mberu, B., & Kanyiva, K. (2012). Determinants for

participation in a public health insurance among residents of urban slums in Nairobi,

Kenya: Results of a cross-sectional survey. BMC Health Services Research , 12(66),

12-66.

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd Edition).

New York: The Guilford Press.

KNBS. (2010). Economic Survey 2010.

KNBS. (2016). Economic Survey 2016.

KNBS. (2007). Kenya Household Integrated Budget Survey 2005/06.

Kothari, C. R. & Garg, G. (2014). Research Methodology: Methods and Techniques (3rd ed).

New Delhi: New Age International Publishers.

233

Krishna, A., & Shrader, E. (1999). Social Capital Assessment Tool. Conference on Social

Capital and Poverty Reduction. Washington DC: The World Bank.

Kruk, M.E., Goldmann, E., & Galea, S. (2009). Borrowing and Selling to Pay for Health

Care in Low- and Middle-Income Countries. Health Affairs , 28:1056-66.

Kumar, R. (2011). Research Methodology: A Step by Step Guide for Begginers . New Delhi:

Sage Publications.

Kutzin, J. (2001). A descriptive framework for country-level analysis of health care financing

arrangements. Health Policy , 56:171-204.

Kutzin, J. (2008). Health Financing Policy: A Guide for Decision-makers. Health Financing

Policy Papers.

Kutzin, J. (2010). Reforms in the pooling of funds . In C. C. Joseph Kutzin, Implementing

Health Financing Reform, Lessons from countries in transition. Copenhagen, Denmark:

WHO: European Observatory on Health Systems and Policies.

Lagomarsino, G., & Kundra, S. S. (2008). Risk Pooling: Challenges and Opportunities.

Technical partner paper 6. The Rockefeller Foundation.

Lagomarsino, G., Garabrant, A., Adyas, A., Muga, E., & Otoo, N . (2012). Moving towards

universal health coverage: health insurance reforms in nine developing countries in

Africa and Asia. Lancet , 380: 933-43.

Langenbrunner, J., & Somanathan, A. (2011). Financing Health Care in East Asia and

Pacific. Washington, DC: World Bank.

Lavrakas, P. J. ed. (2008). Encyclopedia of Survey Research Methods. California: Sage

Publications.

Leftley, R. (2005). Technical assistance for the promotion of microinsurance: The

experience of Opportunity International, CGAP Working Group on Microinsurance,

Good and Bad Practices Case Study, No. 11. Geneva: ILO.

LeRoy, P., & Holtz, J. (2012). Third-party payment mechanisms in health microinsurance. In

C. Churchill and M. Matul (Eds.), protecting the poor: a microinsurance compendium

volume II (pp. 132-155). Geneva : International Labour Organisation (ILO).

Levesque, J., Harris, M.F., & Russell, G. (2013). Patient-centred access to health care:

conceptualising access at the interface of health systems and populations. International

Journal for Equity in Health , 12:18.

234

Lin, N. (2001). Social Capital: A theory of social structure and action. Cambridge , UK:

Cambridge.

Litwin, M. S . (1995). How to measure survey reliability and validity: The Survey

Kit(Volume 7). Thousand Oaks, CA: Sage Publications.

Luse, A., Mennecke, B., & Townsend, A. (2012). Selecting a research topic: A framework

for doctoral students. International Journal of Doctoral Studies , 7: 143-152.

Lysaght, Z. (2011). Epistemological and paradigramatic ecumenism in 'Pasteur's Quadrant.'

Tales from doctoral research. Official conference proceedings of the third Asian

Conference on Education in Osaka, Japan. Osaka.

Makaka, A. (2012). Role of government stewardship and leadership in SHP. Regional

Conference on Social Health Protection in the East African Community, Kigali. Kigali.

Maleika, M., & Kuriakose, A.T. (2008). Microinsurance: Extending Pro-Poor Risk

Management through the Social Fund Platform. Washington, DC: World Bank.

Marsh, D., & Sharman, J. C . (2009). Policy Diffusion and Policy Transfer. Policy Studies ,

30 (3): 269–288.

Martz, E. (2013). Enough is Enough! Handling Multicollinearity in Regression Analysis.

Mathauer, I., & Carrin, G. (2010). The role of institutional design and organizational

practice for health financing performance and universal coverage. Discussion Paper

No. 5. Geneva: WHO.

Mathauer, I., Schmidt, J., & Wenyaa, M. (2007). Extending social health insurance to the

informal sector in Kenya. An assessment of factors affecting demand. The International

Journal of Health Planning and Management , 23(1): 51-68.

Matul, M., Tatin-Jaleran, C., & Kelly, E. (2011). Improving client value from

microinsurance: Insights from India, Kenya, and the Philippines. Microinsurance

Paper No. 12, Microinsurance Innovation Facility. Geneva: ILO.

McCord, M. J., Steinmann, R., Tatin-Jaleran, C., Ingram, M., & Mateo, M. (2012). Making

finance work for Africa: the landscape of microinsurance in africa 2012. Munich Re

Foundation, GIZ-Program Promoting Financial Sector Dialogue in Africa, African

Development Bank Group, Microinsurance Network and Microinsurance Innovation

Facility: ILO.

235

McCord, M., Osinde, S. (2003). Reducing Vulnerability: The supply of Health Insurance in

East Africa. A synthesis Report. Nairobi: MicroSave Africa.

