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The Pharmacy DiabetesCare Program is funded by the Australian Government Department of Health & Ageing
as part of the Third Community Pharmacy Agreement.
Pharmacy DiabetesCare Program
PDCP
FINAL REPORT APRIL 2005
THE UNIVERSITY OF SYDNEY FACULTY OF PHARMACY
1
Chief Researcher
Assoc. Prof. Ines Krass, BPharm, DHP, PhD, Dip Ed Studies (Health Ed), MPS
Institution and contact person for correspondence:
Assoc. Prof. Ines Krass, Faculty of Pharmacy, University of Sydney
Tel: (02) 9351-3507
Fax: (02) 9351-4451
E-mail: inesk@pharm.usyd.edu.au
Project team University of Sydney, New South Wales
Assoc. Prof. Ines Krass
Professor Carol Armour
Dr Sue Taylor
Dr Bernadette Mitchell
Dr Martha Brillant
Monash University, Victoria
Dr Kay Stewart
Dr Phyllis Lau University of Tasmania, Tasmania
Professor Greg Peterson
Ms Rachel Dienaar
Ms Bronwen Colhoun
Curtin University, Western Australia
Mr Jeff Hughes
Ms Jenny Wilkinson
Ms Gill Pugh Economic Analysis
Dr Philip Clarke (University of Oxford, University of NSW)
2
Professor Stephen Colagiuri (Department of Endocrinology and Diabetes, Prince of Wales Hospital)
Acknowledgements
Australian Government Department of Health and Ageing – For funding this
project through the Third Community Pharmacy Agreement.
Pharmacy Guild of Australia – For having the vision to support research into new
clinical services for community pharmacy.
Abbott Diagnostics – For supplying the Medisense meters at cost, the Precision
Link software at no cost and supporting the monitoring of blood glucose levels in
community pharmacy. We gratefully appreciate the assistance of Kim Gosbell who
presented at our Training Workshops and supported individual pharmacists as
required.
The Pharmacists – For enthusiastically taking on this program and giving us
valuable feedback on the implementation of screening and disease state
management services in community pharmacy. The pharmacists who participated in
each State were as follows:
New South Wales: Louise Dixon, Albert Regoli, Anderson Leong,
Stuart Ludington, Jane Ludington, Patricia Thatcher, Catherine De Jonge, Divesh
Kana, Hans Kasch, Sara (Rowena) Kasch, Phil Davies, Khang Nyugen, Mark
Sampson, Michelle Spiro, Kannas Wong, Mark Davis, Sarah Stephenson, Roger
Sham, Roger Hankins, Cheryl Nobb, Suzanah Natos, David Phillips, Ros
Stonehouse, Glenn Steele, Alan Martin, Anne O’Leary, Alison Clark, Anisa Hayati,
Kenneth Wicks, Karl Ehmann and Warwick Bremner.
Victoria: Ben Le, Marsha Watson, Olga Radywyl, Ah Kow Foo, Sherri Rinaldi,
Raymond Chan, Ian Davis, Stephanie Cheng, Cathy Stamboulakis, Dinesh
3
Solanki, Orna Tuckman, Nicki Le, Graeme Weideman, Thanh Tran, Elizabeth
Lucas, Christine Mak and Shirley Neoh.
Western Australia: David Henrisson, Jocelyn Gamble, Kendal Heal, Frank
Wallner, Darren Phoon, Cathy Larkin, Cathy Green, Leonie Cooke, Amilia Chung,
Merelle Perrozzi, Vincent Cosentino and Craig Clarke
Tasmania: Christine White, Claire Nankivell, Shane Jackson, Kendall Carswell,
Sophie Bishop, Greg Finlayson, Theresa Niekrasz, Elizabeth Hope, Madeline
Bowerman, Olivia Gillie and Ian Montgomerie.
Training Workshops: We are grateful to the following people for their contribution:
New South Wales: Professor Jennie Brand-Miller (Glycaemic Index), Ms Kim
Gosbell (Abbott Diagnostics), Ms Megan Spindler (Fingerprick Techniques), Dr
Lorraine Smith (Communication), Professor Don Chishohm (Endocrinologist), Ms
Jan Alford (Diabetes educator), Ms Linda Turner (Diabetes Educator) and Ms
Carlene Smith (Home Medicines Review).
Victoria: Ms Karen Hirth (Team Leader, General Medicine Pharmacy Team,
Alfred Hospital), Ms Helen Matters (Dietician), Mr Joseph Chamoun (Abbott
Diagnostics), Ms Susan North (Diabetes Educator), Dr Jenny Gowan (Home
Medicines Review).
Western Australia: Mr Mark Coles (Community Pharmacist and Diabetes
educator), Ms Susan O’Hara (use of BG meter and associated software).
Tasmania: Dr Tim Greenaway (Endocrinologist, Royal Hobart Hospital), Ms Anne
Muskett (Diabetes Educator), Ms Helena Hain (Diabetes Educator), Ms Tracey
Tasker (Dietician), Mr Camron Randall (Clinical Pharmacist, Royal Hobart
Hospital).
Steering Committee Members: Dr John Primrose (Dept of Health and Ageing), Mr
Lance Emerson (Pharmacy Guild of Australia), Dr Simone Jones (Pharmacy Guild of
Australia), Mr Rob Foster (Pharmacy Guild of Australia), Dr John Aloizos (National
4
Integrated Diabetes Program Advisory Group), Dr Ralph Audehm (Australian Division
of General Practice), Mr John Chapman (Australian Institute of Pharmacy
Management), Ms Ruth Colagiuri (Australian Centre for Diabetes Strategies),
Professor Stephen Colagiuri (Director of Diabetes Services, POW Hospital), Mr Brian
Conway (Diabetes Australia), Dr. Margo Hoekstra (Australian Division of General
Practice), Mr Allan Neate (Department of Health and Ageing), Mr Brendan O’Loughlin
(Pharmacy Guild of Australia), Mr Albert Regoli (Community Pharmacist), Mr
Matthew Ryan (Pharmaceutical Society of Australia).
Ms Clare Delaney – For enthusiastically assisting in conducting the Screening
program follow-up patient survey and for conducting the DMAS program patient
satisfaction survey.
5
Table of Contents
Project Team 1
Acknowledgements 2
Executive Summary 12
1. Background 19
1.1 Diabetes – Scope of the Problem 19
1.2 Screening for Type 2 Diabetes 20
1.2.1 Opportunistic Screening in the Health Care System – the Case
for Pharmacy 22
1.2.2 Guidelines for the Screening and Evaluation of High Risk
Individuals 23
1.3 Optimum Care for Type 2 Diabetes 24
1.4 Disease State Management (DSM) Programs for Patients with
Type 2 Diabetes 26
1.5 Cost Effectiveness of Diabetes Care Programs 27
1.5.1 Screening 27
1.5.2 DSM Programs 27
1.6 Study Rationale 29
2. Methods 30
2.1 Steering Committee 30
2.2 Research Design 30
2.3 Recruitment of Pharmacies 32
2.4 Screening Program 33
2.4.1 Screening Program – Study Design 33
2.4.2 Screening Program – Training for Pharmacists 37
2.4.3 Screening Program – Resources for Pharmacists 38
2.4.4 Screening Program – Exit Surveys for Observable Risk
Factors 38
2.4.5 Screening Program – Patient Follow-up Survey 39
2.4.6 Screening Program – Pharmacist Satisfaction 40
2.4.7 Screening Program – Statistical Analysis 40
2.4.8 Screening Program – Economic Analysis 41
6
2.5 Diabetes Medication Assistance Service (DMAS) 45
2.5.1 DMAS Program – Study Design 45
2.5.2 DMAS Program – Sample Size 46
2.5.3 DMAS Program – Development of the Clinical Protocols 46
2.5.4 DMAS Program – Training of Intervention Pharmacists 47
2.5.5 DMAS Program – Resources for Pharmacists 49
2.5.6 DMAS Program – Patient Recruitment 49
2.5.7 DMAS Program – Evaluation of the Service 52
2.5.8 DMAS Program – Questionnaire Properties 54
2.5.9 DMAS Program – Communication with Pharmacists 55
2.5.10 DMAS Program – Quality Control 55
2.5.11 DMAS Program – Patient Satisfaction 55
2.5.12 DMAS Program – Pharmacist Satisfaction 56
2.5.13 DMAS Program –Statistical Analysis 56
2.5.14 DMAS Program – Economic Analysis 58
3. Results – Screening Program 65
3.1 Screening Program 65
3.1.1 A Comparison of the two Screening Protocols 66
3.1.2 Characteristics of the Screened Population, Study Participants,
and Diagnosed Participants 69
3.1.3 Results of Blood Glucose Testing in the SS Method 73
3.2 Exit Surveys for Observable Risk Factors 73
3.3 Patient Follow-up Survey 75
3.3.1 Awareness of the Service 76
3.3.2 Health Information 76
3.3.3 Approval of the Service 77
3.3.4 Satisfaction with the SS Method 79
3.3.5 Preference for Location of Service 79
3.3.6 Willingness to Pay 80
3.4 Pharmacist Satisfaction 80
3.5 Economic Analysis of the Screening Program 82
3.5.1 Costs 82
3.5.2 Outcomes 83
3.5.3 Cost-effectiveness 83
7
3.5.4 Sensitivity Analysis 85
4. Results – DMAS Program 87
4.1 Recruitment and Completion 88
4.1.1 Pharmacies 88
4.1.2 Study Participants 89
4.2 Baseline Assessment 91
4.2.1 Participant Demographics 91
4.2.2 Diabetes History 93
4.2.3 Clinical Parameters at Baseline 93
4.2.4 Humanistic Parameters at Baseline 95
4.2.5 Medications Use at Baseline 98
4.3 Service Evaluation 99
4.3.1 Process Evaluation 99
4.3.2 Clinical Outcomes – Blood Glucose Readings 103
4.3.3 Clinical Outcomes – Blood Pressure Readings in Pharmacy 104
4.3.4 Clinical Outcomes – Clinical Parameters at Baseline and
Completion 104
4.3.5 Humanistic Outcomes 108
4.3.6 Medication Usage 113
4.3.7 DMAS Program – Patient Satisfaction 118
4.3.8 DMAS Program – Pharmacist Satisfaction 121
4.4 Economic Analysis 126
4.4.1 Outcomes 126
4.4.2 Costs 127
4.4.3 Cost-effectiveness 130
4.4.4 Sensitivity Analysis 134
5. Discussion 136
5.1 Screening Program 136
5.1.1 Consumer Perceptions of the Pharmacy Diabetes Care
Screening Program 139
5.1.2 Economic Analysis of the Screening Program 140
5.1.3 Limitations of Study 141
5.1.4 Conclusion 142
8
5.2 DMAS Program 144
5.2.1 DMAS Program – Patient Satisfaction 148
5.2.2 DMAS Program – Pharmacist Satisfaction 149
5.2.3 Economic Analysis of the DMAS Program 149
5.2.4 DMMR – Domiciliary medicine management review 151
5.2.5 Study Limitations 151
6. Conclusion 153
7. References 154
List of Figures: Figure 1: Research Design of the Pharmacy Diabetes Care Program 31
Figure 2: Study Design and Hypotheses of Screening Service 34
Figure 3: Sequential Screening Protocol (based on NHMRC guidelines) 36
Figure 4: Assumptions regarding timepaths of HbA1c 61
Figure 5: Flowchart of outcomes of the diabetes screening program 67
Figure 6: Percentage of people screened who qualified for referral using either the TTO or SS method 68
Figure 7: Percentage of people who qualified for referral who subsequently took up the referral using either the TTO or SS method 68
Figure 8: Percentage of people screened who were diagnosed with prediabetes or diabetes using either the TTO or SS method 68 Figure 9a: Random blood glucose measurements (NSW & TAS) 74
Figure 9b: Fasting blood glucose measurements (NSW & TAS) 74
Figure 10: The effect of receiving information/advice on exercise and healthy eating 77 Figure 11: Preference for the location of the screening service 79
Figure 12: Cost-effectiveness plane of TTO vs. SS 85
Figure 13: Flowchart of DMAS recruitment and completion 90
Figure 14: Percentage of patients who received interventions 100
Figure 15: Percentage of patients who received interventions related to adherence 101
Figure 16: Percentage of patients who received interventions related to medication history 101
Figure 17: Percentage of patients who received interventions related to home blood glucose monitoring 102
9
Figure 18: Percentage of patients who received interventions related to lifestyle 102
Figure 19: Blood glucose readings (mean ± 95%CI) at the four pharmacy visits 103
Figure 20: Percentage of blood glucose readings (mean ± 95%CI) within the target range at the four pharmacy visits 103
Figure 21: Blood pressure (mean ± 95%CI) at each intervention visit 104
Figure 22: HbA1C at baseline and completion of the DMAS study 107
Figure 23: Percentage of participants who reached target BP 107
Figure 24: Difference in the estimated proportion of patients surviving between the control and intervention groups 127
Figure 25: Life Years - Scenario A 131
Figure 26: Life Years - Scenario B 131
Figure 27: Quality Adjusted Life Years - Scenario A 132
Figure 28: Quality Adjusted Life Years - Scenario B 132
Figure 29: Cost effectiveness acceptability curves indicating the probability that the DMAS is cost effective (y axis) for different levels of willingness to pay for a life year 133
Figure 30: Cost effectiveness acceptability curves indicating the probability that the DMAS is cost effective (y axis) for
different levels of willingness to pay for a QALY 133 List of Tables: Table 1: Breakdown of Target Sample Size for the Screening Program by State 34
Table 2: Main unit costs by type and stage of screening 44
Table 3: Breakdown of target DMAS sample size by State 46
Table 4: Evaluation of the Service 53
Table 5: Main unit costs for selected therapies & cost of complications 64
Table 6: Summary of numbers screened and diagnosed 66
Table 7: Risk estimates of qualifying for referral, referral uptake, and diagnosis of prediabetes or diabetes using the SS method compared to the TTO method 69 Table 8: Number of diabetes risk factors possessed by the screened population .69
Table 9: Distribution of risk factors for type 2 diabetes within the screened population by screening method 71
Table 10: Distribution of risk factors for type 2 diabetes within the screened population by diagnostic category 72
Table 11: Demographic and lifestyle characteristics of the study participants 72
10
Table 12: Demographic and lifestyle characteristics of participants diagnosed with
prediabetes or diabetes 73 Table 13: Customers with one or more observable risk factors 75
Table 14: Estimated rate of at-risk population captured by the screening program 76
Table 15: Summary of numbers surveyed 77
Table 16: Approval of diabetes screening being available in community pharmacy 78
Table 17: Reasons for approval of screening in community pharmacy 79
Table 18: Costs and effects by allocation group 83
Table 19: Impact of different assumptions regarding increases in costs 86
Table 20: Demographic characteristics of pharmacists 88
Table 21: Demographic characteristics of pharmacies 89
Table 22: Breakdown by State of enrolled patients 91
Table 23: Breakdown by State of completed patients 91
Table 24: Demographic characteristics of DMAS participants 92
Table 25: Diabetes history of DMAS participants at baseline 94
Table 26a: Clinical parameters of DMAS participants at baseline 95
Table 26b: Smoking status and physical activity of participants at baseline 96
Table 27: Humanistic parameters of DMAS participants at baseline 97
Table 28: Mean numbers of medications at baseline 99
Table 29a: Clinical parameters of participants at baseline and completion of DMAS study 105
Table 29b: Clinical parameters of participants at baseline and completion of the DMAS study 106
Table 30: Comparison of change in HbA1c between control and intervention groups 106
Table 31a: Humanistic parameters of participants at baseline and completion of DMAS study 109
Table 31b: Humanistic parameters of participants at baseline and completion of DMAS study 111
Table 31c: Humanistic parameters of participants at baseline and completion of DMAS study 112
Table 32: Mean numbers of medications per patient at baseline and completion of the DMAS 114
Table 33: Defined daily doses of most commonly used medications at baseline and completion of the DMAS 116
11
Table 34: Most common antidiabetic medication combinations at baseline and completion of the DMAS program 117
Table 35: Antihypertensive regimen at baseline and completion of DMAS program 117
Table 36: Modelled outcomes based on a DMAS of 10 years duration 128
Table 37: Modelled costs in 2004 A$ based on a DMAS of 10 years duration 129 Table 38: Summary of results from the sensitivity analysis (2004 A$) 135
Appendices:
Appendix 1 Minutes of Steering Committee Meetings
Appendix 2 Ethics Approval
Appendix 3 Screening Documentation
Appendix 4 Training of Pharmacists
Appendix 5 Promotional Material
Appendix 6 DMAS Protocols
Appendix 7 GP Documentation
Appendix 8 Communication with Pharmacists
Appendix 9 Patient and Pharmacist Satisfaction with DMAS
Appendix 10 Additional DMAS Patient Information on Demographics and
Medications
12
Sequential Screening (SS)
NSW Tas
Target 750
Tick test only (TTO)
WA Vic
Target 750
H0: No difference between the case detection methods in rates of
• referral to GPs • uptake of referrals • diagnoses of IFG, IGT and type 2 diabetes
EXECUTIVE SUMMARY
PHARMACY DIABETES CARE PROGRAM (PDCP) The Pharmacy Diabetes Care Program was designed to investigate a Disease State
Management (DSM) Model for people with type 2 diabetes. The model consists of two
components, a Screening service and a Diabetes Medication Assistance Service
(DMAS). The critical elements of the service include patient education, support and
monitoring to facilitate self-management in those with established disease. For those at
risk, the focus is on education and referral.
SCREENING PROGRAM - OBJECTIVE The specific aim of the screening program was to investigate the capacity of community
pharmacies to identify and refer people at risk of type 2 diabetes to their General
Practitioner (GP). Thirty community pharmacies were recruited across 4 States – NSW,
VIC, TAS and WA.
SCREENING PROGRAM - RESEARCH DESIGN
The screening service delivered through the pharmacy, utilised two screening protocol
variants, the sequential screening (SS) and tick test only (TTO). Both protocols used a tick
test risk assessment to determine if risk factors for type 2 diabetes were present. In the SS
protocol, any person with at least one risk factor was also offered a fingerprick test for
capillary blood glucose in the pharmacy. Patients whose blood glucose levels were higher
than a predefined level were referred to their GP. In the TTO protocol, no fingerprick testing
was performed in the pharmacy and if the patient had at least one risk factor for type 2
diabetes they qualified for a referral to the GP.
Study Design and Hypotheses of Screening Service
13
SCREENING PROGRAM – CONCLUSIONS
In conclusion, the SS method was significantly more efficient and cost-effective than the TTO
method and could be successfully implemented in community pharmacies resulting in fewer
unnecessary referrals to the GP while resulting in a higher rate of diagnosis. The benefits of
conducting the capillary blood glucose testing (fingerprick testing) in the pharmacy appear to
be twofold; it eliminates those people with risk factors whose blood glucose levels are
normal, and people who receive the fingerprick test in the pharmacy take the screening
service more seriously than those who receive the TTO method and are more likely to act
upon a referral to the GP.
Consumers were very satisfied with and strongly approved the diabetes screening in
community pharmacy. Community pharmacies provide an ideal environment for the
provision of extended pharmacy services. Over time patients have become more accepting
Screening Program - Key Findings:
A total of 1286 people were screened in 30 pharmacies.
Twenty-four people were diagnosed with prediabetes (1.9% of the total screened),
and 10 people were diagnosed with diabetes (0.8% of the total screened).
Rates of qualifying for referral were lower in the sequential screening (SS) method
compared to the tick test only (TTO) method.
Rates of referral uptake were higher for the SS method compared to the TTO
method.
Rates of diagnosis of diabetes were higher for the SS method (1.7%) compared to
the TTO method (0.2%).
The most common risk factors amongst participants diagnosed with prediabetes or
diabetes were: 1) being over 55 yrs of age and 2) being over 45 with a body mass
index (BMI) greater than 30 kg/m2.
Patients were 7 times more likely to be identified as having diabetes using the SS
method than the TTO method.
The median approval rating of the screening service was high (5 out of 5).
The average cost per case detected was A$788 for SS method compared to
A$6,000 for the TTO method.
If 100,000 individuals were opportunistically screened using the SS method then
the total cost would be in the order of A$2.18 million dollars, of which approximately
A$1.26 million would be incurred at the pharmacy level.
Overall the SS method was superior both from a cost and efficacy perspective.
14
and welcoming of extended services in community pharmacy, largely due to convenience
and increased likelihood of service participation. Our results suggest that future provision of
extended services, including diabetes screening, would be adopted and supported by
patients in Australian community pharmacies.
Our results indicate that the SS method should be considered as the preferred option for
screening if a community based pharmacy screening program was to be established in
Australia. Given the potential number of undiagnosed diabetes patients in the community,
community pharmacy screening could have a high impact. The effectiveness of the SS
program at detecting undiagnosed cases of prediabetes and diabetes in community
pharmacy compares favourably with other studies. It is also cost effective when compared
with other studies.
DMAS PROGRAM - OBJECTIVE The specific aims of the Diabetes Medication Assistance Service (DMAS) were to examine
the role of the community pharmacist in the disease state management for type 2 diabetes;
to implement a specialized service for patients with type 2 diabetes; to evaluate the model in
terms of process and outcomes indicators; and to investigate patient and pharmacist
satisfaction with the service.
