METHODS FOR POPULATION-BASED ASSESSMENTS IN POST CONFLICT SETTINGS: HEALTH SERVICE PERFORMANCE, ECONOMIC STATUS AND EQUITY OF UTILIZATION IN AFGHANISTAN

METHODS FOR POPULATION-BASED ASSESSMENTS IN POST-

CONFLICT SETTINGS: HEALTH SERVICE PERFORMANCE,

ECONOMIC STATUS AND EQUITY OF UTILIZATION IN

AFGHANISTAN

By

Shivam Gupta

A dissertation submitted to the Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy

Baltimore, Maryland

October 2008

© 2008 Shivam Gupta

All Rights Reserved

UMI Number: 3357166

Copyright 2008 by

Gupta, Shivam

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Abstract

There is an urgent need for innovative methods to generate information to

evaluate post-conflict reconstruction, especially in the health sector. In order to translate

the immediate response into a systematic medium and long term health strategy, baseline

estimates of health service performance are required. In war torn Afghanistan, faced with

a monumental task of laying the foundations for an equitable and quality oriented health

system, the Ministry of Public Health used the outdated information from the 1979

census to conduct the first population based health assessment in 2003. Results from a

comparison of these estimates with those generated using the pre-census conducted in

2004 indicate that the originally reported estimates provided information that was

adequate for cross-sectional assessment but of limited use for assessing trends over time.

Logistical concerns of restricted access in post-conflict countries like Afghanistan

require a measure of living standards to be based on information that is easy to collect,

observe and verify. Comparison of principal components analysis based asset index with

expenditure estimates based on out of sample prediction indicated that predicted

expenditure was a more reliable measure to track poverty over time. Regular collection of

data on asset variables to predict expenditure can improve the accessibility of this

information to policy makers. In addition, the predicted expenditure can form the basis

for poverty mapping and targeting through the social protection programs.

Equitable health systems require a sustained increase in utilization of health

services by groups that suffer from the greatest burden of disease - females and the poor.

In Afghanistan, a trend towards equitable utilization in the last four years has occurred

along with simultaneous improvement in quality of health services. The association

u

between different characteristics of health care delivery system and utilization rate

differed across the three outcome groups in the facility catchment area - total population,

the poor and females. Improvement in staffing and service capacity was associated with a

significantly greater increase in utilization in a later year as compared to a similar

improvement in an earlier year. User fee collection was associated with a decrease in

utilization rate, especially by the poor.

Thesis readers:

Dr. David H. Peters (Advisor), Department of International Health

Dr. Laura Morlock (Chair), Department of Health Policy and Management

Dr. Gilbert M. Burnham, Department of International Health

Dr. Stan Becker, Department of Population, Family and Reproductive Health

111

Acknowledgements

I am grateful to the people of Afghanistan, whose generosity enabled the fruition

of this thesis. More than 500 people have worked on the Afghanistan Monitoring and

Technical Support Project since 2004.1 owe a debt of gratitude to all the people who

collected the information; sometimes under difficult circumstances and at great personal

risk to themselves. The team members from JHU and IHMR have facilitated and

supported my thesis research at every stage. I have great appreciation for their efforts and

encouragement.

I feel very lucky and honored to have had teachers who were not only

intellectually stimulating but also very kind and considerate. My advisor Dr. David

Peters, with his cheerful humor and engaging nature, made the whole journey very

enjoyable and interesting. Dr. Peters gave me the freedom to explore different ideas and

ensured that when the need arose, he had the time to help and guide me. I am thankful to

Dr. Gilbert Burnham, Dr. Stan Becker and Dr. Laura Morlock. Their comments and

suggestions during proposal and thesis writing helped me tremendously in improving the

quality of my research. I am also thankful to Dr. Marie-Diener West, Dr. David Bishai,

Dr. Hugh Waters, Dr. Larry Moulton and Dr. Abdullah Baqui. Their help has been

instrumental in clearing the conceptual and methodological roadblocks that I faced during

different phases of my thesis research.

Dr. Mathuram Santosham and his caring wife Dr. Patricia Santosham have always

welcomed me in their home with open arms and have been a wonderful source of moral

support and encouragement for the past five years. Dr. Leon Gordis and his clear and

timely advice about my research and life in general are a treasure that I will cherish for a

iv

long time. I am also thankful to Dr. Robert Black for kindling my interest in International

Public Health and enabling me to pursue the doctoral program at Hopkins.

My life at the school has been truly enriched due to the interactions that I have

had with a wonderful group of friends and colleagues. During the past five years, I have

derived immense pleasure and comfort in the company of Konstantinos Tsilidis, William

Farris, Sandhya Sundaram, Laura Steinhardt, Peter Hansen, Kavitha Viswanathan,

Aneesa Arur, Nagesh Borse, Rebekah Heinzen, Zohra Patel, Nirali Shah, Manjunath

Shankar, Adrijana Corluka, Junko Onishi, Lindsay Grant, Jennifer Moisi, Ehtisham

Akhter, Vikas Dwivedi, Melissa Opryszko, and Krishna Rao. A special thanks to Ms.

Carol Buckley, who has always been available for rescue with her comforting smile and

answers to all administrative questions.

This thesis would not have been possible without the love and support of my

caring family. My parents, Shiv Dutt and Rajeshwari, provided me with a strong

foundation by teaching me the values of compassion, hard work and to provide care to

people who are least capable of doing it on their own. With their lives, they have set an

inspiring example for me to follow. I have been truly blessed to have Andrea as my wife.

Andrea has stood by me through every step of the process and bolstered my confidence

during times of uncertainty. In addition to being a loving wife, Andrea has been a great

friend and I truly respect her advice and enjoy her company. I am very thankful to

Andrea's parents, John and Debbie. Their encouraging words were especially helpful

during times when Andrea was not around due to her work related travel commitments.

Last but certainly not the least, a big thanks to my sister Preeti for being a patient listener

of my endless advice and stories.

v

Table of Contents

Chapter 1 Introduction 1

1.1 Conceptual framework 3

1.2 Study Context: Afghanistan 6

1.3 Problem description 8

1.5 Organization of thesis document 16

Chapter 2 Is an outdated sampling frame adequate for Maternal and Child health care delivery estimates in post-conflict Afghanistan? 18

2.1 Introduction 19

2.2 Methods 22

2.3 Results 31

2.4 Discussion 33

2.5 Conclusion 40

Chapter 3 Use of household asset data to measure living standards and track poverty in post-conflict Afghanistan 47

3.1 Introduction 48

3.2 Methods 54

3.3 Results 60

3.4 Discussion 63

3.5 Conclusion 70

Chapter 4 Equity effects of quality improvements on health service utilization in post-conflict Afghanistan 78

4.1 Introduction 80

4.2 Rationale 82

4.3 Conceptual Framework 85

4.4 Methods 90

vi

4.5 Results I l l

4.6 Discussion 116

4.7 Conclusion 126

Chapter 5 Summary: Findings and implications 137

Appendices 144

Additional tables for Chapter 2 (Study 1) 144

Additional tables for Chapter 4 (Study 3) 150

References 154

Curriculum Vita 168

vii

List of Tables

Table 2.1: Seven priority health indicators for MICS 2003 41

Table 2.2: MICS 2003: Revised (Re-weighted) health service delivery estimates for women 42

Table 2.3: MICS 2003: Revised (Re-weighted) health service delivery estimates for children 43

Table 2.4: MICS 2003: Revised (Re-weighted) confidence intervals for service delivery estimates for women 44

Table 2.5: MICS 2003: Revised (Re-weighted) confidence intervals for service delivery estimates for children 45

Table 3.1: Description of asset variables 72

Table 3.2: Difference in mean of asset variables 72

Table 3.3: Estimated log linear regression coefficients for the 2005 sample 73

Table 3.4: Difference in predicted expenditure between 2004 and 2005 samples 74

Table 3.5: Estimated principal component wealth index coefficients for pooled 2004 and 2005 samples 75

Table 3.6: Difference in wealth index between 2004 and 2005 samples 76

Table 3.7: Estimated probit regression coefficients for the 2005 sample 77

Table 3.8: Difference in predicted probability of a household being poor between 2004 and 2005 samples 77

Table 4.1: Outpatient visit study sample by year of survey 130

Table 4.2: Outpatient visit study sample by number of repeated surveys 130

Table 4.3: Utilization rates by outcome group and year of survey: Mean (standard deviation) 130

Table 4.4: Predictor variables by year of survey: Mean (standard deviation/ percent)... 131

vm

Table 4.5: Estimated rate ratios for Outcome 1: Overall utilization 132

Table 4.6: Estimated rate ratios for Outcome 2: Utilization by females 133

Table 4.7: Estimated rate ratios for Outcome 3: Utilization by poor 134

Table 4.8: Estimated (interaction) rate ratios for Outcome 3: Utilization by poor 135

Table 4.9: Estimated rate ratios for the three outcome groups excluding data from year 2004 136

List of Figures

Figure 1.1: A framework for post-conflict health systems rehabilitation 17

Figure 2.1: Boxplot of difference in point estimates 46

Figure 2.2: Boxplot of difference in confidence interval width 46

Figure 3.1: Kernel density plots for predicted total expenditure - 2004 & 2005 samples 74

Figure 3.2: Kernel density plots for wealth index: 2004 & 2005

samples 76

Figure 4.1: A framework to study the access and utilization of health services 128

Figure 4.2: Mean utilization by outcome group 129

ix

Chapter 1 Introduction

Armed conflicts are an unfortunate reality of this world. In the past thirty years,

more than fifty countries have experienced violent conflicts. These conflicts are most

common in the poorest and least developed countries. In 2000, among the twenty five

countries experiencing conflict, fifteen were from the low-income group (Gleditsch et al.,

2002). Conflicts are a wasteful use of precious national resources and the adverse effects

last longer than the actual conflict itself. In these poor countries, the diversion of social

and health expenditure to fund military spending makes the precarious health situation

worse (Waters et al, 2007).

The direct effects on public health are manifested in terms of a disproportionate

increase in morbidity and mortality, increased susceptibility to communicable diseases,

and long term disability in the population. The indirect effects are more widespread as

they affect every sphere of national well being. The destruction of infrastructure,

equipment and supplies leads to interruption in availability of essential goods like water,

food, and energy. The provision and quality of health care available to the general

population is reduced significantly (Toole et al, 2001). In addition, the foreign

investment decreases and the economic growth stagnates, and in some cases even

reverses. Increase in poverty and worse health situation are common short-term

consequences of armed conflicts (Bantvala & Zwi, 2000; Pedersen, 2002; World Bank,

1998).

Post-conflict reconstruction is a very time and resource intensive activity that

requires active involvement of the population in affected countries and the international

community (Waters et al, 2007). An initial response to the immediate needs of the

1

affected population is important, but economic and social development along with

rehabilitation of health systems is considered essential for long term peace and prosperity

(World Bank, 1998).

A good evidence base on the public health effects of conflict is increasingly

becoming available but studies to monitor and evaluate post-conflict reconstruction are

limited (Bantvala & Zwi, 2000; Waters et al, 2007). A number of problems make the

task of measuring the effects of conflict and the reconstruction efforts difficult. These

include methodological and theoretical shortcomings, inconsistencies in definitions and

terms, restricted access to certain areas and sources of information, lack of current data,

political manipulation of data, and resource constraints (Toole et al., 2001). The scarcity

of reliable, comprehensive data is considered one of the greatest challenges in evaluating

post-conflict reconstruction (Waters et al, 2007). There is an enormous need for

information on health and economic status in post-conflict countries. In the period after

cessation of fighting, this information provides guidance towards prioritization among

reconstruction efforts, donor coordination and policy formulation; while in the long run it

enables sustainability of programs and establishes legitimacy of the national governments

in these countries (Buse & Walt, 1997; Pedersen, 2002; Waters et al, 2007).

Using the example of post-conflict Afghanistan, in this thesis we identify three

specific problems related to lack of data, and provide potential solutions. The three

problems are:

1. Lack of a current sampling frame to enable probability based sampling of

population for baseline evaluation of health service delivery in a post-

conflict population.

2

2. Lack of easy to collect, reliable measures to track economic status and

poverty in a post-conflict population.

3. Lack of information on health service quality and utilization by the

disadvantaged - female and the poor population.

1.1 Conceptual framework

One framework for understanding the information needs for post-conflict

reconstruction, and therefore the linkages between the three studies included in this

document, is the framework proposed by Waters and colleagues for post-conflict health

system rehabilitation (Waters et al, 2007). The relevant parts of this framework have

been discussed in detail in the remaining part of this section, while the other parts are

explained in brief.

The main purpose of the framework proposed by Waters and colleagues is to

enable identification of commonalities across countries - in terms of the inputs and

policies necessary to make crippled health systems function effectively, equitably and in

a sustained manner (Figure 1.1).

The immediate context in terms of pre-conflict health system, the conflict itself

and the post-conflict rehabilitation is provided by national political and economic

framework. The wider international context beyond the national environment is

represented by the donors, as in modern conflicts the ensuing relief and rehabilitation are

strongly influenced by this group of actors.

The key inputs needed for post-conflict rehabilitation include financing, human

resources, physical infrastructure, information systems and essential drugs.

3

The principal policy issues include coordination among donors; political commitment by

host governments; partnership with NGO's; planning, prioritization, and integration of

health services; and the sustainability of the rehabilitation effort.

According to the framework, post-conflict rehabilitation of the health sector can

be viewed as three components: (1) an initial response to immediate health needs; (2) the

restoration or establishment of a package of essential health services; and (3)

rehabilitation of the health system itself. The three components are viewed as parts of a

continuum and where possible, should operate synergistically. There is an enormous need

for information on health and other aspects of the population to enable this synergism

among the three components.

The first step in rehabilitation is to address the immediate health needs of the

displaced and distressed population groups by providing services like: basic and

emergency curative health care, obstetric services, communicable disease control,

immunizations, and supplementary feeding programmes. The incorporation of this fast-

track response into a systematic medium and long term response is considered essential

for the successful reconstruction of the national health system and the first study in this

thesis caters to the information needs of policy makers at this crucial juncture by

evaluating the use of an outdated sampling frame for generation of baseline estimates.

The first study contributes to the pool of information required to anticipate future policies

and programmes and enables the movement towards the principle objectives of all health

systems - efficiency, equity, and positive population health outcomes.

In the second step, based on the information gathered in the relief phase, most

health sector rehabilitation efforts move to restore systematic delivery of essential health

4

services. These efforts focus on a package of cost-effective interventions that address the

greatest health needs of the population.

According to the framework, the implementation of a basic package of health

services should be accompanied by additional components of a comprehensive approach

to health system rehabilitation. In this third step, along with the restoration of essential

health services, additional resources should be directed towards the medium and long-

term needs in the areas of management, financing and health policy. The second and the

third studies in this thesis help in generating some of the required information to enable

this transition.

The second study provides a method to track poverty and economic status in the

post-conflict population. Multiple studies from developing countries have reported that

wealth is intricately linked to health in a population, with poor suffering from a greater

burden of disease (Gwatkin et al., 2005; Peters et ai, 2008; Wagstaff, 2002). A change in

the proportion of poor should prompt an appropriate change in the health policy so that

health services can be offered in an equitable and efficient manner.

Health challenges in post-conflict countries often exist in a broader context of

constitutional weakness with concentration of wealth and power in the hands of a military

and political elite being an important problem that the new national governments face

(Cox, 2001). In the fragile post-conflict environment, ensuring that opportunities for

growth are accessible to the poor is crucial for welfare of ordinary people as well as long

term peace and prosperity. At the national level, the method proposed in the second study

can help in restoring the economic balance by identifying the economically

5

disadvantaged groups, and thereby preferentially directing development programs

towards them.

The third study also provides information towards rehabilitation of health systems

by identifying characteristics of the delivery system that promote greater utilization by

population groups that tend to suffer the greatest burden of disease during and after the

conflict. With the overall long term objective of efficiency, equity and improvement in

population health outcomes in sight, the second study provides information on potential

users of the health system, while the third study provides equity oriented information on

the health system itself.

Using the example of post-conflict Afghanistan, all the three studies provide

guidance towards some of the methods that can be used for gathering important

information about the population and its health status. These studies can help the policy

makers and researchers in operationalizing an information system that can aid in

sustainability of programs and improve the legitimacy of the national government.

1.2 Study Context: Afghanistan

Afghanistan is a land locked country situated at the junction of the middle-eastern

crescent and South-east Asia. The country has suffered almost continuous conflict for the

past century. In recent times, Afghanistan's troubles started in 1979 with Soviet invasion,

followed by a long civil war that ended with Taliban gaining control over majority of the

country. The rule of Taliban led to complete international isolation of Afghanistan,

turning the situation for Afghans from bad to worse. The US intervention in 2001 led to

the removal of Taliban government, though certain parts of Afghanistan still remain

6

volatile and insecure due to a strong presence of Taliban in these areas. The provinces in

the south - Kandahar, Zabul, Uruzgan and Helmand have seen a resurgence of Taliban in

recent times, which is a major cause of concern for Afghan government and its

international partners.

At the fall of Taliban in 2001, the impoverished people of Afghanistan were left

with dysfunctional health system and widespread destruction and dilapidation of the

infrastructure in every sector. Afghanistan ranked among the bottom five countries on the

human development index, which incorporates information on life expectancy, GDP per

capita, literacy and school enrollment (UNDP, 2004). The new democratically elected

government of Afghanistan was charged with recreating a country-wide infrastructure,

including a health system to cope with some of the world's worst health indicators. The

maternal mortality ratio for the country was estimated to be higher than 1,600 per

100,000 live births, with the highest ever recorded ratio of 6,507, being reported from the

province of Badakshan (Bartlett et al, 2005). The infant mortality rate was estimated at

165 per 1000 live births and nearly one in four children died before their fifth birthday

(UNICEF, 2004b).

More than two decades of civil war, and the absence of a central government to

control or deliver services to the entire country, led to a situation where the information

available about health resources in Afghanistan was out of date and scattered. In

anticipation of a large-scale reconstruction in Afghanistan, the Ministry of Public Health1

(MOPH) and its partners decided to conduct a comprehensive inventory of all known

health facilities and related health resources. A comprehensive list of health facilities

1 Before 2004-05, the official name for the Ministry of Public Health was the Ministry of Health.

7

from various sources was created in April 2002 and surveyed by a team of surveyors

using a set of questionnaires specifically created for this purpose.

This assessment revealed a grossly inadequate system with unqualified, under

paid health professionals, unreliable health care facilities lacking in hygiene and proper

equipment, and unlicensed pharmacies selling adulterated drugs. In the immediate post-

conflict period, the ratio of health workers per 1000 population was very low. However,

within the available health workforce there was a relative excess of physicians with a dire

lack of workers among other categories. A majority of the facilities offered some type of

curative care services but severely lacked in provision of maternal and child health

services. In addition, among the available workforce the male to female ratio was

reported to be highly skewed in favor of males with large variations by type of facility.

The availability of health facilities for the general population was very inequitable with

large variations between and within provinces (Management Sciences for Health, 2002).

1.3 Problem description

Problem #1: Lack of a current sampling frame to enable probability based sampling of

population for baseline evaluation of health service delivery in a post-conflict population.

While the absence of a sampling frame of health facilities was circumvented by

surveying all the facilities in Afghanistan, the generation of population level estimates of

health service delivery and utilization presented the first problem that was included as

part of this thesis: the lack of a sampling frame that represented the population living in

Afghanistan in 2003.

8

The use of a sampling frame enables the selection of a random sample of people

that is representative of the population for which the estimates are being generated. In the

post-Taliban period, the first population based health survey of national scope was

conducted in 2003 by UNICEF to generate estimates of delivery practices, immunization

rates, and prevalence of diarrhea and acute respiratory infections (ARI) among children

(UNICEF, 2004a). This Multiple Indicator Clusters Survey (MICS) was required to

provide baseline data for planning a national health strategy to lay the foundations for

equitable, quality health care for the people of Afghanistan. The outdated population

census from the year 1979 was used as the sampling frame for MICS because it was the

only data source available at that time that provided information at a national level. The

survey implementers were well aware of the potential for biased results due to the

inability of 1979 census to account for changes in the population distribution over time.

This pragmatic decision was taken because no national census had been conducted since

1979 and there was an urgent need to collect data on population health, including seven

priority health service delivery indicators. A pre-census enumeration was conducted in

2004 providing an up-to-date source of population distribution and the opportunity to

revise the earlier estimates. A new set of sample weights was generated and used to

calculate provincial and national estimates. By enabling comparison of re-weighted

estimates with those originally reported in the MICS report, the new set of weights

provided an answer to the policy question on adequacy of baseline estimates generated

using an outdated sampling frame.

9

Problem #2: Lack of easy to collect, reliable measures to track economic status and

poverty in a post-conflict population.

In a fragile post-conflict environment, Afghanistan has been undergoing profound

economic, political and social change. Ensuring that the opportunities of developement

are accessible to the poor is crucial for welfare of ordinary people as well as long term

peace and prosperity. The second problem identified and studied as part of this thesis was

the lack of easy to collect, reliable measures to track economic status and poverty in a

post-conflict population.

Improvement in the living standards of the Afghan population has been an explicit

aim of the Government of Islamic Republic of Afghanistan and the donor community. A

pertinent example of this emphasis is the Afghanistan National Development Strategy

(ANDS), which is the centerpiece of Government of Afghanistan's National

Development Framework. ANDS has been produced as a major collaboration between

Afghan and international community to promote growth, generate wealth and reduce

poverty and vulnerability in Afghanistan (T.I.S.A., 2004).

There have been reports that while most of the rural Afghan economy has been

benefiting from economic growth and increase in agricultural harvest, the poorest

sections of the society are still lagging behind (World Bank, 2005). The assessment of

living standards of the population and reduction in poverty is a growing priority for

public policy in Afghanistan. Regular information on economic status over short periods

of time is needed to assess the effects of development efforts on the populations as well

as to identify population groups that might need targeted interventions to improve their

health and develop economically.

10

In Afghanistan, logistical concerns of restricted access to unstable areas and

ongoing security problems favor a more expeditious approach to measuring living

standards. In order to assess the change in economic status over short periods of time,

there is a need for clear indicators that are reliable, easy to collect and easy to verify.

Household income and consumption are the most widely used measures of

economic status in developing countries and have strong theoretical foundations in utility

theory. These measures are absolute in nature. They are considered to be important in

understanding the economic aspect of poverty as consumption is widely used to generate

poverty thresholds (lines) in many countries (Hentschel & Lanjouw, 1996). Consumption

and income surveys require extensive resources of time, money, and personnel, making

them unwieldy for purposes of tracking economic inequality in a population. On the other

hand, regular data collection on asset variables is easier and less resource intensive than

regular income or consumption surveys. Survey modules for asset variables require fewer

questions, which can be collected from a single respondent in a household. The use of

asset variables to rank households and assess the effect of economic status on health

outcomes is fairly common; however, limited research has been conducted on the use of

asset variables to generate an absolute measure of economic status like consumption

expenditure. An urgent need for practical measures for steadily tracking poverty emerged

from international endorsement of Millennium Development Goals and led to recent

studies that have used advanced prediction techniques to link the asset variables directly

to household consumption (Mathiassen, 2007; Stifel & Christiansen, 2007). These studies

provide an inexpensive and efficient technique to utilize information on asset variables

and estimate a measure of economic status that is absolute in nature. The authors

11

employed out of sample prediction techniques to estimate household consumption over

time and generated robust measures to track poverty and inequality in a population.

In this study, we have used the asset variables to assess difference in economic

status and poverty levels between two population samples collected at an interval of one

year. This was done by comparing the results of principal components analysis (PCA)

and out of sample prediction by estimating analogous measures using each of the two

techniques. The two outcomes estimated using out of sample prediction were total

household expenditure and the probability of a household to spend less than $2 US

dollars per day.

Problem #3: Lack of information on effect of health service quality on utilization by the

disadvantaged - the female and the poor population.

Based on the information collected from national surveys like ANHRA and

MICS, the Ministry of Public Health (MOPH) worked closely with development partners

to define a strategy for rapidly expanding the geographic scope and quality of health

services. In 2003, the MOPH and its partners identified a core set of basic health services

and included them in a Basic Package of Health Services (BPHS). The BPHS was an

important policy milestone with streamlining of a fragmented health sector as its primary

goal. The BPHS consisted of cost-effective primary care services designed to meet the

priority needs of rural population, particular women, children and other vulnerable

groups. The MOPH used the BPHS as a central element of its National Health Policy to

"strengthen the delivery of sustainable, quality, accessible health services, especially

12

targeted at women, through planning for, and effective and efficient implementation of

the basic package of health services"(MOPH, 2003a, 2003b).

By 2004, BPHS was being implemented by more than 1,000 health facilities in

the 33 provinces of Afghanistan. There have been encouraging reports of increase in

utilization of health services in the past few years, especially by the female and the poorer

sections of Afghan society. The quality of services provided at health facilities has also

shown improvement (JHU and IHMR, 2008a). However, the association between quality

improvements and service utilization had not been studied in Afghanistan. Increase in

utilization of health services by the poor and females and improvement in quality of

health services are both important policy concerns in Afghanistan. In a majority of

developing countries, the poor and females not only suffer from a greater burden of

disease, but also utilize health services of lower quality. This study was conducted to

assess whether the quality improvements were associated with increase in use of services

by the disadvantaged groups - the poor and females. Of particular interest was to assess if

certain characteristics of health care delivery promoted greater utilization by these

disadvantaged groups. This study investigates the impact of health system development

in Afghanistan on utilization of health services by females and the poor over a period of

four years.

1.4 Relationship between thesis papers and the project from which the data

derive

All three studies in this thesis are based on data from the Afghanistan Monitoring and

Technical Support Project. Since early 2004, The Johns Hopkins University and Institute

13

of Health Management Research have been contracted by the Ministry of Public Health

of the Islamic Republic of Afghanistan to provide technical assistance in a broad range of

areas, including:

1. Developing a framework for monitoring performance in delivery of a Basic

Package of Health Services

2. Conducting primary care facility performance assessments throughout the country

on an annual basis

3. Conducting analysis of surveys implemented by other agencies, including the

2003 Multiple Indicator Cluster Survey and the 2005 National Risk and

Vulnerability Assessments

4. Developing a framework for monitoring performance in delivery of hospital

services

5. Conducting hospital performance assessments throughout the country on an

annual basis

6. Conducting household surveys to assess care seeking behaviors and health

expenditures

7. Conducting a community randomized trial of health financing pilots

8. Conducting a community randomized trial of safe water systems

9. Conducting an assessment of capacity building and learning needs at the Ministry

of Public Health and Provincial Public Health Offices

10. Conducting an assessment of the quality of drugs at primary care facilities

throughout the country and private pharmacies in five major cities

14

11. Conducting an assessment of the performance of community health workers and

their potential to contribute to health improvements in Afghanistan

12. Designing the health module for the 2007 National Risk and Vulnerability

Assessment

13. Advising on the development of monitoring and evaluation plans in other

departments within the Ministry of Public Health, and providing assistance to

Ministry of Public Health leadership and technical staff in the interpretation of

data and its applications for policy development and decision-making

14. Building the capacity of Ministry of Public Health staff in collecting, analyzing

and interpreting data

A large number of staff in Baltimore and Kabul has worked on the Afghanistan

Monitoring and Technical Support Project. I had been employed by the Johns Hopkins

University from June 2004 till June 2007 to work on this project. My role, as it pertains

to the content of this thesis, included the following:

• Participate in survey design, instrument development, training and field

implementation of the National Health Services Performance Assessment

• Participate in the development of the Afghanistan Health Sector Balanced

Scorecard and selection of domains and indicators

• Participate in finalizing the operational definitions and protocols for analyzing

the indicators on the Balanced Scorecard

• Participate in conducting analysis for the indicators on the Scorecard in 2004,

2005, 2006 and 2007

15

• Participate in finalizing the toolkit to provide detailed information on how the

Balanced Scorecard was developed and implemented in Afghanistan.

• Participate in analysis of surveys implemented by other agencies, including the

2003 Multiple Indicator Cluster Survey and the 2005 National Risk and

Vulnerability Assessments.

1.5 Organization of thesis document

This document is organized as follows:

• Chapter two looks at the effect of an outdated sampling frame on adequacy of

health care delivery estimates in post-conflict Afghanistan.

• Chapter three looks at the use of household asset data to measure living standards

and tracking poverty in post-conflict Afghanistan

• Chapter four looks at the equity effects of quality improvements on health service

utilization in post-conflict Afghanistan

• Chapter five provides a summary of the findings and recommendations for policy

makers

The figures and main tables for each study are placed at the end of the specific study.

Additional tables for chapters 2 and 4 are presented in appendix 1 and 2 respectively.

16

Figure 1.1: A framework for post-conflict health systems rehabilitation

Soiree: Waters, H„, Garrett, B. & Bumtiam, G. {200?) Rehabilitating health system in post-conflict situations. Unfed Nations University - Weald ii Research Paper No. 2007106.

for Development Economics Research.

17

Chapter 2 Is an outdated sampling frame adequate for Maternal and Child

health care delivery estimates in post-conflict Afghanistan?