McIntyre, D., Thiede, M., Dahlgren, G., & Whitehead. (2006). What are the economic

consequences for households of illness and paying for health care in low- and

middleincome country contexts? Social Science & Medicine , 62(4), 858-865.

McIntyre, D.,& Meheus, F. (2014). Fiscal Space for Domestic Funding of Health and Other

Social Services. Working Group on Financing Paper 5. London: CHATHAM HOUSE.

McPake B. (1993). Community financing of health care in Africa: An eveluation of the

Bamako Initiative.

Mebratie, A. D., Sparrow, R., Alemu, G., & Bedi, A.S. (2013). Community-Based Health

Insurance Schemes A Systematic Review Working Paper No. 568 . International

Institute of Social Studies.

Meng, Q., & Xu Ling. (2014). Monitoring and evaluating progress towards universal health

coverage in China. PLoS Med , 11(9).

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded source

book (2nd ed.). Newbury Park, CA : Sage.

Mills, A., Ally, M., Goudge, J., Gyapong, J., & Mtei, G. (2012). Progress towards universal

coverage: the health systems of Ghana, South Africa and Tanzania. Health Policy and

Planning , 27 (Suppl. 1), i4-i12.

Ministry for Medical Services. (2011). Market Assessment of Private Prepaid Schemes in

Kenya. Niarobi: MOMS.

Ministry of Health. (2015). Kenya National Health Accounts 2012/13. Nairobi: Ministry of

Health.

Ministry of Medical Services and Ministry of Public Health and Sanitaion. (2012). Kenya

Health Policy 2012-2030. GoK.

Mladovsky, P. (2014). Social capital and enrolment in community-based health insurance in

Senegal. London : London School of Economics and Political Science Doctor of

Philosophy.

Mladovsky, P., & Mossialos, E. (2006). A conceptual framework for community-based health

insurance in low-income countries: social capital and economic development. London:

LSE Health, The London School of Economics and Political Science.

236

MoH. (2014). 2013 Kenya Household Health Expenditure and Utilisation Survey. Nairobi:

MoH.

Moreno-Serra, R., & Smith, P. C. (2012). Does progress to universal coverage improve

population health? Lancet , 380: 917-23.

Morestin, F., & Ridde, V. (2009). How can the poor be better integrated into health

insurance programs in Africa? An overview of possible strategies. Université de

Montréal.

Msuya, J.M., Jütting, J.P., & Asfaw, A. (2008). Impact of Community Health Funds on the

Access to Health Care: Empirical Evidence from Rural Tanzania. International Journal

of Public Administration , 30(8): p. 813-833.

Mugenda, O. M., & Mugenda, A. G. (2003). Research Methods: Quantitative and

Qualitative Approaches . ACTS press.

Muiya, B. M., & Kamau, A. (2013). Universal health care in Kenya: Opportunities and

challenges for the informal sector workers. International Journal of Education and

Research , 1(11):1-10.

Munge, K., Mulupi, S., & Chuma, J. (2016). Strategic purchasing for universal health

coverage: A critical assessment Community-Based Health Insurance Schemes in

Kenya. RESYST.

Munguti, R. (2010). New hospital fund rates blocked. Daily Nation , 1 october.

Musau, S. N. (1999). Community-based health Insurance: Experiences and Lessons learned

from East and Southern Africa. Technical report no. 34. Bethesda, MD. Partnerships

for health reforms, Abt associates Inc.

Nahapiet, J., Gratton, L., & Rocha, H. O. (2005). Knowledge and relationships: When

cooperation is the norm. European Management Review , 45: 1-12.

Ndiaye, P., Soors, W., & Criel, B. (2007). Editorial: a view from beneath: community health

insurance in Africa. Tropical Medicine and International Health , 12(2), 157-161.

Neuman, W.L. (2011). Basics of Social Research: Qualitative and Quantitative Approaches

(7th Ed). Bacon, Boston: Pearson Education.

Nunnally, C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill. OAU. (2001).

Abuja declaration on HIV/AIDS, tuberculosis and other related infectious diseases.

Addis Ababa: Organization of African Unity.

237

OECD-DAC secretariat simulation of DAC members‘ net ODA volumes in 2008 and 2010.

(2010). OECD.

Okech, C.T. (2013). Review of on-going efforts in the implementation of the Health Care

Financing Functions and Proposal for feasible Financing Options. Unpblished report.

Oxfam International. (2008). Health Insurance in low-income countries. Joint NGO Briefing

Paper.

Oxfam International. (2013). Universal Health Coverage: Why health insurance scheme are

leaving the poor behind. Oxford : Oxfam GB.

Ozawa, s., & Walker, D.G. (2009). Trust in the context of Community Based Health

Insurance Schemes in Cambodia: Villagers trust in health insurers . Advances in Health

Economics and Health Services Research , 12:107-32.

Panda, P., Chakraborty, A., Dror, D. M., & Bedi, A.S. (2013). Enrolment in community-

based health insurance schemes in rural Bihar and Uttar Pradesh, India. Health Policy

and Planning , 1-15.

Parmar, D., Allegri, M.D., Savadogo, G. & Sauerborn, R. (2013). Do community-based

health insurance schemes fulfil the promise of equity? A study from Burkina Faso.

Health Policy and Planning , 29(1), pp. 76-84.

Pauly, M. V., Zweifel, P., Scheffler, R. M., Preker, A. S., & Bassett, M. (2006). Private

health insurance in developing countries. Health Aff (Millwood) , 25, 369-79.