DMAS PROGRAM – RESEARCH DESIGN The DMAS utilised a multisite clustered, randomised control versus intervention, repeated
measures design within four states in Australia. The 56 community pharmacies recruited for
the DMAS program came from a representative sample of urban and rural Local Government
Areas across four States – NSW, VIC, TAS and WA.
Intervention patients received the DMAS, an on-going cycle of assessment, management
and review provided at 4 visits at regular intervals over 6 months in the pharmacy. These
services included blood glucose monitoring, education, adherence assessment, and
reminders of follow-up checks for complications related to diabetes. Control patients were
assessed at 0 and 6 months and received no intervention other than the usual pharmacist’s
advice/care over the 6 month period.
15
Study Design and Hypotheses of DMAS
30 Intervention Pharmacies
Diabetes Medication Assistance Service
30 Control Pharmacies
No Service
H0: There will be no significant difference between intervention and control groups pre- and post intervention in
mean HbA1c, blood glucose levels, BP, TC and medication adherence mean Quality of Life, and well-being scores cost per life year and cost per quality adjusted life year
DMAS Program - Key Findings:
High completion rates for the DMAS were achieved – 84% (149/176) for
intervention patients and 88% (140/159) for control patients.
Over the course of the DMAS, intervention pharmacists delivered a mean of 29
interventions per patient; 36% related to home blood glucose monitoring, 31%
related to medication adherence and 29% related to lifestyle and foot care issues.
For the intervention subjects:
o The mean blood glucose levels steadily decreased over the four visits
from 9.4mmol/L at the first visit to 8.5mmol/L at the final visit (p<0.01).
o Mean systolic BP dropped from 143mmHg at the first visit to 137mmHg at
the final visit (p<0.01).
By the end of the study, significantly greater improvements in glycaemic control
were seen in the group who received the DMAS compared to those who did not
receive the service; i.e., a mean reduction in HbA1C of -0.97% (95%C: -0.8, -1.14)
in the intervention group compared with -0.27% (95% CI: -0.15, - 0.39) in the
control group.
Important improvements in humanistic outcomes seen only in the DMAS group
included increased understanding of long term management of diabetes (p<0.01),
and better adherence to medications (p<0.01). There were also trends to
improvement in QOL (EQ-5D utility score) (p=0.07) and well being (p=0.06).
16
DMAS - CONCLUSIONS
The DMAS was effective at improving diabetes control as measured by blood glucose levels
and HbA1C. The service increased patients understanding of long-term management of their
diabetes and improved their adherence to medications. Pharmacists identified and utilized a
range of interventions (4309 for 149 patients) to improve the care and well-being of their
patients. Monitoring of the progress of the disease appeared to be an essential element of
the disease state management process. Both pharmacists and patients identified several
benefits of the service and expressed great satisfaction with the service. The DMAS is cost
effective when compared to other programs.
HbA1C at baseline and completion of the DMAS study
8.08.3
7.9
8.9
7.07.5
8.08.59.0
9.510.0
baseline final
Mea
n H
bA1C
(%)
Control (n = 107) Intervention (n = 125)
DMAS Program - Key Findings Continued:
Patients reported great satisfaction with the DMAS, citing improvements in their
knowledge about diabetes, self confidence, self efficacy and motivation in its
management, as major benefits.
Pharmacists also expressed great satisfaction with their involvement in the
delivery of DMAS especially in terms of knowledge and confidence gained,
benefits for their business and improvements in self management observed in
their patients.
If the reduction in HbA1C achieved during the trial continued over a ten year
period it would produce an increase in life expectancy up to 0.23 (95%CI:-0.10,
0.55) and 0.18 (95%CI: -0.08, 0.45) quality-adjusted life years per patient.
The cost effectiveness of DMAS compares favourably with other accepted uses of
health care resources funded by the Australian Government. The cost per annum
of the service would be $340. The cost per life year was estimated to be from
$17,752 to $24,029 and the cost per QALY was estimated to be from $22,486
to $30,582 (depending on the scenario used).
17
RECOMMENDATIONS The Pharmacy Diabetes Care Program, a specialised service for diabetes
screening and disease state management, is a clinically effective professional
service suitable for implementation in a broad range of community pharmacy
settings.
The program is cost effective relative to other health interventions that have been
funded by the Australian Commonwealth Government.
There are significant health benefits for the diabetes patient and satisfaction for
pharmacists in delivering the service.
For implementing the Pharmacy Diabetes Care Program
o The screening service in community pharmacy should utilise the sequential
screening protocol.
o The DMAS should target patients who require support to achieve optimal
control (therapeutic targets) for HbA1C, blood pressure and other modifiable
cardiovascular risk factors.
Prior to implementing the Pharmacy Diabetes Care Program a suitable
accreditation process needs to be established to ensure standards of service
delivery by pharmacists.
A professional pharmacy fee for the delivery of the screening and DMAS services
is recommended to enable the wide adoption of this enhanced professional role
by community pharmacists.
Implementation of the Pharmacy Diabetes Care Program should align with other
Diabetes Care initiatives eg (National Integrated Diabetes Programme) to ensure
inter-professional collaboration and seamless care for the patient.
18
Further research should investigate the optimum intensity of DMAS services and
monitor patient outcomes including complications over a meaningful time period
to assess the long term impact of the program on diabetes health care.
The sustainability of the program should also be assessed over a period of at
least 2 years rather than the 6 month period used in this study. The cost per
patient/year of such a program is likely to be reduced if the program is extended
beyond the initial intervention and monitoring period.
19
1. BACKGROUND 1.1 DIABETES – SCOPE OF THE PROBLEM
Diabetes is a common condition that contributes significantly to premature
mortality, morbidity, disability and loss of potential years of life 1, 2. The
incidence and prevalence of diabetes are on the rise worldwide 3-6. In particular,
type 2 diabetes is also increasingly occurring at a younger age, including in
adolescence and childhood 6-9.
The Australian Diabetes, Obesity and Lifestyle Study (AusDiab), was the first
ever national study to determine the prevalence of diabetes, obesity and other
cardiovascular disease risk factors including hypertension and abnormal serum
lipid profiles 10. This study has shown that by world standards for a Western
nation, the prevalence of diabetes and its co-morbidities is very high 10. An
estimated 940,000 Australians over 25 have diabetes and around half of these
people are currently undiagnosed 10, 11. Almost 1 in 4 Australians aged 25 years
and over has diabetes or a condition of impaired glucose metabolism 10. The
number of adults with diabetes has trebled since 1981 and the high rates of
diabetes and impaired glucose metabolism, coupled with those of obesity,
dyslipidaemia and hypertension, constitute a significant threat in terms of the
socioeconomic burden of cardiovascular disease and diabetic complications for
Australia 3, 10.
Diabetes and its associated complications, which include cardiovascular, kidney
and eye diseases, compromise the quality of life of a large number of
Australians 1, 12. They also constitute a sharply increasing component of health
care costs, and this increase is likely to continue as the population ages
further 13. The direct annual healthcare costs of diabetes in Australia in 2003
were A$2.2 billion 14. In recognition of the burgeoning health threat posed by
diabetes, Australian Health Ministers declared it as the fifth National Health
Priority Area in 1996 2.
20
Diabetes can often be prevented or controlled using cost-effective intervention
strategies. Early detection is important because diabetes, in particular type 2
diabetes, can remain asymptomatic for many years and significant diabetes-
related complications may set in before the diagnosis is made 1. There is a
recognised need to improve community awareness of the importance of early
detection of diabetes and its complications. Raising awareness about
undiagnosed diabetes among health professionals and improving screening and
detection skills are important in increasing rates of early detection 1.
1.2 SCREENING FOR TYPE 2 DIABETES
There are three broad approaches to diabetes screening; population-based, selective
and opportunistic case detection. Population-based approaches attempt to screen
everybody while selective screening targets groups at high risk in the community.
Opportunistic case detection involves screening individuals during routine encounters
with the health care system 15.
Generally, population based-screening in asymptomatic populations is appropriate if
all of the following conditions are met:
1. The disease is a significant health problem
2. The natural history of the disease is understood
3. There is an identifiable pre-clinical stage of the disease
4. Tests are reliable
5. The benefits of treatment after early detection are better than those obtained if
treatment is delayed
6. The process is cost effective
7. The screening will be a systematic ongoing process
How well does type 2 diabetes meet these conditions? Unquestionably it is a
significant health problem, which can be identified by the presence of post-prandial
and /or fasting hyperglycaemia through reliable laboratory blood testing procedures,
even before typical symptoms develop. Hence conditions 1-4 are clearly met 15, 16.
21
Whether the benefits of treatment after early detection are better than those obtained
if treatment is delayed are far from clear. The proponents of broader population
screening in asymptomatic people, base their rationale on three main arguments: 1)
one third to one half of type 2 diabetes is undiagnosed; 2) complications are
frequently present on diagnosis; and 3) earlier diagnosis and hence treatment is
believed to prevent or delay complications 15.
However, little is known about adherence to lifestyle changes and/or medication by
people who have been diagnosed through screening. To date, no randomised
controlled trials (RCTs) have been conducted to assess the effectiveness of
screening programs in decreasing mortality and morbidity from diabetes. In other
words, studies which apply available treatments to a screened group but not to a
control group have not been conducted because of issues such as feasibility and
ethical concerns related to denying treatment to a diagnosed patient. Moreover,
because the benefits of screening may be small and accrue over a long period, the
number of patients who would need to be recruited would be substantial, making the
research very costly 17, 18.
Notwithstanding the above, the United Kingdom Prospective Diabetes Study
(UKPDS) demonstrated that earlier diagnosis of type 2 diabetes was associated with
better outcomes 19, 20. More recently the findings of the Finnish trial 21 and the
Diabetes Prevention Program (DPP) 22 demonstrated the benefits of early
interventions (lifestyle or metformin) for patients with impaired glucose tolerance
(IGT) detected through a screening program and since the release of these results,
interest has increased in the screening of high risk individuals.
Whether or not screening is cost effective depends on the approach to screening.
Population-based approaches are very costly and inefficient because of the relatively
low prevalence of diabetes in the community 15. Both selective screening and
opportunistic case detection require fewer resources and are popular approaches 15,
but if conducted in the general community may be less effective because of the
failure of people with a positive test to seek and obtain appropriate follow-up for
22
diagnostic testing and care 16. Of the three approaches the best case can be made
for opportunistic case detection 23.
1.2.1 Opportunistic Screening in the Health Care System – The Case for Pharmacy
Since the favoured strategy for case detection of type 2 diabetes is through
opportunistic screening during routine contact with the health care system, we
need to examine the availability of such opportunities.
Clearly, consultations with general practitioners (GPs) provide opportunities for
screening and prevention. During the consultation, GPs can assess risk factors
and perform finger prick blood glucose testing if required. They can also raise
awareness of the risks associated with certain behaviours (e.g. being
overweight, having high blood pressure, blood lipid disorders or smoking
cigarettes) and help to modify them. However, the proportion of consultations in
which GPs undertake preventive activities for cardiovascular disease and
diabetes is relatively infrequent 24. The main focus of medical attention is still
directed at treating the consequences of cardiovascular disease and diabetes,
rather than preventive measures such as assessing and modifying risk factors
for these conditions 25.
Research suggests that the primary health care consultation rate in Australian
pharmacies may be as high as 43 million per year 26. While the social mandate
of the pharmacy profession is to ensure the safe and effective drug therapy of
individual patients 27, pharmacists also frequently provide advice on minor
health problems and lifestyle to people who consider themselves as basically
well. Pharmacists also play a significant role in the early detection of more
serious conditions and in recommending that the consumer seeks a more
extensive medical assessment. Community pharmacies provide an established
and visible network, extending to remote areas, of easily accessible health
professionals. The consumer can consult a pharmacist without an appointment,
with minimal waiting times. Thus, visits to the community pharmacy offer an
excellent opportunity to undertake screening, education and referral of
individuals at risk of diabetes 28.
23
Research also shows that a high proportion of consumers currently support
pharmacist provision of health testing services both in Australia 29 and overseas 30. With an increasing proportion of pharmacists now providing blood pressure,
blood cholesterol and glucose screening/monitoring services, community phar-
macists are able to access people who are apparently healthy and who rarely
come into contact with GPs or nurses 26, 31.
In summary, community pharmacists are ideally placed to assist in the
detection, education and referral of individuals at risk of diabetes. Because they
are accessible, available, and in frequent contact with the public, community
pharmacists represent an important channel for delivery of these kinds of
activities.
1.2.2 Guidelines for the screening and evaluation of high risk individuals
Based on the available evidence, a new set of evidence based Australian guidelines
for the case detection and diagnosis of the type 2 diabetes were published in 2000
and endorsed by the National Health and Medical Research Council 23. These set
out a stepped approach for case detection and diagnosis of individuals at high risk of
type 2 diabetes. The initial step involves assessment of an individual’s risk status and
is followed by measurement, where possible, of Fasting Plasma Glucose (FPG) in
individuals with high risk. Further testing with an Oral Glucose Tolerance Test
(OGTT) is required if the FPG values fall between 5.5 and 6.9 mmol/L 23.
Risk Factors for Type 2 Diabetes
The risk factors for type 2 diabetes are listed below23.
People aged 55 and over.
People aged 45 and over with one or more of the following:
o Obesity (BMI ≥30)
o Hypertension
o First degree relative with type 2 diabetes
24
People aged 35 and over from certain high risk ethnic groups e.g., Australian
Aborigines, Pacific Islander people, people from the Indian subcontinent and
of Chinese origin.
Mothers of babies with birth weight of more than 4.5kg or with a poor obstetric
history, or previous gestational diabetes.
All people with clinical cardiovascular disease.
Women with polycystic ovarian syndrome who are obese.
1.3 OPTIMAL CARE FOR TYPE 2 DIABETES
Once diagnosed, type 2 diabetes represents a chronic disease whose long-term
management poses significant challenges for the health care system. Several
barriers to optimal care for patients with type 2 diabetes have been identified. These
include health system resources, prescriber behaviour and patient adherence to
treatment.
As the number of cases of diabetes and the costs of care increase there will be
increased pressure in the health system to provide more intensive care to more
patients with diabetes 32. It has been suggested that the Australian health care
system could not afford intensive management of type 2 diabetes delivered solely by
GPs 33. Moreover, research has demonstrated that doctors do not follow all diabetes
care guidelines, and that compliance is lower for type 2 diabetes than type 1 33.
Implementation of guidelines in Australia varies, with research showing that 33% of
patients have never had their feet examined and only 71% had their glycosylated
haemoglobin (HbA1c) within recommended targets 34.
Another barrier to the ideal management of diabetes is patients’ adherence to a
diabetes regimen. In one study, only 7% of patients were found to be fully adherent
to all aspects of their anti-diabetic regimen which included adherence to medication,
diet, exercise and self-monitoring of blood glucose 35. With regard to adherence to
anti-diabetic medication, reported rates vary depending on the sample used and the
methods of measuring adherence. For example, two US studies using retrospective
review of pharmacy records and claims databases and a study in Dutch diabetic
25
patients using Medication Event Monitoring Systems (MEMS) for measurement, have
reported similar adherence rates to antidiabetic medication of between 70% - 83% 36-
38. A large population based study in Scotland, using prescription records found
adequate adherence (> or = 90%) in 31% of patients prescribed sulphonylureas
alone and in 34% of those prescribed metformin alone 39.
The management of diabetes is also hampered by the prevalence of medication
problems related to sub-optimal prescribing or drug misadventure through adverse
events or drug interactions. Studies have shown that a substantial proportion of
medication-related problems that exist within the health care system are related to
patients with diabetes 40, 41.
A recent initiative to improve diabetes management in Australia is the National
Integrated Diabetes Program (NIDP) 42. In 2001, the Government provided A$43.4
million over four years to improve prevention, provide earlier diagnosis and improve
management of people with diabetes through general practice 43. This package
provides incentives for GPs for earlier diagnosis and effective management for
people with diabetes and support for Divisions of General Practice to work with GPs
and other health care professionals to remove barriers to better care for people with
diabetes. One key strategy to enable structured and systematic care with regular
follow up and recall of patients is the CARDIAB database, a centralised register and
recall system. Research has already demonstrated that GPs participating in the
share care registers such as CARDIAB, see their patients with diabetes more
regularly and order tests, such as HbA1c and microalbuminuria, more frequently than
those not using registers 43.
Notwithstanding these improvements, there remains a need to explore different
models of care delivery to patients with type 2 diabetes and make better use of
health care resources in the community.
26
1.4 DISEASE STATE MANAGEMENT (DSM) PROGRAMS FOR PATIENTS WITH TYPE 2 DIABETES
The preferred model for management of chronic diseases such as diabetes is
Disease State Management (DSM). This is an approach to patient care that co-
ordinates medical resources for patients across the entire health care delivery
system 44 and as such is more likely to meet the ongoing needs of the patient with
diabetes. Central to this approach is the establishment of effective communication
and collaboration between all health care professionals involved in the care of
patients with diabetes. The health care professional who has consistently been
omitted from the loop is the pharmacist, even though the literature provides many
examples of positive health outcomes when pharmacists provide diabetes DSM
services in research situations in both the clinic 45-49 and community pharmacy
settings 41, 50-52. These diabetes care models implemented by pharmacists in the US
and Australia have demonstrated clinically significant improvements in glycaemic
control (HbA1C and fasting blood glucose (FBG)), in the intervention groups
compared with the control group over periods ranging from 4 months to 5 years 45-48,
41, 50-52.
Specific services that pharmacists may and have offered include the following:
The provision of diabetes self-management education and coaching to assist
in empowerment of the patient 41, 46, 48-50, 52.
Monitoring and promoting patient adherence with medication and other
components of self-management 41, 51.
Ensuring the quality and evidence-based use of medications in the complete
management of the patient’s diabetes, including the prevention of diabetic
complications 41, 45-51.
Monitoring and documenting easily measurable key clinical outcome
measures: o Blood glucose levels (BGL) 41, 46-48, 51, 52.
o Blood pressure 46, 48, 52.
o Lipid levels 46-48, 52.
Reminding patients of the importance of regular examinations for the presence
of diabetic complications, e.g., eye and feet examinations 41, 52.
27
1.5 COST EFFECTIVENESS OF DIABETES CARE PROGRAMS
Given limited resources within the health care sector it is also important to evaluate
new interventions from an economic perspective. The standard tool for conducting
his type of evaluation is cost-effectiveness analysis, where the costs and effects are
quantified and expressed as a ratio in order to compare different programs.
1.5.1 Screening
While long-term evidence on the effectiveness of diabetes screening is limited it has
been recommended by several international organisations 53. If Australia is to follow
these recommendations and adopt a screening program it is important to evaluate
alternative modes of delivery and procedures for screening to determine the most
cost-effective screening strategies. Given the uncertainties over long-term outcomes,
it has not been possible to calculate the cost-effectiveness of screening relative to
other health care interventions in terms of a cost per life year. The only comparison
possible between different methods to evaluate cost effectiveness, and to determine
the most efficient mode of delivering this service through community pharmacies, is
the incremental cost and outcomes in terms of the proportion of people detected with
diabetes.
1.5.2 DSM programs
Previous economic evaluations of large randomised controlled trails such as the
UKPDS 20 have demonstrated that the cost-effectiveness of several strategies for
improving the management of people with diagnosed type 2 diabetes compares
favourably with other accepted health care interventions. This includes intensive
blood-glucose control with insulin or sulphonylureas 54, 55, intensive blood-glucose
control with metformin 56 and tight blood pressure control in hypertensive patients
with type 2 diabetes (UKPDS, 1998) 57. Previous studies have also shown that
pharmacy based interventions are able to reduce HbA1c 41 and hence may play a role
in the intensive management of type 2 diabetes.
28
The evaluation of alternative policies for the treatment of chronic diseases such as
type 2 diabetes poses challenges, as the health and economic consequences of
interventions are likely to accrue over a patient’s lifetime. However, in the case of
DSM programs and many other interventions, evidence of their effectiveness is
obtained through clinical trials of a limited duration (i.e., 6 months). Hence there is a
need to project the outcomes beyond the period of follow-up. For this reason,
computer simulation models are increasingly being used to evaluate the likely impact
of interventions on the progression of the disease.
Although there have been several attempts to develop computer simulation models
for type 2 diabetes all have been hampered by the lack of long-term data on the
relationship between risk factors, treatment and outcomes in type 2 diabetes. For
example, the first computer simulation model for type 2 diabetes developed by
Eastman and colleagues 58 relied on data synthesised from a variety of sources
including data from the Diabetes Control and Complications Trial (DCCT) which
involved only patients with type 1 diabetes. Hence their model had to ignore
important differences between type 1 and type 2 diabetes. Further, all existing
models use cardiovascular risk estimates from the Framingham cohort study that had
a relatively small number of people with type 2 diabetes 59.
Both these limitations have been overcome through the development of the UKPDS
Outcomes Model based on the data from 5,102 newly diagnosed patients with type 2
diabetes who were followed for a median duration of 10.3 years. This is the largest
cohort of its type in the world.