Abstract

Household surveys are important sources for information on population health. In

post conflict countries, logistical difficulties add to the methodological and theoretical

problems of survey research. Despite their importance, few studies have been conducted

on methods to collect and analyze data is such settings. Afghanistan is a country

emerging out of more than two decades of civil war. There was an urgent need for

information on health of women and children after the fall of Taliban regime. UNICEF

conducted a Multiple Indicators Cluster Survey (MICS) in 2003 to collect baseline

information on the status of health service delivery to children and women in

Afghanistan. An outdated sampling frame based on 1979 census was used to select

households in every province. New census figures for the population became available in

2005 and based upon these estimates, a new set of sampling weights were generated. The

population estimates for seven priority indicators generated using these new weights were

compared with the originally reported estimates. The re-weighted estimates confirmed

the poor status of health service delivery in Afghanistan, especially for women. The

average absolute difference in province level estimates for the seven indicators ranged

from 1.0 to 4.3 percentage points. The average absolute difference in widths of

confidence intervals ranged from 1.8 to 5.5 percentage points. The re-weighting provides

unbiased estimates of population parameters but they are also less precise. The study

concludes that use of an older sampling frame for household surveys can provide

18

adequate baseline estimates for planning and policy implementation in post-conflict

countries. However, the policy makers and researchers should be very aware that this

inexpensive correction in bias is not a substitute for more complex evaluation designs,

which are needed to assess trends and rule out the effect of external factors on health

system performance.

2.1 Introduction

Population surveys are important tools for planning and monitoring health

programs in developing countries. These surveys are also used for performance and

impact evaluation of public health programs. These summative evaluations influence

significant policy decisions on program continuation, allocation of resources and

restructuring (Rossi et al, 1999). In recent times, as a greater proportion of decisions on

program oversight are directly based on these results, such use of evaluation results is

also referred to as 'instrumental' use of evaluation results (Habicht et al, 1999). This

'instrumental' use enables the decision makers to ascertain what information is necessary

for the decision-making, unlike earlier situations when evaluations affected programs and

policies less directly, through changing perceptions.

The basic approach in population based surveys is to collect information from a

random sample of people which is representative of the population (Levy & Lemeshow,

1999). The sampling and data collection is usually conducted in multiple stages to

overcome constraints of time, money and other logistical concerns. Demographic and

Health surveys (DHS) and Living Standards Measurement surveys (LSMS) are examples

of such surveys that provide valuable information on health and economic status in

19

various developing countries. In order for the results to reflect the situation in the

population from which the data are collected, the sampling scheme must be incorporated

in the analysis. This usually requires the use of sampling weights and statistical

techniques to accommodate the multi-stage sampling design. The purpose of weighting

sample data is to assure the representativeness of the sample vis a vis the study

population. The inverse of the selection probability of a sampled unit is used as the

sample weight for that unit. The population estimates generated without sampling

weights and adjustments for multi-stage cluster design could be biased (Korn &

Graubard, 1999; Levy & Lemeshow, 1999). Despite the importance of surveys, research

into this aspect of survey methods is limited. This is especially true in post-conflict

settings where logistical concerns such as restricted access to unstable areas and ongoing

security problems add to the methodological and theoretical shortcomings. The lack of

good routine health information systems, vital registration systems and census data, make

household surveys indispensable in such countries (Bostoen et al, 2007).

The country of Afghanistan is emerging out of more than two decades of civil war

and unrest. The long civil war, the reign of the Taliban, and invasion by the United States

led to massive destruction of infrastructure in the country. At the time of fall of the

Taliban regime, information on health and economic status of the population was very

limited and if available, outdated. Since 2002, the Afghanistan Ministry of Public Health

(MOPH) has pursued a health strategy to "lay the foundations for equitable, quality

health care for the people of Afghanistan" (MOPH, 2003a). The MOPH and other

stakeholders required baseline data for planning, implementation and monitoring of this

health strategy. In the post-Taliban period, the first population based health survey of

20

national scope was conducted by UNICEF and the Central Statistics Office (CSO)

Afghanistan for the MOPH in 2003. This Multiple Indicator Cluster Survey (MICS) used

the outdated population census from the year 1979 for sampling. The survey

implementers were well aware of the potential for biased results due to the non-

representative sampling frame. This pragmatic decision was taken because no national

census had been conducted since 1979 and there was an urgent need to collect

information on population and health estimates (UNICEF, 2004a). A pre-census

enumeration was conducted in 2004-05 providing an opportunity to revise the earlier

estimates. In the present study, a new set of sample weights was generated and used to

calculate provincial and national estimates for seven health service delivery indicators.

These seven indicators (Table 2.1) were considered a top priority by the MOPH and had

definitions that were similar to those of the earlier/original MICS report. The new set of

weights provided a unique opportunity for research into survey methods. This study

compared the re-weighted estimates with those originally reported in the MICS report.

This enabled the measurement of the effect of sampling weights and adjustments for

multi-stage clustered data collection on estimates.

This study had two objectives:

1. Calculate re-weighted estimates for the seven indicators by generating new

weights and using appropriate analytic procedures to account for the multi-stage

clustered sampling design.

2. Compare originally reported estimates with estimates generated using the new

weights to assess the effect of weighting.

21

The MICS survey was the first quantitative evaluation of health programs

implemented in post-Taliban period and provided baseline estimates for future

evaluations of health system performance in Afghanistan. The estimates from MICS have

been put to 'instrumental' use as official health indicators for Afghanistan and have

directly affected health policies and programs in Afghanistan (MOPH, 2004). These

country-wide data for Afghanistan have become more important recently as the MICS

survey from 2003 is one of the few household surveys to date with a national scope and

the health of the population as the specific focus.

2.2 Methods

Original MICS Methodology

Sampling frame

The target population for the MICS survey was the sedentary population of

Afghanistan living in 32 provinces2. The survey planners understood the potential effects

of population displacement due to civil war and change in mortality over time but the

need to generate population estimates was considered urgent. It was decided to use two

sources of data for the sampling frame; the 1979 census data and the National

Immunization Day (NID) coverage data. Both sources had strengths and limitations. The

census data were collected for every province in 1979 using a standardized format, but

these data were 25 years old. The NID data were produced based on the NID coverage in

every (at least yearly) round after 2002. These data were current but some areas of the

country had incomplete coverage and the format of the data in NID was not standardized

for all the provinces. The survey planners carried out a comparative analysis to determine

2 Provinces of Panjsher and Daykundi were created after MICS 2003 survey.

22

the better sampling frame and it was apparent that census data were better to use for rural

areas and the NID for the six largest cities (UNICEF, 2004a). The sampling frame

excluded a number of villages due to loss of 1979 census documents over so many years.

The loss was estimated as 10% of all villages spread over the country. In light of this loss

of data over time, the information from the census that was available at the time of

sampling is provided in table A2.1 of the appendix. Table A2.2 in the appendix is the

updated version released later by the CSO Afghanistan after the completion of actual data

collection for MICS 2003.

Sample size and sample design

The indicators which required the largest sample size were those dealing with

vaccinations. The target group for these indicators was children 12-23 months of age.

An earlier MICS survey estimated that there were 0.26 children aged 12-23 months per

household (UNICEF, 2000). It was desired to estimate immunization coverage at the

province level with a precision of 10%. With this specified and assuming a design effect

of 1.5, the needed sample size was 138-144 children aged 12-23 months in every

province. This figure would be met by surveying 550 households in every province

assuming an immunization prevalence of 50%. The sampling error would be lower for

indicators for which target age groups were broader e.g. Vitamin A supplementation.

A multi-stage cluster sampling was used for the 32 provinces. Information on the

number of households in each village and town of every province was collected from the

1979 census database. A total of twenty clusters was systematically selected without

replacement in each province with probability of selection being proportional to size

23

(PPS), where size was the number of households in a cluster. These clusters were

specified as the primary sampling units (PSU). No attempt was made to exclude villages

and towns that were part of the provincial center district except for provinces where the

six largest cities were located. In order to collect information on 550 households per

province, the total number of households surveyed in every cluster ranged from 27 to 28.

For the six largest cities, the 1979 census had data on the total number of households and

persons only, with no breakdown of the reported figure by clusters. This made these data

unusable for sample site selection by systematic PPS methods. Information on children

under-five years of age and number of households collected as part of yearly national

immunization day (NTD) was used for sampling the clusters in each of the cities. For each

of the six largest cities except Kabul, 20 clusters were selected. Twenty-five clusters were

selected for Kabul city, owing to its larger size. The MICS sample had information on a

total of 20,804 households available for analysis after collection and data cleaning

(UNICEF, 2004a).

Stratification

Data were collected to represent 38 strata (domains); the 32 provinces and the six

largest cities constituted these 38 strata. The number of households sampled and surveyed

in each strata is reported in table A2.3 of the appendix.

Selection of households in a cluster

In each of the 32 provinces, a cluster was a village or a town, with the exception

of six cities where a segment (described in detail later) was considered as a cluster. A

24

household was defined as the people (men and women) usually taking their meals from

the same cooking pot, and those who share household assets and accumulate their

earnings to procure food and other household materials. The possibility of a

dwelling/structure being inhabited by more than one household was considered and

surveyors were instructed to count each household separately in such cases. In each

sampled cluster the number and location of households was verified with the elderly local

residents and a sketch map indicating well known landmarks like mosques, schools, and

health centers was prepared. Every sampled cluster was partitioned into segments of

approximately 55 households each and one segment was selected randomly (Tables A2.4

and A2.5). All households in the selected segment were listed separately even if they

lived in the same structure, such as an apartment house or multi-family compound, and

every alternate household was interviewed with a random start (1st or 2nd). Data were

collected on the number of households in a sampled dwelling, numbers of males and

females in the house, languages spoken in the household, and a limited number of other

variables pertaining to the availability of public infrastructure in the cluster (e.g. road

conditions, schools, clinics). The selection of households in sampled clusters that were

part of the provincial center district/town according to 1979 census was conducted

similarly. The primary purpose of the MICS survey was to generate provincial estimates,

and the villages as well as the towns were selected as part of the 20 sampled clusters in

every province using the PPS technique.

A slightly different sampling methodology was used in the six cities where

information on geographically identifiable segments was available either from the

National Immunization Day (NID) or UN Habitat records. These segments with distinct

25

boundaries and information on number of households and number of under-five children

were created to plan and implement the vaccination drives as part of NID. These

segments were analogous to the clusters in the 32 provinces. A segment in each of the

cities was sampled randomly as all segments were of roughly equal size and PPS

technique would have had no additional benefit. The sampled segment in four of the six

cities was divided into a number of sub-segments of approximately 55 households each

after consulting the elderly local residents. These four cities were Kunduz, Jalalabad,

Kandhar and Mazar-e-sharief. The sub-segments were numbered sequentially and one

sub-segment was selected at random. The sub-segment, thus selected, was listed and

every alternative household was interviewed. In the remaining two cities of Kabul and

Herat, information on household listing was available in each segment; therefore

households were selected randomly within each segment without creating any sub-

segments.

Though a formal ethics committee was not constituted to review the MICS survey

questionnaire, representatives from the MOPH, Ministry of Rural Rehabilitation and

Development (MRRD), Kabul University, international agencies and non-governmental

organizations were involved in the technical review of the survey. The committee

reviewed the entire questionnaire and the methodology. Consent was taken at the

beginning of the questionnaire and the interviewer read out the statement before

administering the questionnaire.

26

2004 Pre-census Data Collection

During 2004, the Central Statistics Office (CSO) Afghanistan sent teams to

conduct door-to-door counts in 29 of 32 provinces, missing only three provinces where

the conditions were deemed too dangerous to send field workers. This pre-census laid the

ground work for future censuses by providing codes for each province, district, village,

sub-village (in large villages), urban sector (nahia), and block. Households were also

numbered. Standardized quality assurance procedures were followed, including several

layers of supervisory teams and systematic re-collection of data from selected sites to

ensure consistency. Based on this work, the CSO published the official population figures

for all provinces (CSO, 2005-06). While the figures for 29 provinces were based on

complete enumeration, the figures for three unsecure provinces were based on

enumeration as well as projections based on demographic models. This study used this

information (refer to table A2.6 in appendix) to generate a new set of sampling weights.

Sampling weight generation based on 2004 pre-census

The original MICS sample was designed to be self-weighting within a province.

Sampling weights were utilized to get estimates representative at the national level. The

original reported estimates did account for the multi-stage clustered design during the

analysis.

To generate weights for the analysis, we could not consider the sample as self-

weighting because the population distribution of Afghanistan changed significantly

between 1979 and 2004. There were massive internal and external population

displacements, causing significant changes in number and distribution of people in the

27

country. The list of villages and towns based on the 1979 census was outdated and

incomplete as new villages had come into existence while some villages had disappeared

due to migration, war, and natural disasters like floods and draughts. In addition, in

Afghanistan there is widely prevalent tradition of naming a village after the name of its

village head. The 1979 census was outdated as the replacement of village heads during

the long gap of 25 years and consequent renaming was not accounted for in the 1979 data

(JHU and IHMR, 2005a). Another argument against self-weighting was the selection of a

segment in majority of the sampled clusters as the households were selected randomly

only in the sampled segment, not in the whole cluster. In this re-analysis, the sampling

design was used to generate new sampling weights as the sample was no longer

considered self-weighting. The sampling weight for every sampled household in a

province was the inverse of the selection probability of that household. In order to

aggregate the sample results at the national level, an additional factor was introduced in

the sampling weight calculation for national estimates only.

The formula used to generate the sampling weight for a household (h) in sampled

cluster (k) in province P was as follows:

Dpih = 1 /(ap/aj) * (1/bpO * (cpih/cpn) [1]

ap = Number of primary sampling units (psu) selected in province P

aj = Number of primary sampling units (psu) in province P

bPk = Number of segment(s) in a selected psu k in province P

Cpih = Number of households selected in a selected segment i in psu k in province P

cPii = Number of households in a selected segment i in psu k in province P

28

The additional factor for a household (h) in province P to generate the national estimate

was as follows:

Ih = £Np/Np [2]

Np = Total number of households in province P

The Dpih value for each household was used as its sampling weight for provincial

estimates. The sampling weight for national estimates was generated by multiplying Dpih

value for each household in a particular province by the Ih value for that province.

Each of these two sets of weights was normalized to sum to the available sample size.

The two provinces of Panjsher and Daykundi were created after the 2003 MICS survey

from Parwan and Uruzgan respectively. The 2004 census figures for Panjsher and

Daykundi were combined with Parwan and Uruzgan respectively. These figures were

then used to generate sampling weights for Parwan and Uruzgan.

Revised Variances and Confidence Intervals

For the calculation of variance estimates taking the design of the survey into

account, we utilized the SVYTAB command in STATA (StataCorp., 2004). The SVY

commands in STATA account for the survey design in the point estimates and variance

estimates. By default, the SVY set of commands compute standard errors by using a

linearized variance estimator based on a first-order Taylor series approximation (Wolter,

2007). In the non-survey context, this variance estimator is referred to as the robust

variance estimator (Huber/White sandwich estimator). Each province was specified as the

strata and cluster as the primary sampling unit (psu). The reported indicators were

proportions which used total numbers of women or children as denominators. Since

29

these were not fixed for a given province, but are random variables, we estimated the

variance of a ratio. This estimation is done automatically when this type of analysis is

specified in the ST ATA program. For proportions, the confidence interval is derived

using a logit transformation so that the interval lies between 0 and 1 (StataCorp, 2004a).

Results for 32 provinces were presented in tabular form and box plots were used

to summarize the findings. The three out of the total of seven indicators on health service

used to describe delivery to women were: 1) mother's last delivery assisted by unskilled

birth attendant; 2) married women currently not using a method to delay pregnancy, and;

3) antenatal consultation not taken from a doctor or trained birth attendant during the last

pregnancy. The four out of the total of seven indicators that provide information on

health service delivery to children were: 1) Children aged 6-59 months who have not

received vitamin-A supplementation; 2) Children aged 9-59 months who have not

received measles immunization; 3) Children aged 12-23 months who have not received

three doses of DPT immunization, and; 4) Children under five years of age who have not

received BCG immunization.

Estimates for the seven indicators from the original MICS report were compared

with values calculated after re-weighting. The definitions of the seven indicators that

were included in this study were the same as in the original MICS report. This was

confirmed by a separate analysis conducted with the weights used in the original report.

In addition, rural and urban estimates have not been compared as the sampling for the

original MICS survey was intended to provide provincial and national estimates only.

Generating weights to derive separate rural and urban estimates would have deviated

from the sampling scheme followed in most of the provinces; therefore it was not done.

30

2.3 Results

Revised point estimates and confidence intervals

We calculated the revised estimates for the seven indicators for each of the 32

provinces and the nation (Tables 2.2 and 2.3), as well as the revised estimates of

confidence intervals for each of the 32 provinces and the nation (Tables 2.4 and 2.5). A

higher point estimate for any reported indicator represented a worse situation. The results

were reported in this format to make comparisons with the original MICS report more

intuitive and clear.

Three out of the seven indicators were related with health service delivery to

women (Table 2.2). The provincial and national estimates had high values representing

the poor status of health service delivery to women. The provinces of Samangan and

Takhar had the lowest (57.7) and the highest (99.5) percentage of deliveries conducted by

unskilled birth attendants respectively, with a national estimate of 87.4 %. The lowest

estimated percentage of married women under 50 years not using a family planning

method was in Herat (59.1) and the highest was in Paktika (99.8), with a nationwide

figure of 91.2%. The percentage of pregnant women who did not receive any antenatal

care from a skilled professional ranged from 54% (Kabul) to 99.8% (Badghis) with a

national estimate of 86.1 %.

Four out of the seven indicators were related with health service delivery to

children (Table 2.3). The results for these indicators reflected a better situation for

children as compared to women in Afghanistan. Among the four indicators, incomplete

DPT immunization in 12-23 month old children had the highest national estimate (71%)

31

with provincial estimates ranging from 29.6% (Nangarhar) to 98 % (Helmand). Absence

of vitamin A supplementation in 6-59 month old children had the lowest nationwide

estimate (12.6%) among the four child health service delivery indicators. The province of

Badakshan had the lowest estimate (2.8%) for absent vitamin A supplementation to

children while the province of Ghazni had the highest (46.2%). The lowest estimated

percentage of children 9-59 months of age who had not received measles immunization is

in Logar (6.6%) and the highest was in Ghor (47.6%), with a nationwide figure of 23.7%.

The percentage of children under five years of age who did not receive BCG

immunization ranged from 14.1% (Logar) to 79.2% (Baghlan) with a national estimate of

41%.

Comparison of point estimates

The re-weighted MICS estimates for all seven indicators were compared with the

original estimates (Figure 2.1). Almost all of the original estimates were within ten

percentage points of the re-weighted estimates and the median difference across

provinces was close to zero for every indicator. The average absolute difference (re-

weighted - original) for the province level estimates ranged from 1.0 to 4.3 percentage

points across the seven indicators. The difference in national estimates ranged from -1.7

to 2.2 percentage points. In addition, the provinces were ranked for each indicator based

on the point estimate. The provinces with the five highest and the five lowest values were

compared. The provinces included among the five highest and lowest were similar

though the relative ranking within the groups of five was not identical. Four out of five

32

provinces were same for all indicators except the indicator on DPT immunization where

only three highest ranked provinces were same.

Comparison of confidence intervals

The widths of 95% confidence intervals for the re-weighted estimates were

compared with original estimates (Figure 2.2). The median difference in width between

re-weighted and original 95% confidence intervals (CI) was very close to zero for every

indicator. The average absolute difference in confidence interval widths ranged from 1.8

to 5.5 percentage points across the seven indicators. The difference in CI width for

national values ranged from 0.2 to 2.5 percentage points.

2.4 Discussion

Re-weighted point estimates

The re-weighted estimates presented a poor picture of health service delivery in

Afghanistan. On a relative scale, the situation for women was worse than that of children.

Afghanistan is among the three countries with highest maternal mortality ratios in the

world with an estimate that ranges between 1600-1900 deaths/ 100,000 live births

(MOPH, 2004). The three indicators for health delivery for women are important direct

determinants of maternal mortality. The extremely high levels of deliveries being

conducted by unskilled attendants, poor status of antenatal care utilization and low levels

of family planning are important factors contributing to the high number of maternal

deaths in Afghanistan.

33

The child mortality estimates for Afghanistan have always been among the

highest among the world (UNICEF, 2006). According to the child survival series

published in The Lancet, Afghanistan belongs to the group (profile) where 48 percent of

deaths are attributed to pneumonia and diarrhea and 34 percent to causes in the neo-natal

period (Black et ah, 2003). According to UNICEF, measles has been one of the major

killers in Afghanistan, contributing to about 35,000 under five deaths per year. In 2001,

Afghanistan still had the second highest number of under five deaths due to measles in

the world. Immunization campaigns in the form of National Immunization Day were the

first health initiative taken up by the government of Afghanistan after the fall of the

Taliban. Successive NID campaigns enabled immunization of millions of children,

especially against measles, polio, and tuberculosis. In addition, supplementation with

Vitamin A was also an integral part of these NID campaigns (UNICEF, 2006). The child

health service delivery estimates presented in this study probably reflected the situation

that had resulted due to these efforts. The maternal and child health estimates reported in

this study were in conformity with the best estimates report published by UNICEF in

2006 (UNICEF, 2006). This report by UNICEF conducted a thorough search of all

available information on Afghanistan related to women and children and generated

estimates that could be agreed upon by various stakeholders. The indicators were

calculated from both adjusted survey results and using models and indirect deductive

estimates.

Comparison of point estimates and confidence intervals

34

There are only a few reliable sources of information on population in most post-

conflict situations and countries under conflict. Afghanistan is an extreme case of

uncertainty due to long duration of the conflict, deprivation and migration. In such a

country with restricted geographic access and limited availability of female surveyors3,

the MICS study team relied on the outdated 1979 census for a sampling frame in order to

gather important health information in a short period of time.

This analysis, with adjusted sampling weights and clustered sampling design

based on 2004 census data, provided unbiased provincial and national estimates. Use of

sampling weights for weighted estimation is a widely agreed method for descriptive

analysis of population (Korn & Graubard, 1999; Levy & Lemeshow, 1999). While

sample weights and adjustment for multi-stage clustered design provide unbiased

estimates, these adjustments increase the variance of the estimates (Korn & Graubard,

1999; Rust, 1985). This study provided a unique opportunity to measure the bias that can

arise from using incorrect sampling weights in the analytic process. The estimates from

original report have incorrect sampling weights but they do account for the clustered

sampling design. The study found that the average difference in the value of point

estimate was not high though the difference in precision varies greatly. The re-weighted

estimates were less biased estimators of population parameters than the originally

reported estimates. The re-weighted estimates had wider confidence intervals and greater

uncertainty around them. By using available current information about the population,

this study utilizes an inexpensive and relatively quick technique of re-weighting to

measure health service performance in Afghanistan. Re-weighting reduces the need for a

new survey, which usually requires significant commitment of time and money.

3 Teams of female interviewers were used in all the provinces except three remote insecure provinces.

35

One of the most important limitations of this study that might have affected the

findings reported is that the villages and settlements that were created after 1979 had a

zero probability of selection. We were aware of the potential bias that might have been

created, but this analysis could not adjust for these missing sampling units. However, in

order to estimate the bias due to these missing sampling units (villages), a useful

technique could have been employed right after the 2004 pre-census. A representative

sample of households from only those villages that were created after 1979 could have

been selected using a sampling technique similar to original MICS survey and the

calculated estimates could have been compared to the re-weighted estimates. The

comparison of these estimates with re-weighted estimates would have allowed a precise

measurement of the bias due to missing villages in 1979 census.

Another issue was that of using number of households as the denominator for

generating sampling weights instead of the number of women and children. The original

report published by UNICEF had used the number of households for sampling as well as

generating sampling weights. All the comparisons in this study were based on sampling

weights generated from number of households in every province. In developing countries

like Afghanistan where the criterion for eligibility for an individual interview is "ever

married women under 50 years of age", there are generally about 1.0 such women per

household (Verma et al., 1980). The number of households is used as the denominator in

calculating sampling weights for household surveys in developing countries as the

probability of a different result using number of women is very low.

Despite these limitations, the findings in this study have important implications

for policy makers, donors and health researchers in post-conflict settings. In the

36

immediate post-Taliban period a number of baseline assessments were conducted by the

Ministry of Health in Afghanistan to create a framework for national health policy and its

implementation. Afghanistan National Health Resource Assessment (ANHRA) and

MICS were two such examples aimed at baseline evaluation of health infrastructure and

health performance respectively. The MICS 2003 was a cross-sectional survey targeted

primarily towards the needs of policy makers and donors. It had the specific aim of

providing baseline data for planning and evaluation of interventions that improve the life

of women and children in Afghanistan. The MICS data were collected in a scientifically

rigorous manner in a very difficult setting using a probability based sampling technique.

The originally reported estimates were generated taking into account the stratification and

clustering of households even though the sampling weights were incorrect. In light of the

evaluation framework proposed by Habicht and colleagues, we believe that the policy

makers in post-conflict settings can be reassured that expected goals of a baseline

evaluation are being met as long as the information is collected and analyzed in a

scientifically rigorous manner, even though it is based on an older sampling frame

(Habicht et al, 1999). However, it is important to emphasize that the generalizability of

our findings should be tested in other post-conflict settings before being widely accepted.

During the data collection for MICS 2003, the clusters (villages) were sampled based on

the information provided in the 1979 sampling frame, but the selection of a segment

within the clusters and subsequent stages of sampling were based on information that was

collected directly from the community members living there on the day of survey. The

use of current information probably led to a reduction in the bias that might have

otherwise occurred due to an older sampling frame. In the case of sampling within the six

37

largest cities, even the clusters were sampled based on current information, thereby

strengthening the explanation towards reduction in bias in calculated estimates.

The results for the delivery indicators analyzed in this study indicate that the use

of information from 1979 census for MICS 2003 is sufficient for baseline evaluation of

health system performance in Afghanistan. The estimates generated using information

from 1979 census are adequate for cross-sectional assessment in the immediate post-

Taliban period because the two sets of point estimates yield similar inferences. However,

use of these estimates for assessing trends is not without caution. The original MICS

survey was conducted to generate estimates for the 12-23 month age group with a

precision level of +/-10% at the provincial level (UNICEF, 2004a). Among the seven

indicators analyzed in this study, the indicator on DPT immunization was the only

indicator related to this age group. Our results indicate that a large proportion of unbiased

re-weighted estimates have a precision level that is lower than the intended level of +/-

10%. The re-weighted estimates are adequate to assess trends in health system

performance in terms of the magnitude of point estimates at two (or more) points in time

but the use of these imprecise estimates for statistical testing of change in performance is

associated with greater variability in statistical power as compared to (original) estimates.

In other words, for a given level of statistical power the original MICS estimates will be

able detect smaller changes in performance as compared to the unbiased re-weighted

estimates.

While the estimates can be corrected for precision by using new sampling weights

based on current sampling frame, we suggest that researchers and policy makers should

be careful in using these estimates to rule out the effect of external factors on health

38

system performance in Afghanistan. We believe that the method of re-weighting used in

this study is an inexpensive and important tool that can enable the post-hoc use of this

data for analyses of trends, but it cannot substitute the need for a more complex

evaluation design and extensive data collection.

The maternal and child health interventions included in the MICS survey have a

proven record of being efficacious and effective in improving their health status but

researchers should be aware that the post-conflict situation limits the scope of plausible

explanations that can be derived from such a population survey. This aspect should be

kept in mind while measuring the effect of health programs on population health in such

settings. The large scale movement of population due to war renders the unstable

population unsuitable for use as a historical control that is needed to rule out other

explanations of changes in health system performance. In addition, in post-conflict

Afghanistan, it is difficult to rule out other competing explanations like the effect of

programs for poverty alleviation and economic development that could potentially

improve health outcomes.

The design and conduct of population based surveys like MICS present problems

for probability based assessment of changes in health system performance as well. The

efficacious and effective interventions under study cannot be randomized to population

groups as randomization will not only be politically unfeasible but also ethically

incorrect. In addition, the probability based assessment of change is based on the

questionable practice of choosing arbitrary values for Type One (I) and Type Two (II)

errors in such a way as to be willing to not identify a beneficial result four times more

often than to be mistaken in declaring such a result when it is absent. The interventions

39

included in this study have repeatedly been reported to be associated with significant

reduction in morbidity and mortality in developing countries; therefore, evaluation should

not be based only on arbitrary values of type I and type II errors. A higher value of error/s

should be used to evaluate programs and interventions that have proven efficacy,

especially in cases where scientific inferences are not being made (Habicht et al, 1999).

2.5 Conclusion

This study provides the best possible estimates for health services delivery at provincial

and national levels in Afghanistan in 2003. In post-conflict settings, when urgent

information must be gathered on the health status of the population, older sampling

frames can be used for household surveys to derive population estimates that are

adequate to guide policy decisions. The re-weighting method proposed in this study

corrects the problems of precision and bias in population based estimates and enables the

use of these data for analysis of trends, but with greater variability in statistical power.