Picazo, O. (2014b). Seven remaining policy and programmatic tasks to achieve effective

UHC coverage in the Philippines. Manila: WHO Country Office in the Philippines.

Poletti, T., Balabanova, D., Ghazaryan, O., Kamal-Yanni, M., Kocharyan, H., Arakelyan, K.,

& Hakobyan, M. (2007). Options for scaling up community-based health insurance for

rural communities in Armenia. Health Systems Development Programme. London

School of Hygiene and Tropical Medicine: London.

Prakongsai, P., Limwattananon, S., & Tangcharoensathien, V. (2009). The equity impact of

the universal coverage policy: lessons from Thailand. Advances in Health Economics

and Health Services Research , 21:57-81.

Preker, A. S., & Carrin, G. (2004). Health Financing for Poor People: Resource

Mobilization and Risk Sharing. Washington. D. C: World Bank.

238

Preker, A. S., Liu, X., Velenyi, E. V., & Baris, E. Eds. (2007). Public Ends, Private Means:

Strategic Purchasing of Health Services. Washington D C: World Bank.

Preker, A.S. (2002). Health Care Financing for Rural and Low-Income Populations: The

Role of Communities in Resource Mobilization and Risk Sharing. Background Reports.

Commission on Macro-Economics and Health.

Private Sector Innovation Programme for Health (PSP4H). (2014). Comparing Kenya’s

Private Health Markets with neighbouring markets; A Focus on Health Financing.

NAIROBI : PSP4H.

Putnam, R. D . (2000). Bowling Alone: the collapse and revival of American community.

New York: Simon & Schuster.

Ranson, M. K., Sinha, T., Chatterjee, M., Gandhi, F., Jayswal, R., Patel, F., Morris, S. S., &

Mills, A. J. (2007). Equitable utilisation of Indian community based health insurance

scheme among its rural membership: cluster randomised controlled trial. BMJ (Clinical

research ed) , 334 (7607). p. 1309.

Rashad, A.S ., & Sharaf, M. F. (2015). Catastrophic Economic Consequences of Healthcare

Payments: Effects on Poverty Estimates in Egypt, Jordan, and Palestine. Economies , 3,

216-234.

Ridde, V., Haddad, S., Nikiema, B., Oudraogo, M., Kafando, Y., & Bicaba, A. (2010). Low

coverage but few inclusion errors in Burkina Faso: a community-based targeting

approach to exempt the indigent from user fees. BMC Public Health , 10(631), 2-8.

Ringle, C. M., Sarstedt, M., & Schlittgen, R., Taylor, C. R. (2013). PLS path modeling and

evolutionary segmentation. Journal of Business Research , 66 (9), 1318-1324.

Robert, A., Bereson, P, J. , & Harlan, M. K. (2013). Achieving the potential Health Care

Performance Measures . Washington: Urban Institute.

Robson, C. (2002). Real World Research. (2nd ed.). Oxford: Blackwell.

Robyn, P.J., Sauerborn, R., Bärnighausen, T. (2013). Provider payment in community-based

health insurance schemes in developing countries: a systematic review. Health Policy

Plan , 28(2):111-122.

Rogers, E. (1962). Diffusion of Innovations, 5th Edition. Simon and Schuster.

Rogers, E. (2003). Diffusion of Innovations, 5th Edition. Simon and Schuster.

239

Ron, A. (1999). NGOs in community health insurance schemes: examples from Guatemala

and the Philippines. Social Science & Medicine , 48, 939-50.

Saksena, P., Xu, K ., & Durairaj, V. (2010). The drivers of catastrophic expenditure:

outpatient services, hospitalization or medicines? World Health Report (2010)

Background Paper, No 21 . Geneva: World Health Organization.

Salomon, J.A., Tandon, A., & Murray, C.J.L. (2004). Comparability of self rated health:

cross sectional multicountry survey using anchoring vignettes. BMJ , 328(7434):258-

61.

Saunders, M., Lewis, P., & Thornhill. (2009). Research Methods for Business Students (5th

ed). Harlow: Prentice Hall.

Saunders, M., Lewis, P., & Thornhill, A. (2012). Research Methods for Business Students

(6th Ed). Essex, England: Pearson Education Ltd.

Saunders, M., Lewis, P., & Thornhill, A. (2016). Research Methods for Business Students

(7th ed). Person Education Limited.

Saunders, M., Lewis, P., & Thornhill, A. (2007). Research Methods for Business Students,

(4th ed,.). Harlow: Prentice Hall Financial Times.

Schieber, G., Cashin, C., Saleh, K., & Lavado, R. (2012). Health Financing in Ghana.

Washington DC: The World Bank.

Schneider, P. (2005). Trust in micro-health insurance: an exploratory study in Rwanda. Soc

Sci Med , 61, 1430-8.

Schneider, P. (2004). Why Should the Poor Insure? Theories of Decision-Making in the

Context of Health Insurance. Health Policy and Planning , 19 (6):349- 355.

Schneider, P., & Diop, F. (2004). Community-based health insurance in Rwanda. In: A.S.

Preker and G. Carrin (eds): Health financing for poor people: Resource mobilization

and risk sharing. pp. 251–74. Washington DC: World Bank.

Sekaran, U., & Bougie, R. (2016). Research Methods for Business (7th ed). West Sussex,

UK: Wiley.