An important component of health economic simulation models in type 2 diabetes are
“risk equations” that are used to estimate the probability of different complications
(e.g., cardiovascular disease) and death occurring based on factors such as a
patient’s age, sex, smoking status and other prognostic risk factors such as levels of
HbA1c. These models then use probabilistic Monte-Carlo analysis to predict the
timing of complications/death for an individual in a particular cohort of interest.
Estimates of the health outcomes for patients within the cohort are then quantified.
Health outcomes are typically measured in terms of either life expectancy or
29
expected quality-adjusted life years (QALYs). The latter takes into account the effect
complications can have on a patient’s quality of life. As complications of type 2
diabetes have also been shown to directly contribute to health care costs 60, these
need to be quantified in economic evaluations and as such analyses must take into
account the health care costs an intervention may avert over a patient’s lifetime.
Hence, it is important for a simulation model to predict a profile of expected health
costs in addition to expected health benefits.
1.6 STUDY RATIONALE
To date, evidence of clinical, humanistic and economic benefits of diabetes care
models derived from well designed large scale randomised controlled trials in
community pharmacy is still lacking. Hence, this project implemented and evaluated
a service model in Australian community pharmacy to address the continuum of care
for people with type 2 diabetes and those at risk. The critical elements of the service
included patient education, support and monitoring to facilitate self-management in
those with established disease. For those at risk and undiagnosed, the focus was on
case detection, education and referral. In this way we addressed the aims of the
Pharmacy Diabetes Care Program which were to:
Identify and refer as appropriate people with undiagnosed diabetes
Support the continuity of care for people with diabetes.
Improve the health of people with diabetes.
30
2. METHODS
2.1 STEERING COMMITTEE Once approvals had been obtained from the Human Ethics Committees of the four
Universities involved in the study (Sydney, Curtin, Tasmania and Monash) (Appendix
2), a Steering Committee was established. This consisted of an Expert Advisory
Group and a Reference Group (Appendix 1). Two meetings of the Steering
Committee have been held – the first meeting in August 2003 and the interim
meeting in August 2004. A final meeting of the Steering Committee will be held at the
conclusion of the study.
2.2 RESEARCH DESIGN The research design of the Pharmacy Diabetes Care Program (Figure 1) aimed to address
the three professional components of the service delivery model as follows:
i) Screening for undiagnosed type 2 Diabetes. Early identification and referral
of people at risk of diabetes to their GP.
ii) Diabetes Medication Assistance Services (DMAS). An on-going cycle of
assessment, management and review provided at regular intervals in the
pharmacy in collaboration with GPs and members of the diabetes team. These
services included:
blood glucose monitoring
education on the condition, medication, and lifestyle issues
adherence assessment and detection of drug-related problems
reminders of follow-up checks for complications related to diabetes
referrals as appropriate to healthcare professionals
iii) Domiciliary Medication Management Review (DMMR) – if the patient was
eligible and on request by the GP.
31
Figure 1: Research Design of the Pharmacy Diabetes Care Program
Screening for undiagnosed diabetes Written material and active promotion by
pharmacy staff Recruit 50 per pharmacy
Total 1500 patients Commence in week one
Final data collection at 6 months Final data collection at 6 months.
Impl
emen
tati
on
30 Intervention Pharmacies 9 NSW ; 9 VIC ; 6 TAS; 6 WA
30 Control Pharmacies 9 NSW ; 9 VIC ; 6 TAS; 6 WA
DMAS Recruit 10 per pharmacy
Total 300 patients Collect baseline data
Commence in week 3 DMMR
Recruit 10 per pharmacy Total 300 patients
Collect baseline data
Commence in week 3
Conduct at least 3 follow-up visits over 6 months.
Eva
luat
ion
1.Screening Proportion of screened clients
qualifying for referral to GP taking up referrals to GP diagnosed with prediabetes and diabetes
2. DMAS Clinical outcomes e.g., HbA1C and blood glucose Medication use e.g., adherence Humanistic e.g., diabetes QOL, well-being Economic e.g., cost effectiveness
Environment /work flow/ education
32
2.3 RECRUITMENT OF PHARMACIES
A sampling frame of all Quality Care Pharmacy Program (QCPP) accredited
pharmacies in NSW, Victoria, Western Australia and Tasmania formed the basis
for selecting a stratified random sample of 60 QCPP accredited pharmacies.
This representative sample from urban and rural Local Government Areas
(LGAs) included 30 intervention and 30 control pharmacies (Figure 1). In the
screening component of the study each intervention pharmacy was asked to
screen 50 people, giving a target sample size of 1500 across the four States. In
the DMAS component of the study, each pharmacy, both intervention and
control, was asked to recruit 10 patients with type 2 diabetes, giving a target
sample size of 300 intervention and 300 control patients.
The procedure for obtaining a random stratified sample of pharmacies consisted of the
following steps:
Pharmacies were selected from those that are QCPP accredited and within
400km of each capital city (Sydney, Melbourne, Hobart and Perth). One of the
criteria for QCPP accreditation is that the pharmacy has a patient counselling
area (PDE-3) 61 which was deemed to be an essential requirement for the
successful implementation of the Pharmacy Diabetes Care Program.
Pharmacies that were Diabetes Australia Sub-Agents were used where
possible to ensure an adequate number of patients with type 2 diabetes for
recruitment purposes.
Strata were determined by region and the percentage population in each
stratum was calculated to facilitate the inclusion of the appropriate number of
pharmacies in each region.
Microsoft Excel™ was used to generate the random numbers required within
each stratum and 3 times the required number of pharmacies was invited to
participate in the study. It was envisaged that this approach would provide an
33
adequate sample size, allowing for pharmacies which were unwilling or unable
to be involved in the program.
The allocation to intervention and control was made on the basis of ability to
attend all training sessions (which in itself was randomly determined).
The final pharmacies were matched by area, e.g., metropolitan, rural, into 2
equal sized groups of intervention and control pharmacies.
Additional prerequisites included (1) a computer system with Windows 98™ or
later for compatibility with the Precision Link Software Device™ which would
be required for downloading of blood glucose results; (2) availability to attend
training workshops; (3) two or more pharmacists on duty for most of the
pharmacy operating hours; and (4) no concurrent involvement in other
research projects.
2.4 SCREENING PROGRAM
The specific aim of the screening program was to investigate the capacity of
community pharmacies to identify and refer people at risk of type 2 diabetes to
their GP. The 30 community pharmacies utilised in the screening program were
those recruited for the intervention arm of the study as shown in Figure 1.
2.4.1 Screening Program - Study Design The screening service delivered through the pharmacies, utilised two screening
protocol variants, the sequential screening (SS) and tick test only (TTO) methods
(Figure 2). Given the capacity of an individual pharmacy to screen patients over a 4
week period, a target of 1500 screened patients was set, i.e., 750 in each group
(Table 1). This sample size provided a power of 80% at the 5% significance level to
detect a 10 fold difference in the rate of diagnosis of diabetes between the two
screening methods.
34
Sequential Screening
(SS) NSW, Tas Target 750
Tick test Only (TTO)
WA, Vic Target 750
H0: No difference between the case detection methods in rates of • referral to GPs
• uptake of referrals • diagnoses of IFG, IGT and type 2 diabetes
The null hypotheses were that there would be no differences between the two
protocol variants in the rates of (1) people who qualified for a referral to the GP; (2)
people who took up the referral to the GP; and (3) people with a confirmed diagnosis
of prediabetes and type 2 diabetes (Figure 2). Prediabetes is defined as impaired
glucose tolerance (IGT) or impaired fasting glucose (IFG).
Figure 2: Study Design and Hypotheses of Screening Service
Table 1: Breakdown of Target Sample Size for the Screening Program by State.
Pharmacies Patients
Tick Test Only
VIC 9 450
WA 6 300
Total 15 750
Sequential Screening
NSW 9 450
TAS 6 300
Total 15 750
35
The two protocols are described in detail in the following section:
Protocol 1- Sequential Screening (SS)
Pharmacists followed the SS protocol (Figure 3) which was based on the NHMRC
evidence-based guidelines for the case detection and diagnosis of type 2
diabetes 19. Initially patients were asked to complete a tick test risk assessment to
determine if they had any risk factors for type 2 diabetes (Appendix 3). If they had at
least one risk factor they were offered a fingerprick test for capillary blood glucose in
the pharmacy using a Medisense OptiumTM meter from Abbott Diagnostics.
Patients with a fasting blood glucose (FBG) ≥ 5.5 mmol/L or a random blood glucose
(RBG) of ≥11mmol/L qualified for an immediate referral to their GP. Patients with an
RBG of between 5.5 mmol/L and 11 mmol/L were asked to return for an FBG test.
Patients who were not referred to the GP were given lifestyle advice (i.e., health
information brochures on “A Guide to Healthy Eating” 62 and “National Physical
Activity Guidelines for Australians” 63 – both publications from the Department of
Health and Ageing) and advised to be retested in 3 years.
Protocol 2- Tick Test Only (TTO)
For the TTO risk assessment, patients were asked to complete a tick test to
determine if they had any risk factors for type 2 diabetes (Appendix 3). No fingerprick
testing was performed in the pharmacy. Patients who had at least one risk factor
qualified for a referral to their GP.
The referral to the GP in both protocols consisted of a triplicate form which was
completed by the pharmacist and was designed to include the patient details and
consent form; results from any random or fasting capillary blood testing conducted in
the pharmacy; and a request for further tests to be conducted by the GP. GPs were
asked to complete and fax back the GP referral form to the project coordinators
showing the results of any further blood glucose testing undertaken. One copy of the
triplicate form was placed in an envelope together with a copy of the NHMRC
guidelines for case detection and diagnosis of type 2 diabetes and the GP
information sheet and given to the patient to take to their GP. The remaining 2 copies
of the triplicate form were kept on record by the pharmacist and project officer in
each State. The steering committee had considered follow up of
36
LESS PREFERRED OPTION
> 1 risk factor present
Blood Glucose Test Random
Blood Glucose Test Fasting
< 5.5 mmol/L
5.5 – 11 mmol/L
> 11 mmol/L
> 5.5 mmol/L
< 5.5 mmol/L
BGL normal Lifestyle Advice & Retest in 3 years
REFERRAL TO GENERAL PRACTITIONER
PREFERRED OPTION
BGL normal Lifestyle Advice & Retest in 3 years
2. Retest (2 hrs)
1. FBG (8 hrs)
3 Options in order of
Preference
3. Refer to GP
Figure 3: Sequential Screening Protocol (based on NHMRC guidelines)
RISK ASSESSMENT I am over 55 years of age. I have heart disease or have had a heart attack.
I am over 45 and am overweight (BMI > 30). I am over 45 and have high blood pressure.
I am over 45 and one or more members of my family has diabetes.
I have had a borderline high blood sugar test, i.e., Fasting Plasma Glucose 5.5 – 6.9 mmol/L.
I am over 35 and am an Aboriginal or Torres Strait Islander.
I am over 35 and am of Chinese, Indian or Pacific Islander Heritage.
I have polycystic ovarian syndrome and am overweight (BMI > 30).
I had high blood sugar levels while I was pregnant (gestational diabetes).
37
referred patients to be crucial. Therefore a system of follow up, which consisted of a
4 week reminder letter or phone call from the pharmacist to the patient to remind the
patient to visit the GP for further investigation, was implemented.
The pharmacy population was the basis for this screening program and to evaluate
the demographic profile of this particular population, demographic data were
collected as part of the GP referral form.
The number of people with no risk factors was recorded by the pharmacist to enable
the proportion of people with risk factors in the screened population to be calculated.
2.4.2 Screening Program - Training for Pharmacists One day workshops were held in each State to train the pharmacists and pharmacy
assistants to deliver the Screening Service in the pharmacy setting. The workshops
were held as follows:
NSW – October 2003
VIC – January 2004
WA – January 2004
TAS – March 2004
The content of the Screening Workshops (Appendix 4) was as follows:
Overview of the screening program
Epidemiology and risk factor assessment
Lifestyle modification
Blood glucose monitoring – fingerprick technique and safety issues
o SS protocol in NSW and TAS only
Instruction on the use of Medisense Optium™ meter
o SS protocol in NSW and TAS only
Communication strategies
Screening protocol, case studies and role-plays
For consistency a training video of the Sydney based training workshop was made so
that each of the States could follow a similar format.
38
2.4.3 Screening Program - Resources for Pharmacists At the end of the screening workshop and on subsequent visits to each pharmacy by
the project officers in each State, sufficient resources were provided for each
pharmacy to screen 50 patients, as follows:
Medisense OptiumTM meters for capillary blood glucose testing
o SS protocol in NSW and TAS only
Fingerprick supplies – Tenderlett™ disposable lancets, spot bandaids, rubber
gloves, surface protectors, test strips, alcohol wipes, cotton balls, sharps
containers
o SS protocol in NSW and TAS only
“Pharmacy Diabetes Care Program” Training Manual
Pharmacy Guild Training Module
PSA - Diabetes Specialty Practice Pharmacist Education Module
PSA - Diabetes Specialty Practice Pharmacy Assistant Education Module
Diabetes Management in General Practice 64
Promotional Material - poster and banner to display in the pharmacy
(Appendix 5)
Tick test brochures in counter display unit for risk assessment (Appendix 3)
Documentation – GP referral forms, 4 week reminder letters, etc.
Exercise & Healthy Eating Booklets - Department of Health and Ageing
publications, Canberra.
o “National physical activity guidelines for Australians” 63
o “Food for health – Dietary Guidelines for Australian Adults” 62
2.4.4 Screening Program - Exit Surveys for Observable Risk Factors Exit surveys were conducted to determine the proportion of the at-risk population
being screened by the pharmacists during the trial period. Observational surveys
were performed in 10 pharmacies, three in each of NSW and VIC, and two in each of
TAS and WA. Observers counted the number of people exiting the pharmacies over
several hours and noted how many had observable risk factors. The observable risk
factors recorded were weight (BMI ≥30), age (>55) and ethnicity (Asian, Pacific
Islander, Indian, Aboriginal, or Torres Strait Islander).
39
2.4.5 Screening Program - Patient Follow-up Survey A telephone questionnaire was developed and implemented 3 months after patients
participated in the screening services for undiagnosed diabetes in the pharmacy.
Different variants of the telephone questionnaire were used for (1) TTO (WA and
VIC) and (2) SS (NSW and TAS) (Appendix 3). The aims of the telephone
questionnaire were to determine patient satisfaction and “willingness-to-pay” (WTP)
for the service. The telephone survey also served to verify the outcomes of referrals
since not all GPs returned the referral form as requested.
Where possible, the project officers in each State attempted to contact all patients
who had participated in the program. At least three attempts were made to contact
each patient.
Patient satisfaction with the screening service
To investigate patient experiences and satisfaction with the Diabetes Screening
Service, the telephone questionnaire included items relating to service approval and
patients were asked to rate their agreement or disagreement on a 5 point Likert
scale. An open-ended question to elicit reasons for the consumer’s approval and
satisfaction ratings was also included. The questionnaire for the SS method also
contained items relating to consumers’ satisfaction and provider preference for
service delivery, i.e., whether patients had a preference to receive the screening
service at the GP’s rooms or at the pharmacy.
Patient “willingness-to-pay” (WTP) for the screening service
Section 5 of the SS questionnaire consisted of five WTP questions (Appendix 3). Two
of the questions related to the respondents' WTP for the service as a whole. Firstly
they were asked whether they were willing to pay for such a service provided on a
regular basis and, if yes, to state the maximum amount they would be willing to pay.
The proportion of respondents who were willing to pay and their overall mean WTP
were calculated. In addition the overall median WTP for the screening service was
calculated.
The remaining questions related to the respondents' preference in terms of location
of service delivery (through community pharmacy or GP) and the extra amount
40
(incremental WTP) that they were willing to pay for their preferred location. The
means of the incremental WTP for each preference, community pharmacy versus the
GP or vice versa were calculated. The respondents were not informed about the cost
of the service before being asked.
2.4.6 Screening Service - Pharmacist Satisfaction To investigate pharmacist experiences and satisfaction, pharmacists who delivered
the screening service were invited to attend focus groups conducted in NSW and
VIC. The pharmacists who attended the focus groups represented a cross section of
pharmacies in NSW and VIC and were selected to provide a representative sample
of pharmacies that conducted the SS and TTO methods, respectively. The focus
group, which took about 1 hour, was audiotaped and was conducted by University
staff members who had not previously been directly involved in the project. The
topics covered in the focus groups were semi structured and contained questions
examining overall pharmacist experience of the screening service, including potential
improvements, GP communication, business impact and implications for future
implementation (Appendix 9).
2.4.7 Screening Program - Statistical Analysis
All data were analysed using SPSS 10.0™ for Windows™.
Screening Program
Frequency tabulations were conducted to examine the following categorical
variables: referrals, referral uptakes, diagnosis with prediabetes or diabetes, number
of risk factors possessed by the screened population, occurrence of each risk factor
in the screened population and in the diagnosed population, and the distribution of
demographic and lifestyle characteristics of the study participants and the diagnosed
participants. The Pearson chi-square test (or Yates’ corrected chi-square in the case
of dichotomous variables) was used to compare the rates for each of these variables
between the SS and TTO methods. Relative risk estimates of the SS vs. TTO
methods were also calculated for the rates of referral, rates of referral uptake, and
rates of diagnosis.
41
Consumer follow-up survey
Frequency tabulations were produced for the following categorical variables:
awareness of service, health information, preference for location of service, and
WTP. Descriptive statistics are reported for continuous variables (i.e., helpfulness of
health information, approval of screening service satisfaction and amount willing to
pay). A Mann-Whitney U test was used to compare the median approval ratings of
the screening service between the SS and TTO methods.
The level of significance for all tests was set at p<0.05.
2.4.8 Screening Program - Economic Analysis Economic evaluation
Two forms of analysis of the alternative screening strategies were undertaken. Firstly
in order to estimate the potential impact of implementing a screening program on the
Australian Government’s budget we have estimated the cost per person screened.
We then estimated incremental net cost and net effectiveness of the SS strategy over
the TTO and where possible we calculated the ratio of costs over effects. The
perspective adopted was that of the health care purchaser and so only direct health
service costs were included in the main analysis. These included costs of
consumables associated with administering the tests at the pharmacy and also the
fixed costs of providing the tests (e.g., counter display to provide information about
the test). It is also important to include subsequent screening costs for patients
referred to the GP for follow-up as this further testing is required to establish a
diagnosis. While non-medical costs such as out-of-pocket expenses incurred when
visiting a GP were not included in the main analysis we have considered the potential
impact of the results in the sensitivity analysis.
Outcomes
As a measure of outcome we estimated the proportion of new cases of diabetes and
prediabetes detected in the screened population. We have chosen this outcome as it
represents a common measure of the effectiveness of the two strategies and
facilitates comparison with other types of screening programs for type 2 diabetes.
42
Resource data and costs
For each patient, costs were calculated based on their degree of progression along
the screening pathway (see Figure 5). Table 2 summarises the main sources of
information on unit cost of consumables (expressed in 2004 Australian dollars), the
cost of pharmacist’s time, fixed pharmacy based costs and the subsequent health
care costs of patients who were referred and attended a GP for follow-up screening.
Data Analysis
Analyses were performed with STATA 8.0™ and Microsoft Excel XP™. Statistical
tests were undertaken at the 5% level of significance. Results are reported as mean
values or mean differences along with standard deviations and 95% confidence
intervals. In studies in which costs or effects accrue at different times economists
normally apply an annual discount rate to future costs and effects 65. However, given
the short time horizon of this study (i.e. less than one year between pharmacy
screening and subsequent diagnosis) no discounting was applied to either costs or
effects.
A significant proportion of patients who were referred to a GP were lost to follow-up
and so their actions and whether they have diabetes is not known. We have used hot
deck method of imputation66 to fill in missing costs and outcomes for these patients
as the economic analysis requires estimates of number of patients at each stage of
the screening pathway in order to full account for costs and benefits of each program.
Hot decking is a standard method for imputing missing data and involves dividing the
sample by screening type and randomly drawing values from the group of individuals
with follow-up information in order to impute the missing data. So for example,
outcomes on 79 people who subsequently visited a GP in the SS group were used to
impute the screening and diabetes status for the 39 individuals where follow-up
information was unavailable. To account for the added uncertainty from replacing
these missing data we imputed five separate data sets using different random draws
from the observed values. The results from this multiple imputation were then
combined using standard rules in order to calculate standard errors for costs and
effects that were adjusted for the added uncertainty from the imputation process.66.
To provide a visual representation of the results, the costs and health outcomes are
43
mapped onto the cost-effectiveness plane. The effect on our main results of
uncertainty surrounding some aspects of cost and outcomes in the study were
examined using sensitivity analyses.