The users of these surveys to rule out other explanations for changes in health system

performance should be careful while conducting these plausibility and probability

assessments. While a more complex survey design is needed for plausibility and

probability based assessment of changes due to health programs, data collection remains

a real challenge in Afghanistan. Population based health surveys like MICS 2003 can

provide valuable information to policy makers in monitoring and evaluating the health

situation in a rapidly changing post-conflict setting.

40

Table 2.1: Seven priority health indicators for MICS 2003

Last delivery of the mother assisted by unskilled birth attendant (in last 2 years)

2 Married women under 50 years of age currently not using a method to delay pregnancy

3 Ante-natal consultation not taken from doctor/ TB A during last pregnancy 4 Children aged 6-59 months who have not received Vitamin-A supplementation 5 Children aged 9-59 months who have not received measles immunization 6 Children aged 12-23 months who have not received 3 doses of DPT immunization 7 Children under 5 years of age who have not received BCG immunization

41

Table 2.2: MICS 2003: Revised (Re-weighted) health service delivery estimates for women

Province Badakshan

Badghis Baghlan Balkh

Bamiyan Far ah

Faryab Ghazni Ghor

Helmand Herat

Jawzjan Kabul

Kandhar Kapisa Khost Kunar

Kunduz Laghman

Logar Nangarhar

Nimroz Nooristan Paktika Paktya Parwan

Samangan Saripol Takhar

Uruzgan Wardak Zabul

National

Last delivery assisted by

unskilled birth attendant (in last

2 years) 96.9 93.6 93.5 86.8 89.1 80.8 95.9 95.7 90.7 97.3 74.9 91.9 58.8 79.5 81.3 83.5 97.1 95.0 86.8 89.9 83.1 93.0 98.3 95.3 85.6 94.8 57.7 99.4 99.5 94.2 88.0 99.1 87.4

Married woman under 50 years of age currently not using a method to delay pregnancy

97.0 97.9 94.6 89.0 94.0 73.0 95.1 98.1 99.2 98.9 59.1 97.0 78.9 82.2 86.1 98.3 99.7 88.1 91.7 87.1 93.0 87.1 99.7 99.8 97.4 93.1 97.3 95.6 99.6 96.5 94.8 98.2 91.2

Ante-natal consultation not

taken from doctor/ TBA during last

pregnancy 95.5 99.8 92.6 87.4 92.5 96.4 87.9 89.0 99.2 89.5 71.8 90.9 54.0 76.2 77.5 85.9 96.8 76.9 79.7 73.1 82.1 94.4 98.0 97.0 90.8 90.7 97.4 96.4 97.8 99.0 90.2 99.1 86.1

Table 2.3: MICS 2003: Revised (Re-weighted) health service delivery estimates for children

Province Badakshan


Bamiyan Far ah Faryab Ghazni Ghor

Helmand Herat

Jawzjan Kabul


Kunduz Laghman

Logar Nangarhar




National

Children aged 6-59 months who have not

received Vitamin-A

supplementation 2.8 5.7 34.8 5.5 16.1 7.7 17.4 46.2 15.6 5.3 6.3 6.9 7.9 6.3

20.4 23.1 7.9 32.6 7.5 5.3 6.2 9.8 35.4 4.2 10.4 15.2 7.5 9.6 7.7 30.2 9.0 17.9 12.6


received measles

immunization 12.8 40.3 44.0 21.4 24.2 18.7 20.8 28.7 47.6 9.9 15.4 22.4 14.4 25.1 34.4 27.4 7.5

45.1 15.8 6.5 12.3 45.1 32.3 17.9 25.3 35.0 12.3 12.8 8.9

42.3 25.7 45.2 23.7


received 3 doses of DPT immunization

58.3 85.1 93.9 74.7 97.4 66.2 54.9 82.8 94.3 98.0 37.9 85.7 35.7 70.8 81.0 77.3 35.4 72.3 55.9 38.5 29.6 77.9 86.5 93.5 51.0 81.7 92.4 83.4 96.8 96.8 73.4 94.8 71.0

Children under 5 years

of age who have not

received BCG immunization

17.4 66.2 79.1 32.7 56.2 35.4 28.7 35.2 53.1 37.1 21.5 57.1 18.3 56.6 69.8 20.4 16.5 52.5 17.2 14.1 15.1 38.6 51.8 32.4 26.9 56.8 56.1 52.4 63.4 78.6 16.9 47.5 41.0

43

Table 2.4: MICS 2003: Revised (Re-weighted) confidence intervals for service delivery estimates for women

Province Badakshan


Bamiyan Far ah Faryab Ghazni Ghor

Helmand Herat

Jawzjan Kabul


Kunduz Laghman

Logar Nangarhar




National

Last delivery assisted by

unskilled birth attendant (in last

2 years) [93.2,98.6] [76.1,98.5] [84.4,97.4] [82.1,90.4] [84.6,92.4] [75.1,85.4] [91.4,98.1] [88.8,98.4] [80.3,95.8] [89.6,99.3] [67.1,81.3] [82.9,96.4] [51.7,65.4] [73.7,84.2] [65.8,90.7] [74.7,89.6] [90.7,99.1] [92.0,96.9] [76.5,93.0] [84.1,93.7] [76.5,88.2] [84.7,97.0] [94.8,99.5] [92.1,97.3] [61.3,95.7] [89.3,97.5] [45.0,69.5] [96.1,99.9] [98.1,99.9] [87.3,97.5] [84.0,91.0] [97.2,99.7] [85.2,89.4]

Married woman under 50 years of age currently not using a method to delay pregnancy

[93.5,98.6] [96.3,98.8] [90.2,97.0] [85.7,91.6] [90.4,96.3] [60.5,82.7] [91.2,97.4] [96.3,99.0] [96.3,99.8] [97.0,99.5] [51.5,66.3] [89.1,99.2] [74.5,82.7] [76.3,87.0] [79.1,91.1] [96.9,99.0] [99.1,99.9] [81.0,92.8] [84.4,95.8] [79.0,92.3] [86.5,96.5] [80.3,91.8] [98.9,99.9] [99.4,99.9] [94.7,98.8] [88.4,96.0] [92.1,99.1] [92.0,97.7] [98.7,99.8] [94.0,97.9] [90.6,97.2] [96.5,99.0] [89.9,92.2]

Ante-natal consultation not

taken from doctor/ TBA during last

pregnancy [83.5,98.8] [98.6,99.9] [73.2,98.3] [82.2,91.2] [84.0,96.6] [91.9,98.4] [79.2,93.3] [79.6,94.4] [96.9,99.8] [65.1,97.5] [64.4,78.2] [84.8,94.6] [46.7,61.2] [69.3,82.06] [63.5,87.2] [77.6,91.4] [92.3,98.7] [65.3,85.5] [71.3,86.1] [67.4,78.1] [71.0,89.6] [87.2,97.7] [92.7,99.5] [93.3,98.7] [72.4,97.4] [80.5,95.8] [89.5,99.4] [88.5,98.9] [92.8,99.3] [97.0,99.7] [86.1,93.2] [96.6,99.7] [83.8,88.0]

44

Table 2.5: MICS 2003: Revised (Re-weighted) confidence intervals for service delivery estimates for children

Province Badakshan

Badghis Baghlan

Balkh Bamiyan

Far ah Faryab Ghazni Ghor

Helmand Herat

Jawzjan Kabul


Kunduz Laghman

Logar Nangarhar

Nimroz Nooristan

Paktika Paktya Parwan


Uruzgan Wardak

Zabul National

Children aged 6-59 months

who have not received

Vitamin-A supplementation

[1.8,4.6] [3.2,10.0]

[20.3,52.7] [2.5,11.7] [8.0,29.8] [5.3,11.0] [7.7,34.7]

[37.9,54.7] [9.5,24.4] [2.3,11.9] [4.5,8.6]

[3.6,12.8] [5.9,10.4] [4.2,9.4]

[14.4,28.2] [16.4,31.6] [4.2,14.4]

[18.7,50.4] [4.5,12.4] [3.9,7.2] [4.5,8.3]

[5.7,16.3] [20.0,54.6]

[2.3,7.4] [6.6,16.1]

[10.6,21.4] [2.0,23.8] [5.6,16.0] [4.1,13.9]

[21.2,41.1] [4.8,16.0]

[10.9,28.0] [11.2,14.2]


received measles

immunization [7.0,22.2]

[29.6,52.0] [29.4,59.8] [15.5,28.8] [12.8,40.9] [12.9,26.4] [13.2,31.0] [18.1,42.2] [33.8,61.8] [5.1,18.3]

[10.3,22.3] [16.4,29.8] [9.0,22.4]

[18.7,32.8] [22.6,48.5] [18.3,39.0] [4.3,12.9]

[27.6,63.9] [11.0,22.1] [3.9,10.9] [7.0,20.7]

[26.4,65.2] [18.2,50.6] [11.7,26.4] [17.8,34.7] [25.8,45.4] [7.6,19.2] [9.1,17.6] [5.0,15.6]

[28.6,57.4] [16.6,37.5] [31.2,60.0] [21.3,26.2]


received 3 doses of DPT

immunization [44.2,71.2] [68.9,93.6] [85.2,97.6] [60.8,84.9] [88.3,99.4] [43.5,83.3] [43.4,65.8] [71.1,90.5] [84.0,98.1] [92.1,99.5] [28.1,48.7] [72.9,93.1] [24.5,48.8] [56.5,81.9] [58.6,92.8] [65.7,85.8] [22.2,51.3] [57.3,83.6] [33.3,76.3] [30.7,46.9] [17.7,45.2] [63.6,87.7] [72.8,93.9] [87.2,96.8] [34.7,67.1] [66.6,90.9] [73.6,98.1] [68.8,91.9] [89.2,99.1] [91.3,98.8] [60.5,83.3] [87.7,97.9] [67.2,74.5]

Children under 5 years

of age who have not

received BCG immunization

[12.8,25.4] [69.4,86.2] [67.3,88.1] [32.6,48.7] [45.1,67.9] [29.8,54.1] [28.5,63.0] [26.2,42.0] [39.1,66.6] [34.0,60.9] [19.1,39.5] [47.4,65.0] [14.2,22.4] [53.8,64.9] [61.0,83.1] [13.5,31.4] [12.7,26.4] [49.6,69.1] [8.3,22.6] [8.0,16.3]

[11.9,23.2] [22.5,48.0] [36.2,64.0] [23.3,36.5] [24.1,40.4] [45.2,64.5] [48.2,76.9] [41.5,64.6] [32.7,58.9] [67.8,86.1] [10.3,20.2] [37.6,64.2] [38.2,43.7]

45

Figure 2.1: Boxplot of Differences* in Point Estimates in Afghanistan 2003 MICS

Unskilled Delivery

No contraceptive use

No antenatal consul.

No Vit.A suppl.

No Measles imm.

No DPT3 imm.

No BCG imm.

" L

• • • I H H

CEQ

— i

10 -20 -10 0 10 20 Percentage

"(Reweighted pt. estimate - Original pt. estimate)in percentage points

Figure 2.2: Boxplot of Differences* in Confidence Interval width in Afghanistan 2003 MICS

Unskilled Delivery

No contraceptive use

No Antenatal consul.

No Vit.A suppl.

No Measles imm.

No DPT3 imm.

No BCG imm.

•OZJ-

• L D — i •

-fr-̂ 4-

i i i

T I I I I

-20 -10 0 10 20 Percentage

*(CI width reweighted-CI width original)in percentage points

30

46

Chapter 3 Use of household asset data to measure living standards and track poverty in post-conflict Afghanistan

Abstract

The country of Afghanistan is emerging out of more than two decades of civil war

and has made significant economic progress. In order to achieve long term peace and

sustained economic growth, a growing priority for public policy in Afghanistan is the

assessment of living standards and reduction in poverty among the population. Regular

data collection on standard economic measures such as income and consumption

expenditure is time consuming and resource intensive in general; and in post-conflict

Afghanistan also unfeasible, due to restricted access to unsecure areas and remote

populations. Regular collection of data on household asset variables is easier and more

reliable. The use of asset variables to generate a relative measure of economic status is

fairly common—however, limited research has been conducted on the use of asset

variables to generate an absolute economic measure that is strongly anchored in utility

theory. In this study, we have compared the results of out of sample prediction and

principal component analysis (PCA) by estimating conceptually analogous measures to

assess the difference in economic status and poverty between two population based

samples collected over an interval of one year in rural Afghanistan. All the estimates

were generated using data on an identical set of asset variables collected from two

separate household surveys conducted in 2004 and 2005. Total household expenditure

was estimated using out of sample prediction, and household asset index was estimated

using PCA. The difference between the two samples in mean expenditure as well as mean

asset index was statistically significant (p-value <0.01). The estimated mean was higher

47

for the 2005 sample for each of the two measures. We also calculated the probability of a

household being poor. A household was defined as poor if the total household

expenditure per day was less than two US dollars. The estimated mean probability of

being poor calculated using out of sample prediction was lower for the 2005 sample by

2.8% and the difference was statistically significant (p-value <0.01). A comparable

analysis based on household asset index resulted in statistically inefficient estimates. In

conclusion, predicted expenditure and asset index are both sensitive to changes in the

estimated mean of asset variables but unlike asset index, predicted expenditure provides

an absolute measure of household economic status. In addition, unlike PCA based asset

index, out of sample prediction provides a simple and statistically efficient tool to track

the economic aspect of poverty. Although our findings lack generalizability to the

Afghan population, they do provide evidence towards improvement in economic status

and reduction in poverty in rural Afghanistan.

3.1 Introduction

Household income and consumption expenditure are the standard economic

measures of material living standards (O'Donnell et al, 2008). Measurement of income

and consumption expenditure is supported by a strong theoretical basis in utility theory

and these two metrics of economic status are absolute in nature. These measures are also

important in understanding the economic aspect of poverty as consumption is a widely

used measure of economic status to generate poverty thresholds (lines) in many countries

(Hentschel & Lanjouw, 1996). Collection of accurate household data on income and

consumption poses various problems for researchers and policy makers in developing

48

countries (Rutstein & Kiersten, 2004). Accurate data collection on household

consumption and income is a very time and resource consuming task (Montgomery et al,

2000). A household can have many earning members with several sources of income.

The respondents might try to hide information from interviewers due to privacy concerns.

In rural households, income and expenditure might not be completely based on market

based transactions due to home production of some goods, which are then consumed

internally and/or traded (Cortinovis et al, 1993). In developing countries, the respondents

might not know their income due to self-employment and other non-cash sources of

income. In addition, a large proportion of households receive income intermittently due

to employment in a large informal sector of the economy. In developing countries,

consumption expenditure is considered a more reliable measure of household living

standard than income (Deaton & Grosh, 2000).

Brief household survey modules on durable consumer goods, housing quality,

water and sanitation facilities and other household characteristics have been used to

expedite the assessment of economic aspect of living standards in developing countries.

These variables on household characteristics are either used individually or in a

combination based on maximizing some statistical property of the resulting measure of

economic status. These household variables are variously referred to as asset variables or

asset indicators. Asset variables have been extensively used to generate a relative

measure of household economic status. The asset index, based on the technique of

principal components, is an example of such a measure where a linear index created from

asset variables captures the largest amount of information that is common to all the

analyzed variables (Filmer & Pritchett, 2001).

49

The use of asset variables to rank households and assess the effect of economic

status on health outcomes is fairly common; however, limited research has been

conducted on the use of asset variables to generate an absolute measure of economic

status grounded in utility theory. An urgent need for practical measures for steadily

tracking poverty emerged from international endorsement of the Millennium

Development Goals and led to recent studies that have used advanced prediction

techniques to link the asset variables directly to household consumption (Mathiassen,

2007; Stifel & Christiansen, 2007). These studies provide an inexpensive and efficient

technique to utilize information on asset variables and estimate a measure of economic

status that is absolute in nature. These authors employed out of sample prediction

techniques to estimate household consumption over time and generated robust measures

to track poverty and inequality in a population.

Afghanistan Context

Afghanistan is a land locked country situated at the junction of the Middle-

Eastern crescent and South-east Asia. A prolonged civil war that lasted more than two

decades, along with foreign occupation and tribal warfare, have severely damaged the

political, social and economic infrastructure of the country. Afghanistan has some of the

worst health indicators in the world and is ranked among the lowest in human

development with one in every two people living in poverty (UNDP, 2004) . Since its

2001 invasion, the USA and other countries, including Japan, the UK and Germany, have

invested billions of dollars in Afghanistan's reconstruction (Bristol, 2005). The economy

has improved significantly since the fall of the Taliban with an infusion of international

50

assistance, recovery of the agricultural sector and growth in the service sector. A recent

report by the World Bank found that the GDP (excluding narcotics) grew by more than

fifty percent in 2003, albeit starting from very low level in 2001. A number of new

employment opportunities were created due to growth in the agricultural sector together

with post war expansion in construction and commercial services (World Bank, 2005).

Available estimates suggest that by March 2006, starting from very low levels in 2001,

the Afghan economy had grown by more than eighty percent (Mali, 2006). .

In this fragile post-conflict environment, Afghanistan is undergoing profound

economic, political and social change and ensuring that the opportunities of growth are

accessible to the poor is crucial for welfare of ordinary people as well as long term peace

and prosperity. Improvement in the living standards of the Afghan population has been an

explicit aim of the Government of Islamic Republic of Afghanistan and the donor

community. A pertinent example of this emphasis is the Afghanistan National

Development Strategy (ANDS), which is the centerpiece of the Government of

Afghanistan's National Development Framework. ANDS has been created as a major

collaboration between Afghanistan and the international community to promote growth,

generate wealth and reduce poverty and vulnerability in Afghanistan (T.I.S.A., 2004).

There have been reports that while most of the rural Afghan economy has been

benefiting from economic growth and increase in agricultural harvest, the poorest

sections of the society are still lagging behind (World Bank, 2005). The assessment of

living standards of the population and reduction in poverty is a growing priority for the

public policy in Afghanistan.

51

In light of these efforts and reports, two important questions that have arisen for

policy makers are:

1. Has the standard of living of the Afghan people improved in the post-Taliban

period?

2. Has the overall rate of poverty changed in light of the overall economic growth in

Afghanistan?

In this study, we have attempted to provide answers to these complex questions

by comparing the results of principal components analysis (PCA) and out of sample

prediction to assess difference in economic status and poverty between two population

based samples collected over an interval of one year. We estimated and compared two

analogous measures of economic status generated using each of the two techniques. The

two outcomes estimated using out of sample prediction are total household expenditure

and the probability of a household spending less than $2 US dollars per day. The two

analogous outcomes estimated using PCA are household asset index and probability of a

household being in the poorest 30% of the index measure. In order to ensure

comparability with the asset index, both the measures estimated using out of sample

regression were based on total household expenditure instead of per capita or other

equivalent measure of expenditure. Asset index based on PCA was calculated at the

household level only, as most of the asset variables included in an asset index are shared

between household members and most are just indicators of possession of at least one or

none, rather than quantities.

The asset variables that were used as predictors in this study can be broadly

classified into three categories: household size, ownership of consumer durables, and

52

dwelling characteristics. The data were collected as part of two separate cross-sectional

household surveys conducted over an interval of one year. The sample for the year 2005

had information on the asset variables and the total household expenditure while the 2004

sample had information on the asset variables only. The use of out of sample prediction

technique allowed the estimation of expenditure for 2004 even though household

expenditure data were not collected from the households surveyed in 2004.

The predicted estimates were then used to test the following hypotheses:

1. The mean total household expenditure differs significantly between the two

samples collected at an interval of one year.

2. The mean probability of a household's total expenditure being less than two

dollars a day differs significantly between the two samples collected at an

interval of one year.

The analogous PCA based measure was mean asset index for the first hypothesis and

mean probability of a household to be in the poorest 30% (by asset index) for the second

hypothesis.

This study is particularly applicable in post-conflict Afghanistan, where logistical

concerns of restricted access to unstable areas and ongoing security problems favor a

more expeditious approach to measuring living standards. There is an urgent need for

measures that are easy to collect, observe and verify. Regular data collection on asset

variables is easier and less resource intensive than regular income or consumption

surveys. Survey modules for asset variables require fewer questions, which can be

collected from a single respondent in a household.

53

Three factors guided our focus on the household level variables that have been

used for estimating outcomes in this study. First, it was our conceptual understanding that

among the various asset indicators of living standards that have been studied in the

literature; in a rapidly developing post-conflict country, ownership of durable goods and

a household's dwelling characteristics are sensitive to a change in economic status of a

population. The second factor was based on studies that had reported that relative

measures of economic status commonly employed in demographic research yield results

that are similar to the absolute measures like consumption (Filmer & Pritchett, 2001;

Filmer & Scott, 2008; Montgomery et al, 2000). We intended to study this issue further

by comparing the predicted absolute measure with the relative measure; both generated

using an identical set of asset variables. The absolute measure in this study is the

predicted total household expenditure and the relative measure is the asset index. The

third was a pragmatic response to a data constraint problem, as only data on household

size, ownership and dwelling characteristics were collected using the same format of

questions in the two surveys, thereby ensuring comparability of results by reducing the

bias that might arise due to difference in survey instruments.

3.2 Methods

Data sources

The two datasets used for the analysis in this study are the National Health

Services Performance Assessment (NHSPA) 2004 and the National Risk and

Vulnerability Assessment (NRVA) 2005. The NHSPA was an annual survey conducted

by the Johns Hopkins University (JHU) and the Institute of Health Management Research

54

(IHMR) for the MOPH in Afghanistan. An important objective of NHSPA was to provide

data on health system performance at provincial and national level and enable the MOPH

to monitor and evaluate the nationwide implementation of the Basic Package of Health

Services (BPHS) (Peters et al., 2007). This study utilizes the household data collected

during the first round of NHSPA conducted in (June-September) 2004. Data were

collected at both household and health facility levels in 2004 but later rounds of NHPS A

have been conducted only at the health facility level. Another household survey was

conducted in (June-August) 2005 as part of the National Risk and Vulnerability

Assessment (NRVA) to collect data at provincial and national levels in Afghanistan. The

NRVA 2005 was undertaken "to collect information to better understand the livelihoods

of both males and females in Afghanistan from rural, urban and migratory

populations"(M.R.R.D., 2005). The NRVA was conducted by the Central Statistical

Office (CSO) for the Ministry of Rural Rehabilitation and Development (MRRD)

Afghanistan.

The data were collected in each the two surveys using probability based multi

stage sampling designs in every province of Afghanistan. However, the actual survey

implementation was not exactly the same between the two assessments. The NHSPA

2004 collected information only from households living within one and a half hour (1.5)

walking distance from a health facility, whereas the sampling of households as part of

NRVA 2005 was not based on any such criteria. Three types of BPHS facilities that were

used to select villages for NHSPA 2004 were, Basic Health Center (BHC),

Comprehensive Health Center (CHC) and District Hospital (DH).

55

In order to circumvent this problem of difference in sampling coverage and to

increase comparability of findings, this analysis used data only from households in

villages within one (1) hour walking distance from a BPHS health facility in either of the

two surveys. The walking time of one hour to the health facility is based on summer

months and only households in rural areas have been included in the study for each of the

two datasets. Urban and rural households in developing countries are considered to differ

significantly in household size, ownership of items and dwelling characteristics (Filmer

& Pritchett, 2001; Vyas & Kumaranayake, 2006). Information on urban households

collected in NRVA 2005 was excluded from this analysis to ensure comparability, as no

information was collected from urban areas as part of NHSPA 2004.

Variable description

Total expenditure was used as the measure of living standard due to conceptual

and pragmatic reasons. Conceptually, asset ownership and dwelling characteristics are

expected to be strongly associated with a household's total expenditure, as both are based

on market transactions. In light of this strong association, and because assets and

dwelling characteristics are the main predictors in this study, the total expenditure was

used as the outcome variable. NRVA 2005 was the most comprehensive assessment of

living standards conducted in Afghanistan since the fall of the Taliban, but it lacked the

information needed to calculate household consumption, thereby guiding the pragmatic

decision to use expenditure as the measure of choice. Consumption is a more

comprehensive measure of living standard as it incorporates monetary value for market

based transactions and the calculation of consumption includes monetary values for home

56

produced food items and the benefit derived from assets like housing. The data on price,

stock and characteristics of consumer durables were not collected from the households

surveyed as part of the NRVA 2005. This prevented the calculation of cost of funds tied

up in these goods as well as the depreciation of these goods. The data on prices of food

items was missing for at least half of the districts that the surveyed households

represented. This prevented the calculation of the value of home produced items for

household consumption.

The expenditure data were collected for a recall period of one month on recurring

items of daily use like food, transport, fuel, soap, detergent, as well as for other items like

taxes. The expenditure data on non-recurrent items like payment for medical services,

education, house repair, special events, debt servicing, house repair, and clothing were

collected for the months that these expenditures existed. The yearly total household

expenditure values were calculated by combining the above two categories to generate

expenditures for a uniform reference period of twelve months.

Fourteen asset variables and their average for each household were calculated

(Table 3.1). The variable on household size represents the number of people (male and

female) in a given household that usually take their meals from the same cooking pot,

share household assets and accumulate earnings to procure food and other household

materials. Nine indicator (dummy) variables were generated, including household

ownership of clock, bicycle, radio, television, sewing machine, refrigerator, car, tractor,

and generator. The four asset variables on characteristics of household's dwelling were:

main source of drinking water, main source of lighting, main source of cooking fuel and

availability of private toilet facilities. The data on these four assets were collected as

57

categorical variables, with limited number of households belonging to some categories.

In order to circumvent this problem, for each of the four variables, categories

representing higher and lower living standards were grouped together to generate binary

variables. In order to maintain comparability, this grouping was based on the technique

used for other reports and studies published using the NHSPA data, especially the

Balanced Scorecard Report (BSC) prepared by the Johns Hopkins University every year

for the MOPH Afghanistan (Hansen et al, 2008c; JHU and IHMR, 2008a).

Statistical Analyses

The analyses for this study were conducted using statistical package by Stata

Corporation (StataCorp., 2004). The sub-sample available for this analysis had data on

8822 households for 2004 and 3844 households for 2005. The estimated mean for each

of the asset variable was calculated for the two samples and the difference was analyzed

for statistical significance using t-test.

The basic approach in out of sample prediction involves two steps. In the first

step, the dataset containing information on the expenditure and assets is used. The

expenditure is treated as the outcome variable in a log linear regression with the

household assets as the explanatory (predictor) variables. In the second step, the

estimated regression coefficients for each explanatory variable and the constant term are

used as weights in a linear equation to predict expenditure for every household in each of

the two yearly samples.

In order to test the first hypothesis we used the generalized linear modeling

approach to fit a log linear model for the 2005 sample.

58

A general equation for log linear regression model is:

Log Yi = po + pXi + £i

i = Household identifier (Unique code for each household)

Yi = Total household expenditure for i* household

xi = Vector of asset variables for ith household

The regression coefficients (P) along with the constant term (Po) were then used to

predict total household expenditure for every household in the 2005 and the 2004

samples. The distribution of predicted total household expenditure for 2004 and 2005

estimates was visually compared by graphing the kernel density plots. The difference in

estimated mean of the predicted total household expenditure between 2004 and 2005

samples was tested for statistical significance by implementing the t-test for two

independent samples.

The asset variables were used to generate an asset index for each of the two years

using principal components analysis. The data for the two years were pooled together to

generate a common set of scoring coefficients for the asset indices. We plotted the kernel

density (probability density) estimates of our index to visually compare the distributions

for 2004 and 2005 samples. The difference in average asset index score was compared

between 2004 and 2005 samples by implementing t-test for two independent samples.

In order to the test the second hypothesis, as a first step, the sampled households

that reported their actual total expenditure to be less than two US dollars per day were

identified for the 2005 sample using the exchange rate of 44.78 Afghanis to 1 US dollar.

This exchange rate is based on the official publication by Afghanistan CSO for 2003

(CSO, 2003). A binary variable was generated where the households spending less than

59

two US dollars per day were coded as being 'poor'. This binary variable was used as the

outcome variable and a probit regression model was fitted for the 2005 sample using the

generalized linear modeling approach.

A general equation for the probit regression model is:

P(yi=l|xi) = 0(p'o + P'xD

i = Household identifier (Unique code for each household)

O = Cumulative normal distribution function

P (yi =1| xi) = Probability for the ith household being poor given the set of asset

variables

X, = Vector of asset variables for i household

The probability of a household being 'poor' was predicted for every household in

the 2005 and the 2004 samples at the estimated mean (of asset variables) for the 2005

sample. The difference in average predicted probability of households between 2004 and

2005 samples was tested for statistical significance by implementing the t-test for two

independent samples.

3.3 Results

As compared to 2004 estimates, the estimated mean was higher for nine and lower

for five asset variables in the 2005 sample (Table 3.2). A total of eleven out of the

fourteen differences were statistically significant (p-value <0.05). The estimated average

household size for the 2005 sample was greater than the estimated average for the 2004

sample by 0.5 (p<0.01). Among the remaining eight out of nine mean estimates that were

higher for 2005 sample, the magnitude of difference was greater than five percent for five

60

variables. Among the five variables that had a lower estimated mean in 2005, the

magnitude of difference was not greater than five percent for any of the variables.