Sinha,T., Ranson, M.K., Chatterjee, M., Acharya, A., & Mills, A.J. (2005). Barriers to

accessing benefits in a community-based insurance scheme: lessons learnt from SEWA

Insurance, Gujarat. Oxford University Press.

240

Smit, N., & Mpedi, L. (2010). Social protection for developing countries: Can social

insurance be more relevant for those working in the informal economy? Law,

Democracy & development.

Smith, P C., & Witter, S. N. (2004). Risk Pooling in Health Care Financing:The Implications

for Health System Performance. HNP Discussion Paper. Washington, DC: World

Bank.

Smith, P. C. (2008). Formula funding of health services: learning from experience in some

developed countries. Geneva: WHO.

Soeters, R., & Griffiths, F. (2003). Improving government health services through contract

management: A case of Cambodia . Health Policy and Planning , 18: 74-83.

Soors, W., Devadasan, N., Durairaj, V., & Criel, B. (2010). Community Health Insurance

and Universal Coverage: Multiple paths, many rivers to cross. World Health Report

(2010) Background Paper, 48. GENEVA: WHO.

Spaan, E., Mathijssen, J., Tromp, N., McBain, F., ten Have, A., & Baltussen, R. (2012). The

impact of health insurance in Africa and Asia: a systematic review. Bulletin of the

World Health Organisation , 90(9),685-692.

Speech by H.E. Hon. Uhuru Kenyatta, C.G.H., President and Commander-in-Chief of the

Defence Forces of the Republic of Kenya During the Madaraka Day Celebrations‖

(Nyayo National Stadium). (2013, June 1). Retrieved October 12, 2015, from

http://www.statehousekenya.go.ke/.

Starfield, B. (2011). The hidden inequity in health care. International Journal for Equity in

Health , 10:15.

Stenberg, K., Elovainio, R., Chisholm, D., Fuhr, D., Perucic, A., Rekve, D., & Yurekli, A.

(2010). Responding to the challenge of resource mobilization- mechanisms for raising

additional domestic resources for health World Health Report (2010) Background

Paper, No 13. Geneva : WHO.

Su, TT., KouyatЙ, B., & Flessa, S . (2006). Catastrophic household expenditure for health

care in a lowincome society: A study from Nouna District, Burkina Faso. Bull World

Health Organ , 84(1):21-7.

Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Harlow,

England : Pearson Education.

241

Tabor, S. R. (2005). Community-based health insurance and social protection policy.

Washington DC: World Bank.

Takakura, M. (2011). Does social trust at school affect students‘ smoking and drinking

behaviour in Japan? . Social Science and Medicine , 72(2), 299-306.

Tangcharoensathien, V., Mills, A., & Palu, T. (2015). Accelerating health equity: the key role

of universal health coverage in the Sustainable Development Goals. BMC Medicine ,

13:101.

Tao, Y., Kizito, H., Qinpei, Z., & Xiaoni. (2014). Methods of measuring horizotal equity in

health resource allocation: a comparative study. Health Economics , 4:10.

Tibandebage, P., & Mackintosh, M. (2005). The Market Shaping of Charges, Trust and

Abuse: Health Care Transactions in Tanzania . Social Science & Medicine , 61(7):

1385-95.

Tipke, M., Diallo, S., Coulibaly, B., Storzinger, D., Hoppe-Tichy, T., Sie A.,& Muller O.

(2008). Substandard anti-malarial drugs in Burkina Faso. Malar J , 7:95.

Travis, P., Egger, D., Davies, P., & Mechbal, A. (2002). Towards better

stewardship:concepts and critical issues. Geneva: WHO.

Tundui, C., & Macha, R. (2014). Social capital and willingness to pay for community based

health insurance: An empirical evidence fro rural Tanzania. Journal of Finance and

Economics , Vol 2, Issue 4, 50-67 ISSN 2291 4951.

Usoroh, E. E. (2012). Achieving universal health coverage in Nigeria: The Nigeria Health

Insurance Scheme as a tool. 48th International Course in Health Development .

Amsterdam: Royal Tropical Institute.

Van Leeuwen, E . (2008). Changing moral experiences in European health care. Medical

Law , 27:57-604.

Van Slyke, M. (2006). Agents or Stewards: Using Theory to Understand the Government-

Nonprofit Social Service Contracting Relationship. Journal of Public Administration

Research and Theory , 17: 157-187.

Veillard, J. H. M., Brown, A. D., Baris, E., Permanand, G., Klazinga, N.S. (2011). Health

system stewardship of National health Ministries in the WHO European region:

Concepts, functions and assessment framework. Health Policy , 103: 191-9.

242

Vinzi, V., Trinchera, L., & Amato, S. (2010). PLS Path Modeling: From Foundations to

Recent Developments and Open Issues for Model Assessment and Improvement.

Chapter 2 Handbook of Partial Least Squares. Springer Handbooks of Computational

Statistics.

Wahyuni, D. (2012 ). The Research Design Maze: Understanding Paradigms, Cases,

Methods and Methodologies. Journal of Applied Management Accounting Research ,

Vol. 10, No. 1, pp. 69-80.

Wang, H., & Pielemeier, N. (2012). Community-Based Health Insurance: An Evolutionary

Approach to Achieving Universal Coverage in Low-Income Countries. Journal of Life

Sciences , 6: 320-29.

Welman, C., Kruger, F., & Mitchell, B . (2007). Research Methodology (3rd). Cape Town:

Oxford University Press.