44
Table 2: Main unit costs by type and stage of screening
Item Unit cost A$ 2004 Source
TTO: PHARMACY BASED COSTS Cost of consumables/ per test Brochures (Including Artwork) Referral forms Reminder forms Pharmacists time (5 min @ $37/h) Pharmacy assistants time (5 min @ $20/h) Total variable costs Fixed Costs Counter display unit Poster/ Banner Total fixed costs
$0.82 $0.97 $0.95 $3.08 $1.67 $7.49
$9.00 $60.50 $69.50
Trial estimates " "
Pharmacy Guild "
Trial estimates "
SS: PHARMACY BASED COSTS Cost of consumables/ per test Brochures (including artwork) Referral forms Reminder forms Tenderletts (disposable lancets) Rubber Gloves Bench coat protector Cotton balls Spot band aids& alcohol wipes Test strips Pharmacists time (5 min @ $37/h) Pharmacy assistants time (10 min @ $20/h) Total variable costs Fixed Costs Counter display unit Poster/ Banner Sharps disposal unit Blood glucose meters Total fixed costs
$0.82 $0.97 $0.95 $1.30 $0.16 $0.10 $0.12 $0.05 $0.53 $3.08 $3.33
$11.42
$9.00 $60.50 $4.18
$35.00 $108.68
Trial estimates “ " " " " " " “
Pharmacy Guild "
Trial estimates " " "
COST OF SUBSEQUENT SCREENING Primary care visit (Standard consultation) Fasting Plasma Glucose test OGTT test
Total costs rounded to nearest cent
$26.25 $8.30
$16.20
2004 MBS schedule
" "
45
2.5 DIABETES MEDICATION ASSISTANCE SERVICE (DMAS) The specific aims of the Diabetes Medication Assistance Service (DMAS) were to
examine the role of the community pharmacist in the disease state management for
type 2 diabetes; to implement a specialised service for patients with type 2 diabetes;
to evaluate the model in terms of process and outcomes indicators; and to
investigate patient and pharmacist satisfaction with the service.
2.5.1 DMAS Program - Study Design The DMAS is outlined in Figure 1 and utilised a multisite, randomised clustered
control versus intervention, repeated measures design within four states in Australia.
The 58 community pharmacies recruited for the DMAS program came from the
representative sample of urban and rural Local Government Areas (LGAs) as
described in detail in Section 2.3. Whilst pharmacies were randomly selected patients
were not randomised as this is not achievable in a community pharmacy setting, due
to the high probability of contamination of controls.
As shown in Figure 1, intervention patients received the DMAS, an on-going cycle of
assessment, management and review provided at regular intervals over 6 months in
the pharmacy. These services included blood glucose monitoring, education,
adherence assessment, and reminders of follow-up checks for complications related
to diabetes. Control patients were assessed at 0 and 6 months and received no
intervention other than the usual pharmacist’s advice/care over the 6 month period.
It is acknowledged that the pharmacists “usual care” may have been influenced by
interviewing the patient and discovering some issues in this process. This was
unavoidable and may occur in all such projects. However they received no special
training in diabetes care and did not deliver a structured monitoring service to their
patients as did intervention pharmacists. Hence, what we compared is the additional
benefit of a regular structured monitoring service on top of the initial interview.
46
2.5.2 DMAS Program - Sample Size To detect a 0.5% reduction in HbA1c in the intervention group compared with the
control group post-intervention (SD 1.3%) with a power of 90% at the 5%
significance level and allowing for a 20% dropout rate, at least 180 patients
were required in each group 36. However, to ensure adequate power for the
study and allowing for the recruitment of ineligible subjects, the target sample
size was increased to 300 intervention and 300 control patients. Thus each
pharmacy was asked to recruit 10 patients with type 2 diabetes (Table 3).
Table 3: Breakdown of target DMAS sample size by State
2.5.3 DMAS Program - Development of the Clinical Protocols Two clinical protocols were developed, one for the “intervention” group and one for
the “control” group (Appendix 6). The protocols were based on the three stages of
patient recruitment, assessment and management, and review. All participating
pharmacists were provided with an extensive patient file for each subject, which
incorporated the protocol for assessment, intervention and follow up of each patient,
as well as worksheets for the documentation of their interventions.
Interventions Controls
Pharmacies Patients Pharmacies Patients
NSW 9 90 9 90 Victoria 9 90 9 90 Tasmania 6 60 6 60 WA 6 60 6 60 Total 30 300 30 300
47
2.5.4 DMAS Program - Training of Intervention Pharmacists
Workshop
All the community pharmacists who were initially recruited and who agreed to
participate in the study attended a two-day workshop in each of the four States on
the following dates:
NSW – November 2003
VIC – January 2004
WA – January 2004
TAS – March 2004
The aim of the workshop was two-fold: to provide the community pharmacists with
the most up to date information on the various aspects of diabetes, and to train the
community pharmacists to deliver the DMAS according to protocol and to document
the service and patient data accurately and appropriately.
The content of the DMAS workshops was as follows (Appendix 4):
Overview of DMAS
Key issues in diabetes care
Pharmacotherapy of diabetes
Home blood glucose monitoring and insulin devices
Instruction on the use of meter and software
Patient education
Living with diabetes and counselling issues
Adherence assessment
Instruction in the use of Precision Link Device™ software and cable for
downloading and interpretation of home blood glucose monitoring results
Instruction in the use of Omron™ blood pressure monitor
Patient recruitment and DMAS protocol
Case studies, group work and role-plays between “patient” and “pharmacist”
DMMR
48
A training video of the Sydney based training workshop was made and copies were
distributed to each state to ensure consistency of training delivery in each of the
States.
During the workshop, an education manual was provided to each participating
community pharmacist. This manual was a substantially revised version of the
“SugarCare” manual used by the researchers in an earlier research study 67.
Appendix 4 lists the names of the units presented in the revised manual.
During the workshops, case material was reviewed, role plays undertaken, and group
discussions held.
Monitoring Equipment One of the initial criteria for pharmacist’s enrolment was to have a computer system
installed with Windows 98™ or later for compatibility with the computer software, and
a Precision Link Direct Device™ for downloading BGL results. As one of the tasks
that the community pharmacists had to perform was to instruct patients involved in
the study to use a Medisense OptiumTM blood glucose meter, the community
pharmacists were therefore trained to a proficient level on fingerprick techniques
using various lancet devices; the use of the Medisense OptiumTM meter; and on the
installation and operation of the Precision Link Direct Device™ for downloading BGL
results. This training was conducted during the workshop by the MediSense™
Products representative and on subsequent dates the representative travelled as
necessary to each individual pharmacy if additional assistance was required.
The pharmacists also received training on how to take the patient’s blood pressure at
each visit. For this purpose, each pharmacist was given an Omron T5™ Digital Blood
Pressure Monitor and trained on its use. This is a fully automatic monitor that
requires no manual adjustments or manual inflation bulb; has a personalised Inflation
level with no pain or discomfort; has a quick deflation and release for fast
measurement which is convenient for frequent BP monitoring; and is clinically
validated for accuracy and reliability. Pharmacists were instructed to follow the PSA
guidelines for accurate BP monitoring and to record the mean of 3 measurements of
systolic and diastolic BP at each visit.
49
2.5.5 DMAS Program - Resources for the Pharmacists At the end of the DMAS Workshops and on subsequent visits to each pharmacy by
the project officers in each State, sufficient resources were provided for each
intervention pharmacy to recruit 10 patients, as follows:
Medisense OptiumTM meters for capillary blood glucose testing – one for each
patient recruited to the DMAS
Precision Link Direct Device™ cable and software for downloading BGL direct
from the patients Medisense OptiumTM meters to the pharmacists computer –
one for each pharmacy
Omron™ Blood Pressure Monitor for measuring patients’ BP at each visit
Diabetes Australia CD rom (2003)
Patient files containing all the necessary documentation for the study
Information on program and “National Integrated Diabetes Program” (NIDP)
Promotional material for recruitment of patients
o Counter display, brochures, poster, etc.
o Proposed content of advertorial for local paper
Control pharmacies received patient folders only.
2.5.6 DMAS Program - Patient Recruitment Initially, pharmacists sourced potential patients with type 2 diabetes from their
customer database by identifying people on diabetes medications, such as metformin
and sulphonylureas. The pharmacists then approached these people and asked
them if they would like to be involved in the study. Other strategies for recruitment
included:
Advertorial in local newspaper (example shown in Appendix 5)
GP mail outs (Appendix 7)
Promotional material in store – DMAS brochure (Appendix 5)
The eligibility criteria for patient enrolment in the DMAS program were as follows:
Patients with established type 2 diabetes with,
HbA1c > 7.0%, who were taking at least one diabetes medication and on at
least one anti-hypertensive, angina or lipid-lowering medication; or
50
HbA1c ≥7.5% and who were taking at least one diabetes medication.
To determine if patients who were interested in participating in the program were
eligible, the pharmacist faxed to the patient’s GP a request for clinical data form; GP
information sheet on the Pharmacy Diabetes Care Program and information on the
Practice Incentives Program (PIP) scheme for GPs. In cases where the GP followed
diabetes care guidelines and performed tests regularly, the clinical data would
usually be easily obtained from GP records. However, it was envisaged that in other
cases recent routine tests would not have been carried out and therefore would
require the patients to go for pathology testing. The process of confirming patient
eligibility for the DMAS was one of the major barriers to recruitment in this study and
many strategies were developed to overcome this. Once the patient was
successfully recruited and met the eligibility criteria, an appointment was made for
their baseline visit (Visit 0) to the pharmacy.
Intervention Patients
Visit 0 – Assessment
On the first visit with the pharmacist (Visit 0), the patient was given a MediSense
Optium™ blood glucose meter, instructed on its use, and then asked to take
measurements at least once daily (preferably at different times) over the ensuing 2
weeks. They were required to hand in their own blood glucose meter (if they had
one) to the community pharmacist until the end of the study, so that they were not
tempted to use a machine other than the Medisense OptiumTM blood glucose meter.
It was crucial that the patients used the Medisense OptiumTM meter, as at the
subsequent four visits, the patients’ blood glucose measurements were downloaded
onto the community pharmacists’ computers using the Precision Link Direct
Device™.
During Visit 0, the patient completed several questionnaires which served as
baseline measures of self reported management of diabetes, quality of life, well-
being, and adherence. The validated instruments used are described in detail in the
next section. During this visit, the pharmacist recorded the patient’s demographic
details, diabetes history and current management, height, weight, blood pressure and
their use of health care services over the preceding 6 months.
51
Visits 1 to 4 – Management and Review
During the next four visits (Visits 1- 4) to the community pharmacist, the patients
received targeted counselling based on the pharmacist’s assessment, taking into
consideration their blood glucose readings. Typical topics discussed included
exercise, diet, foot care, and issues relating to their medication. The pharmacists
also provided adherence support, discussed potential or actual drug related
problems, and prompted for medical checks. Goals to be achieved by the next visit
were negotiated with the patient and documented on a worksheet.
At each of the visits to the pharmacy, pharmacists followed a protocol to deliver
targeted interventions based on any adherence issues, medication related problems,
problems with glucose control, diet, exercise and foot care and other lifestyle issues
identified in the patient assessment. Examples of intervention strategies to support
patient adherence included feedback on self-monitoring of blood glucose levels,
education about the disease and medications, adherence devices, reminders and
regular follow-up.
Where possible, the intervals between the visits were as follows:
Visit 0 and Visit 1 - 2 weeks
Visit 1 and Visit 2 - 1 month
Visit 2 and Visit 3 - 2 months
Visit 3 and Visit 4 - 2 months
Ideally the five visits to the community pharmacists could be completed in just less
than 6 months.
At the Final Visit (Visit 4) with the pharmacist, the patient completed the same set of
questionnaires as at Visit 0 to obtain post intervention scores for self reported
management of diabetes, quality of life, well-being, and adherence. At this time a
request for 6 month clinical data was given to the patient to take to the GP to
facilitate the return of post-intervention clinical data for HbA1C, blood pressure (BP)
and lipids profile.
52
Control Patients
Once the control patient was successfully recruited and met the eligibility criteria, an
appointment was made for their baseline visit (Visit 0) to the pharmacy. During this
visit the patient completed several questionnaires which served as baseline
measures of self reported management of diabetes, quality of life, well-being, and
adherence. The pharmacist recorded the patient’s demographic details, diabetes
history and current management, height, weight, blood pressure and their use of
health care services over the preceding six months. During the next 6 months, they
received “usual care” (i.e., no specialised diabetes care in the pharmacy). After 6
months the patient returned to the pharmacy for the Final Visit and completed the
same set of questionnaires. Clinical data were then sought from the patient’s GP i.e.,
HbA1C, blood pressure (BP) and lipids profile.
2.5.7 DMAS Program - Evaluation of the service The ECHO 68 (economic, clinical and humanistic outcomes) model was used to
evaluate the service. The ECHO parameters utilised in this study are indicated in
Table 4.
Hypotheses:
There will be no significant difference between intervention and control groups pre-
and post-intervention in:
mean HbA1C, blood glucose levels, Blood Pressure, lipid profile and
medication adherence
mean self reported management of diabetes, quality of life, well-being, and
adherence scores
cost per life year and cost per quality adjusted life year
53
Table 4: Evaluation of the Service
Outcomes Measures
Clinical HbA1c
Blood pressure (BP) – systolic and diastolic
Total Cholesterol (TC)
High Density Lipoproteins (HDL)
Triglycerides (Trig)
BMI
Humanistic Risk of non-adherence (BMQ)
Well-Being Questionnaire 12 (WB-12)
Diabetes Care Profile (DCP)
Questionnaire on Stress in Patients with
Diabetes – Revised (QSD-R)
EuroQol (EQ-5D)
Consumer Satisfaction with DMAS (DMET)
Economic Cost effectiveness
54
2.5.8 DMAS Program - Questionnaire properties The following questionnaires were used during the DMAS:
The Brief Medication Questionnaire (BMQ) is a quick structured interview that has
been validated to have high sensitivity for non-adherence in patients taking multiple
medications 69. The BMQ consists of three scores (screens): a regimen screen,
belief screen, and recall screen. In all screens, a higher score indicates poorer
adherence or higher risk of non-adherence.
The Well-Being Questionnaire 12 (W-BQ 12) 70, 71 is a validated 12-item instrument.
It consists of three four-item subscales: Negative Well-Being, Energy, and Positive
Well-Being. Each item is scored on a scale from 0 (“not at all’) to 3 (“all the time”).
Following recoding of the negatively worded items, a Total Well-Being score may
also be computed by summing the individual item scores, yielding a total score
ranging between 0 and 36. The higher the score, the greater the individual’s Well-
Being.
The Diabetes Care Profile (DCP) is a validated instrument which assesses the social
and psychological factors related to diabetes and its treatment 72. It comprises 14
subscales. Five selected scales which represent important facets of patient
adjustment to diabetes used in this study were included: control problems (5 items),
positive attitude (5 items), negative attitude (6 items), self care ability (4 items), and
understanding long-term management (12 items). Each item is scored from “1” to
“5”. Scale score are computed by summing the individual item scores and weighting
each scale by the number of items comprising the scale. Higher scores indicate a
greater presence of the characteristic.
The Questionnaire on Stress in Patients with Diabetes – Revised (QSD-R) is a
reliable and valid self-assessment questionnaire for type 1 and type 2 diabetes,
which is designed to measure everyday problems in coping with the illness 73. We
used the self medication/diet scale (9 items) which deals with problems encountered
with the treatment plan, e.g., injecting, giving up tasty foods, etc. Each item is scored
on a 5 point scale from “1” (only a slight problem) to “5” (a very big problem).
55
The EuroQol – 5D (EQ-5D) is a validated instrument for use as a measure of health
outcomes which is applicable to a wide range of health conditions and treatments 74.
It provides a simple descriptive profile of 5 items, with each item scored on a scale of
1-3 with a higher score indicating a poorer health condition. It also provides a single
health status index where the patient rates their present health state on a scale of 0
(worst state they can imagine) to 100 (best state they can imagine).
2.5.9 DMAS Program - Communication with Pharmacists The project officer used a range of communication strategies to ensure that the
community pharmacists were kept informed of the latest developments of the study,
and to provide ongoing support to the pharmacists and these included:
Regular phone calls
Emails
Mail-outs
Monthly newsletters - the intervention pharmacies and the control pharmacies
received a different variation of this newsletter (Appendix 8)
Visits to the pharmacies on a regular basis
2.5.10 DMAS Program – Quality Control Adherence to protocols was monitored on visits to each pharmacy by the project
officers. Patient data files were checked for accuracy and completeness to ensure
the quality of the data.
2.5.11 DMAS Program - Patient Satisfaction To investigate patient experiences and satisfaction with the DMAS, qualitative
interviews (face to face and telephone) were conducted with a convenience sample
of patients who had completed the DMAS from 5 of the 10 intervention pharmacies in
NSW (Appendix 9). This represented a cross section of pharmacies in NSW. The
interviews took approximately 20 minutes, were semi structured and contained
questions examining five core issues including overall experience, patient
understanding, patient expectations, pharmacist interaction and future intentions.
56
Sixteen items from the Diabetes Measurement Evaluation Tool (DMET) 75, a
validated instrument to measure patient satisfaction with diabetes DSM programs,
were included in the patients interviews. These asked patients about satisfaction with
provision of information during the service, understanding of diabetes management,
relationship quality with the pharmacist and convenience of location. Respondents
were asked to rate each item on a 5 point Likert scale scored from 1 (very
dissatisfied) through to 5 (very satisfied).
The patient interview data were analysed using thematic analysis. The text was
coded into manageable thematic categories representing patient opinions of the
service. The coding was performed independently by two researchers.
2.5.12 DMAS Program - Pharmacist Satisfaction To investigate pharmacist experiences and satisfaction with the DMAS, pharmacists
who delivered the DMAS service were invited to attend focus groups conducted in
NSW and VIC (Appendix 9). The pharmacists who attended the focus groups
represented a cross section of pharmacies in NSW and VIC. The focus groups which
took about 1 hour were audiotaped and were conducted by a facilitator who had not
previously been directly involved in the project. The topics covered in the focus
groups consisted of overall pharmacist experience of the DMAS service, including
consumer perspective, GP communication, business impact and implications for
future implementation.
2.5.13 DMAS Program – Statistical Analysis The data analysis was conducted on the evaluable group using SPSS 10.0™ for
Windows™. The evaluable group comprised all control patients and any intervention
patient who had completed at least visit 2 were included in the analysis as long as
final clinical data were available.
57
Demographic Characteristics and Diabetes History at Baseline
Frequency tabulations were conducted to examine the distribution of demographics
and diabetes history at baseline. Pearson chi-squared test for independent samples
(with Yates’ continuity correction in the case of dichotomous variables) was used to
test for differences in the proportions of categorical characteristics (e.g., gender,
employment status, current diabetes management) between the intervention and
control groups. An independent samples t-test or a Mann Whitney U test was used to
test for differences between the intervention and control groups in normally (e.g.,
age) and non-normally distributed (e.g., years since diagnosis, use of medical
services) continuous variables respectively.
Clinical, Humanistic and Medication Parameters at Baseline
Continuous parameters of the intervention and control groups at baseline (e.g., %
HbA1C, systolic BP, total cholesterol) were compared using an independent samples
t-test (if normally distributed) or a Mann-Whitney U test (if non-normally distributed). If
possible, non-normally distributed variables were transformed to generate normal
distributions. Categorical parameters (e.g., smoking status) were compared using
Pearson chi-squared test (with Yates’ continuity correction in the case of
dichotomous variables).
Clinical, Humanistic and Medication Outcomes
If normally distributed, continuous parameters were compared at baseline and final
visit using a paired t-test. A general linear model repeated measures multivariate
ANOVA was then used to test for differences between the intervention and control
groups.
For clinical data, if there was a significant difference between the control and
intervention groups and if the baseline value for a given parameter differed between
the intervention and control groups (as with HbA1C), the repeated measures ANOVA
was followed by a general linear model univariate analysis of covariance (ANCOVA)
to control for the baseline difference and thus provided a much more conservative
test for differences between the intervention and control groups. The ANCOVA was
also used to estimate an adjusted effect of the DMAS on HbA1C in the intervention
58
compared with the control group while controlling for baseline76. Random effects due
to the clustered design, (i.e, pharmacy) were initially included but were excluded from
the final analysis as variation due to pharmacy was consistently found to be non-
significant.
Non-normally distributed continuous parameters were compared at baseline and final
visit using a Wilcoxon signed ranks test. A Mann Whitney U test was also used to
test for differences in score changes between the intervention and control group at
final visit.
Categorical parameters (e.g., smoking status, medication regimens) were compared
at baseline and final visit within each group using McNemar’s test. This was followed
by a chi-square test to check for differences at final if the two groups had been
similar at baseline for that particular parameter.
General linear model repeated measures ANOVA was used to test for differences in
the mean blood glucose, percent of blood glucose readings in target range and blood
pressure over the 4 intervention visits. This was followed by a linear test for trend to
test for changes over time.
The level of significance for all tests was set at p<0.05.
2.5.14 DMAS Program - Economic Analysis The economic analysis of the DMAS Program involved an incremental cost-
effectiveness analysis in which the net cost and effectiveness of the DMAS provided
by pharmacists was calculated and expressed as a ratio (e.g. cost per life year
gained). The main perspective used in the economic evaluation was that of the
healthcare purchaser. We therefore excluded indirect costs (e.g. the loss of earning
for patients with diabetes-related complications) in order for the results to be
comparable with other evaluations of health care funded by the Commonwealth
Government such as submissions to the PBAC (Commonwealth Government of
Australia) 77. Note it is important to consider system-wide health care costs in the
evaluation. For example, patients who receive DMAS may make more (or less) use
59
of GP services and this increase (or reduction) in costs should be accounted for in
the evaluation.