Table 3.3 provides the coefficients estimated from the log linear regression model

that was fitted to test the first hypothesis. Household size4, dwelling characteristics and

ownership of consumer durables were important in explaining the variability in

household expenditure for the 2005 sample. The shape of the probability density plot of

the deviance residuals generated from this model had heavier tails indicating a higher

kurtosis than a density plot of normally distributed residuals. The R squared estimate for

a similar model fitted using ordinary least square (OLS) regression technique was 0.22

and it explained twenty two percent (22%) of the variation in total household

expenditure. The fifteen coefficients along with the constant term were then used to

predict total household expenditure for the 2005 and 2004 samples. The kernel-density

estimates of the distribution of predicted expenditures for each of the two years were

plotted in a graph and visually compared (Figure 3.1). The density functions were similar

in shape with the 2005 distribution slightly shifted to the right, indicating a higher value

for mean expenditure. Both the distributions were skewed to the right, which is a

characteristic of expenditure data in general. In addition, both of the expenditures had a

heavy tailed distribution indicating kurtosis higher than a normal distribution.

The mean estimate of the total household expenditure for the 2005 sample was

greater than mean for 2004 sample by 1466.4 Afghanis (US $32.7) per household (Table

3.4). In addition to using the constant term, these estimates were generated using

coefficients of all the asset variables included in table 3.3 to account for the differences

in household size, ownership of consumer durables and housing characteristics between

4 Square value of household size was included in the model to allow for non linear pattern in the data.

61

the two samples. The t-test implemented towards testing the first hypothesis found the

difference to be statistically significant (p-value <0.01).

The first component explained nearly 20% of the total variability in the asset

variables generated through PCA on the pooled data for 2004 and 2005 (Table 3.5).

Every asset variable was associated with a positive coefficient indicating that ownership

of assets is associated with a higher estimate on the household asset index. The kernel

density plots for the asset indices for 2004 and 2005 samples indicated that the mean

value for the 2005 sample was higher as compared to the 2004 sample. In addition, the

two distributions differed in shape. The 2004 index had lighter tails with a higher degree

of skewness to the right as compared to the 2005 index. The estimated mean of the asset

index for the 2005 sample was greater than mean for 2004 sample and this difference was

statistically significant (p-value <0.01) (Table 3.6).

Household size, dwelling characteristics and ownership of consumer durables

were significant predictors of a household's probability of being poor (Table 3.7). The

fifteen coefficients along with the constant term were then used to predict a household's

probability of being poor for the 2005 and 2004 samples. The average probability of

sampled households to being poor was 31.9% for 2004 and 29.1% for 2005 (Table 3.8).

The t-test implemented towards testing the second hypothesis found the difference of

2.8% to be statistically significant (p-value <0.01). This indicates that as compared to the

2004 sample the proportion of poor households is lower in the 2005 sample by 2.8%.

The households in the poorest 30% had the lowest scores on the asset index and

hence were the poorest on a relative scale. Along the lines of testing the second

hypothesis, we attempted to predict the probability of a household to be in the poorest

62

30% of the 2005 sample. The binary nature of majority of predictors led to some of the

predictors being dropped from the regression model as they predicted the probability of a

household to be in the bottom 30% perfectly. The predictors dropped from the analysis

on pooled dataset were generator, car and tractor. Moreover, the number of predictors

that were dropped from the analysis differed if only the 2005 data were used instead of

pooled dataset. The results of this model were found to be highly inefficient and therefore

not reported here. We were unable to calculate the difference in average probability of

being in the poorest 30% for the 2004 and 2005 samples.

3.4 Discussion

A surge of reconstruction efforts in Afghanistan has created the potential for rapid

improvement in economic status of the population. The findings of our study indicate a

small but statistically significant difference in average expenditure as well as the

proportion of poor households between the two samples.

Other studies have employed a similar technique to track poverty through use of

asset variables to predict consumption. The results of our study compare favorably with

these studies by Mathiassen (2007), Stifel and Christiansen (2007) and Filmer and Scott

(2008). The fifteen predictors included in the log linear model to predict total household

expenditure help in explaining 22% of the variability in expenditure. The study by Stifel

and Christiansen included thirteen predictors to explain 2 1 % of the variability in

consumption per adult equivalent in rural Kenya (Stifel & Christiansen, 2007). The

model implemented by Mathiassen included a higher number of predictors that are able

to explain 39% of the variability in per capita consumption in rural Mozambique

63

(Mathiassen, 2007). Both the studies employed out of sample prediction technique to

generate robust estimate of the poverty measure of headcount ratio and used it to track

poverty over time. The poverty headcount ratio is the proportion of the national

population whose consumption is below the official threshold (or thresholds) set by the

national government. In Afghanistan, currently no such national threshold/s exists due to

lack of nationally representative consumption data. The study by Filmer and Scott was

conducted to compare the effect of different approaches used to aggregate asset variables

in literature with per capita expenditure in terms of the association between economic

status and population level outcomes on health and development. This study used

datasets from eleven countries, each containing an average of thirty asset variables, to

predict per capita expenditure. The R squared for this study ranged from 19% - 72%, with

higher estimates for countries that had data available on greater number of asset variables

(Filmer & Scott, 2008).

The finding of a statistically significant difference in economic status persists

even if the metric is asset index instead of predicted expenditure. This indicates that the

predicted expenditure and the asset index are sensitive to differences in independent

variables between the two samples. We suggest that despite qualitatively similar results,

expenditure is a better measure of economic status as it has strong foundations in utility

theory. Expenditure is an absolute measure of economic status unlike asset index, which

is a relative measure. The coefficients for predicted expenditure are based on maximizing

their capacity to explain the variability in actual expenditure whereas the coefficients in a

PCA based asset index maximize their capacity to explain the variability in the asset

variables that are used to generate the index. The coefficients used to generate an asset

64

index are solely dependent on the asset variables and lack an underlying theoretical basis

making it possible for assets of low monetary value to get a higher coefficient than an

item of high monetary value.

Our findings suggest that out of sample prediction provides a practical method to

measure and steadily track poverty over time. As compared to asset index, this technique

has two distinct advantages. First, unlike asset index it provides a theoretical basis for

identification of poor households by enabling the use of a specified cutoff value that is

based on an absolute measure like expenditure. Second, the probability of a household

being poor based on an absolute cutoff is statistically more efficient than computation

based on a relative cutoff. The ranking of households by asset index is completely based

on the asset variables used to generate the index, therefore the variables that exhibit no

variability in predicting the probability of being poor are dropped from the regression

model.

In addition to above advantages over PCA based asset index, out of sample

prediction has certain useful characteristics of its own. In the absence of regular,

comparable data on actual expenditure, out of sample prediction is an economically

intuitive and inexpensive method for measuring economic status and steadily tracking

poverty in a population over time. Two recent studies have pointed out that predicted

expenditure mimics the 'best possible' linear prediction in situations where asset

indicators are available in more than one datasets, but they can only be related to

expenditures in one (Filmer & Scott, 2008; Stifel & Christiansen, 2007). The ranking of

households based on economic status enables researchers and policy makers to study the

association between economic status and important outcomes on population health and

65

development. Expenditure is one of the most widely prevalent measures of economic

status used to rank households for this purpose. Among all the possible linear

aggregations of asset indicators to rank households based on economic status, predicted

expenditure matches the ranking based on actual expenditure most closely (Filmer &

Scott, 2008).

A majority of variables used in this study were binary in nature, thereby

restricting the combinations of predictors available to predict household expenditure.

This provides a plausible explanation for the heavy tailed distribution of predicted

expenditures for the NHSPA 2004 and NRVA 2005 samples. In the 2005 sample, the

estimated proportion of poor households calculated via predicted expenditure and the

mean probability of households being poor should be equal to the proportion of poor

households estimated using actual expenditure. The heavy tailed distribution of predicted

expenditure limited its ability to correctly identify the poor households. Predicting the

probability of being poor and calculation of the mean did result in an estimate of 0.29,

which is the same as the proportion of poor households estimated using actual

expenditure.

In addition to the above problem, another issue that might have affected the

results is the low number of asset variables available for this study. This is a data

limitation that arose because the 2004 and 2005 datasets were not collected with the

original intent of tracking poverty over time. This low number of variables might have

adversely affected the ability of assets to explain the total variability in expenditure for

prediction as well as the total variability of indicators for asset index.

The findings in this paper are internally valid but have limited external validity.

66

The two datasets used in this study are from households living within one hour walking

distance of a BPHS health facility. The applicability of these findings at a national level

is limited as this study excludes villages that are further away. Earlier studies have

reported that in developing countries, distance from a health facility is related to

economic status and health services utilization by the people, especially in rural areas

(Tlebere et al, 2007). This suggests that the household samples analyzed in this study

might be economically very different than the households living further away. In

addition, the two sample were collected using probability based multi-stage designs but

with different sampling schemes. The standard errors of the estimates in this study have

not been adjusted to account for the difference in sampling schemes, making the

estimates imprecise, even if they are considered to be valid. As a result, the findings in

this study have limited capability to reflect a population level increase in ownership of

durables goods or improvement in a household's dwelling characteristics. In addition,

NRVA 2005 was collected to be representative at national and provincial levels. Unlike

NHSPA 2004 the sample of households from NRVA included in this study might not be

representative of all the households in Afghanistan that are located within one hour of a

BPHS health facility.

Despite these limitations, this study does provide some indication of improvement

in economic status and reduction of poverty in Afghanistan. These findings are limited to

households living within one hour of a BPHS facility but they do provide evidence

towards initial success of national policies implemented as part of ANDS. This is the first

study in a post-conflict setting to use asset variables to track poverty by generating an

absolute measure of economic status. The findings of this study have important

67

implications for poverty reduction in Afghanistan. Unlike the PCA coefficients, the

regression coefficients can be used as weights to create an economic measure of living

standards in a population. The Core Welfare Indicator Questionnaire (CWIQ) technique,

developed by the World Bank, uses a method similar to the one implemented in this study

for identifying poverty predictors and estimation of predicted welfare function for

ranking households for poverty mapping (Fofack, 1999). The CWIQ technique uses a

combination of two different sources - a qualitative survey and a comprehensive

integrated household survey, to identify a set of explanatory variables that explain over

40% of the total variance observed in household aggregate total expenditure. The

predicted welfare function is expressed as the weighted sum of these poverty predictors.

The results of this technique have been successful in accurately identifying different

welfare groups in population in countries like Ghana and Uganda. The out of sample

prediction technique can form the basis for identification of poor households in

Afghanistan. The recent completion of the national pre-census enumeration in

Afghanistan has provided an opportunity to conduct poverty mapping of the Afghan

population to identify the poorest and the most vulnerable groups.

Out of sample prediction technique has important implications for the targeting

approach employed for the much needed social protection programs for the poor in

Afghanistan. A recent assessment by The World Bank has highlighted the lack of

information on extreme poverty and vulnerability in Afghanistan (World Bank, 2005).

The report emphasizes the urgent need for data collection and analysis on this aspect

before launching any new social protection programs. In countries like Chile and Egypt, a

proxy means test approach to predict consumption has been successfully used to target

68

subsidies to the poor households. The term "proxy means test" is used to describe a

situation where information on household or individual characteristics correlated with

welfare levels is used in a formal algorithm to proxy household income, welfare or need

(Grosh & Baker, 1995; Grosh & Glinskaya, 1997). This approach was developed to

improve the targeting accuracy of social protection programs in developing countries

where reliable income and expenditure records are seldom available. A comparative

study of 30 targeted social programs in Latin America reveals that, among all targeting

methods, the proxy means tests used in Chile resulted in the highest targeting rate to the

poor, both in terms of sensitivity (coverage) and specificity (leakage) of the methods

(Grosh, 1994). The Ficha CAS program in Chile used a proxy means test to achieve its

goal of ensuring that the poorest 30 percent of the population receive 72 and 62 percent

of the benefits of the family subsidy and the old age assistance pension programs

respectively (Grosh & Baker, 1995). In Egypt, the technique has been used to achieve the

dual purpose of increasing the equity of food subsidy program and lowering of the

program's budgetary cost to the government (Ahmed & Bouis, 2002). In light of the

evidence from programs in Egypt and Chile, the technique implemented in this study can

be used to gain insight into the economic dimension of poverty in Afghanistan and

identify the potential beneficiaries of social protection programs.

Since 1984, Demographic and Health surveys (DHS) have been conducted in

more than 75 developing countries and have provided valuable nationally representative

data on fertility, family planning, maternal and child health, as well as child survival,

HIV/AIDS, malaria, TB, and nutrition. A PCA based asset index, similar to this study,

generated from the DHS data has been used by the World Bank to report on economic

69

inequalities in health outcomes for many developing countries (Gwatkin et ah, 2000;

McKenzie, 2003). The technique proposed in this study can be used to generate a

measure of economic status for the countries where DHS has been conducted. Unlike

asset index, this will provide an economic measure of living standard that is absolute in

nature as the weights to be used for combining assets will be derived from a regression on

an absolute measure. Like the CWIQ method described above, this would need additional

information from a comprehensive survey that has detailed information on an absolute

measure of economic status like income or consumption. A good example of these types

of surveys is Living Standards Measurement Study (LSMS) that have been conducted by

the World Bank in more than 40 countries since 1980. A set of regression coefficients can

be generated for the list of assets that have the maximum R squared value in predicting

this absolute measure. The predicted measure of economic status will be the weighted

sum of these predictors, with the weights being the regression coefficients.

3.5 Conclusion

Afghanistan provides a unique opportunity to study the relation between a

household's dwelling characteristics and ownership of assets with expenditure in a

rapidly changing economic environment. The use of asset variables to predict total

household expenditure is a simple and effective way to meet the urgent need for practical

methods for steadily tracking poverty over time in Afghanistan. Our findings indicate that

there is some evidence of an improvement in economic status and reduction of poverty in

Afghanistan, though our findings only reflect the situation of households living within

one hour of a BPHS facility. With the availability of nationally representative

70

consumption data in future, this technique can be used to improve the efficiency of

targeting public health interventions and services towards the poorer sections of society

as part of the much needed social protection programs in the country. The proposed

technique can also form the basis for poverty mapping in Afghanistan.

71

Table 3.1: Description of asset variables

Variable name hhsize sew clock radio tv bike motorbike generator car tractor lighting water fuel toilet

Variable description Household size Own sewing machine Own clock/watch Own radio Own television Own bicycle Own motorcycle Own generator Own car Own tractor Main lighting source - electricity/generator/battery Main water source - well/pump/piped Main cooking fuel - electricity/gas/kerosene Private toilet

Table 3.2: Difference in mean of asset variables «

Variable Household size Own sewing machine Own clock/watch Own radio Own television Own bicycle Own motorcycle Own generator Own car Own tractor Main lighting source -electricity/generator/battery Main water source -well/pump/piped Main cooking fuel -electricity/gas/kerosene Private toilet

20<) Mean 06.87 48.10 70.80 53.40 14.80 22.80 08.20 07.20 04.10 01.70

19.60

56.60

10.40 72.60

4 SDA

2.52 0.50 0.45 0.50 0.35 0.42 0.27 0.26 0.20 0.13

0.40

0.50

0.31 0.45

200 Mean 07.41 45.20 89.60 79.90 16.00 37.50 11.90 04.20 02.80 01.20

20.40

64.40

09.30 82.00

5 SDA

2.84 0.50 0.31 0.40 0.37 0.48 0.32 0.20 0.17 0.11

0.40

0.48

0.29 0.28

(2005-2004) Difference

0.54** -2.90** 18.80** 26.50**

1.20 14.70** 3.70** -3.0**

-1.30** -0.50*

0.80

7.80**

-1.10 9.40**

Number of households: NHSPA 2004 - 8822; NRVA 2005 - 3844 A Standard deviation * Statistically significant with p-value <0.05 ** Statistically significant with p-value <0.01 w Estimated mean of all the variable except hhsize is a percentage

72

Table 3.3: Estimated log linear regression coefficients for the 2005 sampleW Outcome Variable: Log Total Household Expenditure

Predictor Household size (Household size) Own sewing machine Own clock/watch Own radio Own television Own bicycle Own motorcycle Own generator Own car Own tractor Main lighting source -electricity/generator/battery Main water source -well/pump/piped Main cooking fuel -electricity/gas/kerosene Private toilet constant

Coefficient 0.09** 0.01** 0.12** 0.08**

0.02 0.08** 0.05**

0.04 0.19** 0.21**

0.01

0.04

-0.06**

0.1** -0.16** 10.19**

** Statistically significant with p-value <0.01 H Coefficients estimated using generalized linear (GLM) modeling approach.

73

Figure 3.1: Kernel Density plots for predicted total expenditure - 2004 & 2005 samples

'tf o O -o Q

m o Q-CO ><o 2 o ... * 8 > s •

to c .gCM

s ° ^t o o o Q . • X CD 1 _ Q - T -

> . o •t: o .. to o f o <D •

• a J ^

0 - +

50000 100000 Predicted Expenditure

150000

2004 2005

Table 3.4: Difference in predicted expenditure between 2004 and 2005 samples

Predicted outcome Total household

expenditure

2004 Mean

48811.1

SDA

13646.2

2005 Mean

50277.5

SDA

13025.5


1466.4** A Standard deviation ** Statistically significant with p-value <0.01

Using coefficients for all the asset variables in table 3.3

74

Table 3.5: Estimated principal component asset index coefficients for pooled 2004 and 2005 samples*

Asset variable Household size (Household size) Own sewing machine Own clock/watch Own radio Own television Own bicycle Own motorcycle Own generator Own car Own tractor Main lighting source -electricity/generator/battery Main water source -well/pump/piped Main cooking fuel -electricity/gas/kerosene Private toilet

Coefficient 0.1735 0.1733 0.2804 0.2942 0.3050 0.3685 0.3142 0.2765 0.3120 0.2405 0.1593

0.2720

0.1771

0.2237 0.1864

* The percentage of covariance explained by the first principal component is 19.77%. The first eigenvalue is 2.96.

75

Figure 3.2: Kernel Density plots for asset index: 2004 & 2005 samples

co -in o o CM CO

o Q.

'55 c\i -c CO

T3

o o CM CO

o Q . 1 - ..

"55 c CO

O -•

-5

I \ 1 \ 1 \ 1 \ 1 \ 1 I

1 \ 1 \ 1 I 1 1 1 1 1 / / / / / / /

I i

\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \

\ \ \ \ \ \

0 5 Household Asset Score

_ ^( j ( j4 *lUUO

Table 3.6: Difference in asset index between 2004 and 2005 samples

Outcome Asset Index

20C Mean -0.154

4 SDA

1.81

20(1 Mean 0.354

15 SDA

1.43


0.509** A Standard deviation ** Statistically significant with p-value <0.01

76

Table 3.7: Estimated probit regression coefficients for the 2005 sample w Outcome Variable: Probability of Household being Poor (Total Expenditure per day less than two US Dollars)

Predictor Household size (Household size) Own sewing machine Own clock/watch Own radio Own television Own bicycle Own motorcycle Own generator Own car Own tractor Main lighting source -electricity/generator/battery Main water source -well/pump/piped Main cooking fuel -electricity/gas/kerosene Private toilet constant

Coefficient -0.27** 0.01** -0.30** -0.26**

0.08 -0.58** -0.10*. -0.12 -0.06

-0.56** -0.17

-0.18**

0.27**

-0.39** 0.18** 1.14**

* Statistically significant with p-value <0.05 ** Statistically significant with p-value <0.01 w Coefficients estimated using generalized linear (GLM) modeling approach

Table 3.8: Difference in predicted probability ° of a household being poor between 2004 and 2005 samples

Predicted outcome Probability of a household

being poor

20(1 Mean

31.9%

14 SDA

0.19

20C Mean

29.1%

15 SDA

0.17


2.8%** A Standard deviation ** Statistically significant with p-value <0.01

Using coefficients for all the asset variables in table 3.7

77

Chapter 4 Equity effects of quality improvements on health service utilization in post-conflict Afghanistan

Abstract

In 2003, the Ministry of Public Health (MOPH) started the nationwide

implementation of a Basic Package of Health Services (BPHS) across public health

facilities in Afghanistan to lay the foundations of an equitable health system. Using data

from first four years of BPHS implementation, we set out to assess whether the

association between characteristics of health care delivery system and utilization of

services differed across three outcome groups living in catchment area of health facilities

- total population, the poor and female population. Specifically, this paper focuses on the

relationship between objective measures of technical quality and utilization of health

services by the poor and female population.

The three analyzed outcomes were overall utilization rate, female utilization rate

and utilization rate for the poor. The year of survey and other quantitative measures of

health service delivery characteristics like quality, user fees, facility type and managing

agency were included as the predictors. In addition to a summary index, structural

domain of quality was measured using four indices related to staffing & service capacity;

infrastructure; equipment; and drug supplies. The process domain was measured using

one summary index and two indices related to patient assessment and counseling. Health

facilities were sampled using stratified sampling technique. After excluding facilities

with missing data, the final sample for 2004, 2005, 2006 and 2007 consisted of 350, 593,

562 and 615 facilities respectively. A longitudinal data analysis was conducted using the

78

generalized estimating equations (GEE) technique with bootstrapped standard errors to

account for clustering of observations over time.

The mean monthly utilization rate increased over time for each of the three

outcome groups, with a statistically significant trend over the four years. As compared to

the overall utilization rate, the mean utilization rate was higher for females and the poor

in 2005, 2006 and 2007. In the multivariate analysis including all the predictor variables

in the model, the index on structural quality was significantly associated with higher

utilization rate in each of the three outcome groups (p-value <0.01), whereas process

index was significant only for the utilization by the poor. Staffing and service capacity

was the only quality index significant among each of the three outcome groups. While

infrastructure was found to be significant only for overall and female utilization, drug

availability and patient counseling were significant for utilization by the poor. In the

facility sample from four years, the adjusted rate ratio for user fees was significant for

overall utilization and utilization by poor but not for utilization by females. The highest

decline in utilization associated with collection of user fees was found among the poor.

An explicit focus towards the health needs of women and the poor in provision

and delivery of services has been an important goal of the Ministry of Health and its

partners in Afghanistan. The study findings provide evidence towards the initial success

of this policy objective of the Basic Package of Health Services. Our findings suggest

that higher quality is associated with greater utilization though the association between

different indices of quality and utilization differed by outcome group. Higher quality in

one year leads to an increase in utilization over subsequent years.

79

4.1 Introduction

Afghan context

The country of Afghanistan has suffered from more than two decades of civil war,

and since emerging from conflict has some of the worst human development statistics in

the world. Afghanistan is also one of the poorest countries in the world. The national

health resource assessment conducted after the fall of the Taliban in 2002, found a health

system in utter ruin with thousands of unqualified, under-paid health professionals,

unreliable health care facilities lacking hygiene and proper equipment, and unlicensed

pharmacies selling adulterated drugs (Management Sciences for Health, 2002). The

number of physicians per 1000 population was 0.1, which is very low as compared to 1.1

on average for other developing countries. The survey reported a total of 12,107 health

providers working in active facilities, 28% of which were physicians and specialist

physicians. This indicated a relative excess of physicians in the health workforce, even

though the ratio per 1000 population was very low and the quality of care very variable.

In addition, the male to female ratio was reported to be 3 to 1 with wide fluctuations by

province and facility type.

A majority of the population lived in rural areas with limited availability of health

services and low utilization rates. This scenario was widely prevalent for curative care

and child health services, and the situation was even worse for antenatal and delivery care

services (JHU and IHMR, 2005a; Waldman & Hanif, 2002). To illustrate this point, only

about 52% of facilities surveyed as part on ANHRA in 2002 reported providing a basic

package of Antenatal Care (ANC) services while just 29% of facilities had the necessary

80

equipment and a female health worker to provide the ANC. At the same time, the ANC

utilization rate in 2003 was 14% and the skilled delivery rate was 13%.

In this context, faced with a dysfunctional health system, the Ministry of Public

Health (MOPH) worked closely with development partners to define a strategy for

rapidly expanding the geographic scope and quality of health services. The MOPH and its

partners identified a core set of basic health services to be included in the Basic Package

of Health Services (BPHS) in mid-2002 and finalized the package in March 2003. The

BPHS consists of cost-effective primary care services designed to meet the priority needs

of rural populations, particularly women, children and other vulnerable groups. The

MOPH has used the BPHS as a central element of its National Health Policy to

"strengthen the delivery of sustainable, quality, accessible health services, especially

targeted at women, through planning for, and effective and efficient implementation of

the basic package of health services" (MOPH, 2003a, 2003b).

There have been encouraging reports of increase in utilization of health services

in the past few years, especially by women and poorer sections of Afghan society (Peters

et al., 2007). The quality of services provided at health facilities has also shown

improvement (JHU and IHMR, 2008a). However, the association between quality

improvements and service utilization has not been studied in Afghanistan.

Increase in utilization of health services by the poor and females, and

improvement in quality of health services are both important policy concerns in

Afghanistan. An important objective of this study is to assess whether the quality

improvements are associated with increase in use of services by the disadvantaged

groups; Poor and females. Of particular interest is to assess if certain aspects of quality

81

promote greater utilization by these disadvantaged groups. This study investigates the

impact of health system development in Afghanistan on utilization of health services by

the females and the poor over a period of four years.

This study seeks answer the following questions:

1. Is utilization of health services by the poor and females changing over time in rural

areas?

2. Are changes in quality of health services at BPHS facilities associated with changes in

utilization of health services by the poor and females in Afghanistan?

3. Do the associations between different aspects of quality and utilization differ by the

group of users?

4.2 Rationale

Equity in general terms means that individuals should have equal opportunities to

pursue a life of their choosing and be spared from extreme deprivations in outcomes.

Equity is instrumentally related with development (World Bank, 2006). For societies to

have sustainable growth and development, all members should have similar chances to be

socially active, politically involved and economically productive. Inequalities in wealth

and power with imperfect markets in many countries translate into unequal opportunities.

This leads to wastage of productive potential and to an inefficient allocation of resources.

Economic and political inequalities are associated with impaired institutional

development. These institutions perpetuate inequalities in power, status and health and

negatively affect innovation, investment and risk taking associated with long-term

growth. Equity is helpful in poverty reduction in two ways. It beneficially affects long-

82

term development of the society and directly provides greater opportunities for weaker

sections of the society.

Health is a crucial part of well-being, and of economic and social development.

Improved health contributes to economic growth by reducing production losses caused by

illness; permitting the use of natural resources that would be inaccessible due to illness;

reducing the cost of illness and allowing resources for alternative uses; and enhancing

school enrolment along with the ability to learn (World Bank, 1993). According to

Macinko and Starfield, equity in health may be defined as "the absence of potentially

remediable, systematic differences in one or more aspects of health across socially,

economically, demographically or geographically defined populations or

subgroups"(Macinko, 2002).

Health outcomes are intricately linked to socioeconomic status and gender. A

lower socioeconomic status is an important indicator of poverty. Poverty and ill health

are part of a vicious circle, where poverty leads to ill health and ill health maintains

poverty (Wagstaff, 2002). Recent studies conducted in Afghanistan have reported that the

illness rates among women and the poor are higher than the rates among men and the

economically well off respectively (JHU and IHMR, 2008b; Steinhardt et al, 2007).

These findings are in agreement with the literature from other developing countries where

the poor and women suffer from a greater burden of disease and in some settings have

shorter life expectancy (Gwatkin et al., 2000; Pande & Yazbeck, 2003; Peters et al,

2002). Most experts as well as the general population feel that this type of inequality

violates a sense of fairness, particularly when the people affected can do very little about

it (Alleyne et al, 2000; Le Grand, 1987). Experimental evidence suggests that most of

83

the people behave in ways consistent with fairness, subsequent to caring how they fare

individually (Andersson & Lyttkens, 1999).

Health services utilization directly affects health outcomes and is one of the

important proximate determinants of health (Mosley & Chen, 2003; Wagstaff, 2002). The

relationship between poverty and utilization is thought to be similar to the association

between poverty and health, where worsening poverty leads to a reduction in utilization

and lower utilization helps in maintaining poverty among the poor (Peters et al, 2008).

As compared to males, females have been reported to have higher infant and child

mortality rates, lower immunization rates and lower rates of utilization of primary health

services in general (Ganatra & Hirve, 1994; Shaikh & Hatcher, 2004). This should lead to

a higher utilization of services by these groups but available studies report that this is

seldom the case (Makinen et ai, 2000; Shaikh & Hatcher, 2004). These disadvantaged

groups of poor and females not only utilize health services less often but also utilize

services that are of lower quality (Barber et al, 2007; Buor, 2004). Improvement in

quality of health services has been shown to increase the overall utilization of health

services in developing countries (Chawla & Ellis, 2000; Haddad & Fournier, 1995). Yet

little is known whether improvement in service quality actually benefits these

disadvantaged groups.