Whetten, D. A. (1989). What Constitutes a Theoretical Contribution? . Academy of

Management Review , Vol. 14, No. 4, 490-495.

WHO South Africa. (2015). Risk pooling in health financing: Rationale and principles.

Working paper. WHO.

Williams, C. (2007). Research Methods. Journal of Business & Economics Research , 5 (7).

Witter, S., & Garshong, B. (2009). Something old or something new? Social health insurance

in Ghana . BMC International Health and Human Rights , 9:20.

Wodtke, G, T., Elwert, F.,& Harding, D.J. (2012). Poor families, Poor neighbourhoods: How

family poverty intesifies the impact of concentrated disadvantage. Report 12-776.

Population Studies Report.

Woolcock, M. (1998). Social Capital and Economic Development: Toward a Theoretical

Synthesis and Policy Framework. In Theory and Society , 27, 151-208.

World Bank. (2012). World Development Report 2013: Jobs. Washington, DC: World Bank.

World Bank . (2014). Philippines: Delivering Successful Sin Tax Reform. Geneva : World

Bank.

World Bank. (2015). World Development Indicators database. Washington DC: World Bank.

World Health Organization. (1948). Constitution of the World Health Organization. Geneva:

WHO.

243

World Health Organization. (1978). Declaration of Alma-Ata:Ata: international conference

on primary health care. Geneva: WHO.

World Health Organization. (1981). Global Strategy for Health for All by the Year 2000.

Geneva: WHO.

World Health Organization. (2000). The World Health Report 2000 - Health systems:

improving performance. Geneva: WHO.

World Health Organization. (2001). Macroeconomics and health: Investing in health for

economic development. Geneva: WHO.

World Health Organization. (2001). Report on WHO meeting of experts on the

StewardshipFunction in Health Systems. Geneva: WHO.

World Health Organization. (2005). Social health insurance: Sustainable health financing,

universal coverage and social health insurance In: 58th World Health Assembly.

WHO.

World Health Organization. (2006). The World Health Report 2006 - Working together for

health. Geneva: WHO.

World Health Organization . (2007). Everybody's Buisness - Strengthening Helath Systems to

improve Health outcomes. Geneva: WHO.

World Health Organization. (2008). The World Health report 2008: primary health care now

more than ever. Geneva : WHO.

World Health Organization. (2009). Taskforce on Innovative International Financing for

Health systems, Working Group 2 Report. Geneva: WHO.

World Health Organization. (2010a). Health Systems Financing - The path to universal

coverage. Geneva: WHO.

World Health Organization. (2010b). Constraints to scaling up the health Millennium

Development Goals: costing and financial gap analysis. Geneva: WHO.

World Health Organization. (2012). Intersectoral Governance for Health in All Policies:

Structures, actions and experiences. European observatory on health systems and

policies. Copenhagen: WHO.

World Health Organization. (2013). The state of health financing in the African Region.

Brazzavile: WHO, Region Office of Africa.

244

World Health Organization. (2014a). WHO African Region Expediture Atlas. Brazzaville:

WHO Regional Office for Africa.

World Health Organization. (2014b). Health Systems Governance for Universal Health

Coverage: Action Plan . Geneva : WHO.

World Health Organization. (2014c). Fiscal space, public finance management and health

financing policy: a collaborative agenda. Montreux, Switzerland: WHO.

World Health Organization. (2015a). Tracking Universal Health Coverage: First Global

Monitoring Report. Geneva: WHO.

World Health Organization. (2015b). Do health expenditures meet health needs? Geneva:

WHO.

World Health Organization. (2015c). Positioning health in the post-2015 development

agenda. Geneva: WHO.

World Health Organization. (2015d). Anchoring universal health coverage in the right to

health: What difference would it make? Policy brief. Geneva: WHO.

World Health Organization. (2015e). World Health Statistics. Geneva: WHO.

World Health Organization. (2015f). External AID for health remains insufficient in low

income countries. WHO/HA Policy Highlight No.3. Geneva: WHO.

World Health Organization. (2016). World Health Statistics: Monitoring health for the

SDGs. Geneva: WHO.

World Health Organization- GTZ. (2004). National Social Health Insurance Financial

projections and future bilateral / multilateral cooperation. sixth joint WHO/GTZ

mission report. Nairobi: WHO - GTZ.

Xu, K., Evans, D.B., Carrin, G., Agilar-Rivera, A.M, Musgrove, P., & Evans, T . (2007).

Protecting households from catastrophic health spending (Millwood). Health Affairs ,

26:972-983.

Xu, K., Evans, D.B., Kawabata., K., Zeramdini, R., Klavus, J., & Murray, C.J. (2003).

Household catastrophic health expenditure: a multicountry analysis. Lancet ,

362(9378): 111-117.

Xu, K., Saksena, P., & Evans, D.B. (2010). Health financing and access to effective

interventions. World Health Report (2010) Background Paper, 8. Geneva: World

Health Organization.

245

Yamane, T . (1967). Statistics: An Introductory Analysis, (2nd Ed.). New York: Harper and

Row.