Outcomes
In this study the main outcome of the service was improved metabolic control as
measured by a reduction in HbA1c over the duration of the study. While this
constitutes a useful intermediate outcome measure, it was necessary to estimate the
likely long-term benefits in a metric such as life expectancy in order to compare the
relative cost-effectiveness of DMAS with other interventions.
For the economic evaluation we focused on two long-term outcome measures: the
estimated change in life expectancy and the change in expected Quality Adjusted
Life Years (QALYs). The QALY adjusts length of life for quality of life by assigning a
value or health utility (where 0 represents death and 1 represents full health) for each
year of life. While the impact of the intervention on patient QALYs were assessed
during the study using the EQ-5D, the longer term impact of the intervention is likely
to derive from its ability to prevent diabetes-related complications that have been
shown to affect the quality of life of patients with type 2 diabetes 78, 79. In this regard
we estimated the effect of complications on QALYs using reference values for health
utilities based the EQ-5D survey that was administered to 3,192 patients still
participating in the UKPDS in 1997. As utility decrements were also derived using
EQ-5D they are likely to produce a set of utility values that are consistent with the
estimated short-run impacts of DMAS of patients’ health-related quality of life.
Using these data the mean utility for a patient with diabetes who is free of
microvascular and macrovascular complications was found to be 0.79, which is
similar to people of the same age who do not have diabetes. Patients with a history
of complications were found to have lower utility, and the following decrements were
estimated: -0.06 for a myocardial infarction(MI); -0.09 for other Ischaemic heart
disease (IHD) or angina; -0.16 for stroke; -0.11 for heart failure; -0.28 for amputation
and –0.07 for blindness in one eye 78. We assumed the renal failure decremented
utility to be 0.26 based on the results of another recent study 80. It was assumed that
the occurrence of multiple complications has an additive effect on utility and the
same decrements were applied to patients with comparable health states regardless
of their treatment policy.
60
Estimates of both life expectancy and outcomes were obtained using the UKPDS
Outcomes Model, a newly developed simulation model which has been fully
documented elsewhere 81 . In brief, the UKPDS Outcomes Model is based on an
integrated system of parametric equations which predict the annual probability of
seven complications (listed above) occurring. Monte Carlo methods are used to
predict the occurrence of these events. A key aspect of this model is its ability to
capture the clustering or interaction of different types of complications at the
individual patient level. This may arise not only because many events share common
risk factors, but also due to event related dependence, i.e. when the occurrence of an
event substantially increases the likelihood of another event occurring. The model is
a probabilistic discrete-time illness-death model rather than a Markov model 82, which
simulates a patient’s life experience using annual cycles to calculate the probability of
death or of experiencing any of the specified complications. Patients start with a
given health status (e.g. no complications) and can have one or more non-fatal
complications and/or die in any model cycle by comparing estimated probabilities
with random numbers drawn from a uniform distribution ranging from zero to one to
determine whether an event occurs. When a patient experiences a complication,
their utility is permanently decremented such that they accumulate QALYs at a
slower rate. Study cohorts were run through the model until all patients had died.
To estimate long-term outcomes of the DMAS we assumed the program continued
for a period of 10 years and employed a range of assumptions regarding its effect on
HbA1c. The average time paths of HbA1c for the two scenarios compared with control
are illustrated in Figure 4. The predicted time-path for control patients has been
predicted by the UKPDS Outcomes Model and reflects the general upward trend in
mean HbA1C experienced by many people with diabetes over time. The time-path of
subjects in the intervention group depends on the following scenarios:
1. Scenario A: Patients in the intervention group have 0.35% lower HbA1c than
controls until the end of the 9th year of simulation (based on the baseline
adjusted difference achieved within the study).
61
2. Scenario B: Patients in the intervention group have 0.7% lower HbA1c than
controls until the end of the 9th year of simulation (based on the unadjusted
difference achieved within the study).
The outcomes in terms of life expectancy/QALYs for patients in the intervention and
control group were then estimated using the time-paths generated by UKPDS
Outcomes Model which are based on the experience of UKPDS patients.
Figure 4: Assumptions regarding timepaths of HbA1c
6
6.5
7
7.5
8
8.5
9
9.5
10
0 1 2 3 4 5 6 7 8 9 10 11 12
Years from baseline
Mea
n H
bA1c
Scenario AScenario BControl
Resource data and costs
The costs of various health care resources used in DMAS are summarised in Table
5. The main costs associated with the intervention are divided between the cost of
providing the service by the pharmacy and the wider impact of the program on
resource use within the health care sector.
Pharmacy based costs
The Pharmacy based costs included both fixed and variable costs. Fixed costs are
costs that are not related to the number of services provided and include the counter
display unit, blood pressure monitor and software. We assumed that these fixed
costs would be reincurred every 3 years due to having to renew or update equipment
62
and apportioned the fixed costs to services by assuming each pharmacy would
provide, on average, 200 patients with the DMAS over the life of the program. The
variable cost per patient over a 6 month period was $272.15 and included the fee to
the pharmacist for providing the DMAS, the cost of printouts of blood sugar levels
and the cost of telephone calls.
Health System costs
To determine the impact of the DMAS on health care costs in other areas of the
health system, data were collected during the study including the number, type, and
doses of medications and number of GP visits. In the case of anti-diabetic
medications we used patient’s defined daily dose to determine the quantity of drugs
used over the course of the study. For other drugs we combined information on the
average benefit (subsidy) by class (e.g. statins) paid by the government under the
Pharmaceutical Benefits Scheme 83. The rate at which the patient visited the GP was
multiplied by 85% of the scheduled Medicare fee 84. While hospital costs were
collected during the study they were not included in the economic analysis due to the
short duration of the study. Instead the potential long-term savings from improved
metabolic control (i.e. reducing the rate diabetes-related complications over the
patients remaining lifetime) were estimated using the UKPDS Outcomes Model. The
predicted event rates were multiplied by an average of the relevant DRG cost
weights from the National Hospital Cost Data Collection Cost Report Round 7 (2002-
03) report 85.
Undiscounted costs are reported as well as net present values using 5% discount
rate in the main analysis. Rates of 3% to 10% were used in the sensitivity analysis.
The effect of either a higher or lower discount rate was examined in the sensitivity
analysis. Discounting takes into account the societal view that costs or benefits are
worth less in the future than today.
Analysis
Results are reported as means with standard deviations or mean differences with
confidence intervals, and as cost-effectiveness ratios. To provide a visual
representation of the results, the costs and health outcomes are mapped onto the
cost-effectiveness plane and reported as acceptability curves 86. It is important to
63
recognise that estimates in this evaluation are subject to uncertainty surrounding
both costs and health outcomes. Two forms of uncertainty are addressed within the
modelling exercise; as the UKPDS Outcomes Model uses Monte Carlo methods
each simulation produces a different set of outcomes. In line with recent guidelines
on computer simulation modelling (American Diabetes Association Consensus Panel,
2004 87) we have removed this first order uncertainty by averaging across a large
number of repeated simulations. Secondly, there is uncertainty in the estimated
parameters in the model and we have used bootstrap methods to estimate standard
errors around the estimates and to facilitate the reporting of the confidence intervals
and acceptability curves for the mean difference in costs and outcomes.
Decision rule
To determine whether the DMAS represents value for money the cost-effectiveness
ratios are compared with other health care programs that are routinely funded by the
Commonwealth. Historically interventions below $37,000 -$69,000 per life year have
been funded by the Australian Government 88.
64
Table 5: Main unit costs for selected therapies & cost of complications
Item Unit cost
A$ 2004 Source
PHARMACY BASED COSTS Cost of consumables/per patient
Brochures (Including Artwork) Printouts for home blood glucose monitoring Pharmacist time (230 minutes @ $70 per hour)
Total variable costs (over 6 month period) Fixed costs Counter Display Unit Blood Pressure Monitor Software Poster/Banner Total fixed costs
$0.82 $3.00
$268.33 $272.15
$9.00 $126.00 $205.00 $60.50
$400.50
Trial Estimates “ “ “ “ ” “ “ “
THERAPY COSTS Anti-diabetic therapy costs (per script)
Metformin (850mg) Glibenclamide(5mg) Gliclazide (80mg) Glipizide (5mg) Glimepiride(2mg) Acarbose (50mg) Insulin Neutral 1000-units Insulin Aspart 1500-units Rosiglitazone (4mg)
$14.74 $10.01 $15.38 $10.12 $12.40 $28.90
$136.36 $270.55 $61.65
PBS Schedule
" " " " " " " " "
Other medications (average cost per script by class) Beta Blockers Calcium Channel Blockers ACE inhibitors Statins
$20.04 $25.07 $24.67 $62.39
Estimates based on unpublished
HIC data
OTHER HEALTH CARE COSTS Primary care visits
$26.25
MBS schedule
Hospital costs associated with selected complications
Myocardial infarction Other ischaemic heart disease Stroke Congestive heart failure Amputation Renal Failure
$4,021.00 $4,454.00 $7,244.00 $2,424.00
$18,848.00 $50,000.00
National Hospital Cost Data
Collection Cost Report Round 7 (2002-03) Report
UKPDS 65
65
3. RESULTS – SCREENING PROGRAM
3.1 SCREENING PROGRAM
As a result of the screening program, 10 people (0.8%) were successfully diagnosed
with type 2 diabetes and 24 people (1.9%) were identified as having prediabetes
(Table 6). A total of 1286 people were screened for diabetes, 802 by the TTO method
Screening Program - Key Findings:
A total of 1286 people were screened in 30 pharmacies.
Twenty-four people were diagnosed with prediabetes (1.9% of the total
screened), and 10 people were diagnosed with diabetes (0.8% of the total
screened).
Rates of qualifying for referral were lower in the sequential screening (SS)
method compared to the tick test only (TTO) method.
Rates of referral uptake were higher for the SS method compared to the
TTO method.
Rates of diagnosis of diabetes were higher for the SS method (1.7%)
compared to the TTO method (0.2%).
The most common risk factors amongst participants diagnosed with
prediabetes or diabetes were: 1) being over 55 yrs of age and 2) being over
45 with a body mass index (BMI) greater than 30 kg/m2.
Patients were 7 times more likely to be identified as having diabetes using
the SS method than the TTO method.
The median approval rating of the screening service was high (5 out of 5).
The average cost per case detected was A$788 for SS method compared to
A$6,000 for the TTO method.
If 100,000 individuals were opportunistically screened using the SS method
then the total cost would be in the order of A$2.18 million dollars, of which
approximately A$1.26 million would be incurred at the pharmacy level.
Overall the SS method was superior both from a cost and efficacy
perspective.
66
Table 6: Summary of numbers screened and diagnosed
Diagnosed State Number
Screened Prediabetes* n (%)
Diabetes n (%)
NSW 260 8 (3.1) 6 (2.3)
TAS 224 2 (0.9) 2 (0.9)
VIC 436 7 (1.6) 1 (0.2)
WA 366 7 (1.9) 1 (0.3)
Total 1286 24 (1.9) 10 (0.8)
* Impaired Glucose Tolerance or Impaired Fasting Glucose
and 484 by the SS method. The outcomes of the two screening methods are
presented as a flow diagram in Figure 5. The results of GP visits were determined by
return of the referral forms from GPs (58%) or were self reported during the follow-up
survey (42%).
It should be noted that the variation in the denominators (n) in the tables presented
throughout the results section is due to missing data for particular variables.
3.1.1 A comparison of the two screening protocols Overall, within the SS arm of the study there were lower rates of people qualifying for
referral but a higher rate of referral uptake and subsequent diagnosis of diabetes,
compared with the TTO arm. Seventy-seven percent (n=619) of people screened
using the TTO method qualified to be referred to the GP compared with 24% (n=118)
of people screened using the SS method (Figure 6) (2 =342, df=1, p<0.01). Of those
who qualified for referral to the GP, the rates of referral uptake (i.e., those who visited
their GP for further testing) were significantly higher for the SS method 42.4% (n=50)
than the TTO method (20.5%, n=127) (2 = 93.5, df=2, p<0.01) (Figure 7). Higher
rates of diagnosis of prediabetes (2.1%) and diabetes (1.7%) were also observed
using the SS method compared with rates of prediabetes (1.7%) and diabetes
(0.2%) using the TTO method (2 = 7.9, df=2, p=0.02). (Figure 8).
67
Tick Test n = 802
Tick Test n = 484
Referred n = 225, 28%
No risks n = 183, 23%
Risks but declined referral
n = 394, 49%
GP visited n = 127
Unknown n = 59
GP not visited n = 39
No tests done n = 24, 3.0%
Tested, no diabetes n = 85, 10.6%
Tested, prediabetes n = 14, 1.7%
Tested, diabetes n = 2, 0.2%
Tested, results unknown n = 2, 0.2%
No risks n = 107, 22%
Fingerprick test n = 304, 63%
Risks but declined
fingerprick test n = 73, 15%
Tick Test only (Vic & WA)
SS (NSW & Tas)
Referred n = 118, 24.4%
Not referred n = 186, 38.4%
GP not visited n = 29
GP visited n = 50
Unknown n = 39
No tests done n = 9, 1.9%
Tested, no diabetes n = 23, 4.8%
Tested, prediabetes n = 10, 2.1%
Tested, diabetes n = 8, 1.7%
Figure 5: Flowchart of outcomes of the diabetes screening program
Note: Only percentages of total number screened are reported.
68
Figure 6: Percentage of people screened who qualified for referral using either the TTO or SS method.
0%
20%
40%
60%
80%
100%
Tick Test Only (n=802) Sequential Screening (n=484)
Case Detection Method
Perc
enta
ge
Figure 7: Percentage of people who qualified for referral who subsequently took up the referral using either the TTO or SS method.
0%
20%
40%
60%
80%
100%
Tick Test Only (n=619) Sequential Screening (n=118)
Case Detection Method
Per
cent
age
Figure 8: Percentage of people screened who were diagnosed with prediabetes
or diabetes using either the TTO or SS method.
0%
2%
4%
6%
8%
10%
Tick Test Only (n=802) Sequential Screening (n=484)
Case Detection Method
Perc
enta
ge diabetes prediabetes
69
Table 7: Risk estimates of qualifying for referral, referral uptake, and diagnosis of prediabetes or diabetes using the SS method compared to the TTO method.
95% Confidence Interval Risk
Estimate Lower Upper
Qualify for Referral 0.10 0.07 0.12
Referral Uptake 5.88 3.57 9.71
Diagnosis of Prediabetes or Diabetes 1.90 0.96 3.76
Diagnosis of Diabetes 6.76 1.43 32.26
The risk estimates for the SS compared to the TTO method are presented in Table 7.
People screened by the SS method were 10 times less likely to qualify for a referral
to GP, but were approximately six times as likely to take up the referral and were
seven times as likely to be diagnosed with diabetes compared to people screened
using the TTO method.
3.1.2 Characteristics of the screened population, study participants
and diagnosed participants The distribution of risk factors was similar between the two methods (Table 8).
Overall, 23% of the screened population had no risk factors for diabetes, a further
50% had one or two risk factors and the remaining 27% had three or more.
Table 8: Number of diabetes risk factors possessed by the
screened population
Number of Risk Factors
TTO n (%)
SS n (%)
Total n (%)
0 184 (23) 115 (24) 299 (23)
1 215 (27) 118 (24) 333 (26)
2 189 (24) 114 (24) 303 (24)
3 or more 214 (27) 137 (28) 351 (27)
70
A significantly higher proportion (74%) of the 34 participants who went on to be
diagnosed with prediabetes or diabetes had three or more risk factors for diabetes
compared to the rest of the screened population (2=35.3, df=1, p<0.01).
The most common risk factor for diabetes amongst the entire screened population
was being over 55 years of age (50%), followed by being over 45 with high blood
pressure (30%). Other common risk factors were, being over 45 with a BMI greater
than 30 (26%) and being over 45 with a family history of diabetes (23%) (Table 9).
The occurrence of risk factors was very similar between the two methods (Table 9).
The only difference being a slightly higher occurrence of people over 35 and of
Chinese, Indian or Pacific Islander heritage in the TTO method.
Of the 34 participants who were diagnosed with prediabetes or diabetes, the most
common risk factor was being over 55 years of age (85%), followed by being over 45
with a BMI greater than 30 (53%) (Table 10). The occurrence of the following risk
factors was significantly higher amongst the diagnosed population (diabetes and
prediabetes) compared to the remainder of the screened population: being over 55
years of age, being over 45 with a BMI greater than 30, having a history of borderline
high blood sugar, being over 45 and having a family history of diabetes and having
polycystic ovarian syndrome with a BMI greater than 30 (Table 10).
Demographic and lifestyle information was only available for those who either agreed
to a referral (TTO method) or underwent a fingerprick test (SS method) (see Figure
5) and therefore filled out the patient information/consent form. The majority of these
study participants were female (68%) and were over 55 years of age (71%) (Table
11). The TTO method had a greater proportion of females participating than did the
SS method (2=5.3, df=1, p=0.02).
The demographics of the 34 participants who were diagnosed with prediabetes or
diabetes were similar to those of the study participants in general, with the exception
that a greater proportion were over 55 (85% vs. 71%) (2=15.6, df=1, p<0.01) (Table
12).
71
Table 9: Distribution of risk factors for type 2 diabetes within the screened population by screening method
Risk Factor TTO
(n = 802) n (%)
SS (n = 484)
n (%)
Total (n = 1286)
n (%) Age > 55 390 (49) 258 (53) 648 (50)
Over 45 & high blood pressure 229 (29) 158 (33) 387 (30)
Over 45 & BMI > 30 206 (26) 132 (27) 338 (26)
Over 45 & family history of diabetes 182 (23) 110 (23) 292 (23)
History of cardiovascular disease 104 (13) 63 (13) 167 (13)
History of borderline high blood sugar 92 (12) 74 (15) 166 (13)
History of gestational diabetes 51 (6) 21 (4) 72 (6) Over 35 & of Chinese, Indian or Pacific Islander heritage * 55 (7) 16 (3) 71 (6) Over 35 & Aboriginal or Torres Strait Islander heritage 16 (2) 6 (1) 22 (2)
Polycystic ovarian syndrome & BMI > 30 17 (2) 3 (0.6) 20 (2) * Indicates significant difference between the two protocols (2=6.6, df=1, p=0.01).
72
Table 10: Distribution of risk factors for type 2 diabetes within the screened population by diagnostic category
Risk Factor Diagnosed with
Prediabetes (n = 24) n (%)
Diagnosed with Diabetes
(n = 10) n (%)
Remainder of Screened
Population (n = 1252)
n (%) Age > 55 22 (92) 7 (70) 619 (49)*
Over 45 & BMI > 30 13 (54) 5 (50) 320 (26)* History of borderline high blood sugar 12 (50) 4 (40) 1102 (88)*
Over 45 & high blood pressure 10 (42) 5 (50) 372(30) Over 45 & family history of diabetes 9 (38) 5 (50) 278 (22)* History of cardiovascular disease 6 (25) 3 (30) 158 (13)
History of gestational diabetes 3 (13) 1 (10) 68 (5) Over 35 & of Chinese, Indian or Pacific Islander heritage 2 (8) 2 (20) 67 (5)
Polycystic ovarian syndrome & BMI > 30 1 (4) 2 (20) 17 (1.4)*
Over 35 & Aboriginal or Torres Strait Islander heritage 1 (4) 0 (0) 21 (1.7)
* Indicates significant difference between the diagnosed population and the remainder of the screened population (chi-square test p<0.05)
Table 11: Demographic and lifestyle characteristics of the study participants*
Characteristic TTO n (%)
SS n (%)
Total n (%)
Female 163 (74) 186 (64) 349 (68) Gender†
Male 57 (26) 104 (36) 161 (32)
≤55 63 (29) 86 (29) 149 (29) Age
>55 153 (71) 209 (71) 362 (71)
Smoker 38 (18) 35 (12) 73 (15)
Physically Active‡ 109 (56) 124 (48) 233 (52)
BMI ≥ 30 78 (38) 98 (42) 176 (40) * Study participants are those who agreed to a referral (WA & VIC) or underwent a fingerprick test (NSW & TAS). † Indicates significant difference between the two protocols at p < 0.05, using a chi-square test. ‡ Engage in physical activity for at least 30min, 5 or more times a week.
73
Table 12: Demographic and lifestyle characteristics of participants diagnosed
with prediabetes or diabetes
Characteristic Diagnosed with
Prediabetes (n = 24)
n (%)
Diagnosed with Diabetes (n = 10)
n (%)
Total (n = 34) n (%)
Female 14 (58) 8 (80) 22 (65) Gender
Male 10 (42) 2 (20) 12 (35)
≤55 2 (8) 3 (30) 5 (15) Age
>55 22 (92) 7 (70) 29 (85)
Smoker 3 (14) 2 (20) 5 (16)
Physically Active† 11 (55) 3 (33) 14 (48)
BMI ≥ 30 10 (53) 6 (67) 16 (57) † Engage in physical activity for at least 30min, 5 or more times a week. 3.1.3 Results of blood glucose testing in the SS method
Of the patients who underwent a fingerprick test in pharmacy (n = 304, see Figure 5),
35.5% had a fasting test only, 50% had a random test only, 10% had both a random
test followed by a overnight fasting test and 2% had a random test followed by a 2h
fasting test (data are missing for the remaining 2.5%).