Despite the importance attributed to gender specific and pro-poor approaches in

health sector, in practice, many national governments have not been able to prioritize

policies accordingly. A number of successful small scale programs and interventions

have been reported in various settings but there is lack of systematic evidence specific to

these groups at the national level (Peters et al, 2008; Standing, 1997). Keeping this in

84

mind, the main purpose of this study is to provide actionable evidence to policy makers

and program planners to improve equity in developing countries through greater

utilization by these disadvantaged groups. This will be done using data from a nationally

representative sample of health facilities to generate quantitative measures of health

service characteristics, and assesses how each of these aspects is related to utilization by

these groups.

4.3 Conceptual Framework

The framework for this study (Figure 4.1) has been adapted from the access to

medical care framework proposed by Aday and Andersen (Aday & Andersen, 1974).

Access was used in the framework to not only mean the availability of health resources

and services, but whether they are actually utilized by those who need them. To explain

the actual use, this framework used health policy as the starting point and proceeds using

factors like characteristics of health delivery system; characteristics of the population at

risk; utilization of health services; and consumer satisfaction with the services received in

the system. The relevant sections of the framework are discussed below.

A. Characteristics of the health care delivery system

The Aday and Andersen framework describes two main elements in a health care

delivery system - resources and organization. In this study, the access framework by

Aday and Andersen has been modified using Donabedian's quality of care framework.

Donabedian proposed three domains for analyzing the quality of care in a health system:

structure, process and outcome (Donabedian, 1980, 1988). Structure relates to the

characteristics of the system in which care is delivered: it includes attributes of material

85

resources (building, equipment, availability of services, examinations and drugs), human

resources (number and qualification of personnel) and organizational structure (medical

staff organization). Structural features of health care provide the opportunity for

individuals to receive care but do not guarantee it, although they can have direct impact

on processes and outcomes. Process is the actual delivery or receipt of health care.

Process of care in a health system has been described under two categories: clinical care

and interpersonal care. Clinical care refers to the application of clinical medicine to a

particular health problem that a particular individual is suffering from, whereas

interpersonal care describes the interaction of health service provider and the treated

individual. Outcome measures the impact of care on the health status of the users. This is

measured in terms of the change in health status (functional status, clinical outcome) and

the user satisfaction.

Aspects of quality associated with structure and process are under direct control

of policy makers and health professionals and precede the actual utilization of services.

Aspects of quality associated with outcome are not under direct control of policy makers

and health professionals and arise as a result of utilization. The other characteristics of

health care delivery system relevant to this study are type of implementing agency and

user fees. In Afghanistan, public health facilities are managed by either MOPH or an

NGO. These two approaches differ in several respects, including source and level of

funds, management structure and incentives. User fees is a component of organizational

structure that has emerged as an important factor in health systems research and practice

in developing countries, due to an increased emphasis on decentralized decision making

and cost recovery.

86

B. Characteristics of the population at risk

Similar to the Aday and Andersen framework, the framework for this study

describes three components of the population at risk - predisposing, enabling and need.

These are the factors that predispose people to seek care, enable them to seek care and

define their need for services. Predisposing characteristics are those variables that

describe a person's predisposition to use services, such as demographic and social

characteristics and beliefs and perceptions about health services. These characteristics

exist prior to the onset of illness. Enabling characteristics are the means that are available

to the people for the use of services, and include resources specific to the individual and

family (e.g. income, ability to afford costs etc.) and community (rural-urban, travel time).

Need refers to illness levels; the most immediate cause of health services use.

C. Utilization of Health services

The framework describes the utilization as a result of interaction between

characteristics of the health system and the population at risk. In the original Aday and

Andersen framework, utilization was described in terms of its type, site, purpose and the

time interval involved. This study exclusively deals with provision of curative care

through three types of public health facilities in rural Afghanistan over a period of four

years.

D. Relationship among the variables in the framework

Health policy is seen as directly affecting characteristics of the delivery system

and the population at risk. Some of the effects of health policy on the population at risk

are mediated directly (insurance, education) and some indirectly through the delivery

system (relocation of facilities). Various population groups are regarded as having

87

different levels of access to care. When differences are based on need, the access is

considered equitable; when differences are based on factors like age, race, income,

education or geographic location, the access is termed as inequitable (Aday & Andersen,

1981). The delivery system affects the utilization of health services and customer

satisfaction with the services delivered. These effects are determined by the structure

itself and not necessarily mediated by the properties of the potential users. The direct

effects of system properties are important for system-level analyses, where the system

itself, rather than the population at risk, is the unit of concern. The system may also

impact on the characteristics of the population and thereby indirectly affect its utilization

of services and the consumer's satisfaction with services. On the other hand, the

characteristics of the population (attitude towards medical care, income etc) may directly

affect use and satisfaction independent of system properties.

Quality and its effects on health service utilization

There is considerable evidence from various settings that under-utilization of

public health facilities is directly attributable to poor quality of services (Lule et ah,

2000; Mwabu et al, 1993; Rao & Peters, 2007; Wong et al., 1987). The two most

common measures of structural quality that have been extensively documented in the

literature are presence of qualified personnel and availability of drugs (Mariko, 2003;

Mwabu et al., 1993). It has been suggested that patients are attracted by the presence of

qualified personnel and that they are prepared to make substantial efforts to use services

that are technically competent (Haddad & Founder, 1995). Also, availability of drugs has

88

a strong positive effect on utilization of primary health care facilities. Evidence

concerning the effects of other structural attributes like quality of infrastructure is limited.

Structural attributes of quality are considered necessary but not sufficient

conditions for the utilization of health services (Mariko, 2003). Information on process of

care, albeit limited, is also considered important to understand the utilization pattern of

health services. This applies to both clinical and inter-personal aspects of the process of

health service delivery. Mariko highlighted the importance of studying the processes

followed by health care practitioners in estimating the effect of quality on health service

utilization after the introduction of cost recovery program in Bamako, Mali (Mariko,

2003). This study reported that the availability of drugs and good process of care

constitute the two main factors, which have a positive and significant impact on the

choice of health service utilized.

Quality of services is considered a more important predictor of utilization as

compared to the distance that the users have to travel to access the health services

(Acharya & Cleland, 2000; Glei et al, 2003). A logical corollary to this is that users

bypass the facilities with poor quality of services in favor of facilities with higher quality

of services (Akin & Hutchinson, 1999). However, distance from a health facility has

consistently been reported to be an important factor affecting utilization with a large

number of studies reporting an inverse relation between utilization and distance (King,

1966; Moisi, 2008).

89

User Fees and its effects on health service utilization

The collection of a user fee from users of health services is widely prevalent in

developing countries. The proponents of user fees promoted it as an important

mechanism to finance the public system of health care delivery and generate resources

for quality improvements in these countries. However, it remains a contentious policy

issue as available evidence of its effect on utilization is mixed (Peters et al, 2008). A

number of studies have shown that collection of user fees leads to a decrease in

utilization of health services (Collins et al., 1996; Jacobs & Price, 2004). Another set of

studies have reported that this decrease in utilization is highest among the poor (Gilson et

al, 2001; Nyonator & Kutzin, 1999). On the other hand, in selected cases user fee

collection has been associated with improvements in quality as well as increase in

utilization (Litvack & Bodart, 1993; Rao & Peters, 2007). The positive influence of

improvement in quality of health services has been shown to outweigh the negative

influences of user fees on health services utilization (Audibert & Mathonnat, 2000;

Chawla & Ellis, 2000).

4.4 Methods

Data sources

The two sources of data used for this study were the National Risk and

Vulnerability Assessment (NRVA) for the year 2005 and the National Health Services

Performance Assessment (NHSPA) that has been conducted annually from June to

September since 2004.

90

The NRVA was conducted by the Central Statistics Office (CSO) Afghanistan

from June to September of 2005 to collect information to better understand the

livelihoods of rural, urban and migratory (kuchi) households in Afghanistan (M.R.R.D.,

2005). The households were selected using a probability based multi-stage sampling

technique and the collected data were statistically representative of the rural households

at the provincial and national levels. The data on 23,220 rural households (out of a

national sample of 30,822 households) collected from 30 provinces were used in this

study. The household questionnaire that formed the core of NRVA 2005 had a total of 18

sections, with separate sections on household expenditure, ownership of assets, dwelling

characteristics and land ownership.

The NHSPA is an annual facility survey that has been conducted by the MOPH to

collect information on service provision and perspectives of the patient and staff at health

facilities all over Afghanistan. It is a yearly cross sectional survey where a total of twenty

five health facilities providing health services according to the basic package are

surveyed in every province. For this study, the three types of BPHS facilities surveyed

were: Basic Health Center (BHC), Comprehensive Health Center (CHC) and the

outpatient clinics of District Hospitals (DH). Each of these facility types differed in

provided services, staffing levels and the size of the population that they serve; therefore

the twenty five facilities were selected using stratified random sampling in every

province. If fewer than the maximum number of each type of health facility to be

surveyed was present in a province, another type of health facility is randomly sampled

so that up to 25 facilities are surveyed in the province. In provinces where the total

number of facilities is less than 25, all facilities are sampled and surveyed. In 2004, the

91

sampling frame for each province was compiled using the list of all facilities from the

MOPH in Kabul and updated with information from the Provincial Health Directorate

and NGO key informants. This list was updated for 2005, 2006 and 2007 and the updated

list for each year was used to sample facilities for that particular year.

Facilities in provinces that were reported to be too unsafe to survey were removed

from the sampling frame. Any province where more than a third of the facilities were

deemed unsafe was not surveyed. This meant that Helmand, Kandahar, Zabul and

Uruzgan provinces were not surveyed in 2005, 2006, and 2007. The household data

collected from these four provinces as part of NRVA 2005 was also excluded to maintain

representativeness of the analyzed sample.

In each health facility, ten 'new' outpatients were systematically sampled. Out of

these ten new outpatients, a maximum of five were under five years of age and a

maximum of five were over five years of age. A systematic random sampling scheme

with a random start and sampling interval based on the expected number of new

outpatients in each age category in a given year was used to sample these ten patients.

Inpatients admitted for medical treatment in a facility were not included as part of this

study. In this study, a 'new' outpatient was defined as a first time visitor to the health

facility for a specific condition or a repeat visitor because of a worsening of symptoms of

a previous condition. New outpatient visits relate to curative care only, as routine follow-

up and preventive care visits were not included in the category.

The data were collected in every facility via separate modules on: a) health

worker observation b) patient exit interview c) facility assessment. The consultation of

each of the ten patients by the health worker was observed by a trained independent

92

observer. Observers filled out a checklist concerned with technical aspects of care,

including courtesy, patient assessment, physical examination and patient/caretaker

counseling. Before departing from the facility, each of these ten patients was interviewed

in a separate room or a location away from the facility staff and the information was

collected via the exit interview module. The interviewer completing this module collected

information on indicators of patient's household economic status as well as his/her

perspective on the health care and advice that he/she received that day. The facility

assessment module was completed in every facility to collect information on equipment,

supplies, drug stocks, staffing, supervision and management. In addition, facility

surveyors recorded information on the volume of new outpatient visits at the facility from

the sampled facility's administrative records meant for the national Health Management

Information System (HMIS). This module was completed using techniques of direct

observation, review of administrative records and interview of the facility administrator.

Only provinces covered in each of the four yearly NHSPA surveys were retained

in this analysis. Facilities from Helmand, Kandahar, Zabul, Uruzgan and Daykundi

provinces were therefore excluded. Furthermore, surveyed facilities that did not have

outpatient visit records for at least one month preceding the survey were also excluded

from the final sample for analysis. The final samples for 2004, 2005, 2006 and 2007

included 350, 593, 562 and 615 facilities respectively. Tables 4.1 and 4.2 provide details

on the study samples.

For data quality assurance purposes three surveyed facilities per province were

randomly selected for re-survey by an individual who had not been a part of the team of

enumerators. If data discrepancies were deemed to be beyond reasonable bounds,

93

facilities enumerated by that team were re-surveyed. However, the occurrence of a high

percentage of data discrepancies between original and independent repeat survey was

very rare. All survey data were double entered and checked for consistency.

Operationalization of variables

The three outcome variables that were used for this analysis are:

1. New outpatient visits per one thousand (catchment area) population per month:

The data on volume of new outpatient visits in previous months and the catchment area

population were available from the administrative records at every health facility. The

volume of new patients was collected as part of the reporting procedure for the national

health management information system (HMIS). The catchment area population was

calculated by the facility staff based on the geographical area under coverage of the

services provided by the health facility. The data for calculation of catchment area

population was gathered through population surveys conducted by the staff or by using

data from the 2004 pre-census. In the absence of these two sources, the catchment area

population was estimated by facility staff based on their knowledge of the local area and

its population. This variable was calculated as the number of new outpatient visits per

one thousand catchment area population per month and referred to as overall utilization

rate in subsequent section of this study.

2. New female outpatient visits per one thousand (catchment area female) population per

month: The data on volume of new outpatient visits by females in previous months were

available from the administrative records at every health facility. The proportion of

females in every province based on the 2004 pre-census figures was multiplied with the

94

catchment area population to estimate the number of females. This variable was

calculated as the number of new outpatient visits by females per one thousand females in

the catchment area for a period of one month and referred to as female utilization rate in

subsequent section of this study. The calculation of this indicator included data on all

females - under-five as well as adults. Females in the reproductive age group of 15 to 45

years are referred to as 'women' in subsequent sections of this study.

3. New outpatient visits by poor patients per one thousand (catchment area poor)

population per month: A poor patient was defined as a patient belonging to the bottom

40% of the national population when ranked by per capita household expenditure. For

every yearly NHSPA sample, the poor population in the catchment area as well as the

volume of visits by poor patients was calculated using the information on rural

households from NRVA 2005.

Poor population in the catchment area

Information on total expenditure in Afghanis (official Afghan currency) was

collected from every household surveyed for NRVA 2005. The total expenditure for

every sampled household was divided by the number of members in the household to

derive per capita expenditure for that household. The proportion of population in every

province constituting the bottom 40% of national per capita expenditure was estimated by

cross tabulating the bottom 40% of the national population by province using sampling

weights. These sampling weights were provided by CSO and have been used in the past

to generate national and provincial estimates for the NRVA report. The NHSPA

catchment area population of poor was calculated by multiplying the total NHSPA

95

catchment area population for every facility with the proportion of sampled NRVA

population belonging to the poor category in that province.

Volume of visits by poor patients

As part of the data collection for NRVA 2005, information was collected on

ownership of assets, dwelling characteristics and total household expenditure for a

representative sample of rural households in every province. In the exit interview module

of NHSPA, the same questions were asked from every patient except the questions on

total household expenditure. The per capita household expenditure of every surveyed

patient in NHSPA was predicted using the generalized linear modeling technique5.

Using the NRVA data, the per capita household expenditure was treated as the

outcome variable in a log linear regression with asset ownership and dwelling

characteristics as the explanatory variables. The estimated regression coefficients were

then used to predict per capita household expenditure for patients surveyed in every

round of the NHSPA. The predicted per capita expenditure was used to calculate the

proportion of surveyed patients belonging to bottom 40% when ranked by per capita

expenditure. The volume of visits by the poor was calculated by multiplying the total

volume of patients recorded at every facility with the proportion of surveyed patients

belonging to bottom 40% in that province. The variable was calculated as the number of

new outpatient visits by poor per one thousand catchment area population (of poor) for a

period of one month and referred to as utilization rate for the poor in subsequent section

of this study.

The predictor variables used in this study were:

5 This technique has been described in detail in the chapter 3 (second study).

96

Name of

variable

Year of survey

Description of variable

2004, 2005, 2006 and 2007

Type

of variable

Categorical

Source

Facility

survey

Facility Characteristics

Type

Managing

Agency

User Fees

Structure

domain of

quality

Process domain

of quality

District Hospital, Comp.

Health Center, Basic Health

Center

Agency that manages facility:

NGO (0), MOPH (1).

User fees being collected: No

(0), Yes (1)

Composite index of items

Composite index of items

Categorical

Categorical

Categorical

Continuous

Continuous

Facility

survey

Facility

survey

Facility

survey

Facility

survey

Health worker

observation

All the predictor variables provide information for facility level data, except the

indicator on process level measures of quality which was collected for every patient

provider interaction. A summary index was calculated for every facility by aggregating

the data from all the patient visits to a particular facility. All the predictor variables

included in this study were time variant.

97

Type of facility: In this analysis facilities were classified as Basic Health Centers

(BHC - reference category), Comprehensive Health Centers (CHC), or District Hospitals

(DH) using the standard MOPH classification. Facility type is a potentially important

determinant of health service utilization as different types of health facilities have

different sizes of catchment area population of users. Inclusion of facility type also

controls for the structural variation in quality of care. According to the BPHS, facilities

are classified as BHC, CHC and DH based on differences in staffing levels and provision

of different sets of services. A BHC is supposed to be staffed by vaccinators and a nurse,

midwife or auxiliary midwife, and cover a population of 15,000 to 30,000. The staffing

level of a CHC should include both male and female doctors and nurses, in addition to

midwives and laboratory and pharmacy technicians. CHCs should cover a population of

30,000 to 60,000 and offer a more extensive range of services than BHCs. District

hospitals are supposed to serve up to four districts containing a population of 100,000 to

300,000 people, perform major surgeries and provide comprehensive emergency obstetric

care, including caesarian sections.

Type of implementing agency was a dichotomous variable that measured whether

the MOPH or a non governmental organization (NGO) (reference category) was the

direct provider of services. MOPH-managed facilities and NGO-managed facilities differ

in several respects, including source and level of funds, management structure and

incentives. There has been a heavy emphasis on use of NGOs to deliver services in

Afghanistan and the services have been rapidly expanded through contracting service

delivery to NGOs. It is important to assess utilization rates at MOPH-managed facilities,

relative to those at NGO-managed facilities, to adjust for potential differences in quality

98

of care due to difference in inputs and supervision at facilities managed by these two

managing agencies.

Collection of user fees at a facility was a dichotomous variable that measured if

financial charges are being collected from patients as a payment for services being

delivered to them. These charges could be a flat fee that patients must pay for receiving

services and drugs, a fee for drugs only, or a separate fee for both service and for drugs.

This paper focused on the relationship between objective measures of technical

quality and utilization of health services by different outcome groups. The measures of

technical quality included in this study were based on the quality of care framework

proposed by Avedis Donabedian (Donabedian, 1980, 1986,1988). In addition, these

measures were under direct control of the MOPH, rendering them amenable to change

faster than other measures of quality.

In this paper, structural and process domains of quality were included as

individual predictor variables. In order to study the relationship between specific aspects

of structural and process quality and utilization, these two domains were further

subdivided into six indices and each of these indices was included as a predictor variable.

All items included in quality indices for this study were binary (Yes '17 No '0').

Structural items included variables for facility staffing, equipment, drugs and supplies

and infrastructure. Process items relate to technical quality of care for children under five

years of age and were based largely on assessment, counseling and care seeking priority

indicators for Integrated Management of Childhood Illness (W.H.O., 1999). These items

are based on the indicators included in the Ministry of Public Health's routine monitoring

system, the Afghanistan Balanced Scorecard (BSC). They were developed through

99

formative research and consultative process involving service providers, officials from

the MOPH and content experts from the government and donor agencies. The MOPH

uses these and other indicators on the BSC to clarify strategy, facilitate decision making

and monitoring progress in implementation of services and achievement of desired

outcomes (Hansen et ai, 2008a; JHU and IHMR, 2008a). As the list of items included

under structure and process domains is based on the BSC, it might not have been the

most comprehensive in the general sense of health service quality. This was especially

true for structural measures related to drugs and supplies and for process measures, which

were related to care delivered to under-fives.

In order to ensure comparability across studies, the indices of quality used in this

study were similar to those generated by Arur. Factor analysis (principal component

factor) methods were used as an exploratory data reduction tool to identify the items to be

retained in each index of technical quality (Arur, 2008).

Factor analysis describes the covariance between multiple variables in terms of a

few underlying (or common) factors (Johnson & Wichern, 2002). The number of

components to be retained was decided by examining a scree plot and the face validity of

each component retained. A scree plot graphs eigenvalues. Eigenvalues are a measure of

the proportion of variance explained by each underlying factor. The norm is to retain all

the factors above the 'elbow' of the scree plot. This implies that factors that do not add

substantially to the proportion of variance explained were dropped. After the selection of

items for each index, two sets of facility scores were generated.

The first set of scores was used for exploratory analysis for each year of survey.

In this set, facility score on each index consisted of the 'yes' responses to each item

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(scored as '1') expressed as a percentage of the total number of items in the index. The

maximum score on each index is 100 and the minimum is 0.

The second set of scores was for the bivariate and multivariate analysis with

pooled data from the four survey years. In this set, the number of 'yes' responses to items

on a particular index was transformed to a standard normal distribution and a

standardized score for each facility was generated.

Indices for structure domain of quality

Based on the exploratory factor analysis, items were assigned to the following four

indices:

Index 1 - Staffing and service capacity: This index assessed the availability of

doctors, nurses and midwives, and the capacity of the facility to provide health

services in terms of equipment, general protocols, laboratory tests and delivery

services. This index included 43 items. Index 1 had an alpha coefficient of 0.94

which indicated relatively high reliability.

Index 2 - Child health services: This index measured facility capacity to provide

child health services, like immunization and growth monitoring, and the presence

of protocols, supplies and equipment. Index 2 included 18 items. Index 2 had an

alpha coefficient of 0.87, suggesting that reliability was relatively high.

Index 3 - Infrastructure: This index was concerned with the presence, physical

condition and cleanliness of infrastructure and the presence and functioning of

basic equipment for clinical services. Index 3 included 19 items. Index 3 had an

alpha coefficient of 0.78.

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Index 4 - Drags and contraceptive supplies: This index was concerned with the

continuous availability over the previous month of drugs and contraceptives and

their quality in terms of non-expired stocks at the facility. Index 4 included 9 items.

Index 4 had an alpha of 0.86. Tables A4.1 to A4.4 list the items in each individual

index.

Correlations between the four indices ranged from 0.24 (index 3 and index 4) to 0.49

(index 1 and index 3). In general index 4 (Drugs and contraceptives supplies) had the

lowest correlation with the other indices (0.24 to 0.38). Index 1 (Staffing and service

capacity) tended to have higher correlations with the remaining three indices. This was

not surprising since facilities with the highest capacity to provide health services were

likely to have better infrastructure, and have better equipment and supplies. The four

indices captured aspects of structural quality that were intuitively distinct. The child

health services index was dominated by immunization-related variables. Emphasis on

immunization through vertical programmatic efforts may mean that the determinants of

capacity to provide immunization services were different from those of other services.

Index 1, on the other hand, was concerned with an assessment of capacity to provide

health services in terms of clinical staff, equipment and services that are not specific to

children. Index 3 was concerned with the basic pre-requisites for health service delivery

like infrastructure and amenities.

Improving health outcomes is a primary goal of health service provision. An

improvement in technical quality should, ideally, increase the likelihood of better health

outcomes. From this it follows that constructs that measure quality should have a direct

relationship and therefore a strong correlation with improved health outcomes.

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However, the relationship between quality of care and health outcomes is complex in

general— and especially complex in the case for structural measures of quality.

Evidence suggests that structural measures of quality are relatively weakly linked with

health outcomes as compared to more proximate process measures of quality

(Donabedian, 1988; Mariko, 2003; Peabody et al, 2006).

Nevertheless there is a strong case in favor of measuring and monitoring

structural measures of quality in Afghanistan. Although structure in itself does not tell the

whole story it tells an important part of it. A multi-dimensional assessment of quality that

also includes structural measures is likely to be more valid than one that focuses on either

structural or process aspects in isolation (Donabedian, 1988). Inputs like equipment, staff

and supplies do not guarantee an improvement in the process of care or in health

outcomes. However, in some developing countries where there is a severe shortage of

these inputs, improving structural aspects of quality may be an important pre-requisite to

improving the process of care and therefore health outcomes. The ANHRA's assessment

of health resources clearly suggests that this is the case in Afghanistan (Management

Sciences for Health, 2002).

Indices for process domain of quality

The individual items included in the final indices were concerned with process

aspects of technical quality for patients under-five years of age. Each index included the

binary items assessed from direct observation of the interaction between providers and

patients and their caregivers. These items relate to patient assessment activities and

caretaker counseling (Tables A4.5 & A4.6).

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Items were assigned to the following two indices based on their factor loadings:

Under 5 index 1- Patient assessment and physical exam index: This index described

the quality of the assessment that the patient receives. The index consisted of 12

items and the cronbach's alpha value for this index was 0.73, which indicated that

reliability was modest but acceptable.

Under 5 index 2- Counseling index: This index was concerned with the quality of

the counseling that the patient receives, including an explanation of the diagnosis,

care to be given at home and danger signs and when to return for a return visit. This

nine item index had a cronbach's alpha value of 0.71 which indicated modest but

acceptable levels of reliability.

The correlation between the two indices was 0.66. Both indices measured the

technical competence of health providers based on Integrated Management of Childhood

Illness (IMCI) protocols. Assessment and caretaker counseling are distinct but integral

aspects of the IMCI strategy (W.H.O., 1999).

Process measures of technical quality are considered to be proximal determinants

of health outcomes (Donabedian, 1988; Peabody et al, 2006) although they tend to be

studied less frequently because of logistical difficulties and problems with measurement

criteria and tools to study the interaction between doctors and patients (Nicholas et al,

1991; Peabody et al, 2006). Recent evidence underscores the importance of directly

examining what providers do rather than what they know or say they do. A study from

India finds that urban doctors operate 'within their knowledge frontier' and do less than

they know they should (Das & Hammer, 2005). The indices in this study measure what

health providers actually do with reference to IMCI protocols. The IMCI approach has

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been validated through rigorous study and is widely applied across the developing world

(Armstrong Schellenberg et ah, 2004; Gouws et ah, 2004)

Analysis plan

The data for this study was analyzed using the statistical software developed by

the Stata Corporation (StataCorp., 2004). Annual facility surveys leads to the possibility

of positive correlations between repeated measurements on the same facilities. Ignoring

these correlations may be associated with misleading precision in results. In general,

ignoring correlations underestimates the standard errors for estimated difference between

facilities in a year and overestimates the standard errors for estimated the difference

within a particular facility over time. This longitudinal data analysis was conducted using

the generalized estimating equations (GEE) technique using bootstrapped method to

adjust for clustering of observations (Diggle etah, 2002).

A general equation for log linear regression model that was fitted for each of the

three outcome variables is as follows:

Log [Mean (ry)] = po + Pistsumy + p2psumij + P3yr05 + p4yr06 + P5yr07 + Pechcy +

p7dhij + pgufy + p9mophij + sy

i = Facility identifier (Unique facility code for each facility)

j = 1, 2, 3, 4; Year of data collection.

rij = Mean utilization rate

stsumjj = Facility level standardized score on structural domain of quality

psumy = Facility level index standardized score on process domain of quality

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yr05: Dummy variable coded as 1 if the observation was from year 2005, coded 0 if

2004


2004


2004

chcij = Dummy variable coded as 1 if the facility was a CHC, coded 0 if BHC

dhjj = Dummy variable coded as 1 if the facility was a DH, coded 0 if BHC

ufy: Dummy variable if user fees was being collected, 0 otherwise

mophjj: Dummy variable if the facility was managed by MOPH, 0 if NGO

The interpretation for the p coefficients is as follows:

• (3stsuiriij = Difference in log rate for a 1 unit increase in standardized quality score

(structure level), i.e., log rate for stsuniij + 1 - log rate for stsumjj, other predictors

held constant

Or equivalently

• e Pstsumjj - exponential form of the p coefficient = Rate ratio for a 1 unit increase

in stsumy, i.e., rate ratio for stsuniij + 1 -vs- stsumy, other predictors held constant

Association between individual indices of quality and utilization was analyzed by

replacing the structure level index by four indices of structural quality and process level

index with the two indices of process level quality.

The analysis accounted for the within facility correlation structure by generating

and comparing standard errors using three different assumptions for the working

correlation model within facilities over time. While misspecification of the working

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correlation does not affect validity of the P coefficient estimates, in most cases it does

affect the efficiency of estimates.

a) The independence working model assumes within facility measurements are

uncorrelated over time.

b) Unspecified correlations working model estimates common correlations from the

data on each facility.

c) Exchangeable correlation working model assumes that any pair of measurements

within a facility over time has the same correlation.

This analysis also checked whether the association between service quality and

utilization differed by year of survey or collection of user fees. To assess this

possibility, interaction between service quality and time and service quality and

collection of user fees were included in the general model described previously in

this section.

The original model modified to assess interaction between structural quality and

year of survey is as follows:

Log [Mean (rij)] = Po + Pistsuniij + P2psum;j + p3yr05 + P4yr06 + p5yr07 + p6chcjj +

P7dhjj + pgufy + P9inophij + PioStsunii*yr + sy

The coefficient Pio represents the interaction term and its interpretation is as

follows:

• pio = Difference in log rate for a 1 unit increase in standardized quality score in a

later year (2005, 2006 or 2007), as compared to a 1 unit increase in year 2004.