246

APPENDICES

APPENDIX 1: Approval Letter from USIU-A

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APPENDIX 2: APPROVAL LETTER FROM NACOSTI

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APPENDIX 3: NACOSTI RESEARCH CLEARANCE PERMIT

249

APPENDIX 4: QUESTIONNAIRE

Instructions

In section I – II of this questionnaire you have been provided with one type of question:

1. Tick (√) the one which closely matches your opinion.

SECTION ONE: GENERAL QUESTIONS

1.1. What is the name of your CBHIs? …………………………………..

1.2. How many years has the CBHIs been operating? ........................................

1-5 years ( ) 6-10 years ( ) 11-15 years ( ) > 15 years ( )

SECTION TWO: HEALTH FINANCING FUNCTIONS

2.1 Enrolment

Indicate with a tick (√) the statement that best describes the enrolment strategy for the

CBHIs. Tick your choice in the appropriate answer box.

1 = Strongly Disagree, 2 = Disagree, 3= Neutral 4 = Agree, 5 = Strongly Agree

2.1.1 Affordability 1 2 3 4 5

We give members a chance to allocate premium among preferred products

Members can pay premium in kind e.g. through farm produce or labour

We give subsidies and exemption of premiums for extremely poor and

vulnerable

We encourage members to use savings-linked premium payment

mechanisms such as rotating saving groups (Chamas), MPESA

Members can make irregular payments of premium

2.1.2 Unit of membership

The CBHIs membership is based on coffee, tea, villages or mutual benefit

societies or administrative areas

The CBHIs have adapted households as unit of membership

CBHIs encourages members to join when they are healthy

CBHIs membership is open to poor and vulnerable groups

Any other, please indicate

2.1.3 Timing of collection

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Members pay in a single annual premium /contribution

Members can pay their premiums in installments

Premium payments correspond with income from e.g. harvest, sale of

livestock or salary payment

Premium payments are linked to loans from SACCOs and banks

Mobile premiums payments are allowed


2.1.4 Trust

Members interact with the scheme‘s administrative / management team and

providers about their needs, concerns and make suggestions for

improvements

Members participate in setting of benefit package

Members participate in setting the premiums

The CBHIs uses existing Chamas, community development projects and

credit schemes as entry points for CBHIs membership

Member of the scheme are willing to cover the poorest and vulnerable

group in the community such as orphans and disabled


2.2 Mix of Contributions

Indicate with a tick (√) the statement that best describes the adequacy and mix of

contributions in the CBHIs. Tick your choice in the appropriate answer box.


1 2 3 4 5

Members‘ contributions are adequate in meeting the cost of the set benefit

package

NHIF covers costs of services not covered by the CBHIs

The CBHIs receives financial support from donor(s)

The poor and vulnerable members of the CBHIs are covered through

government subsidies

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2.3 Indicate with a tick (√) the statement that best describes the risk pooling strategies that

are employed by the CBHIs. Tick your choice in the appropriate answer box.


RISK POOLING 1 2 3 4 5

i) Enhanced risk pooling

Members of CBHIs comes from a wide range of social economic

background

The scheme targets a large geographical /administrative area

Community members are encouraged to join CBHIs when they are healthy

We have a waiting period before one can benefit from CBHI

The CBHIs has other branches in other geographical or administrative areas

CBHIs reinsures its risks

The CBHIs has a partnered with the county / national government and or

NHIF

The CBHIs merged with other CBHIs to form a network or a federation


ii a) Social solidarity

Members of the scheme have expressed the opinion that if they would not

need health care themselves, at least they had done something good for the

community by contributing to the insurance fund


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Indicate with a tick (√) the statement that best describes the risk pooling strategies that are

employed by the CBHIs. Tick your choice in the appropriate answer box.

1 = None of the cost, 2 = Some of the cost, 3= Half of the cost 4 = Most of the cost, 5 = All

of the cost

ib) Social solidarity

How much do you think members of the CBHIs are willing to contribute to

pay for health care services used by the sick?

How much do you think members of the CBHIs are willing to contribute to

pay for health care services used by the poor?


2.4 Indicate with a tick (√) the statement that best describes the strategic purchasing activities

undertaken by the CBHIs. Tick your choice in the appropriate answer box.


STRATEGIC PURCHASING 1 2 3 4 5

We have signed a contract with all our health services providers

We only select providers who are accredited by NHIF

The providers must provide health services according to conditions put


We allocate resources based on population needs

The CBHIs has been successful in negotiating agreeable terms and contract

with service providers – in terms of service quality, fee, and reduction in

unnecessary services/prescription (moral hazard)


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2.5 Indicate with a tick (√) the statement that best describes the role played the government

and or donors‘ financial sustainability of your company. Tick your choice in the appropriate

answer box.


GOVERNMENT ROLE 1 2 3 4 5

A. ADVISER ON DESIGN OF CBHIS: The Government recommends

Startup of CBHIs when a minimum percentage of population in enrolled

A waiting period

Enrollment of households as opposed to individuals

A flexible premium collection system

A benefit package that reflect the needs of the target population

A standard treatment protocols for members of CBHIs

A standard referral procedure

Consolidation of CBHIs through a federation or a network

Creation of a risk equalization fund or a reinsurance mechanism

Community participation in management and decision making


B. MONITORING OF CBHI ACTIVITIES: The government

Tracks the progress of CBHIs through time

Monitors the basic performance of CBHIs


C. TRAINING: The government organizes trainings on

Determination of benefit packages

Determination of contributions

Collection of contributions

254

Claims processing

Use of Management Information Systems

Establishment of Health Insurance Development Plans

Exchange visits


D. CO- FINANCING: The government and/ or donors

Partially or fully subsidizes the poorest and vulnerable members of the

community

Has set a solidarity fund for financing epidemics and deficits of the CBHIs


2.6 EQUITY IN HEALTH CARE

2.6.1 Indicate with a tick (√) the statement that best describes access to health services to

members of your CBHIs. Tick your choice in the appropriate answer box.