The mean blood glucose level for the random tests was 6.5±2.8 mmol/L (mean± SD)
(n = 214), while the mean for the fasting blood glucose tests was 5.7±1.2 mmol/L
(mean± SD) (n = 139) (Figure 9a and 9b).
3.2 EXIT SURVEYS FOR OBSERVABLE RISK FACTORS
Fifty-two percent of the people exiting the 10 pharmacies surveyed (three in each of
NSW and VIC, two in each of TAS and WA), had at least one observable risk factor
for type 2 diabetes. This compares to 63% of the screened population having
observable risk factors (Table 13). This indicates that the screening program may
have been selectively capturing customers with risks for diabetes at a higher rate
74
Figure 9a: Random blood glucose measurements (NSW & TAS) (n = 214).
Figure 9b: Fasting blood glucose measurements (NSW & TAS) (n = 139).
Fasting Blood Glucose (mmol/l)
24.0 - 24.5
22.0 - 22.5
20.0 - 20.5
18.0 - 18.5
16.0 - 16.5
14.0 - 14.5
12.0 - 12.5
10.0 - 10.5
8.0 - 8.5
6.0 - 6.5
4.0 - 4.5
2.0 - 2.5
0.0 - .5
Freq
uenc
y
60
50
40
30
20
10
0
level (5.5 mmol/L) at which referred to GP
Random Blood Glucose (mmol/l)
24.0 - 24.5
22.0 - 22.5
20.0 - 20.5
18.0 - 18.5
16.0 - 16.5
14.0 - 14.5
12.0 - 12.5
10.0 - 10.5
8.0 - 8.5
6.0 - 6.5
4.0 - 4.5
2.0 - 2.5
0.0 - .5
Freq
uenc
y
60
50
40
30
20
10
0
level (5.5 mmol/L) at which asked to return for fasting test or referred directly to GP
75
Table 13: Customers with one or more observable risk factors.
NSW n (%)
VIC n (%)
TAS n (%)
WA n (%)
All n (%)
exit survey 335 (41) 400 (63) 268 (64) 191 (46) 1194 (52)
screening 180 (69) 256 (59) 126 (56) 250 (68) 812 (63)
than were present in the overall customer base (although this varies greatly between
states). Using the data from the screened population it was estimated that an
additional 13.6% of people screened had unobservable risk factors, such as high
blood pressure, unaccompanied by observable risk factors. These people would not
have been captured by this exit survey.
Using the exit survey data it is estimated that during the period of screening an
average of 8 out of every 1000 people entering a participating pharmacy per week
with observable risk factors were screened (Table 14). To estimate the number of
people at risk per week, the number of people observed to have risk factors in an
hour was multiplied by the average number of opening for the pharmacy per week.
The screening rate varied greatly between states depending on the intensity of the
screening effort (i.e., the number of weeks the screening was spread over).
3.3 PATIENT FOLLOW-UP SURVEY
A total of 289 follow-up surveys were conducted (22.5% of the total number of people
screened). The majority of people screened (58.1%) could not be surveyed as, per
the study protocol, they had not filled out a patient information/consent form and
therefore did not provide their contact details. A further 5.5% filled out the consent
form but did not give their phone number and 13.1% could not be reached by phone.
Only 0.6% refused to take part in the survey once contacted (Table 15).
76
Table 14: Estimated rate of at-risk population captured by the screening program
State Duration of Screening
(weeks)
No of People Screened per
week
Estimated No of people at risk
per week
Screening rate (per 1000 people at
risk)
NSW 12 3 995 3.0
TAS 9 5 1341 3.7
VIC 2 25 1760 14.2
WA 5 11 1436 7.7
Average 7 11 1383 8.0
3.3.1 Awareness of the service When asked “How did you first become aware of the diabetes screening service?”,
66% responded that the pharmacist had invited them to participate, while a further
26% indicated an advertisement in pharmacy.
3.3.2 Health Information Overall, 35% of respondents indicated that they had been given verbal or written
information or advice on physical activity and 33% had been given information or
advice on healthy eating. Of these, 41% reported that they had made a change in
their diet since receiving the information and 36% reported that they now exercised
more (Figure 10). Of those who did not make any changes, the majority considered
that they were already adhering to a healthy lifestyle.
When asked to rate the helpfulness of the information received on a 5 point Likert
scale (1 = very unhelpful, 5 = very helpful), the median responses were 4 (mode 4,
range 1-5, n = 61) for verbal information on physical activity, 4 (mode 4, range 3-5, n
= 50) for written information on physical activity, 4 (mode 4, range 3-5, n =55) for
verbal information on healthy eating and 4 (mode 4, range 3-5, n =49) for written
information on healthy eating.
77
0
20
40
60
80
100
exercise (n = 100) diet (n = 95)
Perc
enta
ge
exercise more or change in diet no change
Table 15: Summary of numbers surveyed
TTO n (%)
SS n (%)
Total n (%)
Surveyed 140 (17.5) 149 (30.8) 289 (22.5)
Did not fill out referral/consent form 576 (71.8) 171 (35.3) 747 (58.1) No phone number provided on form 5 (0.6) 66 (13.6) 71 (5.5)
No answer/ Not at home 76 (9.5) 92 (19.0) 168 (13.1)
Did not want to participate in survey 5 (0.6) 3 (0.6) 8 (0.6)
Not Surveyed
Could not remember participating in screening
0 (0.0) 3 (0.6) 3 (0.2)
Total 802 (100) 484 (100) 1286 (100)
Figure 10: The effect of receiving information/advice on exercise and healthy eating.
3.3.3 Approval of the service Overall respondents strongly approved the diabetes screening service in community
pharmacy. The median approval rating was 5 (mode 5, range 2-5, n = 281) on a 5
point Likert scale (1 = strongly disapprove, 5 = strongly approve). There was a higher
approval rating from the respondents in the SS method than from those in the TTO
method (Table 16). In response to an open ended question concerning the reason for
approval of the service, the most common response for both methods was ‘increases
78
awareness of diabetes’ (Table 17). Otherwise the responses from the two methods
were quite different, with the TTO respondents frequently pointing to ‘reminder to be
tested’ and ‘early detection is important’ as reasons for approval, while the SS
respondents commonly indicated ‘convenient, accessible, or easy’ and ‘good service,
friendly staff’. Some verbatim comments from participants are given below.
“Just to be given some incentive to go and have my blood glucose levels
checked was fantastic.” – TTO participant
“Diabetes is so common these days. It’s much better to know early than to
know too late and you have to have insulin.” – TTO participant
“It drew my attention to the fact that I could have diabetes.” - SS participant
“There is a long wait at GP; pharmacy is less hectic and more private.” - SS
participant
“You can walk in and have it done anytime; you don’t have to make an
appointment.” - SS participant
“The pharmacist was very helpful and supportive, stressed the importance of
going to the doctor.” - SS participant
Table 16: Approval of diabetes screening being available in community pharmacy*
TTO† (n = 132) SS
(n = 149) Total (n = 281)
median mode range median mode range median mode range
4 5 2-5 5 5 2-5 5 5 2-5
* Rated on a 5 point Likert scale (1 = strongly disapprove, 5 = strongly approve). † There is a significant different in the mean approval scores between the two methods (Mann Whitney U=6618, p<0.01).
79
community pharmacy,
n=83GP, n=20
either, n=44
community pharmacy,
n=83GP, n=20
either, n=44
Table 17: Reasons for approval of screening in community pharmacy
TTO SS
Increases awareness of diabetes Increases awareness of diabetes
Reminder to be tested Convenient, accessible, or easy
Early detection is important Good service, friendly staff
Good Improves peoples health
Convenient, accessible, easy Visit more often than GP
3.3.4 Satisfaction with the SS method The participants in the SS method were asked about their satisfaction with the
service and any health information received (rated on a 5 point Likert scale, 1=
extremely dissatisfied, 5 = extremely satisfied). Satisfaction with the service was high
with a median at 5 (mode 5, range 2-5, n=149), as was satisfaction with the health
information received 4 (mode 4, range 2-5, n=99).
3.3.5 Preference for location of service When the participants in the SS method were asked whether they preferred to have
the service delivered in the community pharmacy or by their GP, the majority (56.5%)
responded that they preferred to have the service delivered in community pharmacy
(Figure 11).
Figure 11: Preference for the location of the screening service.
80
3.3.6 Willingness to pay The participants in the SS method were asked about their willingness to pay for a
diabetes screening service. Sixty-two percent of the 148 respondents were willing to
pay for the service, 37% were not willing to pay and 1% were unsure. Of the 92
people who were willing to pay, 61 gave an amount: the mean maximum WTP was
A$15.09±$11.66 (mean ± SD) and the overall median maximum WTP was A$10.00
(the distribution was around the median).
Those who indicated a preference for the location of the service to be delivered were
asked how much more they would be willing to pay to have it delivered there. Of the
83 (56.5%) who said they preferred the service to be delivered through a community
pharmacy only seven were prepared to place a value of greater than A$0 on their
preferred location of service delivery. This indicated that the majority would pay the
same for the test at the pharmacy as they would at the GP surgery (the gap between
Medicare and the GP’s fee). The mean incremental WTP for the seven who said they
would pay more at the pharmacy was A$1.43±$4.08 (mean ± SD). Among those who
preferred the service to be delivered by their GP only four responded to the WTP
question and of these, only one respondent valued their preference for receiving the
service through the GP more than A$0.00.
3.4 PHARMACIST SATISFACTION
Five pharmacists from NSW who delivered the SS method and nine pharmacists
from VIC who delivered the TTO method attended the focus groups. Overall, there
was a very high approval and satisfaction with the service with very little difference
observed between the groups. Various topics were discussed and verbatim
examples of pharmacists’ responses on aspects of the program are presented below.
Training Workshop for Screening
NSW “I found it very good, can’t fault it. Maybe we needed to get more on the marketing of the idea
to get the message out to the community”
“The training has been very useful and has an ongoing effect in that you might have
suspicions about someone having diabetes I always offer them a chance for screening.”
81
VIC
“Learned a lot from the program”
Pharmacist satisfaction with the screening service
NSW “The most rewarding part is if you actually diagnose someone who has diabetes and they
come back and let you know the outcome”
“If diagnosed, the patient usually comes back because you’re the one who found it for them.”
“I found the protocols very useful and easy to follow”
“We had been screening previously but this made it more of a formal thing - Its an ongoing
thing and we still screen people for diabetes.”
VIC “Tick Test appealed to customers”
“Pharmacy assistants were encouraging”
“Diabetes is on the increase and early stages are often symptomless so early detection
would help to reduce long-term complications”
“Patient follow up important but difficult for busy pharmacy”
Future improvements and implementation of the screening service
NSW “Most pharmacists would say we already do that – it would be rare for a pharmacist not to
back a request for a blood pressure test or a blood glucose test.”
“Its good to be able to offer a screening service that follows the correct guidelines and has
protocols”
VIC “If we were allowed to do blood glucose test in pharmacy patient with potential diabetes
would probably see doctor sooner”
“The screening service in pharmacies all the time would make people more aware”
82
3.5 ECONOMIC ANALYSIS OF THE SCREENING PROGRAM
3.5.1 Costs The cost of consumables was estimated to be A$2.74 for the TTO and A$5.00 for SS
per case screened. The cost of the pharmacist’s time for administering either test
was estimated to be A$3.08 based on the recommended remuneration rate of A$37
per hour. The cost of a pharmacy assistant (Level 3) was estimated to be A$1.67 (5
minutes) for the TTO and A$3.33 (10 minutes) for SS. An average fixed cost per
person screened was calculated under the assumption that five patients per
pharmacy would be screened per week and that the life of the program was one
year. On this basis the average fixed cost was A$0.27 for the TTO and A$0.42 for
SS. Based on these estimates the total cost of screening each person in a pharmacy
is A$7.76 for the TTO and A$11.83 for the SS service (Table 18).
While information on whether referred patients had attended a GP was available on
166 patients in the TTO group and 79 patients in the SS group, resource use
information was not routinely collected on these patients. We have assumed all these
patients had a standard consultation (cost A$26.25, i.e. 85% of the scheduled fee)
and those patients who were tested for diabetes followed the Australian protocol for
diabetes screening. As a recent study has indicated 53 this involves fasting plasma
glucose (FPG) blood test (cost A$8.30) at the initial consultation and a follow-up
consultation to obtain the result. Patients whose FPG is between 5.5 and 5.9 mmol/L
can be regarded as having inconclusive results and so will have an OGTT at a cost
of A$16.20 and then return for the final results (cost A$26.25). Based on AusDiab
study we assume that 48% of persons who were screened by their GP have an FPG
in the range that would require an OGTT test. Following this protocol the expected
cost of screening each patient who attends a GP and was screened is estimated to
be A$54.97.
Table 18 shows the average cost per patient over the duration of the study by
category of cost and type of screening. The total incremental cost of SS for the
pharmacy was A$4.07 per patient more than the TTO alone. The cost of the
subsequent GP based screening to diagnose diabetes was A$14.03 for the TTO and
83
Table 18: Costs and effects by allocation group
Mean cost (A$)/Effect (S.D) Category
TTO SS Difference (95% CI)
Costs
Pharmacy based costs $7.76 $11.83 $4.07
GP based costs $14.03 $9.35 -$4.68 (-8.78, -0.57)
Total costs $21.79 (31.84) $21.18 (28.06) -$0.37 (-4.47, 3.73)
Effects
% diagnosed with diabetes 0.35% 2.73% 2.38% (0.46%, 4.30%)
% diagnosed with IGT or IFG 2.44% 2.69% 0.24% (-1.54%, 2.02%)
Cost per diabetes case detected $6241 $788
A$9.35 for SS. The higher cost of subsequent screening in the TTO group is due to
the greater proportion visiting a GP for testing than in the SS group. The incremental
cost of SS within the pharmacy is entirely offset by these lower subsequent costs, the
overall incremental average cost was A$0.37 lower per person screened in the
sequential group and this difference was not significant.
3.5.2 Outcomes After imputing for the patients lost to follow-up, a total of 0.35% of people who had
the TTO and 2.73% who had SS were subsequently diagnosed with diabetes within
the duration of the study. Hence the incremental proportion diagnosed using SS was
2.38% (95% CI: 0.46%, 4.30%). A total of 2.44% in the TTO group and 2.69% in the
SS group, were diagnosed as having prediabetes, however, there was no significant
difference in the proportion diagnosed between these groups.
3.5.3 Cost-effectiveness The combinations of cost and effect difference reported above for the intervention
can be represented by plotting them on a cost-effectiveness plane (Figure 12). The
plane extends the traditional statistical analysis of either costs (y-axis) or effects (x-
axis) into two dimensions, both of which are considered simultaneously. Points on
84
the plane represent extra cost and effect of SS over the TTO. The plane can be
divided into four quadrants (labelled North East (NE), South East (SE), South West
(SW) and North West (NW)), with each quadrant representing a different relationship
between cost and outcome. For example, points in the NE indicate that a greater
incremental outcome can only be achieved at a higher incremental cost. Figure 12
shows the estimated mean cost and effect differences between the two protocols
with the 95% confidence intervals (CI) and 95% confidence ellipses defining the joint
probability distribution of incremental costs and effects (i.e., the area into which we
would expect 95 combinations of costs and effects to fall if the study were conducted
100 times).
The diagram shows that there is no significant difference in costs because the 95%CI
is equally spread above and below the x-axis. However, the ellipse lies to the right of
the y-axis (positive) indicating that there is a significant difference in effects. The
mean incremental difference in costs is A$0.37 (favouring SS) and the mean
incremental increase in the percentage diagnosed with diabetes is 2.38%. As there
is no significant difference in costs between the two methods of screening, but a
significant difference in effects, calculation of a cost-effectiveness ratio is
inappropriate in these circumstances, because it would fail to differentiate between
an intervention that is cost-saving and has better outcomes vs. an intervention that is
more costly and has poorer outcomes. However, Figure 12 would indicate that it is
likely there is no trade-off between cost and outcomes (i.e., the cost is not
significantly different in the two strategies, but SS is the superior strategy in terms of
detecting new cases of diabetes). The higher rate of detection using SS also greatly
reduces the average cost per case detected. The cost per case diabetes detected is
$21.79 divided by 0.35% or $6241 in the TTO group and $21.18 divided by 2.73% or
$788 (Table 18).
With regard to the budgetary impact, if 100,000 individuals were opportunistically
screened using the sequential method then the total cost would be in the order of
A$2.18 million dollars, of which approximately A$1.26 million would be incurred at the
pharmacy level.
85
Figure 12: Cost-effectiveness plane of TTO vs. SS
-$10
-$8
-$6
-$4
-$2
$0
$2
$4
$6
$8
$10
-0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 0.06
Proportion diagnosed with diabetes
Incr
emen
tal c
osts
(200
4 A
us$)
NE
SE
NW
SW
3.5.4 Sensitivity analysis Sensitivity analyses were performed to examine whether the results in the main
analysis are robust to different assumptions concerning each of the interventions
(see Table 19). For the sake of brevity, we have only considered increases in the
cost of screening. Firstly, a 50% increase in the remuneration to the pharmacist and
pharmacy assistant (i.e. A$37 to A$55 and A$20 to A$30) would increase the cost
per person screened to A$10.31 and A$15.04 for the TTO and SS groups
respectively. This increases the incremental cost saving to -A$0.94, but the
difference between the groups remains non-significant. Secondly, we have used the
scheduled Medicare fee rather than the 85% rebate to include some of the out-of-
pocket costs incurred by the patients. While the average cost per person screened
increases to around A$26 the difference between the groups does not change.
Thirdly we have assumed that the pharmacist primarily administers the SS test
increasing the time required to 10 minutes (and reducing pharmacy assistant time 5
86
minutes). This has little effect on the difference in costs. To examine the impact of
changes in fixed costs we assumed the number of patients screened per week in
Table 19: Impact of different assumptions regarding increases in costs
Mean cost (A$)/Effect (S.D) Category
TTO SS Difference (95% C.I.)
50% increase in the Pharmacy fees
24.56 (36.17) 23.62 (31.93) -0.94 (-7.39, 5.52)
Increase in GP based costs
25.63 (36.17) 24.92 (31.93) 0.72 (-6.92, 0.49)
Increase in screening time (10 minutes)
21.79( 30.77) 22.07 (27.07) 0.28 (-2.98, 3.54)
Increase in No. patient screened per week
21.79 (31.85) 21.06 (32.02) 0.05 (-5.05, 5.15)
Increase in proportion having an OGTT
23.79 (35.06) 22.53 (35.06) -1.26 (-6.33, 3.80)
Increase in GP visits in SS group
21.79 (30.75) 23.33(27.21) 1.54 (-3.52,6.60)
each pharmacy increased from 5 to 20. This had little impact on the average, or
incremental cost. To examine how the subsequent GP based costs impacted on the
analysis we have examined the effect of changing two assumptions. If the proportion
of patients having an OGTT test increased from the AusDiab rate of 48% to 75%
then the cost per patient screened would increase to around A$24 per person. Finally
if the proportion being referred to the GP in the sequential group increased from 14%
in the main analysis to 24% then the average cost would increase to A$24, but the
difference between groups would remain insignificant. Overall the conclusion that SS
is not significantly more expensive than a TTO would appear robust to a wide range
of assumptions.
87
4. RESULTS – DMAS
Key Findings: High completion rates for the DMAS were achieved – 84% (149/176) for
intervention patients and 88% (140/159) for control patients
Over the course of the DMAS intervention, pharmacists delivered a mean of 29
interventions per patient; 36% related to home blood glucose monitoring, 31%
related to medication adherence and 29% related to lifestyle and foot care issues.
For the intervention subjects:
o The mean blood glucose levels steadily decreased over the four visits
from 9.4mmol/L at the first visit to 8.5mmol/L at the final visit (p<0.01).
o Mean systolic BP dropped from 143mmHg at first visit to 137 mmHg at
final visit (p<0.01).
By the end of the study, significantly greater improvements in glycaemic control
were seen in the group who received the DMAS intervention compared to those
who did not receive the service; i.e., a mean reduction in HbA1C of -0.97% (95%C:
-0.8, -1.14) in the intervention group compared with -0.27% (95% CI: -0.15, -
0.39) in the control group.
Important improvements in humanistic outcomes seen only in the DMAS group
included increased understanding of long term management of diabetes (p<0.01),
and better adherence to medications (p<0.01). There were also trends to
improvement in QOL (EQ-5D utility score) (p=0.07) and well being (p=0.06).
Patients reported great satisfaction with the DMAS, citing improvements in their
knowledge about diabetes, self confidence, self efficacy and motivation in its
management, as major benefits.