Or equivalently

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• e pio - exponential form of the P coefficient = Rate ratio for a 1 unit increase in

quality in a later year, as compared to a 1 unit increase in year 2004; i.e., rate

ratio for stsum, + 1 in 2005 -vs- stsum; + 1 in 2004.

Unless otherwise indicated, bootstrapped standard errors and 95% confidence

intervals (CI) are presented here. Bootstrapped confidence intervals and standard errors

are empirical and based on repeated sampling of the available data (Mooney & Duval,

1993). Bootstrapping does not require any assumptions about variable distributions, and

the approaches are based on an analogy between the sample and the population (Mooney

& Duval, 1993) since they sample repeatedly to create a distribution of the test statistic.

The sample of facilities included more than 50% of all eligible public sector facilities

(including NGO-managed) in each of the four years for the provinces included in this

analysis. The high proportion of surveyed facilities suggests that the sample may mirror

the population of public sector facilities well. This also indicates the relevance of

bootstrapping methods to calculate standard errors and confidence intervals.

Although a sample of health facilities was drawn independently in each year, the

high proportion of total facilities sampled indicates that about 18% of facilities with

outpatient visits data were surveyed in each of the four years. The non-parametric

bootstrapping method employed takes the complex survey design and other issues

generated by the design into account. The bootstrapped standard errors were calculated

by re-sampling (with replacement) from the sample of health facilities.

Two additional analyses were conducted for each outcome to check whether

facilities with missing outpatient visit records were systematically different than facilities

that did not have missing outpatient visit records. In the first analysis, a linear regression

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model was used to assess the association between missing outpatient records and

structural and process quality as follows:

Yij = p0 + Pichcy + p2dhij + p3yr05 + p4yr06 + p5yr07 +p6Missingij + p7ufij +

p7mophij + £„

Yij; Index of staffing and service capacity

chcij: Dummy variable coded as 1 if the facility was a CHC, coded 0 if BHC

dhij: Dummy variable coded as 1 if the facility was a DH, coded 0 if BHC


2004


2004


2004

Missingij: Dummy variable for whether the facility was missing outpatient records

coded 1 if records are missing and coded 0 otherwise

ufjj: Dummy variable if user fees was being collected, 0 otherwise

mophij: Dummy variable if the facility was managed by MOPH, 0 if NGO

Here fie measured the association between missing outpatient visit records and

score of structural quality. If the coefficient was statistically significant this would

indicate that facilities with missing outpatient visit records had systematically different

quality scores from facilities without missing outpatient visit records holding other

variable constant.

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In the second additional analysis, all the data from the year 2004 was excluded and the

original model was refitted. The data from 2004 had outpatient records missing for 38%

of the surveyed facilities, whereas rates of missing data were 4.5%, 9% and 1.5% for

2005, 2006, and 2007 respectively (Table 4.1). The purpose of this was to ascertain

whether the high percentage of missing facilities from 2004 threatens the validity of

findings by fundamentally altering the association between utilization and other

predictors, especially quality of care.

Multicollinearity between independent variables was tested by calculating the

variance inflation factors for the set of explanatory variables for each outcome

individually. The data on utilization rate for each of three outcome groups were over

dispersed as the variance in utilization rate for was higher than the mean of each group.

The log linear models with poisson distribution did not result in a good fit according to

pearson's goodness of fit test (p-value <0.05). The goodness of fit statistic did not

improve with inclusion of interaction terms or multiplying the standard error by a scale

factor. The log linear model fitted with different working correlation models yielded

inconsistent findings for the coefficients and the associated standard errors.

Refitting the subsequent log linear models with a negative binomial distribution

provided a good fit for the over dispersed utilization rate data resulting in lower log

likelihood values and dispersion parameters of around 1.00 for each outcome group.

Multiple iterations of this model with different working correlation structures yielded

consistent results for the coefficients and the associated standard errors. The final model

was fitted assuming an independent working correlation structure and bootstrapped

standard errors. The coefficients for bivariate and multivariate analyses reported in

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subsequent sections were generated using generalized estimation equation with negative

binomial family of distribution and logarithmic link and bootstrapped standard errors.

4.5 Results

Time trends for outcome groups and predictor variables

Outcome groups

The three outcomes groups analyzed in this study were overall utilization rate,

female utilization rate and utilization rate for the poor.

The mean monthly utilization rate increased over time for each of the three outcome

groups (Figure 4.2). The trend was statistically significant (p-value <0.01) for all three

outcome groups (Table 4.3). As compared to the overall utilization rate, the mean

utilization rate was higher for females and the poor for 2005, 2006 and 2007. Among the

three outcome groups, the highest proportional increase over time was among females

where the utilization rate in 2007 was 2.9 times higher than the rate in 2004.

Predictor variables

Quality of health services

The mean facility score for each index of health service quality improved over

time (Table 4.4). The mean facility score on the summary index for the structure domain

of service quality increased from 39.1 in 2004 to 61.2 in 2007. The mean facility score on

the summary index for the process domain of service quality increased from 37.4 in 2004

to 52.6 in 2007. The trend was statistically significant (p-value <0.01) for the two

summary indices and each of the six indices. Among the six indices of quality, the

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highest proportional increase over time was in the staffing and service capacity index

where the mean facility score in 2007 was 2.1 times higher than the score in 2004.

Type of health facility

Health facilities belonging to the category of BHC constitute the largest

proportion (0.58) among the three types of facilities. The difference in distribution of

facility type over the four years was not statistically significant (Table 4.4).

User fees

The proportion of facilities collecting user fees increased over time and the

difference in proportion was statistically significant (p-value <0.01) over the four years

(Table 4.4). In the analyzed sample, 79% of the facilities collected user fees.

Managing Agency

The proportion of facilities being managed by an NGO increased over time and

the difference in proportion was statistically significant (p-value <0.01) over the four

years (Table 4.4). In the analyzed sample, 86% of the facilities were supported by an

NGO.

Bivariate and multivariate analyses

Results are presented for each outcome group separately.

Outcome 1: Overall utilization rate (Table 4.5)

The bivariate rate ratios estimated for every predictor variable were statistically

significant, except for facility type, managing agency and user fees. The estimated rate

ratio for user fees was 0.95 (CI 0.84 - 1.09). The estimated rate ratios for the six indices

of quality ranged from 1.05 (CI 1.00 - 1.10) to 1.22 (CI 1.17 - 1.27). In the multivariate

112

analysis including all of the predictor variables in the model, the adjusted rate ratios were

statistically significant for the facility type, year of survey, the quality indices of staffing

and service capacity and infrastructure, and user fees. The adjusted rate ratios for the two

statistically significant indices of quality were 1.23 (CI 1.14 - 1.34) and 1.09 (CI 1.02 -

1.17). The adjusted rate ratio for user fees was 0.85 (CI 0.75 - 0.96).

In the multivariate model the interaction between each index of quality and year

of survey was tested for statistical significance. The interaction term between the quality

index on staffing and service capacity and year of survey was statistically significant.

Interaction between other indices of quality and year of survey were not found to be

statistically significant. The interaction terms between each index of quality and user fees

were also not found to be statistically significant.

Given the high proportion of facilities without records of outpatient visits (range

1.5 % to 38%) an important concern was that facilities having missing outpatient visit

records tended to be those that are poorly run. The additional analysis checked for

correlation between missing outpatient visit records and each index of quality. The

association was statistically significant (p-value <0.05) only between missing outpatient

visit records and child health services index, where missing visit record was associated

with a lower score on the index. Re-estimation of the multivariate model after the

exclusion of all of the facilities surveyed in 2004 resulted in adjusted rate ratios that were

very similar to the original estimates, with the exception of the rate ratio for the year

2006 (Table 4.9). The adjusted rate ratio for 2006 was not statistically significant (p-

value < 0.07) in the re-estimated model.

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Outcome 2: Utilization rate for females (Table 4.6)

The bivariate rate ratios estimated for every predictor variable were statistically

significant, except for facility type, user fees and managing agency. The estimated rate

ratio for user fees was 1.00 (CI 0.88 - 1.14). The estimated rate ratios for the six indices

of quality ranged from 1.07 (CI 1.02 - 1.13) to 1.27 (CI 1.22 - 1.32). In the multivariate

analysis including all the predictor variables in the model, the estimated adjusted rate

ratios were similar to the rate ratios with overall utilization rate as the outcome. The rate

ratios were statistically significant for the year of survey, quality index of staffing and

service capacity and quality index of infrastructure, and the facility type. The coefficient

for user fees was not statistically significant. The adjusted rate ratios for the two

statistically significant indices of quality were 1.29 (CI 1.19 - 1.39) and 1.09 (CI 1.02 -

1.16).

In the multivariate model, the interaction between each index of quality and year

of survey was tested for statistical significance. The interaction term between the quality

index on staffing and service capacity and year of survey was statistically significant.

Interaction between other indices of quality and year of survey were not found to be

statistically significant. The interaction terms between each index of quality and user fees

were found not to be statistically significant.

In order to assess the effect of missingness, re-estimation of the multivariate

model after the exclusion of all of the facilities surveyed in 2004 resulted in adjusted rate

ratios that were very similar to the original estimates, with the exception of the rate ratios

for the year 2006 and user fees (Table 4.9). As compared to the original model, the

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coefficient for user fees was statistically significant (p-value < 0.05) while rate ratio for

2006 was not statistically significant (p-value <0.1) in the re-estimated model.

Outcome 3: Utilization rate for the poor (Tables 4.7 & 4.8)

Bivariate rate ratios estimated for every predictor variable except facility type and

user fees were statistically significant. The estimated rate ratio for user fees was 0.93 (CI

0.80 - 1.07). The estimated rate ratios for the six indices of quality ranged from 1.18 (CI

1.12 - 1.25) to 1.26 (CI 1.18 - 1.35).

In the multivariate analysis including all of the predictor variables in the model,

the adjusted rate ratios were statistically significant for facility type, year of survey, user

fees, managing agency and quality index of staffing and service capacity, quality index of

drug availability and quality index of patient counseling. The adjusted rate ratio for user

fees was 0.73 (CI 0.64 - 0.85) and for supporting organization it was 0.80 (CI 0.67 -

0.95). The adjusted rate ratios for the three indices on staffing capacity, drug availability

and patient counseling were 1.15 (CI 1.05 - 1.26), 1.12 (CI 1.05 - 1.20) and 1.10 (CI

1.04 - 1.17) respectively.

In the multivariate model, the interaction between each index of quality and year

of survey was tested for statistical significance (Table 4.8). The interaction term between

the year of survey and quality indices on staffing and service capacity, drug availability

and patient counseling were statistically significant for at least two of the three years. The

interaction terms between each index of quality and user fees were not found to be

statistically significant.

In order assess the effect of missingness, re-estimation of the multivariate model

after the exclusion of all of the facilities surveyed in 2004 resulted in adjusted rate ratios

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that were very similar to the original estimates, with the exception of the rate ratio for the

year of survey and drug availability (Table 4.9). The adjusted rate ratios for the year of

survey and drug availability were not statistically significant in the re-estimated model.

4.6 Discussion

The utilization of health services has increased significantly over time in post

conflict Afghanistan. As compared to 2004, the overall utilization rate for the year 2005

increased by 50%. By 2007 the rate increased by 86% from the 2004 level. A number of

studies published recently report an increase in health services utilization at public sector

health facilities in Afghanistan in the initial years after implementation of Basic Package

of Health Services (Hansen et al, 2008b; JHU and fflMR, 2008a). The findings of this

study indicate that the initial increase in utilization of services has continued over time in

Afghanistan.

The findings in this study indicate that this increase in utilization rate by new

outpatients is evident for total catchment area population as well as the two priority

groups; females and the poor. The mean monthly utilization rate for females and the poor

is higher than the overall rate in 2005, 2006, and 2007. The poor have the highest

utilization rate among the three outcome groups in each of the four years. The higher

facility level utilization rates in this study are corroborated by the findings of two recent

household level studies from rural Afghanistan on care seeking practices that reported

that females and the poor households utilize public health facilities more often than males

and richer households, respectively (JHU and fflMR, 2008b; Steinhardt et al., 2007). An

explicit focus towards the health needs of females and the poor in provision and delivery

116

of services has been an important goal of the Ministry of Public Health and its partners in

Afghanistan. The study findings provide evidence towards the initial success of this

policy objective of the Basic Package of Health Services.

The quality of health services delivered at public health facilities has also

improved significantly over time. Health facilities providing higher quality of services

also have a higher rate of utilization in each of the three outcome groups. A large number

of studies have reported this association in various settings; the study of association

between quality and three distinct outcome groups over time is a first. The use of a

comprehensive yet distinct list of facility level characteristics provides actionable

evidence towards promoting equity in health service use by studying the trend in

utilization by individual priority groups. All six indices of quality reflect aspects that are

amenable to change. Each of these indices has high validity and reliability and is

associated with a different aspect of health system management. These indices provide

operational guidance towards monitoring and evaluating the equity promoting policies of

the health system. Based on the differences in association between individual aspect of

quality and utilization by the three outcome groups, policy makers and program planners

can identify aspects that not only promote utilization but also promote equity.

Among the two domains of quality explored in this study, both structure and

process are strongly associated with utilization in each of the three outcome groups,

though in multivariate analysis process level quality is significant only for utilization by

the poor. Among the individual indices of quality, the index measuring the staffing and

service capacity in a facility was the only index significantly associated with each of

three outcomes. In multivariate analysis, one standard unit increase in the measured value

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of facility score on this index lead to a 23%, 29% and 15% increase in utilization rate

among all new outpatients, new female outpatients and new outpatient visits by the

poorest 40%, respectively. The items included in this index relate to availability of

adequate staff for provision of basic medical services at a health facility and its ability to

perform basic laboratory tests and examinations. HI health is an uncomfortable situation

for the suffering patient and the family. Once the decision to use health services is made,

the patient prefers to use a facility that provides the basic services, has adequate staff and

can perform the necessary laboratory tests and exams, thereby highlighting the all around

association for this index. Each year as part of NHSPA, formative research was

conducted in the community living in catchment areas of surveyed health facilities to

assess their perceptions about quality and barriers to utilization. The two factors that were

included in the description of good quality by a majority of community members were

presence of qualified staff, especially doctors, and the facility's capability to conduct

laboratory tests and examination (JHU and IHMR, 2005b). These qualitative findings

further strengthen the overall importance of our quantitative analyses.

The three outcome groups differ in terms of importance of the second index.

Infrastructure index of structural domain is significantly associated with higher overall

and female utilization rates, while drug availability is the other index important for

greater utilization by poor outpatient. A one standard unit increase in quality on the

infrastructure index was associated with a 9% increase in the rate of utilization for the

females as well as the total catchment area population. Good infrastructure requires a

building that appears to be structurally sound on visible inspection with adequate number

of clean rooms. A facility with good infrastructure is one that provides the patient with a

118

greater sense of comfort before and during the consultation process with a health worker.

In a highly traditional society like Afghanistan, a facility with good infrastructure

provides a greater opportunity for privacy during the consultative process, especially for

reproductive age women. The poor patients, due to lower socio-economic status, might

have a lower sensitivity to issues of privacy and comfort, thereby leading to a non-

differential pattern of utilization in this group based on this factor.

A large number of studies exploring the effect of health service quality on

utilization have reported the importance of regular availability of drugs at a facility in

affecting the utilization of services, though none of them studied the role of drug

availability in utilization by different outcome groups (Lule et al., 2000; Mariko, 2003;

Mwabu et al, 1993). In our study, drug availability is an important predictor of

utilization rate in each of the three outcome groups in bivariate analysis, but after

adjusting for other aspects of quality it is statistically significant only for outpatient visits

by the poor. One standard unit increase in facility score on the drug availability index is

associated with a 12% increase in rate of utilization by the poor, after adjusting for other

predictors. The poor are the most economically vulnerable part of the population and

suffer from a greater burden of disease as compared to those economically well off.

Unlike the general population they are the least capable of consulting private providers or

buying drugs in the open market. While the finding that drug availability is not a

statistically significant factor in overall utilization is a bit surprising, its importance in

affecting the utilization by the poor is a logical expectation.

The process domain of quality was a significant predictor of increase in utilization

rate for each of three outcome groups in bivariate analysis. After adjusting for structure

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and other predictors, it was a significant predictor only for the outpatient visits by the

poor. Among the two indices representing the process domain, the index on counseling of

patients was significant only for the utilization by the poor group. One standard unit

increase on the counseling index was associated with a 10% increase in utilization by the

poor.

The poor patients represent the lowest 40% of the population in terms of

household expenditure and they have the lowest level of education and exposure to mass

media like radio and television (JHU and IHMR, 2008b). The availability of good

counseling as a source of information and awareness about disease and illness might be

relatively more important for the poor as compared to the general population, thereby

leading to this important association. Another possible explanation for this association

might be that a larger proportion of poor patients are less than five years of age. The poor

tend to have larger families on average as compared to the non poor (Filmer & Scott,

2008; Rutstein & Kiersten, 2004). The poor not only have larger families, they might also

have a greater proportion of members belonging to younger age groups. The process

indices used in our study mostly deal with observation of delivery of services to the

children below five years of age. If the counseling is as important in the total population

of the catchment area as it is for the poor in the same area, a greater number of children

per family among the poor and the measurement of process aspect of quality while

delivering services to children might lead to a significant association when the outcome

of interest is the utilization by the poor.

The lack of strong association between process measures of quality and utilization

of services by females might also be explained by the lack of indices measuring delivery

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of services specific to females, especially reproductive age women. As compared to the

structure domain of quality, the process domain is more difficult to measure as the

process of health services delivery is specific to patient factors like age, gender and

disease symptoms (Nicholas et ah, 1991; Peabody et ah, 2006). This study was

performed only in general outpatient clinics of health facilities in order to perform facility

assessment with the total catchment area population in perspective. The data collection of

health worker observation was stratified by patient age and the instruments were based on

BVICI instruments in order to ensure comparability with other studies. No data was

collected on process measures of quality in antenatal or postnatal clinics within facilities,

where services specific to women are delivered.

The collection of user fees at health facilities is a variable that has been

extensively studied in the literature. It is a contentious policy issue as available evidence

of its effect on utilization is mixed (Peters et ah, 2008). A pilot study was conducted in

Afghanistan to assess the effect of user fees on various aspects health service delivery,

including its effect on utilization. In the final report published recently, it was found that

the utilization of services increased over time across all the facilities under study, but the

increase in utilization of health services at facilities providing services free of charge was

significantly greater than among facilities charging some form of user fees (JHU and

IHMR, 2008c). In our study, user fee collection at primary health care facilities was

associated with a statistically significant reduction in utilization rates for the total

catchment area population and the poor. The magnitude of reduction in utilization rate for

facilities collecting user fees was 15% for overall utilization and 26% for utilization by

the poor. The findings of our study are in agreement with other studies that report a

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similar decline in utilization at facilities where user fees are collected (Burnham et al,

2004;Gilson<??a/.,2001).

Our findings suggest that the effect of user fees is not uniform across different

outcome groups. The population group that is most strongly affected by user fees is the

poor and our findings reflect this association by the additional 11% decline in adjusted

rate ratio for the poor as compared to the total catchment area population. The user fee

collection had no significant association with utilization rate by females. Using data from

the four years of survey, the adjusted rate ratio for utilization by females was 0.89 in

magnitude and was not statistically significant, whereas using data from the last three

years, the adjusted rate ratio was 0.85 and statistically significant. According to Hansen et

al, among the BPHS facilities surveyed as part of NHSPA, the provision of antenatal

services for women increased from 58.5% in 2004 to 79.3% in 2005, and this change was

statistically significant. During the same interval, the proportion of women among the

new outpatients also increased significantly from 55.2% to 57.4% (Hansen et al, 2008b).

In 2004, while the general population had a greater choice of facilities to access health

services, the women not only visited the BPHS facilities less often but also had much

lower number of facilities to choose from in order to receive antenatal services. The

situation for women trying to access antenatal care might have changed from 2004 to

2005, leading to user fees becoming a significant factor in choice of health facility.

Our findings indicate that the type of health facility associated with highest

utilization rate is BHC, followed by CHC and then by DH. This finding is evident in each

of the three outcome groups. The most probable explanation for this difference might be

due to the fact that this study was conducted only in the general outpatient clinics of

122

health facilities. BHC's have the smallest catchment area population, and if equal number

of patients visit outpatient clinics across each of the three types of health facilities,

BHC's will have the highest utilization rate. Alternatively, BHC's are the nearest health

facility available to a patient among the three types of public health facilities surveyed in

this study,. Under the assumption of a functioning referral system, an outpatient's first

contact with the health system will be at the level closest to his/her residence, which

among the three types in this study is the BHC.

The management of health facility solely by the MOPH is associated with a 20%

decrease in utilization rate by the poor. The association for other two outcome groups is

not statistically significant. In an earlier study by Hansen et al on significant determinants

of quality of health services in Afghanistan, lower socioeconomic status was found to be

associated with receipt of lower quality services at MOPH facilities only, not at facilities

supported by NGO's (Hansen et al, 2008a). This difference provides a plausible

explanation for the poor utilizing the MOPH facilities less often than the non-poor.

In each of the outcome groups, the statistical significance of an interaction

between staffing and structural capacity and the year of survey suggest that an increase of

one standard unit in quality score on this index leads to a significantly greater increase in

utilization at a facility in a later year as compared to a similar increase in quality an

earlier year. One probable explanation for this interaction is the change in perception of

quality in catchment area population. A good perception of quality among the new users

in one year might have led to an increase in utilization rate over and above what was

expected due to the provision of better quality services at a particular health facility.

123

Study findings for outpatient visits by the poor from the interaction between drug

availability and year of survey yielded statistically significant rate ratios. The overall

coefficient for drug availability was positively associated with utilization though the

coefficients for this interaction were negative, suggesting the possibility of a threshold

value on the index after which there is a reversal of trend. Refitting the model after

excluding data from 2004 resulted in a non-significant coefficient for drug availability,

thereby lending support to hypothesis that increased drug availability was linked with

increase in utilization by the poor but its effect waned after reaching a threshold value at

the end of first year of BPHS implementation. The interaction between counseling with

year of survey also yielded statistically significant results for utilization rate by the poor.

Though the interaction terms for counseling were not found to be significant for each of

the three years of survey, the findings are highly suggestive.

The findings in this study are subject to some limitations. The lack of community

based data precludes us from studying other factors that might affect the choice of health

facility by different outcome groups. Two potentially important variables (enabling

factors) affecting the choice of health facility were cost and travel time. The total cost of

utilizing a particular health facility, besides the actual expenditure on consultation and

drugs, consists of the loss in income of the patient and the accompanying caretaker and

the expenditure incurred on traveling to and from the health facility. A number of studies

have reported that a higher cost or a longer traveling time to a particular health facility

reduces the probability of a patient to use that facility (Akin et ah, 1995; Haddad &

Fournier, 1995; King, 1966; Moisi, 2008; Shaikh & Hatcher, 2004). In order to have

124

adequate information on these important variables, yearly household surveys in the

catchment area would have been needed.

Another potential limitation of this study is the lack of data on private providers

of health services competing with the public health facilities. In addition to medical

providers, this group includes pharmacies and other traditional healers. Though the

longitudinal nature of data enables the association of the increasing trend in utilization to

public health facilities, it cannot measure the proportion of users that have switched over

from private providers (or vice versa) versus the users that are actually 'new'.

The change in security situation in Afghanistan is a potentially important variable

that was not included in this analysis. We tried to control for this variable by excluding

the most insecure provinces from this analysis, though the effect of security on utilization

could not be explored.

The outcomes analyzed in this study rely heavily on the routine reporting system

at health facilities. Exploratory analyses revealed that 38% of the facilities from the year

2004 were missing information on the outcome variable. An important concern was that

these facilities with missing information were providing services of lower quality or were

markedly different from the facilities with information on the outcome variable. The

potential effect of these facilities was studied by performing two additional analyses. The

first analysis was based on the fact that these facilities with missing information had data

available on the quality variables. In the first analysis, the association between missing

value on utilization rate and quality was tested for statistical significance. Each index of

structure domain of quality was included as the outcome variable in a regression model

with a binary explanatory variable that was coded as ' 1' if the facility had missing

125

information on number of visits or catchment area population, and '0' otherwise. The

association was statistically significant (p-value <0.05) only between missing outpatient

visit records and child health services index, where missing visit record was associated

with a lower score on the index. In order to assess the effect of this finding, the

multivariate models for each of the three outcomes were refitted after excluding all the

facilities from the year 2004. The refitted models without data from 2004 yielded results

that were qualitatively similar to the original models fitted with data from 2004. This

implied that the findings of this study are robust to the missing data from the year 2004.

The high proportion of missing outpatient visit rate (12.6% when all years are considered

together) is not entirely unexpected given that both facilities and the reporting system

were being set up during the 2004-2005 period. Based on the two analyses, it is fairly

likely that the missing values reflect the incremental health sector reconstruction

process— rather than poor performance by facilities with missing data.

4.7 Conclusion

The utilization of public health facilities in Afghanistan increased significantly

over the four years. Females and the poor had utilization rates that were higher than the

overall population. The quality of services at health facilities had also improved

significantly over time. Facilities providing services of better quality had higher

utilization rates than facilities providing lower quality services. While higher quality in

one year was associated with greater utilization, higher quality also lead to an increase in

utilization over subsequent years.

126

Both structure and process aspects of quality were significantly associated with

increase in overall utilization as well as utilization by females and the poor, though

among the facilities having similar levels of structural quality, process aspect of quality

was significant only for utilization by the poor.

In each of the three outcome groups, a higher availability of qualified staff and

ability to perform clinical tests and examinations in one year was associated with an

increase in utilization in the next year, which was significantly higher than what was

expected with greater availability of staff or passage of time alone. Increased availability

of drugs and good patient counseling were associated with increase in utilization among

the poor, though the association with drug availability was subject to threshold effects.

Collection of user fees was associated with decline in utilization, with the poor

experiencing the highest decline in rates.

127

Figure 4.1: A Framework to study the Access and Utilization of Health services

Health Policy

Characteristics of Health care delivery

system Structural Quality Human Resources Material Resources

Organizational Structure Process Quality

Clinical care Interpersonal care

Managing Agency User Fees

i '

Utilization of Health Servi ces

Characteristics of the population at risk

Predisposing Age Sex

Enabling Socioeconomic status

Travel time Cost

Perception Need Illness

t Outcome Quality Health Outcomes

Patient Satisfaction

Adapted from: Aday L.A. & Andersen R. (1974). A Framework for the study of access to medical care. Health Serv Res 9(3): 208-220.

128

o CM

Figure 4.2:Mean utilization rate by outcome group •-• Overall • Female • Poor i ••—! 95% CI

o ~ o

1" I >-^" * £_ I 3r.»"T a. \ ^ ^ ..••r.r:

CD Q.

o . ^

22 co - . ' .•••!' •

T^T

o CM _ _. . .