Access 1 2 3 4 5

There is distribution of enrolment across income categories 1

The contracted providers are within the proximity of covered population 2

We cater for transport / accommodation cost related to healthcare utilization 3

The covered population is entitled to similar benefits 4

The number of members seeking services has increased in the past 12

months

5


255

2.6.2 Indicate with a tick (√) the statement that best describes contribution across different

income categories within the CBHIs. Tick your choice in the appropriate answer box.


Equity in Contribution 1 2 3 4 5

Everyone pays the same amount

Everyone pays an equal amount of their income

We allow flexible premium payments

We offer allow members to match premium or products to their income

We offer premium subsidies

We allocate a larger claim budget for low cost products


2.6.3 Indicate with a tick (√) the statement that best describes perceived quality of care from

contracted health care providers. Tick your choice in the appropriate answer box.


2.6.3 Quality of care 1 2 3 4 5

The CBHIs has a standard client compliant management mechanism

Members have complained about long queues before being seen

Members have complained on availability of health services

Members have complained about lack of key prescribed medicines

Members have raised concerns relate to cleanliness

Members have raised concerns on availability of trained staff in the

contracted health facilities

The CBHIs have put in place mechanisms to check on patient perceived

quality of care in contracted health facilities on issues concerning waiting

time, availability of staff, services, drugs and supplies

There are other organization(s) that conduct quality checks in the contracted

health facilities

These organizations share their findings with the CBHIs


256

2.6.4.1 Indicate with a tick (√) the statement that best describes the administrative and

managerial capability of the CBHIs management team. Tick your choice in the appropriate

answer box.


1 2 3 4 5

Administrative and managerial capability: The administrative committee

has basic skills in

Setting of contributions

Collection of contributions and compliance

Determination of the benefit package

Claim management

Marketing and communication

Contracting with providers

Management information systems

Accounting


2.6.4.2 Financial Sustainability

Indicate with a tick (√) the statement that best describes the practice of the CBHIs. Tick your

choice in the appropriate answer box.

1 = Strongly Disagree, 2 = Disagree, 3 = Agree, 4 = Strongly Agree

Financial sustainability 1 2 3 4 5

We have partnered with organizations that assist in collection of premiums

Premiums are not paid on time

The CBHIs is funded through a mix of contributions from county / national

government / donors and members contributions

Government and or donors‘ covers health cost for those who cannot afford

257

to pay premiums

Chronic conditions are covered by the CBHIs

The CBHI have put in place mechanisms to check whether the invoices sent

from the health facilities are correct

There are instances which health facilities tried to overstate the

reimbursement request amount

The CBHIs is part of a network of CBHIs

We have merged with other CBHIs

We are in partnership with NHIF

Besides treatment we finance community prevention, promotion and

rehabilitation activities


Thank you for your responses

258

APPENDIX 5: SECONDARY DATA SHEET

Instructions

In parts I – IV of this questionnaire you have been provided with two types of questions:

1. Tick (√) the one which closely matches your opinion.

2. Tables which require that you indicate the values in each category for a specific year.

SECTION ONE: GENERAL QUESTIONS

1.1 How many households are targeted by the CBHIs? ………………………….

0-500 ( ) 501-1000 ( ) 1001-2000 ( )

1.2 How many households are covered by the CBHIs? ..................................

0-500 ( ) 501-1000 ( ) 1001-2000 ( )

1.3 Indicate the percentage of members that lives within the stated distance from the

participating health facilities.

Within 5km …………Between 5-10km ………………More than 10km ………………

1.4 Which methods of payment is used by the CBHIs?

i) Inpatient services

Capitation method ( ) Fee for service method ( ) Case based payment ( )

Mixed methods ( )

ii) Outpatient services

Capitation method ( ) Fee for service method ( ) Case based payment ( )

Mixed methods ( )

1.5 How many members of CBHI have drop out of NHIF in the last one year? ………

259

1.6 Indicate the insurance products offered by the CBHIs and premiums per product

CBHIs only products Composite product (NHIF and CBHIs)

Product Premium Product Premium

i CBHIs cover for small households

(public hospital services)

i Compounded CBHIs & NHIF cover

ii CBHIs cover for expanded households

(public hospital services)

ii Compounded CBHIs & NHIF expanded

cover

iii CBHIs general outpatient cover iii Cover with NHIF outside CBHIs

iv CBHIs general outpatient and inpatient

cover

iv Cover with NHIF outside CBHIs

(expanded benefits)

v CBHIs general outpatient and inpatient

cover with ambulance services

vi CBHIs general outpatient and inpatient

cover with ambulance services and

burial expenses

260

SECTION TWO: ENROLMENT AND CONTRIBUTIONS

Indicate the number of households enrolled and the contributions for the following years?

Item Population

2015 2014 2013 2012 2011 2010

i) Target Population

iv) Total members of the CBHIs

Members covered by both

CBHIs and NHIF

Members covered by CBHIs

only

SECTION THREE: MIX OF CONTRIBUTIONS

Indicate the amount of Contributions from each category

Category 2015 2014 2013 2012 2011 2010

Membership

contributions

Donor

NHIF

Government

261

SECTION FOUR: FINANCIAL STATUS OF CBHI SCHEME

What have been the income, reimbursements, costs and surplus/deficits for your CBHI for the following years?