Pharmacists also expressed great satisfaction with their involvement in the
delivery of DMAS especially in terms of knowledge and confidence gained,
benefits for their business and improvements in self management observed in
their patients.
If the reduction in HbA1C achieved during the trial continued over a ten year period
it would produce an increase in life expectancy up to 0.23 (95%CI: -0.10, 0.55)
and 0.18 (95%CI: -0.08, 0.45 ) quality-adjusted life years per patient.
The cost effectiveness of DMAS compares favourably with other accepted uses of
health care resources funded by the Australian Government. The cost per annum
of the service would be $340. The cost per life year was estimated to be from
$17,752 to $24,029 and the cost per QALY was estimated to be from $22,486 to
$30,582 (depending on the scenario used).
88
4.1 RECRUITMENT AND COMPLETION
4.1.1 Pharmacies A total of 28 intervention and 28 control pharmacies participated in the DMAS study.
In the intervention pharmacies, 31 pharmacists participated in the study while in the
control arm 32 pharmacists took part. A comparison of the demographic
characteristics of control and intervention pharmacists (Table 20) and pharmacies
(Table 21) confirmed their similarity. No statistically significant differences between
groups were found with respect to any of the characteristics.
Table 20 : Demographic characteristics of pharmacists
Control n=32
Interventionn=31
Number (%)
male 13 (39) 14 (45) Gender female 19 (61) 17 (55)
18-25 4 (13) 3 (10) 26-35 9 (28) 7 (22) 36-45 9 (28) 9 (29) 46-55 7 (22) 11 (34) 56-65 4 (13) 1(3)
Age group (yr)
> 65 1(3) 0 (0)
Owner/partner 19 (59) 19 (61) Status Salaried pharmacist 12 (41) 12 (39)
89
Table 21 : Demographic characteristics of pharmacies
Control n=28
Interventionn=28
Number (%)
1-2 1 (4) 1 (4)
3-4 9 (32) 16 (57) Number of staff
5-6 18 (64) 11 (39)
1 23 (82) 20 (70)
>1-2 1 (4) 3 (11)
>3-4 3 (11) 4 (14)
PHARIA rating
>5-6 1 (4) 1(4)
Stand alone/strip 15 (54) 15 (54)
Shopping centre 11 (39) 8 (29)
Mall 1 (4) 3 (11) Location
Medical Centre 1 (4) 2 (7)
4.1.2 Study Participants A total of 335 eligible participants were fully recruited into the DMAS study, 176 into
the intervention group and 159 into the control group. Two hundred eighty-nine (149
intervention, 140 control) participants fully completed the study. Forty-six participants
partially completed the study (27 intervention, 19 control) (Figure 13). Final clinical
data were obtained for 232 of the participants. A breakdown of enrolled and
completed patients in each State is shown in Table 22 and Table 23, respectively.
It should be noted that there is variation in the denominators (n) in the tables
presented throughout the results section and this is due to missing data for any
particular variable.
90
Figure 13: Flowchart of DMAS recruitment and completion
> 400 patients agreed to participate
> 65 ineligible
335 patients enrolled
159 Control Baseline Visit
140 Control Final Visit (6 mos)
18 withdrew 1 death
176 Intervention Baseline Visit
171 Intervention Visit 1 (2 wks)
163 Intervention Visit 2 (1.5 mos)
153 Intervention Visit 3 (3.5 mos)
149 Intervention Final Visit (6 mos)
5 withdrew
8 withdrew
10 withdrew
4 withdrew
91
Table 22: Breakdown by State of enrolled patients (n=335)
NSW VIC TAS WA
Intervention 81 45 12 38
Control 70 37 23 29
TOTAL 151 82 35 67
Table 23: Breakdown by State of completed patients (n=289)
NSW VIC TAS WA
Intervention 74 37 10 28
Control 57 34 20 29
TOTAL 131 71 30 57
All participants who fully completed the study were included in the following analyses
as well as non-completers for whom final clinical data were available (299 in total,
157 intervention, 142 control).
A comparison of completers and non completers is provided in Appendix 10. The
non-completers were younger on average than the completers (55 yr vs. 62 yr), had
a higher mean baseline HbA1C (9.1% vs. 8.5%), a higher mean BMI (33.6 vs.
31.6kg/m2) and were more likely to be current smokers (36% vs. 11%).
4.2 BASELINE ASSESSMENT
4.2.1 Participant Demographics The demographics of the interventions and controls were very similar (Table 24). The
mean age was 62 ±11 years, around half were male (51%) and the majority were
born in Australia (66%). Most (78%) lived with someone else. Approximately half had
continued their education past the minimum school-leaving age and 26% had a
degree or professional qualification. Fifty-two percent of the participants were retired
92
Table 24: Demographic characteristics of DMAS participants
Control Intervention All
Number (%) or Mean ±SD
Age (yr) (n = 293) 62.9 ±11.4 60.8 ±10.3 61.8 ±10.8
male 71 (50) 81 (52) 152 (51) Gender female 71 (50) 75 (48) 146 (49)
Australia 96 (68) 100 (65) 196 (66) Country of Birth Other 45 (32) 55 (36) 100 (34)
alone 31 (22) 35 (23) 66 (22) Reside with someone 111 (78) 119 (77) 230 (78)
Education continued past minimum school-leaving age 69 (50) 72 (47) 141 (48)
Degree or professional qualification 37 (28) 38 (25) 75 (26)
Employment Status
retired 84 (59) 70 (45) 154 (52)
employed or self-employed 33 (23) 52 (33) 85 (29)
unable to work due to health 8 (6) 12 (8) 20 (7)
other 17 (12) 22 (14) 39 (13)
Receive pension 87 (61) 85 (55) 172 (58)
Concession card for prescriptions 92 (72) 96 (68) 188 (70)
and the majority (58%) received a pension. Seventy percent had a concession card
for prescriptions.
93
4.2.2 Diabetes History Overall, the diabetes history of the two groups was similar (Table 25). Most (67%)
reported having had prior diabetes education and 90% were currently monitoring
their blood glucose at home. The most common self-reported diabetes co-morbidities
were hypertension (70%) and high cholesterol (61%). Approximately one fifth of
patients reported macrovascular complications such as angina and microvascular
complications such as eye problems. The mean number of years since diagnosis of
diabetes was 9.5 (range: <1 - 42) with the control group having been diagnosed
somewhat longer than the intervention group (10.4 yr versus 8.6 yr; p=0.04). Most
patients (79%) reported being treated with oral hypoglycaemics alone, however the
intervention group had a higher proportion of patients on a combination of insulin and
oral hypoglycaemics than the control group (25% vs. 13%; p=0.01). Importantly,
though, these patients on combined therapy did not differ between groups with
respect to baseline HbA1C and therefore diabetes regimen did not need to be
controlled for in the analysis of clinical outcomes (see section 4.2.3). Use of medical
services in the 6 months prior to the study was also similar between the intervention
and control groups. Patients reported a mean of 0.03 admissions to hospital and 0.5
visits to GP per month. The number of work days missed in the 6 months prior to the
study was higher in the intervention group than in the control group (0.25 vs. 0.03
days per month; p=0.02).
4.2.3 Clinical Parameters at Baseline The control and intervention groups at baseline were similar for most clinical
measures (Table 26a). They differed in mean baseline HbA1C with the intervention
group having a higher baseline HbA1C than the control group (8.8±1.4% vs.
8.2±1.4%, p<0.01). All other clinical measures were similar at baseline. The mean
BMI was 31.6 ±6.7kg/m2 which falls into the obese category according to the World
Health Organization classification scheme 89. Mean BP was 134/78 mmHg which falls
in the high-normal category according to the National Heart Foundation of Australia 90. Only 11% reported being current smokers and 30% reported doing exercise or
physical activity (such as brisk walking, dancing, active work around the home, using
stairs or more vigorous exercise) five or more times per week (Table 26b).
94
Table 25: Diabetes history of DMAS participants at baseline
Control Intervention All
Number (%) or Mean ±SD
Years since diagnosis (n=294) * 10.4 ±7.5 8.6 ±6.5 9.5 ±7.0
Current management †
oral hypoglycaemics only 117 (85) 109 (71) 226 (78)
insulin only 2 (2) 6 (4) 8 (3)
insulin & oral hypoglycaemics 18 (13) 38 (25) 56 (19)
Prior diabetes education 90 (65) 105 (69) 195 (67)
Monitoring blood glucose at home 130 (92) 139 (89) 269 (90)
Self-reported history of diabetes complications & co-morbidities
high blood pressure 101 (71) 107 (69) 208 (70)
stroke 11 (8) 12 (8) 23 (8)
angina 25 (18) 32 (21) 57 (19)
heart attack 17 (12) 24 (16) 41 (14)
high cholesterol 86 (61) 93 (61) 179 (61)
eye problems 36 (25) 28 (18) 64 (22)
kidney problems 21 (15) 18 (12) 39 (13)
feet problems 31 (22) 35 (22) 66 (22)
Use of medical services in the previous 6 months (no per month) (n=296)
admissions to hospital 0.03 ±0.08 0.04 ±0.09 0.03 ±0.09
days in hospital 0.11 ±0.50 0.18 ±0.90 0.14 ±0.73
visits to emergency 0.02 ±0.08 0.03 ±0.09 0.02 ±0.08
visits to GP 0.41 ±0.42 0.50 ±0.50 0.46 ±0.47
work days missed * 0.03 ±0.28 0.25 ±2.48 0.15 ±1.80 * Significant difference between intervention and control at baseline using an independent t-test. † Significant difference between intervention and control at baseline using a chi-square test.
95
Table 26a: Clinical parameters of DMAS participants at baseline
Control Intervention All n Mean ±SD n Mean ±SD n Mean ±SD
HbA1C (%)* 134 8.2 ±1.4 155 8.8 ±1.4 289 8.6 ±1.4
BMI (kg/m2) 136 31.2 ±6.6 147 32.1 ±6.7 283 31.6 ±6.7
Systolic BP (mmHg) 124 134 ±13 143 135 ±14 267 134 ±14
Diastolic BP (mmHg) 124 78 ±9 143 79 ±8 267 78 ±8
Lipids Profile (mmol/L)
TC 133 4.79 ±1.01 154 4.85 ±1.03 287 4.82 ±1.02
HDL 118 1.32 ±0.43 143 1.26 ±0.44 262 1.31 ±0.55
Trig 131 2.02 ±1.06 153 2.33 ±1.46 284 2.19 ±1.30
* Significant difference between intervention and control at baseline using an independent t-test.
4.2.4 Humanistic Parameters at Baseline
EuroQol (EQ-5D)
The patient scores on the mobility, self care, usual activity, pain and anxiety items of
the EuroQol (EQ-5D) questionnaire were adjusted using an algorithm 78 to derive a
utility score which ranged from -0.6 (worst health state) to 1.0 (best health state). The
utility scores were similar at baseline for control and intervention groups with a mean
score of 0.76 (± 0.25).
Patients were also asked to rate their present health state on a scale of 0 (worst state
imaginable) to 100 (best state imaginable). The control group rated themselves
higher on the health state scale than the intervention group (72 ±19 vs. 67 ±19,
p=0.02) at baseline (Table 27).
96
Table 26b: Smoking status and physical activity of participants at baseline
Control Intervention All
Number (%)
Smoking Status:
current smoker 15 (11) 18 (12) 33 (11)
ex-smoker 63 (45) 69 (44) 132 (45)
never a smoker 62 (44) 69 (44) 131 (44)
Smoking frequency amongst current smokers (times per day):
≤10 3 (20) 5 (28) 8 (24)
11-20 5 (33) 7 (39) 12 (36)
21-30 7 (47) 3 (17) 10 (30)
≥31 0 (0) 3 (17) 3 (9)
Physical Activity:
never 15 (11) 18 (12) 33 (11)
seldom 22 (16) 20 (13) 42 (14)
1-2 times a week 28 (20) 35 (22) 63 (21)
3-4 times a week 36 (26) 34 (22) 70 (24)
5 or more times a week 39 (28) 49 (31) 88 (30)
Well-Being Questionnaire 12 (WB-12)
Control and intervention participants scored similarly on the energy (6.3 out of a
possible 12) and positive well-being (8 out of a possible 12) subscales of the well-
being questionnaire at baseline. The intervention group had a higher score on the
negative well-being subscale than the control (2.2 vs. 1.6 out of a possible 12, p =
0.03). The total well-being scores were similar between the two groups (24 out of a
possible 36) (Table 27).
Diabetes Care Profile (DCP)
Control and intervention patients scored similarly on the five subscales of the DCP
although the intervention group scored slightly lower than the control group on the
97
Table 27: Humanistic parameters of DMAS participants at baseline
n Control n Intervention All
Mean ±SD or Number (%)
EQ-5D
Utility score (range -0.6 to 1.0) 140 0.77 ±0.25 154 0.75 ±0.25 0.76 ±0.25
Health state scale * (range 1-100) 139 72.3 ±18.9 152 67.1 ±19.3 69.6 ±19.2
Well-Being Questionnaire 12 (range 1-12)
Negative well-being * 141 1.62 ±2.25 154 2.24 ±2.59 1.94 ±2.45
Energy 138 6.54 ±2.76 151 5.97 ±2.68 6.25 ±2.73
Positive well-being 141 8.16 ±3.29 154 7.79 ±2.99 7.96 ±3.14
Total (1-36) 138 25.0 ±6.8 151 23.6 ±6.5 24.3 ±6.7
Diabetes Care Profile (range 1–5)
Control problems 135 1.33 ±0.49 135 1.42 ±0.61 1.38 ±0.55
Positive attitude 140 3.62 ±0.80 154 3.48 ±0.77 3.55 ±0.79
Negative attitude 140 2.51 ±0.89 153 2.67 ±0.90 2.60 ±0.90
Self-care ability † 140 3.38 ±0.90 154 3.05 ±0.80 3.21 ±0.86
Understanding of long-term management † 138 3.53 ±0.86 154 3.21 ±0.80 3.36 ±0.84
QSD-R (range 0–5)
Self medication/ diet scale * 139 1.17 ±0.89 154 1.51 ±1.06 1.35 ±1.00
Brief Medication Questionnaire
Regimen screen† (range 0-8) 138 1.04 ±1.05 157 1.57 ±1.32 1.32 ±1.22 Belief screen† (range 0-2) 138 0.54 ±0.72 157 0.78 ±0.80 0.67 ±0.77 Recall screen† (range 0-2) 138 0.99 ±0.43 156 1.15 ±0.58 1.07 ±0.52
Total adherence† (range 0-12) 138 2.58 ±1.63 156 3.50 ±2.05 3.07 ±1.92
Problems accessing medications (range 0-6) 138 0.62 ±1.13 151 0.65 ±1.08 0.63 ±1.10 Informed about medications 94 (68) 108 (69) 202 (69) * Significant difference between intervention and control at baseline using the Mann Whitney U test. † Significant difference between intervention and control at baseline using an independent t-test.
98
“self care ability” (3.1 vs. 3.4 out of a possible 5; p<0.01) and “understanding of long-
term management” (3.2 vs. 3.5 out of a possible 5; p<0.01) subscales (Table 27).
Questionnaire on Stress in Patients with Diabetes – Revised (QSD-R)
The intervention group scored higher on the QSD-R self medication/diet scale than
the control group at baseline (1.5 vs. 1.2 out of a possible 5, p=0.01) indicating that
diet and self-medication issues caused them more stress (Table 27).
Brief Medication Questionnaire (BMQ)
The intervention group scored higher on all screens of the BMQ indicating a greater
risk of non-adherence (Table 27). In addition to the BMQ screens, patients were also
assessed on their ability to match their medications to disease states and on
problems accessing their medications. Problems included opening or closing the
medicine bottle, giving themselves injections, reading the print on the bottle, getting
refills on time and taking many medications at the same time. Both groups reported
few problems with accessing medications (mean of 0.6 problems) and both were
equally informed about medications (69%) (Table 27).
4.2.5 Medications Use at Baseline The average number of medications that each patient was prescribed was similar
between the intervention and control groups at baseline (Table 28). The exception
was antihypertensive medications where the control patients were taking more
antihypertensive medications on average than the intervention group (1.46 ±1.04 vs.
1.20 ±0.97; p=0.02). The defined daily doses for all classes of medications were
similar between the two groups at baseline (Table 33).
As discussed in section 4.2.2, there was a significant difference in the antidiabetes
medication regimen between the intervention and control groups at baseline with a
greater proportion of the intervention group using insulin than in the control group
(Table 25). There were no other differences in the medication regimens of the two
groups at baseline either in the proportion of patients receiving therapy or in the
types of medications used.
99
Table 28: Mean numbers of medications at baseline
Medication type n Control Mean ±SD n Intervention
Mean ±SD n All Mean ±SD
antidiabetes 138 1.79 ±0.64 157 1.83 ±0.77 295 1.81 ±0.71
antihypertensive* 138 1.46 ±1.04 157 1.20 ±0.97 295 1.32 ±1.01
lipid-lowering 137 0.66 ±0.55 157 0.54 ±0.51 294 0.60 ±0.53
anti-coagulation 138 0.49 ±0.53 157 0.42 ±0.54 295 0.45 ±0.54
other cardiovascular 138 0.22 ±0.54 157 0.24 ±0.54 295 0.23 ±0.54
other 139 2.19 ±2.43 157 2.25 ±2.09 296 2.22 ±2.26
Total 137 6.85 ±3.33 157 6.47 ±2.97 294 6.65 ±3.14 * Significant difference between intervention and control at baseline using an independent t-test.
4.3 SERVICE EVALUATION
4.3.1 Process Evaluation A total of 4309 interventions were delivered by the intervention pharmacists and
documented in the worksheets during the study. The mean number of interventions
per patient was 29 (SD ±24). Thirty-six percent of these interventions were related to
home blood glucose monitoring (checking technique, strategies to address
hypoglycaemia and hyperglycaemia). Thirty-one percent were related to adherence
to medications (education about medicines, ability to access medicines, and insulin
administration). Twenty-nine percent of the interventions dealt with lifestyle (physical
activity, nutrition, alcohol consumption and smoking) and foot care issues. The
remaining 4% of interventions addressed checks of medication history (drug/dose
discrepancies and potential therapeutic problems).
Ninety-five percent of intervention patients received interventions related to home
blood glucose monitoring, 92% received lifestyle interventions and 89% received
interventions related to adherence (Figure 14). The distribution of interventions
100
Figure 14: Percentage of patients who received interventions
(n=148)
0%
20%
40%
60%
80%
100%
Adherence MedicationHistory
Home BGMonitoring
Lifestyle Foot care
% o
f pat
ient
s w
ho r
ecei
ved
inte
rven
tion
delivered and documented by the pharmacists within the broader categories is shown
in Figures 15 to 18. Pharmacists also set and documented an average of 5.9 (range
0-15) goals per patient.
101
Figure 15: Percentage of patients who received interventions
related to adherence (n=147)
0%
20%
40%
60%
80%
100%
education aboutmedications
ability to accessmedications
insulinadministration%
of p
atie
nts
who
rece
ived
inte
rven
tion
Figure 16: Percentage of patients who received interventions
related to medication history (n=147)
0%
20%
40%
60%
80%
100%
drug/dosediscrepancies
potentialtherapeuticproblems
other
% o
f pat
ient
s w
ho re
ceiv
ed in
terv
entio
n
102
Figure 17: Percentage of patients who received interventions related to home blood glucose monitoring (n=148)
0%
20%
40%
60%
80%
100%
technique hypoglycaemia hyperglycaemia% o
f pat
ient
s w
ho r
ecei
ved
inte
rven
tion
Figure 18: Percentage of patients who received interventions
related to lifestyle (n=148)
0%
20%
40%
60%
80%
100%
physicalactivity
nutrition alcohol smoking foot care
% o
f pat
ient
s w
ho re
ceiv
ed in
terv
entio
n
103
4.3.2 Clinical Outcomes - Blood Glucose Readings The mean blood glucose levels for the intervention participants steadily decreased
over the four visits from 9.4mmol/L at the first visit to 8.5mmol/L at the final visit
(Figure 19). There was a significant difference between the mean BGL over the four
visits (p<0.01) with a significant downward trend over time (p<0.01).
The percent of readings which fell within normal range increased over the four
intervention visits from 39% at visit one to 51% at the final visit (Figure 20). There
was a significant difference between the percentage in normal range over the four
visits (p<0.01) with a significant upward trend over time (p<0.01).
Figure 19: Blood glucose readings (mean ± 95%CI) at the four pharmacy visits
(n = 123)
77.5
88.5
99.510
Visit 1 Visit 2 Visit 3 Visit 4
Blo
od G
luco
se (m
mol
/L)
Figure 20: Percentage of blood glucose readings (mean ± 95%CI) within the
target range at the four pharmacy visits (n =123)
25
35
45
55
65
75
Visit 1 Visit 2 Visit 3 Visit 4
% o
f rea
ding
s in
targ
et
rang
e (4
-8m
mol
/L)
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4.3.3 Clinical Outcomes - Blood Pressure Readings in Pharmacy Pharmacists took blood pressure readings on the intervention patients at each visit.