2004 2005 2006 2007 Year of survey

129

Table 4.1: Outpatient visit study sample by year of survey

Original dataset: Province surveyed in each year Number of facilities

Year of survey 2004 562

2005 621

2006 619

2007 624

Total 2426

Analyzed dataset: Facilities with routine reporting system data Number of facilities 350 593 562 615 2120

Table 4.2: Outpatient visit study sample by number of repeated surveys

Original dataset: Province surveyed in each year Number of facilities

Number of repetitions 4

196 3

246 2

230 1

444 Total 1116

Analyzed dataset: Facilities with routine reporting system data Number of facilities 104 263 257 401 1025

Table 4.3: Health Service utilization rates by outcome group and year of survey: Mean (standard deviation)

Outcome group Overall1

Female2

Poor3

Year of survey 2004

32 (43.9) 32 (40.4) 40 (56.3)

2005 54 (108.7) 63 (125.1) 73(93.1)

2006 70(74.1) 83 (85.3) 93(115.7)

2007 78 (64.8) 95 (78.2)

105(111.3)

p-value <0.01 <0.01 <0.01

1 New outpatient visits per one thousand (catchment area) population per month New female outpatient visits per one thousand (catchment area female) population per month

3 New outpatient visits by poor per one thousand (catchment area poor) population per month

130

Table 4.4: Predictor variables by year of survey: Mean (standard deviation/ percent)

Predictor Year of survey

2004 2005 2006 2007 p-value Structure domain of quality

Summary Index (stsum) Staffing and service capacity

index (indxl) Child health services index

(indx2) Infrastructure index (indx3)

Drug availability index (indx4)

34.8 (15.6)

21.6(20.4)

60.8 (24.3) 51.0(28.5) 54.0 (33.5)

39.9(17.5)

29.1(23.5)

68.8 (23.4) 51.1 (26.4) 58.7 (36.5)

50.3 (14.7)

39.0 (22.9)

79.2 (16.4) 63.4 (24.5) 80.2 (20.9)

54.5 (14.4)

46.0 (23.2)

83.9 (14.9) 63.0 (24.3) 84.3 (19.9)

<0.01

<0.01

<0.01

<0.01 <0.01

Process domain of quality Summary Index (psum)

Pt. assessment & physical exam index (indx5)

Counseling index(indx6)

7.4 (3.5)

36.3 (17.2) 38.8 (22.0)

8.2 (3.2)

43.2 (16.5) 38.7 (19.7)

9.3 (3.6)

49.6(18.7) 42.8(21.0)

10.5 (3.6)

54.3 (18.8) 50.7 (20.9)

<0.01

<0.01 <0.01

Facility type Basic Health Center (bhc)

Comprehensive Health Center (chc)

District Hospital (dh)

179(51.1)

143 (40.8) 28(8)

343 (57.8)

210 (35.4) 40 (6.7)

340 (60.5)

181 (32.2) 41 (7.3)

366 (59.5)

202 (32.8) 47 (7.6) >0.05

User fees Collected Yes (uf) 254 (72.5) 466 (78.5) 445 (79.1) 516(83.9) <0.01

Managing agency Ministry of Public Health

(moph) 69 (19.7) 88 (14.8) 60 (10.6) 70(11.3) <0.01 @ Name in the parentheses is the acronym used in analyses

131

Table 4.5: Estimated rate ratios for Outcome 1: Overall utilization

Predictor Summary index: Structural quality Summary index: Process quality

Staffing and service capacity index Child health services index

Infrastructure index Drug availability index

Pt. assessment & physical exam index Counseling index

Year 2005 A

Year 2006 A

Year 2007 A

Comprehensive Health Center District Hospital User fees Collected

Ministry of Public Health Year 2005 X Staffing and service

capacity index A

Year 2006 X Staffing and service capacity index A


Bivariate model 1.26 ** 1.08 ** 1.22** 1.18** 1.19 ** 1.12* 1.11 ** 1.05*

1.67 ** 2.18** 2.43 ** 0.90 N/s

1.13 N/s

0.95 N/s

0.86 N/s

Multivariate model with summary

measures of quality** 1.25 ** 0.98 N/s

— — --— — —

1.52** I 7i **

1.86** 0.75 ** 0.73 ** 0.84* 0.90 N/s

Multivariate model with six indices of qualityH

— —

1.23 ** 0.99 N/s

1.09** 0.97 N/s

1.01 N/s

0.98 N/s

1.49 ** 1.72 ** 1.86** 0.70 ** 0.63 ** 0.85*

0.90 N/s

Multivariate model with interaction

termn (year X indxl)

— —

0.91N/s

1.00 N/s

1.10** 0.97 N/s

1.01 N/s

0.97 N/s

1 -7-7**

2.01** 2.08 ** 0.67 ** 0.54 ** 0.83 ** 0.90 N/s

1.3**

I.47 **

1.54 ** A Reference year 2004

Reference facility type Basic Health Center * p-value <0.05; ** p-value <0.01; N/S: Non-significant at 5% level of significance a Ratios adjusted for other variables (with reported estimate) in each respective column

132

Table 4.6: Estimated rate ratios for Outcome 2: Utilization by females





Year 2005 A

Year 2006 A

Year 2007 A



capacity index A



Bivariate model 1.32 ** 1.11 ** 1.27 ** 1.22 ** 1.22** 1.15*

1.14 ** 1.07 ** 1.98 ** 2.62 ** 2.98 ** 0.91 N/s

1.13 N/s

1.00 N/s

0.85 N/s

_


measures of quality** 1.29 ** 1.00 N/s

— — — — — —

1.77 ** 1.97 ** 2.16** 0.74 ** 0.68 ** 0.89 N/s

0.92 N/s

..

Multivariate model with

six indices of quality**

— —

1.29 ** 0.99 N/s

1.09 ** 0.97 N/s

1.01 N/s

0.99 N/s

1.49 ** 1.72** 1.86** 0.69 ** 0.57 ** 0.89 N/s

0.91 N/s

Multivariate model with interaction

term** (year X indxl)

— —

0.98 N/s

1.01 N/s

1.10** 0.98 N/s

1 0 2 N/S

0.98 N/s

1 77 **

2.01 ** 2.08 ** 0.65 ** 0.50 ** 0.88*

0.91N/s

1.25 **

1.42**

1.48 ** A Reference year 2004

Reference facility type Basic Health Center * p-value <0.05; ** p-value <0.01; N/S: Non-significant at 5% level of significance H Ratios adjusted for other variables (with reported estimate) in each respective column

133

Table 4.7: Estimated rate ratios for Outcome 3: Utilization by poor





Year 2005 A

Year 2006 A

Year 2007 A


Ministry of Public Health

Bivariate model 1.31 ** 1.22 ** 1.24 ** 1.24** 1.18** 1.26** 1.20 ** 1.20** 1.79 ** 2.28 ** 2.59 ** 0.91 N/s

1.24 N/s

0.93 N/s

0.82*


measures of quality** 1.29** 1.08 **

— — — — — —

1.61 ** 1.64 ** 1.79 ** 0.76 ** 0.78 N/s

0.75 ** 0.79 **

Multivariate model with six

indices of quality**

— —

1.15** 1.04 N/s

1.03 N/s

1.12** 0.98 N/s

1.10** 1.66 ** 1.68 ** 1.80** 0.78 ** 0.82 N/s

0.73 ** 0.80*

A Reference year 2004 Reference facility type Basic Health Center

* p-value <0.05; ** p-value <0.01; N/S: Non-significant at 5% level of significance H Ratios adjusted for other variables (with reported estimate) in each respective column

134

Table 4.8: Estimated (interaction) rate ratios for Outcome 3: Utilization by poor





Year 2005 A

Year 2006 A

Year 2007 A



capacity index A



Year 2005 X Drug availability index A



Year 2005 X Counseling index A




interaction term** (year x indxl)

— —

0.96 m

1.06 m

1.04 m

1.13** 0.98 N/s

1.09 ** 1.80** 1.82** 1.85 ** 0.74 ** 0.72* 0.73 ** 0.79 **

1.09 N/s

1.29 **

1.40 ** — — — — — --


interaction termH (year X indx4)

— —

1.15** 1.06™ 1.03 m

1.32** 0.98 *"5

1.11 ** 1.50 ** 1.55 ** 1.69** 0.78 ** 0.82 N/s

0.73 ** 0.79 **

0.80 ** 0.84 NK

0.80* — — --


interaction term** (year X indx6)

— —

1.16** 1.04 m

1.03 NIS

1.11 ** 0.98 w s

0.96™ 1.75 ** 1.76** 1.90** 0.77 ** 0.82 N/s

0.72 ** 0.78 **

— — —

1.26** 1.16*

U3m A Reference year 2004

Reference facility type Basic Health Center * p-value <0.05; ** p-value <0.01; N/S: Non-significant at 5% level of significance Q Ratios adjusted for other variables (with reported estimate) in each respective column

135

Table 4.9: Estimated multivariate rate ratios for the three outcome groups excluding data from year 2004

Predictor Staffing and service capacity index

Child health services index Infrastructure index

Drug availability index Pt. assessment & physical exam index

Counseling index Year 2006 A

Year 2007 A


Ministry of Public Health

Overall utilization**

1.35 ** 0.99 N/S

1.10** 0.95 N/s

0 9 9 N/S

0 9 7 N/S

1.13 N/s

1.21 * 0.64 ** 0.55 ** 0.80 ** 0.97 N/s

Utilization by females** 1.43 ** 0.99 N/s

1.10** 0.95 N/s

1.00 N/s

0.98 N/s

1.11™ 1.19*

0.60 ** 0.47 ** 0.85* 1.00 N/s

Utilization by poorB 1.22* 1.03 N/s

1.05 N/s

1.06 N/s

0.96 N/s

1.15** 1 Q 2 N/S

1.08 N/s

0.75* 0.80 N's

0.72 ** 0.82*

A Reference year 2005 Reference facility type Basic Health Center

* p-value <0.05; ** p-value <0.01; N/S: Non-significant at 5% level of significance ct Ratios adjusted for other variables (with reported estimate) in each respective column

136

Chapter 5 Summary: Findings and implications

Good policies require good information, and the health sector is no exception to

this rule. Adequate resources and infrastructure are available in developed countries to

gather, analyze and synthesize this information, and yet developing countries have

chronically suffered from lack of such necessities. Nowhere is this problem more evident

than in countries recovering from decades of conflict, where not only are the resources

constrained but the need for population level information is more urgent.

In post-conflict countries, collection of baseline information on health status of

the population at the cessation of fighting enables the policy makers to formulate future

policies for the successful reconstruction of the national health system. In war torn

Afghanistan, faced with an enormous task of laying the foundations for an equitable and

quality oriented health system, the Ministry of Public Health needed data on health

service delivery in the country. The survey planners used an outdated sampling frame to

gather baseline data through Multiple Indicators Cluster Survey (MICS) in 2003. The

estimates reported in the original MICS report were generated using the information from

the 1979 population census; and were considered to be biased. In the first study, we

generated a new set of reanalyzed estimates using the data from the pre-census conducted

in 2004 and compared them with the originally reported estimates. From a policy

perspective the two sets of estimates provided similar cross-sectional information about

the status of health care delivery in the immediate post-Taliban period. However, the two

sets of estimated differed in statistical precision, thereby affecting the potential use for

assessing trends.

137

In post-conflict settings, when urgent information must be gathered on the health

status of the population, older sampling frames can be used for household surveys to

derive population estimates. The policy makers in post-conflict settings can be reassured

that expected goals of a baseline evaluation are being met as long as the information is

collected and analyzed in a scientifically rigorous manner, even if it is based on an older

sampling frame. However, the generalizability of the reported findings should be tested in

other post-conflict settings before being widely accepted. During the data collection for

MICS 2003, the clusters (villages) were sampled based on the information provided in

the 1979 sampling frame, but the selection of a segment within the clusters and

subsequent stages of sampling were based on information that was collected directly from

the community members living there on the day of survey. The use of current information

probably led to a reduction in the bias that might have otherwise occurred due to an older

sampling frame. In the case of sampling within the six largest cities, even the clusters

were sampled based on current information, thereby strengthening the explanation

towards reduction in bias in calculated estimates.

The estimates generated using older sampling frames are biased and have false

precision, but availability of new data in future can correct for some of these errors. The

method of re-weighting used in this study can enable the post-hoc use of this data for

statistical analyses of trends, but the users should be cautious of the greater variability in

statistical power of these new estimates. In addition, the policy makers and researchers

should be aware that re-weighting has a limited capability to enable study of the effect

due to health programs on population health, because a more complex evaluation design

and extensive data collection are usually needed to rule out the effect of external factors.

138

The findings in the first study indicate that the precision and complexity of an

evaluation must relate to the needs of the user of the particular evaluation and to the type

of inference that will be made based on the particular evaluation. The indicators on

maternal and child health delivery relate to interventions that have proven effectiveness

and efficacy in improving health outcomes in various developing country settings. An

improvement in health service delivery should lead to a reduction in morbidity and

mortality in the population. The evaluation of such interventions should not be evaluated

only on the basis of arbitrary values of precision. The selection of arbitrary values for

Type I and Type II errors is questionable. The commonly used values for probability of

Type I and Type II errors are 0.05 and 0.20, respectively. These values indicate that the

evaluator is willing to not identify a beneficial result four times more often than to be

mistaken in declaring such a result when it is absent. It has been suggested that a higher

value of error should be used to evaluate programs with proven efficacy, especially in

situations where scientific inferences are not being made. We believe that evaluation of

health care delivery in post-conflict settings represents such a situation. There is also a

limited need for complex evaluation designs that include a control group to enable ruling

out the effect of other factors on population health outcomes, especially in the immediate

post-conflict period.

Policy makers in post-conflict countries need to ensure that the opportunities of

growth are accessible to the poor. This requires a measure of living standards that is easy

to collect, observe and verify, so that data can be gathered at regular intervals to monitor

economic status and track poverty. In the third chapter we found that an out of sample

prediction of expenditure using asset variables enables measurement and tracking of

139

poverty in a population over time, thereby making this information more accessible for

policy makers and researchers alike. The study uses data collected from two separate

household surveys over an interval of one year; therefore the results might not be

generalizable to the population of Afghanistan. However, our results do indicate an

improvement in economic status and reduction in poverty over the interval of one year.

The potential implications of our findings are wide ranging. Regular collection of

information on asset variables to predict an absolute measure of economic status like

consumption or expenditure can enable tracking of poverty over time. In addition, the

predicted expenditure can form the basis for poverty mapping and targeting through the

social protection programs. This information on economic status and poverty can also be

linked to reallocation of resources in the health sector, thereby improving the efficiency

and equity of programs to improve health outcomes in the population.

Future research on the use of asset variable to predict consumption should focus

on a more comprehensive set of indicators that can help in explaining a greater proportion

of the variability in consumption. This will help by improving the predictive ability of the

asset variables. This list can include variables that vary by time, like rainfall; or vary by

location, like average number of households with good housing characteristics in a

cluster/district. An important limitation of this technique in general is that the coefficients

used to predict economic status and poverty are stable for only short periods of time.

Repeated household surveys at short intervals to collect information on asset variables

can serve as a complement to consumption surveys conducted at longer intervals. Both

these source of data on economic status can together provide a comprehensive set of

140

information to formulate and implement policy decisions to reduce poverty in post-

conflict settings.

In Afghanistan, the implementation of a Basic Package of Health Services

(BPHS) to address the biggest health needs of the conflict affected population was an

important step towards establishment of an equitable health system. The analysis of

utilization rates over four years showed increasing levels of utilization among the two

priority groups - the poor and females. Barring the first year of implementation of BPHS,

the utilization rates for these two groups has been higher than the overall population

living in facility catchment area. This trend towards equitable utilization has occurred

along with a simultaneous improvement in quality of health services.

We found that improvement in facility staffing and services capacity was the

strongest factor associated with increase in utilization among the overall population as

well as the poor and the females. Improvement in infrastructure was strongly associated

with increase in utilization by females and overall population but not with utilization by

poor. Increased drug availability was linked with increase in utilization by the poor but its

effect waned after reaching a threshold value at the end of first year of BPHS

implementation. The counseling of patients and caretakers about the illness and treatment

lead to an increase in utilization by the poor only. Across all the three, an improvement in

staffing and service capacity appears to increase utilization at a greater rate in later years.

The collection of user fees at health facilities was associated with decrease in

utilization across each of the three outcome groups but the strongest decline was seen in

the rate among the poor. Management of health facilities by a non governmental

141

organization appears to benefit the poor most, though utilization among females and by

the total population also showed an increase.

While the improvement in staffing and service capacity appears to be reinforcing

overall increase in utilization, identification of specific characteristics in the health care

delivery system associated with increased utilization by females and the poor can help the

MOPH in making equity a more sustainable and long lasting feature of public health

system in Afghanistan. An important step can be the improvement in infrastructure of

health facilities with a specific focus on needs of female clients; such as separate waiting

and consultation rooms for women. These improvements along with greater availability

of female oriented services provided by female staff can synergize together and lead to

greater female utilization and quality improvement.

Increased availability of drugs and better counseling of patients and their

caretakers appears to increase the utilization by the poor, though collection of user fee is

associated with a decrease in use by the poor. Among the various interventions available

to reduce the financial burden on the poor, user fee exemptions are being practiced at a

number of public health facilities in Afghanistan. It appears that these exemptions have

not been effective in countering the negative effects of user fees on the poor (JHU and

IHMR, 2008c). An improvement in the targeting of user fee exemptions to the poor might

be a prudent way to achieve a dual objective - to reduce the negative effect of user fees

on utilization by the poor and still generate resource to enhance financial sustainability of

the health system.

The data collection for the national round of National Risk and Vulnerability

Assessment (NRVA 2008) is currently being conducted all over Afghanistan. This

142

assessment will provide nationally representative estimates of household consumption.

The out of sample prediction method used in the third chapter can be used to identify and

target the poor with exemptions for user fees. The information on consumption

expenditure collected from every household can be regressed over a set of asset variables.

The estimated regression coefficients for the assets variable can be used to predict

expenditure and identify the economically weaker households in a community. These

economically disadvantaged households can be provided with user fee exemption cards

and thereby reduce some of the financial constraints faced by them in utilizing health

services.

The sustained increase in utilization by females and the poor is in line with the

MOPH's overall vision of equitable growth in the health sector. Improvement in quality

across all facility types and a higher rate of utilization at the lowest level are also

significant achievements of the primary health care approach pursued by the fledgling

MOPH in Afghanistan. Further investigations are required, however, to determine

whether the increased levels of utilization by the poor and females is also associated with

improvement in health outcomes for these disadvantaged groups.

Reduction in poverty and improvement in quality and utilization might be directly

linked to the long term peace and prosperity in a volatile country like Afghanistan. The

evidence provided in this study can provide useful information to other post-conflict

countries striving to rehabilitate their health systems.

143

Appendices

Additional tables for Chapter 2 (Study 1)

Table A2.1: Data of 1979 census available for MICS

Province

Badakhshan

Badghis

Baghlan Balkh

Bamiyan

Farah

Faryab

Ghazni

Ghor

Helmand

Herat

Jawzjan

Kabul

Kandahar

Kapisa

Khost

Kunar

Kunduz

Laghman

Logar

Nangarhar

Nimroz

Nooristan $

Paktika

Paktya

Parwan

Samangan

Saripol

Takhar

Uruzgan

Wardak

Zabul

Afghanistan

City

---

Mazar

------

Herat

-Kabul

Kandahar

---

Kunduz

--

Jalalabad

-----------6

Number of households

(City)

---

24970

------

107204

-406544

85676

---

24478

--

37678

-----------

686550

Number of Villages/ Towns

1920

944

1411

924

1890

922

863

3034

2290

1411

1654

375

662

1344

559

876

722

496

701

748

1057

677

227

794

822

1190

828

820

1276

2532

840

1485

36294


(Villages/Towns)

78605

40869

66294

76367

43177

38128

84767

97474

57401

79470

110033

39614

64546

35514

45111

25141

33764

54438

40206

31699

94558

18517

17612

26587

33568

82923

38328

49304

77224

69176

21994

27789

1700198

Total number of households

78605

40869

66294

101337

43177

38128

84767

97474

57401

79470

217237

39614

471090

94190

45111

25141

33764

78916

40206

31699

132236

18517

17612

26587

33568

82923

38328

49304

77224

69176

21994

27789

2359748

$ Sample of Nooristan was drawn from REMT (Regional EPI Management Team) data as the province was created after 1979 census.

144

Table A2.2: Updated (complete) 1979 census data

Province

Badakhshan

Badghis

Baghlan

Balkh

Bamiyan

Farah Faryab

Ghazni

Ghor

Helmand

Herat

Jawzjan

Kabul

Kandahar

Kapisa

Khost

Kunar

Kunduz

Laghman

Logar

Nangarhar

Nimroz

Nooristan $

Paktika

Paktya

Parwan

Samangan

Saripol

Takhar

Uruzgan

Wardak

Zabul

Afghanistan

City

---

Mazar

------

Herat

-Kabul

Kandahar

---

Kunduz

--

Jalalabad

-----------6


(City)

---

24970

------

107204

-406544

85676

---

24478

--

37678

-----------

686550


1978

977 1441

946 1935

959 1031

3059

2367

1466

1697

381

666 1634

579 906 729 498 706 824 1071

693

227 993 834

1218

837 828 1297

2567

1702

1538

38584


(Villages/Towns)

79263

41157

70261

76986

43824

38594

105284

97689

58425

79968

110179

40203

422852

35795

49875

25356

33822

54892

40215

31712

94597

18940

17612

26866

34220

83245

40534

49366

86076

69836

22053

27891

2107588


79263

41157

70261

101956

43824

38594

105284

97689

58425

79968

217383

40203

829396

121471

49875

25356

33822

79370

40215

31712

132275

18940

17612

26866

34220

83245

40534

49366

86076

69836

22053

27891

2794138

$ Sample of Nooristan was drawn from REMT (Regional EPI Management Team) data as the province was created after 1979 census.

145

Table A2.3: Number of selected clusters and completed households in MICS 2003

Province

Badakhshan

Badghis

Baghlan

Balkh

Bamiyan

Farah

Faryab

Ghazni

Ghor

Helmand

Herat

Jawzjan

Kabul

Kandahar

Kapisa

Khost

Kunar

Kunduz

Laghman

Logar

Nangarhar

Nimroz

Nooristan

Paktika

Paktya

Parwan

Samangan

Saripol

Takhar

Uruzgan

Wardak

Zabul

Afghanistan

Cities

-

-

-

Mazar

-

-

-

-

-

-

Herat

-

Kabul

Kandahar

-

-

-

Kunduz

-

-

Jalalabad

-

-

-

-

-

-

-

-

-

-

-6

Cities: Number of segments

-

-

-

20

-

-

-

-

-

-

20

-

26

20

-

-

-

20

-

-

20

-

-

-

-

-

-

-

-

-

-

-126

Total number of

clusters

20

20

20

20 + 20

20

20

20

20

20

20

20 + 20

20

26 + 20

20 + 20

20

20

20

20 + 20

20

19

20 + 20

20

20

20

20

20

20

20

20

20

20

20

765

Number of households completed

551

545

566

530 + 520

546

504

533

515

491

523

555 + 533

488

817 + 568

577 + 542

552

479

566

623 + 564

562

530

638 + 601

578

537

557

482

564

492

461

542

538

516

518

20804

146

Table A2.4: MICS 2003: Number of segments in sampled cluster (1-10) of 32 provinces/ Number of sub-segments in sampled segment (1-10) of 6 cities.

Province

Badakshan

Badghis

Baghlan

Balkh

Bamyan

Farah

Faryab

Ghazni

Helmand

Herat

Jawzjan

Kabul

Kandhar

Kapisa

Khost

Kunar

Kunduz

Laghman

Logar

Nangarhar

Nimroz

Nooristan

Paktika

Paktya

Parwan

Samangan

Saripol

Takhar

Uruzgan

Wardak

Zabul

City

Herat City

Jalalabad City

Kabul City*

Kandahar City

Kunduz City

Mazar City

Cluster number 1 1

7

2

20

3

8 10 1

14

8 7

24

2

1

3

1

2

3 3

23 2

6 1 4

8

7

6

6

7 2

3

2

6

1

1 11

5

3 3 1

3

9 5

2

1

1

1

2

7

2

12 12

1 1 1 2

6

2

3

3

3 2

2

3

3

3

6 9

1

10 4

1

3

5 25

3

1

8

1

3

2

2

21 7 1

7 2

5 14

12

2

7

1 2

2

4

2

2

1

13

1

20 5 1

1

6 25

4

11

13

1

4

11

4

3

16 2

6 21 21

3

7

6

1

3

5 1

Se 1 1

5 1

3 4

4

2 1

5 1 2

7

13

3 1

5 1 2

6 21

4 1

5 1

1

9

35

5 2

5

2 4

1

15 11

1

1

4 4

3

1

11

1

1

17

1

3 1

2 1 3 3

16

3

1

2

2

1

2

6 1

1

2

12

7 1 1

1

10

5 4

4

1

9 1

1 2

5 4 3

5 1 6 3

3 2

6

3

1

1

4

7

4

5

4

8 4

1 3

3 12

3 3

2

1

12

1

2

10

5

3 10 1 2 12 4

8

13

10

6

1

1

1

8

1

3 2

2

3

1 2

1

1

3 5

12

1

21

1

2

13

4

1 2 1

1 5 3

3 1

11

3

3 1

1

9 14

13 2

3

2

2 2 1

1

6 2

3

1

1

1

6 8

4

3 14

2 1 2 2

1

3

2

1

1

1

1

10 7

1

6

3 1

2 1

2

4

4

8

1

6

3

1

1

7

3

2 45 1

1 1 3

9

2

9

4

2 1

2

anient number

5 1

5 1 2

8

7

6 1

5 1

2

8 10

7

1

5 1

5 5

17

8 1

4 1

11 1

19

9 1 4 1 2

1

18

10

1 4 1

3

5 5

A A total of 25 segments were surveyed in Kabul city. The remaining 5 were sampled and surveyed the same way as the 20 shown here.

147

Table A2.5: MICS 2003: Number of segments in sampled cluster (10-20) of 32 provinces/ Number of sub-segments in sampled segment (10-20) of 6 cities.

Province

Badakshan

Badghis

Baghlan

Balkh

Bamyan

Farah

Faryab

Ghazni

Helmand

Herat

Jawzjan

Kabul

Kandhar

Kapisa

Khost

Kunar

Kunduz

Laghman

Logar

Nangarhar

Nimroz

Nooristan

Paktika

Paktya

Parwan

Samangan

Saripol

Takhar

Uruzgan

Wardak

Zabul

City

Herat City

Jalalabad City

Kabul City*

Kandahar City

Kunduz City

Mazar City

Cluster number

11 1

3 2

9 1

1

5

3

3

2

12

5

3

3

2

1

7

1 1 1 4

1

1

5

5

2

7

7

2

1 1

12

1

1

1

5

1

8

34

5

1

2

6

1

8

6

1

1

6

2 2

6

2

2

2

1

1

2

4

9

2

1

3

13 1

1

2

19

1

2

11

3

3

4

20

4

4

2

1

3

5

10

1 2

2

5

1

2

1

3

2

10

3

2

1

14

8

4

2

6

2

1

14

5

5

13

5

7

2

4

3

1

17

5

1

13

1

4

2

3

3

3

2

34

1

1

1

15 2

2

4

5

1

7

2

5

8

3

39

1

2

3

3

4

8

1 4

12

1

7

22

2

16

2

1

52

1

2

1

16 1

3

1

7

1

3

6

1

5

4

19

5

1

16

2

2

5

2

2

9

4

4

9

3

5

72

7

11

3

3

1

17 1

2

9

1 1

1

6

1

3

14

14

1

1

3

1

8

7

2

10

6

1

5

1

13

1

3

7

6

3

5

1

18 2

2

1

2

1

6

14

1

3

2

15

3

1

3

1

3

2

2

9

15

13

3

1

1

2

1

7

3

1

2

1

19 3

38

8

2

1

2

12

1

4

2

30

1

1

2

2

10

3

3

1 15

4

4

1

1

6

8

1

4

3

1 1

20

5

2

4

7

1

7

24

3

7

2

17

3

1

1

1

6

3

1

1 2

7

4

1

3

4

7

15

5

1

2

2

Segment number 11 1 4

1

14

5

16

12 1 4

1

15

2

4

13 1 4

1

3

2

6

14 1

4

1

9

2

5

15 1 4

1

8

2

12

16 1 4

1

1

73*

18

17 1

3

1

4

73*

8

18 1 3

1

7

4

15

19 1

3

1

2

7

4

20 1

3

1

4

7

10 * Segment 16 and 17 were the same in Kunduz city.

A A total of 25 segments were surveyed in Kabul city. The remaining 5 were sampled and surveyed the same way as the 20 shown here.