2015 2014 2013 2012 2011 2010

Total income of the CBHI

scheme

Reimbursement of the cost of

OPD and inpatient services in

health facilities

Administrative cost

Total cost of the scheme

Surplus / Deficit

262

APPENDIX 6: List of CBHIs (registered and sampled) and their respective Networks

263

APPENDIX 7: Cross Loadings of Constructs

Cross loadings for Enrolment

Pattern Matrixa

Component

1 2 3 4 5

AFF1 0.915 -0.025 0 0.074 -0.198

AFF2 0.024 -0.035 0.056 0.013 -0.019

AFF3 0.031 -0.03 -0.086 0.07 0.013

AFF4 0.901 -0.027 0 0.071 -0.19

AFF5 -0.017 0.002 -0.102 0.095 0.02

MEM1 0.013 0.043 -0.003 0.057 -0.033

MEM2 -0.019 0.023 0.063 -0.053 -0.102

MEM3 -0.018 0.039 0.258 0.431 0.254

MEM4 0.014 0.017 -0.02 0.036 -0.071

TM1 0.873 0.018 -0.003 -0.038 0.207

TM2 -0.011 0.039 0.006 -0.039 0.031

TM3 0.884 -0.004 0.024 0.029 0.217

TM4 -0.011 0.039 0.006 -0.039 0.031

TRU1 0.87 0.012 0.002 -0.012 0.247

TRU2 0.905 -0.005 -0.001 0.045 -0.229

TRU3 0.849 0.003 0.029 -0.071 0.231

TRU4 0.905 0.013 -0.013 0.049 -0.222

TRU5 0.589 0.033 -0.045 -0.287 0.008

Extraction Method: Principal Component Analysis.

Rotation Method: Promax with Kaiser Normalization.

a. Rotation converged in 5 iterations.

264

APPENDIX 7: Cross Loadings of Constructs (Continued)

Extraction Method: Principal Component Analysis

Rotation Method: Promax with Kaiser Normalization

a. Rotation converged in 3 Iterations

Cross loadings for Strategic Purchasing

Pattern Matrixa

Component

1

SP1 .783

SP2 .876

SP3 .861

SP4 .849

SP5 .476

Cross loadings for Mix of Contribution

Pattern Matrixa

Component

1 2

MC1 .327 -.209

MC2 .984 .084

MC3 .256 .068

MC4 .964 .084

Cross loadings for Risk pooling Pattern Matrix

a

Component

1 2 3 4

ERP1 .801 .103 .003 -.114

ERP2 .015 .052 -.786 -.221

ERP3 .971 -.039 -.016 -.019

ERP4 -.044 .995 -.027 .010

ERP5 .015 .055 .024 -.249

ERP6 .745 .055 .027 .041

ERP7 .018 .045 -.001 .141

ERP8 .823 -.050 .014 .145

265


Extraction Method: Principal Component Analysis

Rotation Method: Promax with Kaiser Normalization

a. Rotation converged in 3 Iterations

Cross loadings for Government Stewardship

Pattern Matrixa

Component

1 2

COF1 .884 -.015

COF2 .904 -.018

COF3 -.002 .099

TR1 .942 -.001

TR2 .964 .009

TR3 .978 .031

TR4 .948 .008

TR5 .045 -.019

TR6 .070 .000

TR7 .012 .024

MO1 .933 -.046

MO2 .936 -.037

AD1 .916 .034

AD2 .945 .027

AD3 .952 -.006

AD4 .924 -.021

AD5 .969 .009

AD6 .973 .018

AD7 .941 .029

AD8 .931 -.008

AD9 .937 -.023

AD10 .057 .002

266


Extraction Method: Principal Component Analysis. Rotation Method: Promax with Kaiser Normalization. a. Rotation converged in 6 iterations.

Cross loadings for Equity in HealthCare

Pattern Matrixa

Component

1 2 3 4

ACC1 .027 -.008 .003 .085

ACC2 .063 .038 .001 .234

ACC3 .081 .008 .010 .055

ACC4 .071 -.049 -.014 .714

AMC1 .099 -.008 .992 -.075

AMC2 .096 .002 .016 .020

AMC3 .004 -.008 .009 -.140

AMC4 .006 .020 .097 -.014

AMC5 -.011 .007 .097 .016

AMC6 .040 .002 .978 .038

AMC7 .037 .817 -.073 .006

AMC8 -.024 -.021 -.007 .091

FS1 .004 .915 .009 -.140

FS2 .398 .002 -.001 .038

FS3 -.001 .099 .011 -.010

FS4 -.001 .099 .011 -.010

FS5 .089 .750 -.068 -.048

FS6 .015 .014 .016 -.082

FS7 .020 .075 -.066 .008

FS8 .321 .015 -.001 .640

FS9 -.044 .091 -.084 -.061

FS10 .988 .988 -.006 -.205

FS11 -.039 .910 .035 .071

QOC1 -.001 -.003 .011 .040

QOC2 -.001 -.001 .011 -.010

QOC3 -.001 -.084 .011 -.010

QOC4 -.001 -.006 .011 -.010

QOC5 -.001 .035 .011 -.010

QOC6 -.001 .011 .011 -.010

QOC7 -.001 .035 .011 -.010

QOC8 .914 -.007 .038 .028

QOC9 .886 .033 -.060 .077

QOC10 .095 -.003 .011 .040

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INNOVATIVE HEALTHCARE FINANCING AND EQUITY …