148

Table A2.6: Data from 2005-06 National Census °

Province

Badakshan

Badghis

Baghlan

Balkh

Bamiyan

Farah

Faryab

Ghazni

Ghor

Helmand

Herat

Jawzjan

Kabul

Kandhar

Kapisa

Khost

Kunar

Kunduz

Laghman

Logar

Nangarhar

Nimroz

Nooristan

Paktika

Paktya

Parwan

Samangan

Saripol

Takhar

Uruzgan

Wardak

Zabul

Afghanistan

City

---

Mazar ------

Herat -

Kabul

Kandhar ---

Kunduz --

Jalalabad ---------. -6


(City)

---

61154 -. ----

66760 -

312957

43132 ---

16119 --

31267 --. --------

531389


1945

1008

1583

1137

1850

1232

1240

3167

2187

1957

2167

546

766

944

616

904

820

902

620

682

1400

412

263

1278

833

1430

695

877

1351

2556

1986

1115

40469


(Villages/Towns)

140052

87140

118805

129243

56720

84420

138457

167826

112515

203459

236239

70087

79137

77527

51788

89446

67235

96222

60477

45084

199704

18685

19811

116324

74309

89872

52930

66136

138282

131050

84210

35306

3138498


140052

87140

118805

190397

56720

84420

138457

167826

112515

203459

302999

70087

392094

120659

51788

89446

67235

112341

60477

45084

230971

18685

19811

116324

74309

89872

52930

66136 138282

131050

84210

35306

3669887

Based on 2004 pre-census

149

Additional tables for Chapter 4 (Study 3)

Indices for the structure domain of quality

Table A4.1: Staffing & service capacity index (Index 1) — List of items

1) Facility has a clean inpatient ward 2) Facility has a clean delivery ward 3) Facility has functioning electrical mains or functioning generator or solar power 4) The facility has a running vehicle to transport patients for referral 5) At least 1 female health worker worked in facility in the past month 6) At least 2 nurses worked in facility in the past month 7) At least 3 nurses worked in facility in the past month 8) At least 4 nurses worked in the facility in the past month 9) At least 1 midwife/ auxiliary midwife worked in facility in the past month 10) At least 2 mid wives/ auxiliary mid wives worked in facility in the past month 11) At least 2 doctors worked in facility in the past month 12) At least 3 doctors worked in facility in the past month 13) Facility can provide antenatal services 14) Facility can provide routine delivery services 15) Facility can conduct caesarian sections 16) Facility can do complete blood counts on the day of the survey 17) Facility can do malaria smears on the day of the survey 18) Facility can do rapid diagnostic test for malaria on the day of the survey 19) Facility can do TB smears on the day of the survey 20) Facility can do gram stains on the day of the survey 21) Facility can do blood type & cross match on the day of the survey 22) Facility can do urine dipstick test on the day of the survey 23) Facility can do HIV testing on the day of the survey 24) Facility can do hepatitis test on the day of the survey 25) Facility can do syphilis test on the day of the survey 26) Facility can do pregnancy test on the day of the survey 27) Facility has a working fetoscope 28) Facility has a tape measure 29) Facility has a working partograph 30) Facility has a working delivery light 31) Facility has a complete delivery kit 32) Facility has a speculum 33) Facility has a working vacuum extractor 34) Facility has a working aspirator/ suction bulb 35) Facility has a working resuscitation bag for newborn 36) Facility has a working microscope 37) Facility has a working centrifuge 38) Facility has a working hemoglobinometer 39) Facility has suction/ aspirating device 40) Facility has an oxygen tank 41) Facility has eye drops or ointment for newborn babies 42) Facility has guidelines for TB diagnosis and treatment 43) Facility has protocols and guidelines for Family Planning services

• All items are binary ('Yes'=l; 'No'=0)

150

Table A4.2: Child health services index (Index 2) — List of items

1) Facility provides immunizations at the facility 2) Facility provides immunizations through outreach 3) Facility has an ORT corner with 1 liter container, cups and spoons and rehydration guidelines 4) Facility has at least one weighing scale 5) Facility has at least one children's scale 6) Facility has at least one height measure 7) Facility has at least one otoscope 8) Facility has a working vaccine refrigerator 9) Facility has a working vaccine thermometer 10) Facility has a working cold box/ vaccine carrier 11) Facility has ice packs that are in good condition 12) Facility has a stock of immunization cards adequate for at least 30 days 13) Facility has graphs for growth monitoring 14) Facility has IMCI chart book/ wall chart 15) Facility has ARI guidelines 16) Facility has guidelines on diagnosis & treatment of diarrhea 17) Facility has immunization schedule 18) Facility has patient education materials prominently displayed


151

Table A4.3: Infrastructure & Basic Equipment index (Index 3) — List of items

1) Few or no repairs needed for windows & doors 2) Interior paint in good condition 3) Few or no repairs needed for facility interior walls 4) Few or no repairs needed for facility exterior walls 5) Few or no repairs needed for roof condition 6) Few or no repairs needed for grounds/ fence/wall 7) Cleanliness is satisfactory in reception rooms 8) Cleanliness is satisfactory in waiting rooms 9) Cleanliness is satisfactory in consultation rooms 10) Cleanliness is satisfactory in treatment/ injection rooms 11) Cleanliness is satisfactory in pharmacy 12) Cleanliness is satisfactory in staff toilets 13) Cleanliness is satisfactory in patients toilets 14) Cleanliness is satisfactory in grounds 15) Waste is adequately disposed with an incinerator 16) Functional burial pit is utilized to dispose of waste and there is no waste lying around the pit 17) Needles and syringes are disposed of in a special sharps container immediately after use 18) Facility has a working sterilizer 19) Facility has minor surgical instruments for procedures like incision & drainage and suturing


Table A4.4: Drugs & Contraceptives index (Index 4) — List of items

1) No stock out in previous month and non-expired tetracycline ophthalmic ointment 2) No stock out in previous month and non-expired paracetamol tablets 3) No stock out in previous month and non-expired amoxicillin syrup or tablets 4) No stock out in previous month and non-expired ORS packets 5) No stock out in previous month and non-expired iron tablets 6) No stock out in previous month and non-expired condoms 7) No stock out in previous month and non-expired oral contraceptive pills 8) No stock out in previous month and non-expired DMPA 9) No stock out in previous month and non-expired IUD


152

Indices for the process domain of quality

Table A4.5: Patient assessment and physical exam index (Index 5) — List of items

1) The provider greeted the child or caretaker 2) The provider physically examined the child at least once 3) The provider checked the vaccination status of the child 4) The provider checked the child's feet or both ankles for edema 5) The provider checked the child's palms for pallor 6) The provider asked about fever in the past 24 hours 7) The provider asked about cough or difficult breathing 8) The provider asked about diarrhea 9) The provider asked if the child had convulsions 10) The provider asked whether the child was lethargic or showed a change in the level of consciousness 11) The provider asked whether the child vomits everything 12) The provider asked whether the child is able to drink or breastfeed


Table A4.6: Counseling index (Index 6) — List of items

1) The provider told the caretaker the name of the disease 2) The provider explained the cause of the disease and its course to the caretaker 3) The provider explained home care for the child to the caretaker 4) The provider indicated the signs or symptoms that should prompt a return to the clinic 5) The provider told the caretaker when to return for a scheduled check-up or return visit 6) The provider asked the caretaker if he or she had any questions

• All items are binary ('Yes'=1; 'No'=0)

153

References

Acharya, L. B., & Cleland, J. (2000). Maternal and child health services in rural Nepal:

Does access or quality matter more? Health Policy Plan, 15(2), 223-229.

Aday, L. A., & Andersen, R. (1974). A framework for the study of access to medical

care. Health Serv Res, 9(3), 208-220.

Aday, L. A., & Andersen, R. M. (1981). Equity of access to medical care: A conceptual

and empirical overview. Med Care, 29(12), 4-27.

Ahmed, A., & Bouis, H. (2002). Weighing what's practical: Proxy means tests for

targeting food subsidies in Egypt. Washington D.C.: International Food Policy

Research Institute.

Akin, J. S., Guilkey, D. K., & Dentmon, E. H. (1995). Quality of services and demand for

health care in Nigeria: A multinomial probit estimation. Soc Sci Med, 40, 1527-

1537.

Akin, J. S., & Hutchinson, P. (1999). Health-care facility choice and the phenomenon of

bypassing. Health Policy Plan, 14(2), 135-151.

Alleyne, G. A., Casas, J. A., & Castillo-Salgado, C. (2000). Equality, equity: Why

bother? Bull World Health Organ, 78(1), 76-77.

Andersson, F., & Lyttkens, C. H. (1999). Preferences for equity in health behind a veil of

ignorance. Health Econ, 8(5), 369-378.

Armstrong Schellenberg, J., Bryce, J., de Savingny, D., Lambrectchts, T., Mbuya, C ,

Mgalula, L., et al. (2004). The effect of integrated management of childhood

illness on observed quality of care of under-fives in rural Tanzania. Health Policy

Plan, 19,1-10.

154

Arur, A. (2008). Contracting for health services in Afghanistan: An analysis of the

changes in outpatient services utilization and quality between 2004 and 2005. The

Johns Hopkins University Bloomberg School of Public Health, Baltimore MD.

Audibert, M., & Mathonnat, J. (2000). Cost recovery in Mauritania: Initial lessons.

Health Policy Plan, 15(1), 66-75.

Bantvala, N., & Zwi, A. B. (2000). Public health and humanitarian interventions:

Developing the evidence base. British Medical Journal, 321, 101-105.

Barber, S. L., Bertozzi, S. M., & Gertler, P. J. (2007). Variations in prenatal care quality

for the rural poor in Mexico. Health Affairs, 26(3).

Bartlett, L. A., Mawji, S., Whitehead, S., Crouse, C , Dalil, S., Ionete, D., et al. (2005).

Where giving birth is a forecast of death: Maternal mortality in four districts of

Afghanistan, 1999-2002. Lancet, 365(9462), 864-870.

Black, R. E., Morris, S. S., & Bryce, J. (2003). Where and why are 10 million children

dying every year? Lancet, 361, 2226-2234.

Bostoen, K., Bilukha, O. O., Fenn, B., Morgan, O. W., Tam, C. C , ter Veen, A., et al.

(2007). Methods for health surveys in difficult settings: Charting progress,

moving forward. Emerging Themes in Epidemiology, 4(13).

Bristol, N. (2005). Editorial: Reconstructing Afghanistan's health system. Lancet, 366,

2075-2076.

Buor, D. (2004). Gender and the utilization of health services in the ashanti region,

Ghana. Health Policy, 69(3), 375-388.

155

Burnham, G. M., Pariyo, G., Galiwango, E., & Wabwire-Mengen, F. (2004).

Discontinuation of cost sharing in Uganda. Bull World Health Organ, 82(3), 187-

195.

Buse, K., & Walt, G. (1997). An unruly melange? Coordinating external resources to the

health sector: A review. Soc Sci Med, 45(3), 449-463.

Chawla, M., & Ellis, R. P. (2000). The impact of financing and quality changes on health

care demand in Niger. Health Policy Plan, 15(1), 76-84.

Collins, D., Quick, J. D., Musau, S. N., Kraushaar, D., & Hussein, I. M. (1996). The fall

and rise of cost sharing in Kenya: The impact of phased implementation. Health

Policy Plan, 11(1), 52-63.

Cortinovis, I., Vela, V., & Ndiku, J. (1993). Construction of a socio-economic index to

facilitate analysis of health in data in developing countries. Soc Sci Med, 36,

1087-1097.

Cox, M. (2001). State building and post-conflict reconstruction: Lessons from Bosnia.

Geneva: Center for applied studies in International negotiations.

CSO. (2003). Afghanistan statistical yearbook. Central statistics office. Afghanistan.

Retrieved 02/28, 2008, from http://www.cso-

af.net/cso/index.php?page=5&language=en&menutitle=Statistical%20Indicators

CSO. (2005-06). Census results. Central statistics office. Afghanistan. Retrieved 12/12,

2007, from http://www.cso-

af.net/cso/documents/estimated%20population%201384.xls

Das, J., & Hammer, J. (2005). Money for nothing: The dire straits of medical practice in

Delhi, India. Journal of Development Economics, 83(1), 1-36.

156

http://www.cso-

http://www.cso-

http://af.net/cso/documents/estimated%20population%201384.xls

Deaton, A., & Grosh, M. (2000). Consumption. In Designing household survey

questionnaires for developing countries: Lessons from 15 years of the living

standards measurement study. Washington D.C.: The World Bank.

Diggle, P. J., Heagerty, P., Liang, K. Y., & Zeger, S. L. (2002). Analysis of longitudinal

data (Second ed.): Oxford University Press.

Donabedian, A. (1980). The definitions of quality and approaches to its assessment. Ann

Arbor: Health Administration Press, School of Public Health, The University of

Michigan.

Donabedian, A. (1986). Criteria and standards for quality assessment and monitoring.

QRB Qual Rev Bull, 12, 99-108.

Donabedian, A. (1988). The quality of care. How can it be assessed? JAMA, 260, 1743-

1748.

Filmer, D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure data -

or tears: An application to educational enrollments in states in India.

Demography, 38, 115-132.

Filmer, D., & Scott, K. (2008). Assessing asset indices. (Policy Research Working Paper

No. 4605). Washington D.C.: The World Bank.

Fofack, H. (1999). Annex 4 - poverty predictors: Estimation and algorithm for computing

predicted welfare function. In T. Marchant (Ed.), Core welfare indicator survey

(CWIQ) handbook. Washington D.C.: The World Bank.

Ganatra, B., & Hirve, S. (1994). Male bias in health care utilization for under-fives in a

rural community in western India. Bull World Health Organ, 72(1), 101-104.

157

Gilson, L., Kalyalya, D., Kuchlerc, F., Lakea, S., Orangad, H., & Ouendo, M. (2001).

Strategies for promoting equity: Experience with community financing in three

African countries. Health Policy, 58(1), 37-67.

Gleditsch, N. P., Wallensteen, P., Eriksson, M., Sollenberg, M., & Strand, H. (2002).

Armed conflict, 1946-2001: A new dataset. Journal of Peace Research, 39(5).

Glei, D. A., Goldman, N., & Rodriguez, G. (2003). Utilization of care during pregnancy

in rural Guatemala: Does obstetrical need matter? Soc Sci Med, 57,2447-2463.

Gouws, E., Bryce, J., Habicht, J. P., Amaral, J., Pariyo, G., Schellenberg, J. A., et al.

(2004). Improving antimicrobial use among health workers in first-level facilities:

Results from the multi-country evaluation of the Integrated management of

childhood illness strategy. Bull World Health Organ, 82(509-15).

Grosh, M., & Baker, J. (1995). Proxy means tests for targeting social programs.

Simulations and speculation. (LSMS Working paper No. 118). Washington D.C.:

The World Bank.

Grosh, M., & Glinskaya, E. (1997). Proxy means testing and social assistance in

Armenia. Washington D.C.: The World Bank.

Grosh, M. E. (1994). Administering targeted social programs in Latin America: From

platitudes to practice. Washington D.C.: The World Bank.

Gwatkin, D. R., Rutstein, S., & Johnson, K. (2000). Socioeconomic differences in health,

nutrition, and population. H.N.PJ poverty thematic group of the World Bank.

Washington, D.C.

158

Gwatkin, D. R., Wagstaff, A., & Yazbeck, A. S. (2005). Reaching the poor with health,

nutrition, and population services. What works, what doesn't, and why?

Washinton D.C.: The World Bank.

Habicht, J. P., Victora, C. G., & Vaughan, J. P. (1999). Evaluation designs for adequacy,

plausibility and probability of public health programme performance and impact.

International Journal of Epidemiology., 28, 10-18.

Haddad, S., & Fournier, P. (1995). Quality, cost and utilization of health services in

developing countries. A longitudinal study in Zaire. Social Science and Medicine,

40(6), 743-753.

Hansen, P. M., Peters, D. H., Niayesh, H., Edwards, A., Gupta, S., Arur, A., et al.

(2008a). Determinants of primary care service quality in Afghanistan. Int J Qual

Health Care, Forthcoming December 2008.

Hansen, P. M., Peters, D. H., Niayesh, H., Singh, L. P., Dwivedi, V., & Burnham, G.

(2008b). Measuring and managing progress in the establishment of basic health

services: The Afghanistan balanced scorecard. Int J Health Plann Manage, 23,

107-117.

Hansen, P. M., Peters, D. H., Viswanathan, K., Rao, K. D., Mashkoor, A., & Burnham,

G. (2008c). Client perceptions of the quality of primary care services in

Afghanistan. International Journal for Quality in Health Care, Forthcoming

December 2008.

Hentschel, J., & Lanjouw, P. (1996). Constructing an indicator of consumption for the

analysis of poverty: Principles and illustrations with reference to Ecuador.

(LSMS Working paper No. 124). Washington D.C.: The World Bank.

159

Jacobs, B., & Price, N. (2004). The impact of the introduction of user fees at a district

hospital in Cambodia. Health Policy Plan, 19(5), 310-321.

Mali, A. A. (2006). The future of Afghanistan. Parameters: The United States Army's

Senior Professional Journal, XXXW(Spring), 4-19.

Johns Hopkins University Bloomberg School of Public Health, and Institute of Health

Management Research (2005a). Afghanistan Multiple Indicator Cluster

Survey(MICS) 2003. A re-analysis of critical health service delivery indicators.

Kabul: Ministry of Public Health. Afghanistan.


Management Research (2005b). Afghanistan National Health Services

Performance Assessment: Results of formative research. Kabul: Policy and

Planning Division. Ministry of Public Health.


Management Research (2008a). Afghanistan health sector balanced scorecard:

National and provincial results. Kabul: Policy and Planning Division. Ministry of

Public Health.


Management Research (2008b). Afghanistan Health Survey 2006: Estimates of

priority health indicators for rural Afghanistan. Kabul: General Directorate of

Policy and Planning. Ministry of Public Health.


Management Research (2008c). Final evaluation report on health financing

160

pilots: The effects of user fees vs. Free services on primary care in Afghanistan.:

General Directorate of Policy and Planning. Ministry of Public Health.

Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis.

Upper Saddle River, NJ 07458: Pearson Education International.

King, M. H. (1966). Medical care in developing countries. Nairobi, Kenya: Oxford

University Press.

Korn, E. L., & Graubard, B. I. (1999). Analysis of health surveys: John Wiley & Sons,

Inc.

Le Grand, J. (1987). Equity, health and health care. Social Justice Research, 1(3), 257-

274.

Levy, P. S., & Lemeshow, S. (1999). Sampling of populations: Methods and

applications.: John Wiley & Sons, Inc.

Litvack, J. I., & Bodart, C. (1993). User fees plus quality equals improved access to

health care: Results of a field experiment in Cameroon. Soc Sci Med, 37(3), 369-

383.

Lule, G. S., Tugumisirize, J., & Ndekha, M. (2000). Quality of care and its effects on

utilization of maternity services at health center level. East African Medical

Journal, 77, 250-255.

M.R.R.D. (2005). National Risk and Vulnerability Assessment (NRVA) 2005 results and

reports. Retrieved 02/28, 2008, from http://www.mrrd.gov.af/nss-

vau/NRV A_2005 .htm

Macinko, J. A., Starfield, B. (2002). Annotated bibliography in equity in health, 1980-

2001. International Journal for Equity in Health., 1(1).

161

http://www.mrrd.gov.af/nss-

Makinen, M., Waters, H., Rauch, M., Almagambetova, N., Bitran, R., Gilson, L., et al.

(2000). Inequalities in health care use and expenditures: Empirical data from eight

developing countries and countries in transition. Bull World Health Organ, 78(1),

55-65.

Management Sciences for Health, Health and Development Services, Management

Sciences for Health/ Europe. (2002). Afghanistan National Health Resource

Assessment. Kabul: Ministry of Health, Transitional Islamic Government of

Afghanistan.

Mariko, M. (2003). Quality of care and the demand for health services in Bamako, Mali:

The specific roles of structural, process, and outcome components. Soc Sci Med,

56(6), 1183-1196.

Mathiassen, A. (2007). A model based approach for predicting annual poverty rates

without expenditure data. Journal of Economic Inequality.

McKenzie, D. J. (2003). Measure inequality with asset indicators. Bureau for Research

and Economic Development. Working Paper No. 042.

Moisi, J. (2008). Mortality, morbidity, immunization coverage and distance to health

facilities in Kilifi district, Kenya., The Johns Hopkins University Bloomberg

School of Public Health, Baltimore MD.

Montgomery, M. R., Gragnolati, K., Burke, A., & Parades, E. (2000). Measuring living

standards with proxy variables. Demography, 37, 155-174.

Mooney, C. Z., & Duval, R. D. (1993). Bootstrapping: A non parametric approach to

statistical inference. Newbury Park, California: Sage Publications.

162

MOPH. (2003a). Interim health strategy 2002-2003. A strategy to lay foundations.

Kabul: Ministry of Health. Transitional Islamic Government of Afghanistan.

MOPH. (2003b). A basic package of health services for Afghanistan. Kabul: Ministry of

Health, Transitional Islamic Government of Afghanistan.

MOPH. (2004). Afghanistan health indicators. Kabul: Ministry of Public Health, Islamic

Republic of Afghanistan.

Mosley, W. H., & Chen, L. C. (2003). An analytical framework for the study of child

survival in developing countries. 1984. Bull World Health Organ, 81(2), 140-145.

Mwabu, G., Ainsworth, M., & Nyamete, A. (1993). Quality of care and choice of medical

treatment. An empirical analysis. Journal of Human Resources, 28(4), 836-862.

Nicholas, D. D., Heiby, J. R., & Hatzell, T. A. (1991). The quality assurance project:

Introducing quality improvement to primary health care in less developed

countries. Quality Assurance in Health Care, 3(3), 147-165.

Nyonator, F., & Kutzin, J. (1999). Health for some? The effects of user fees in the volta

region of Ghana. Health Policy Plan, 14(A), 329-341.

O'Donnell, O., van Doorslayer, E., Wagstaff, A., & Lindelow, M. (2008). Measurement

of living standards. In Analyzing health equity using household survey data. A

guide to techniques and their implementation. Washington D.C.: The World

Bank.

Pande, R. P., & Yazbeck, A. S. (2003). What's in a country average? Wealth, gender, and

regional inequalities in immunization in India. Soc Sci Med, 57(11), 2075-2088.

163

Peabody, J. W., Taguiwalo, M. M., Robalino, D. A., & Frenk, J. (2006). Improving the

quality of care in developing countries: The World Bank & Oxford University

Press.

Pedersen, D. (2002). Political violence, ethnic conflict, and contemporary wars: Broad

implications for health and social well-being. Social Science and Medicine, 55(2),

175-190.

Peters, D. H., Garg, A., Bloom, G., Walker, D. G., Brieger, W. R., & Rahman, M. H.

(2008). Poverty and access to health care in developing countries. Annals of New

York Academy of Sciences., 1136, 161-171.

Peters, D. H., Noor, A. A., Singh, L. P., Kakar, F. K., Hansen, P. M., & Burnham, G.

(2007). A balanced scorecard for health services in Afghanistan. Bulletin of World

Health Organization., 85(2), 145-151.

Peters, D. H., Yazbeck, A. S., Sharma, R. R., Ramana, G. N. V., Pritchett, L. H., &

Wagstaff, A. (2002). Better health systems for India's poor: Findings, analysis

and options. Washington D.C.: IBRD/ World Bank.

Rao, K. D., & Peters, D. H. (2007). Quality improvement and its impact on the use and

equality of outpatient health services in India. Health Econ, 16, 799-813.

Rossi, P. H., Freeman, H. E., & Lipsey, M. W. (1999). Evaluation: A systematic

approach (6th ed.). Thousand Oaks CA: SAGE Publications, Inc.

Rust, K. (1985). Variance estimation for complex estimators in sample surveys. Journal

of Official Statistics, 7(4), 381-397.

Rutstein, S. O., & Kiersten, J. (2004). The DHS wealth index. (No. 6). Calverton, MD:

ORC Macro.

164

Shaikh, B. T., & Hatcher, J. (2004). Health seeking behavior and health service

utilization in Pakistan: Challenging the policy makers. Journal of Public Health,

27(1), 49-54.

Standing, H. (1997). Gender and equity in health sector reform programmes: A review.

Health Policy and Planning, 72(1), 1-18.

StataCorp. (2004a). Survey data and other reference manuals. Stata statistical package.

Release 8. College Station, TX: Stata Corp LP.

StataCorp. (2004). Stata statistical package. Release 8. College station, TX.: Stata Corp

LP.

Steinhardt, L. C , Waters, H., Rao, K. D., Naeem, A. J., Hansen, P. M., & Peters, D. H.

(2007). The effect of wealth status on care seeking and health expenditures in

Afghanistan.Unpublished manuscript.

Stifel, D., & Christiansen, L. (2007). Tracking poverty over time in the absence of

comparable consumption data. The World Bank Economic Review, 27(2), 317-

341.

T.I.S.A. (2004). Securing Afghanistan's future: Accomplishments and the strategic path

forward. (Government/ International agency report). Kabul.

Tlebere, P., Jackson, D., Loveday, M., Matizirofa, L., Mbombo, N., Doherty, T., et al.

(2007). Community-based situation analysis of maternal and neonatal care in

south africa to explore factors that impact utilization of maternal health services.

Journal of Midwifery & Women's Health, 52(4), 342.

165

Toole, M. J., Waldman, R. J., & Zwi, A. B. (2001). Complex humanitarian emergencies.

In M. H. Merson, R. E. Black & A. J. Mills (Eds.), International public health:

Diseases, programs, systems, and policies. Gaithersburg: Aspen.

UNDP. (2004). Afghanistan National Human Development Report. Security with a

human face: Challenges and responsibilities. Kabul.

UNICEF. (2000). Afghanistan multiple indicator cluster survey. Situational analysis of

children and women in east of Afghanistan. Retrieved 12/12, 2007, from

http://www.childinfo.org/MICS2/newreports/afghanistan/Afghanistan2.PDF

UNICEF. (2004a). Afghanistan: Progress of provinces. Multiple indicators clusters

survey 2003. Retrieved September 12, 2008, from

http://www.childinfo.org/MICS2/newreports/afghanistan/AfghanistanMICS2003.

£df

UNICEF. (2004b). The State of Worlds Children 2004. New York.

UNICEF. (2006). Best estimates of social indicators for children in Afghanistan: 1990-

2005. New York: United Nations Children's Fund.

Verma, V., Scott, C , & O'Muircheartaigh, C. (1980). Sample designs and sampling

errors for the world fertility survey. Journal of the Royal Statistical Society. Series

A (General). 143(4), 431-473.

Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How

to use principal components analysis. Health Policy and Planning, 21(6), 459-

468.

W.H.O. (1999). IMCI information package: IMCI indicators, monitoring, and

evaluation. Geneva: WHO.

166

http://www.childinfo.org/MICS2/newreports/afghanistan/Afghanistan2.PDF

http://www.childinfo.org/MICS2/newreports/afghanistan/AfghanistanMICS2003

Wagstaff, A. (2002). Poverty and health sector inequalities. Bull World Health Organ,

80(2), 97-105.

Waldman, R., & Hanif, H. (2002). The public health system in Afghanistan: Current

issues. Kabul: Afghanistan Research and Evaluation Unit.

Waters, H., Garrett, B., & Burnham, G. (2007). Rehabilitating health systems in post-

conflict situations. (Research Paper): United Nations University - World Institute

of Developement Economics Research.

Wolter, K. M. (2007). Introduction to variance estimation. (2nd ed.). New York:

Springer.

Wong, E. L., Popkin, B. M., Guilkey, D. K., & Akin, J. S. (1987). Accessibility, quality

of care and prenatal care use in the Philippines. Soc Sci Med, 24(11), 927-944.

World Bank. (1993). World Development Report: Investing in health. Washington D.C.:

The World Bank.

World Bank. (1998). Post conflict reconstruction: The role of the World Bank.

World Bank. (2005). Afghanistan: Poverty, vulnerability and social protection - an initial

assessment. Washington D.C.: The World Bank.

World Bank. (2006). World development report: Equity and development. Washington

D.C.: The World Bank.

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Curriculum Vita Shivam Gupta

[email protected]

EDUCATION:

9/04 -10/08 The Johns Hopkins Bloomberg School of Public Health. Doctor of Philosophy, International Health, Health Systems Program. Thesis topic: Methods for population-based assessments in post-conflict settings: Health service performance, economic status and equity of utilization in Afghanistan.

7/03 - 5/04 The Johns Hopkins Bloomberg School of Public Health. Master of Public Health. Program concentration: Women's and Reproductive Health.

7/97 - 3/03 Manipal Academy of Higher Education. Bachelor of Medicine and Bachelor of Surgery (equivalent to M.D.), Kasturba Medical College, Karnataka, India.

SELECTED WORK EXPERIENCE:

10/07-3/08 Consultant, World Bank. Analyzed the differences in health outcomes, the main determinants of health outcome variations, and helped in identifying the main policy interventions that have taken place within Afghanistan after the removal of Taliban in 2001.

6/04 - 6/07 Monitoring and Evaluation Specialist, Johns Hopkins University. Assisted with design, development of instruments, writing field manuals, conducting training, supervising data collection and conducting analysis for the Afghanistan National Health Services Performance Assessment.

3/03 - 7/03 Research Officer, Institute of Health Management Research, India. Assisted the faculty with conceptualizing research questions, development of research proposal and study designs, data analysis and writing draft reports.

2/02 - 2/03 Medical Intern, Sawai Man Singh Medical College, Jaipur, India. One year rotating internship. Worked as physician's assistant at the rural health training center for six weeks, urban health training center for two weeks, immunization center, infectious disease hospital, family planning clinic (one week each). Also worked as an assistant to the chief resident in departments of Pediatric Medicine, Internal Medicine, Surgery, Ophthalmology and Otolaryngology.

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mailto:[email protected]

SELECTED TEACHING EXPERIENCE:

6/04 - 12/06 Graduate Teaching Assistant, Johns Hopkins Bloomberg School of Public Health. Assisted the faculty in teaching the following courses: Pregnancy Outcomes in Developing and Developed Countries (summer intersession 2004), Principles of Epidemiology (2004), Managing Health Services Organizations (2004 - 2006).

PUBLICATIONS:

Hansen PM, Peters DP, Niayesh H, Edward A, Gupta S, Arur A, Burnham G. Determinants of primary care service quality in Afghanistan. International Journal for Quality in Health Care. Forthcoming, December 2008.

Pandian V, Vaswani R S, Mirski M A, Haut E, Gupta S, Bhatti N I. Safety of Percutaneous Dilatational Tracheostomy in Coagulopathy Patients. Ear, Nose and Throat Journal, (Accepted for publication September 12, 2008)

LANGUAGES: • English & Hindi: native fluency. • Dari: beginner.

FELLOWSHIPS AND AWARDS:

• 2005 - 2008: Department Fellowship, International Health, Bloomberg School of Public Health, Johns Hopkins University.

• 2002: Fellowship by the International Epidemiological Association (IEA) to present a paper at the XVI World Congress of Epidemiology in Montreal, Canada.

PERSONAL DATA: Born July 16, 1979 in Jaipur, Rajasthan, India.

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Documents

METHODS FOR POPULATION-BASED ASSESSMENTS IN POST CONFLICT SETTINGS: HEALTH SERVICE PERFORMANCE, ECONOMIC STATUS AND EQUITY OF UTILIZATION IN AFGHANISTAN