Systolic BP measured in pharmacy decreased over the visits from 143 mmHg at the
baseline visit to 137mmHg at the final visit. There was a significant difference
between the mean systolic BP over the four visits (p<0.01) and a significant
downward trend over time (p<0.01). Diastolic BP also decreased slightly over the
intervention visits from 82mmHg to 79mmHg. There was a significant difference
between the mean diastolic BP over the four visits (p=0.05) and a significant
downward trend over time (p=0.02) (Figure 21).
Figure 21: Blood pressure (mean ± 95%CI) at each intervention visit
(n =108)
70
90
110
130
150
Visit 0 Visit 1 Visit 2 Visit 3 Visit 4
Blo
od P
ress
ure
(mm
Hg)
Systolic BP Diastolic BP
4.3.4 Clinical Outcomes – Clinical Parameters at Baseline and
Completion There was a significantly larger reduction in HbA1C over the 6 month study in the
intervention group (-0.97%; 95%CI: -0.8, -1.14) compared with the control group (-
0.27%; 95% CI: -0.15, -0.39) (Figure 22, Table 30) and this was statistically
significant (MANOVA: interaction term F=15.0; df=1; p<0.01) (Table 29a). This
difference between intervention and control groups was sustained when controlling
for baseline difference in HbA1C (ANCOVA: fixed factor term F=5.2; df=1; p=0.02).
When the baseline difference in HbA1C is controlled for, the adjusted effect of the
intervention was a decrease in HbA1C of 0.35%.
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Table 29a: Clinical parameters of participants at baseline and completion of DMAS study
n Baseline Mean ±SD
Final Mean ±SD
Baseline vs. Final
p value*
Intervention vs. Control
p value†
intervention 125 8.9 ±1.4 7.9 ±1.2 <0.01 HbA1C (%) control 107 8.3 ±1.3 8.0 ±1.2 0.01 <0.01
intervention 136 31.4 ±5.9 31.1 ±5.6 <0.02 BM I (kg/m2) control 131 31.3 ±6.7 31.1 ±6.6 0.31 0.37
intervention 87 135 ±14 133 ±15 0.17 Systolic BP (mmHg) control 92 133 ±12 135 ±15 0.18 0.06
intervention 87 79 ±8 77 ±8 0.07 Diastolic BP (mmHg) control 92 77 ±9 76 ±9 0.33 0.52
intervention 112 4.89 ±1.07 4.67 ±1.10 0.01 TC (mmol/L) control 98 4.85 ±1.03 4.66 ±1.03 0.04 0.85
intervention 96 1.23 ±0.43 1.24 ±0.31 0.11‡ HDL (mmol/L) control 84 1.32 ±0.38 1.32 ±0.36 0.46‡ 0.67 §
intervention 112 2.45 ±1.42 2.19 ±1.58 <0.01‡ Trig (mmol/L) control 97 2.19 ±1.13 2.07 ±1.33 0.05‡ 0.39 §
* paired t-test unless otherwise noted; †repeated measures multivariate ANOVA unless otherwise noted; ‡ Wilcoxon signed ranks test; § Mann Whitney U test
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Table 29b: Clinical parameters of participants at baseline and completion of the DMAS study
Baseline Final p value*
Number (%) Number (%)
Current smoker
intervention 15 (10) 16 (11) 1.00
control 15 (11) 17 (12) 0.63
Exercise 3 or more times a week
intervention 79 (54) 91 (62) 0.07
control 73 (53) 75 (54) 1.00
Exercise 5 or more times a week
intervention 46 (32) 54 (37) 0.24
control 38 (28) 39 (28) 1.00 * McNemar test
Table 30: Comparison of change in HbA1c between control and intervention groups
Control Intervention p value*
n mean ±SD n mean ±SD
Change in HbA1c 107 0.27 ±1.25 125 0.97 ±1.4 <0.01
*independent samples t-test
Other effects observed in the intervention group included a significant reduction in
mean BMI (Table 29a). There was also an increase in the proportion of participants
who exercised three or more times per week from 54% to 62% (Table 29b), but this
was not statistically significant. There was no change in the proportion exercising
more than five times per week, which is the recommended target frequency 63.
107
Figure 22: HbA1C at baseline and completion of the DMAS study
8.08.3
7.9
8.9
7.07.5
8.08.59.0
9.510.0
baseline final
Mea
n H
bA1C
(%)
Control (n = 107) Intervention (n = 125)
Figure 23: Percentage of participants who reached target BP (130/80 mmHg))
0
20
40
60
80
100
control intervention
% w
ho re
ache
d ta
rget
BP
baseline final
45% 42%
55%
42%
Systolic and diastolic BP decreased in the intervention group between baseline and
final but the decrease was not statistically significant (Table 29a). However the
percentage of patients who achieved target BP (130/80 mmHg) increased in the
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intervention group from 42% at baseline to 55% at final (p=0.07), while the control
group decreased slightly (45% to 42%; p=1.00) (Figure 23). The BP measures
reported in Table 26a and 29a were obtained from the participant’s GP and differ
significantly from the measurements made by the intervention pharmacists using the
Omron T5™ digital blood pressure monitor in pharmacy as discussed in section 4.3.3
(Figure 21). Unfortunately comparable in pharmacy BP measures are not available
for the control patients.
Both the intervention and control groups showed a reduction in total cholesterol and
mean triglycerides, however the reduction in triglycerides was greater in the
intervention group than the control group (Table 29a).
4.3.5 Humanistic Outcomes The outcomes of the questionnaires administered at the beginning and completion of
the DMAS are presented in Tables 31a, 31b and 31c. The intervention group
exhibited significant improvements in several humanistic parameters that were not
paralleled in the control group.
EuroQol 5D
The mean EQ-5D utility score improved in the intervention group from 0.75 to 0.79
however the improvement was not significant (p=0.08). The control group utility score
did not change (Table 31a).
The health state scale measure increased significantly from 66 ±20 (mean±SD) at
baseline to 72 ±17 (mean±SD) at the final visit in the intervention group while no
change was seen in the control group. When the two groups were compared using
repeated measures MANOVA, the effect of the intervention was not quite large
enough to be statistically significant (p=0.07) (Table 31a).
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Table 31a: Humanistic parameters of participants at baseline and completion of DMAS study
Baseline Final n
Mean ±SD
Baseline vs. Final
p value*
Intervention vs. Control
p value† EQ-5D
intervention 143 0.75 ±0.25 0.79 ±0.22 0.08‡ utility score (range -0.6 – 1.0) control 137 0.77 ±0.25 0.77 ±0.27 0.93‡ 0.07§
intervention 142 66.3 ±19.5 71.6 ±17.2 <0.01 health state scale (range 1 – 100) control 137 72.2 ±18.9 73.3 ±15.8 0.41 0.07
Well-Being Questionnaire (range 1 – 12)
intervention 143 2.33 ±2.64 1.80 ±2.23 <0.01‡ Negative well-being control 136 1.63 ±2.26 1.39 ±1.99 0.14‡ 0.23§
intervention 140 5.99 ±2.71 6.86 ±2.71 <0.01 Energy control 133 6.53 ±2.73 6.80 ±2.61 0.23 0.06
intervention 142 7.82 ±3.05 8.21 ±3.04 0.11 Positive well-being control 134 8.20 ±3.24 8.07 ±3.38 0.61 0.14
intervention 138 23.51±6.73 25.25 ±6.51 <0.01
Total control 130 25.06 ±6.78 25.52 ±6.56 0.33 0.06
* paired t-test unless otherwise noted; † repeated measures multivariate ANOVA unless otherwise noted; ‡ Wilcoxon signed ranks test; § Mann Whitney U test
110
Well-Being Questionnaire 12
In the Well-Being Questionnaire, significant improvements were seen on the
“negative well-being”, “energy” and “total” scores in the intervention group but not in
the control group (Table 31a). There was no change in the “positive well-being”
subscale in either group. The two groups were then compared using a repeated
measures MANOVA on the energy and total scales and the effect of the intervention
was close to statistical significance for both measures (p=0.06). A Mann Whitney U
test on the change in the negative well-being subscale showed no significant
difference between the change in the two groups.
Diabetes Care Profile
In the DCP, the intervention group showed significant improvements in the “self-care
ability” and “understanding of long term management” subscales while the control
group showed no change (Table 31b). When the two groups were compared with
repeated measures MANOVA, the effect of the intervention on “self-care ability” was
not great enough to be statistically significant (p=0.07), however there was a highly
significant effect of the intervention on “understanding of long-term management”.
Both the intervention and control groups showed significant improvements in the
DCP “negative attitude” subscale (Table 31b).
Questionnaire on Stress in Patients with Diabetes
Both the intervention and control groups showed an improvement in the QSD-R self
medication/diet scale although the improvement was somewhat greater in the
intervention group (Table 31b).
Brief Medication Questionnaire
The intervention group exhibited significant improvements in the “regimen” and “total
adherence” screens of the BMQ while the control group did not (Table 31c).
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Table 31b: Humanistic parameters of participants at baseline and completion of DMAS study
Baseline Final n
Mean ±SD
Baseline vs. Final
p value*
Intervention vs. Control
p value † Diabetes Care Profile (range 1 – 5)
intervention 122 1.43 ±0.62 1.39 ±0.49 0.49‡ Control problems control 131 1.33 ±0.49 1.34 ±0.51 0.81‡ 0.80 §
intervention 144 3.48 ±0.77 3.52 ±0.81 0.49 Positive attitude control 137 3.60 ±.80 3.59 ±0.83 0.86 0.56
intervention 143 2.69 ±0.92 2.45 ±0.81 <0.01 Negative attitude control 136 2.52 ±0.89 2.37 ±0.87 0.02 0.30
intervention 145 3.07 ±0.79 3.36 ±0.76 <0.01 Self-care ability control 136 3.39 ±0.91 3.51 ±0.77 0.10 0.07
intervention 145 3.20 ±0.82 3.69 ±0.71 <0.01 Understanding of long-term management control 134 3.54 ±0.86 3.49 ±0.82 0.32 <0.01
QSD-R (range 0 – 5)
intervention 145 1.54 ±1.09 1.32 ±0.94 <0.01‡
Self medication/diet scale control 135 1.19 ±0.90 1.06 ±0.89 0.06‡ 0.40 §
* paired t-test unless otherwise noted; † repeated measures multivariate ANOVA unless otherwise noted; ‡ Wilcoxon signed ranks test; § Mann Whitney U test
112
Table 31c: Humanistic parameters of participants at baseline and completion of DMAS study
Baseline Final n
Mean ±SD or Number (%)
Baseline vs. Final
p value*
Intervention vs. Control
p value† Brief Medication Questionnaire
intervention 144 1.57 ±1.33 1.23 ±1.11 <0.01 Regimen screen (range 0 – 8) control 133 1.07 ±1.05 1.06 ±0.95 0.93
0.02
intervention 145 0.80 ±0.80 0.53 ±0.71 <0.01 Belief screen (range 0 – 2) control 135 0.56 ±0.72 0.41 ±0.76 0.05
0.19
intervention 145 1.13 ±0.57 1.04 ±0.48 0.10 Recall screen (range 0 – 2) control 135 0.99 ±0.43 1.01±0.42 0.55 0.09
intervention 144 3.49 ±2.07 2.80 ±1.62 <0.01 Total adherence (range 0 – 12) control 133 2.62 ±1.65 2.50 ±1.53 0.37 <0.01
Problems accessing medications (range 0-6)
intervention 137 0.63 ±1.08 0.32 ±0.63 <0.01 control 133 0.62 ±1.14 0.49 ±1.04 0.13 0.15
Informed about medications
intervention 147 102 (69) 114 (78) 0.08 ‡ control 136 92 (68) 100 (74) 0.22 ‡ 0.41§
* paired t-test unless otherwise noted; † repeated measures multivariate ANOVA unless otherwise noted; ‡ McNemar test; § Chi-square test
113
Repeated measures MANOVA confirmed a significant effect of the intervention on
regimen and total adherence. Both the control and intervention groups improved
significantly on the “belief” screen (Table 31c).
The intervention group reported significantly fewer problems accessing medications
at the completion of the study than at baseline, while the control group remained the
same (Table 31c). However the effect of the intervention was not large enough to be
statistically significant when the two groups were compared with repeated measures
MANOVA. Both groups were somewhat more informed about medications at the
completion of the study than they were at baseline but the improvement was not
statistically significant (Table 31c).
4.3.6 Medication Usage Number of Medications
The mean number of antidiabetes medications that each patient was taking
increased from 1.8 at baseline to 2.0 at final in the intervention group but did not
change in the control group. Repeated measures MANOVA confirmed a significant
difference between the intervention and control groups (Table 32).
The mean number of antihypertensive medications increased between baseline and
final in the control group but not in the intervention group. However there was no
significant difference between the two groups. Both the intervention and control
groups had an increase in the mean number of lipid-lowering medications. There
were no changes in the mean number of anti-coagulation or other types of
medications. The mean total number of medications increased between baseline and
final in the control group but not in the intervention, again there was no significant
difference between the two groups (Table 32).
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Table 32: Mean numbers of medications per patient at baseline and completion of the DMAS.
Baseline Final
n
Mean ±SD
Baseline vs. Final p value*
Intervention vs. Control
p value†
intervention 150 1.83 ±0.78 1.95 ±0.77 <0.01 antidiabetes control 136 1.79 ±0.65 1.80 ±0.69 0.84
0.04
intervention 150 1.22 ±0.98 1.26 ±0.96 0.18 antihypertensive
control 136 1.46 ±1.05 1.54 ±1.10 0.02 0.31
intervention 149 0.52 ±0.51 0.60 ±0.56 <0.01 lipid-lowering control 135 0.65 ±0.55 0.75 ±0.53 <0.01
0.56
intervention 150 0.43 ±0.55 0.43 ±0.54 0.83 anti-coagulation control 136 0.49 ±0.53 0.47 ±0.53 0.60
0.85
intervention 150 0.25 ±0.55 0.27 ±0.58 0.25 other cardiovascular control 136 0.22 ±0.54 0.24 ±0.59 0.32
0.89
intervention 150 2.24 ±2.06 2.15 ±2.17 0.50 other control 137 2.20 ±2.45 2.31 ±2.41 0.36
0.26
intervention 149 6.50 ±2.95 6.66 ±3.12 0.29 total control 135 6.84 ±3.35 7.13 ±3.20 0.05
0.54
* paired t-test; † repeated measures multivariate ANOVA
115
Defined Daily Doses
The mean defined daily doses (DDD) of metformin increased significantly between
baseline and final in the intervention group but not in the control group. The
difference between the two groups was not statistically significant (Table 33). The
DDD of beta blockers also increased significantly in the intervention group but
declined slightly in the control group. Again the difference between the two groups
was not large enough to be statistically significant. Otherwise the DDD of the most
common medications did not change over the duration of the program (Table 33).
Antidiabetic Regimen
There were no significant changes in the proportion of patients taking insulin in the
intervention group between baseline and completion of the study. There was
however an increase in the percentage of control group patients who were taking
insulin alone or in combination (15% vs. 18%; p=0.03).
The most common antidiabetic regimen was metformin plus sulphonylurea, followed
by metformin alone. The third most common was either sulphonylurea alone or a
combination of sulphonylurea, metformin and insulin (Table 34).
Antihypertensive Regimen
The proportion of participants on antihypertensive medications increased slightly but
not significantly in both the intervention (76% – 79%) and control (81% – 83%)
groups between baseline and final. Of the participants who were on antihypertensive
medications the majority (77%) were on an ACE inhibitor, an A2 antagonist or a
combination of the two (Table 35). There were no significant changes between
baseline and final in the types of antihypertensive medications taken. Lipid-lowering and Anti-Platelet Regimen
There was a significant increase in the proportion of control patients who were on
lipid-lowering medications between baseline and final (62 % vs. 70%, p<0.01) but no
change in the intervention group (55% vs. 57%). Of the participants who were taking
lipid-lowering medications the majority (92%) were on statins.
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Table 33: Defined daily doses of most commonly used medications at baseline and completion of the DMAS. Baseline Final
n Mean ±SD
Baseline vs. Final
p value*
Intervention vs. Control
p value† antidiabetes medications
intervention 33 64 ±52 63 ±52 0.85 insulins (units) control 12 64 ±48 61 ±45 0.66
0.63 intervention 87 114 ± 121 120 ±119 0.46 sulphonylureas (mg)
control 85 99 ±114 107 ±126 0.20 0.82
intervention 111 1814 ±776 2016 ±806 <0.01 metformin (mg)
control 113 1882 ±777 1958 ±799 0.09 0.11
intervention 14 27 ±18 28 ±18 0.34 glitazones (mg)
control 4 17 ±15 18 ±14 0.39 0.97
antihypertensive medications intervention 37 9.4 ±8.7 9.4 ±8.7 0.91
thiazide diuretics (mg) control 36 10.8 ±9.7 10.8 ± 9.7 0.32
0.95
intervention 26 75 ±72 85 ±74 0.04 beta blockers (mg)
control 21 98 ±91 89 ±63 0.36 0.07
intervention 54 15 ±43 16 ±44 0.10 ACE inhibitors (mg)
control 55 16 ±24 16 ±24 0.29 0.98
intervention 47 194 ±132 206 ±167 0.38 A2 receptor antagonists (mg)
control 42 209 ±124 220 ±124 0.07 0.97
intervention 29 70 ±92 85 ±109 0.24 calcium channel blockers (mg)
control 42 89 ±125 97 ±123 0.29 0.67
lipid-lowering medications intervention 75 34 ±19 35 ±20 0.09 statins (mg) control 73 30 ±23 32 ±22 0.31 0.98 * paired t-test, † repeated measures multivariate ANOVA
117
Table 34: Most common antidiabetic medication combinations at baseline and completion of the DMAS program
Control Number (%)
Intervention Number (%)
Baseline Final Baseline Final
metformin & sulphonylurea 62 (45) 62 (44) 51 (33) 57 (38)
metformin 31 (23) 29 (21) 29 (19) 23 (15)
sulphonylurea 10 (7) 9 (6) 13 (8) 6 (4)
insulin & metformin & sulphonlyurea 8 (6) 9 (6) 13 (8) 15 (10)
insulin & metformin 7 (5) 8 (6) 13 (8) 11 (7)
Insulin 2 (1) 4 (3) 6 (4) 8 (5)
insulin & sulphonylurea 2 (1) 1 (1) 5 (3) 3 (2)
insulin & metformin & glitazone 1 (1) 0 (0) 5 (3) 6 (4)
other 15 (11) 18 (13) 18 (14) 21 (14)
Table 35: Antihypertensive regimen at baseline and completion of DMAS program
Control Number (%)
Intervention Number (%)
Baseline Final Baseline Final
ACE inhibitor 47 (34) 45 (32) 47 (30) 44 (29)
A2 antagonist 35 (25) 33 (24) 42 (27) 42 (28)
ACE & A2 antagonist 3 (2) 4 (3) 8 (5) 8 (5)
other 28 (20) 34 (24) 22 (14) 25 (17)
none 25 (18) 24 (17) 38 (24) 31 (21)
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There were no changes between baseline and final visits in the proportion of
participants who were on antiplatelet medications in either the control (46% vs. 44%)
or intervention group (40% vs. 40%).
A detailed breakdown of the proportion of participants on each of the classes of
medications is available in Appendix 10.
4.3.7 DMAS program – Patient satisfaction Fourteen patient interviews were conducted with intervention patients. Content
analysis of the interviews reported several themes including patient evaluation of the
service, service impacts, provider preference, patient satisfaction and perceived need
for the service. Each of the themes are represented below using illustrative quotes.
Evaluation of the service
Patient evaluation of the service was largely very positive. Many patients were
content with the service and reported the service being very worthwhile. “Service was a good thing; it was very useful”
“The service was very worthwhile; a good addition to medication”
Patients did not convey any negative feedback about the DMAS and did not suggest
any service improvements for future delivery. “I had no dislikes; everything was positive from my point of view”
“I can’t think of how the service could be improved; no improvements are necessary”
There were a minority of patients who did not envisage the DMAS as a service but
rather as a university study. These patients, however, did report contentment with the
results (e.g. decrease in HbA1C and better control of diabetes). “I didn’t really see the service as a service – it was more a benefit for the uni although I
did learn a little from it and my diabetes is under control more than ever now”
Impact of the Service
Patients reported an increase in knowledge of diabetes after participation in the
DMAS. Patients also affirmed an increased awareness and better understanding of
diabetes, self monitoring, role of medications and lifestyle regulation (specifically diet
and exercise). “I have learnt a lot about diabetes”
119
“I learnt a lot about medications and lifestyle during the service”.
Patients also reported gaining better control of their diabetes and diabetes
management. “I have better control and management of my diabetes since the service”
“I feel I am more in control of the disease and am not scared anymore”
A minority of patients believed their level of diabetes knowledge did not change with
participation in the DMAS, however, did report the information provided served as a
reminder and reinforcement and was therefore very beneficial. “I knew most of the information before but it was a good reminder”.
“The service reinforced issues to prevent me going back to my old ways”
Many patients spoke of an improvement in self efficacy including achieving a sense
of accomplishment after participation in the DMAS and upon achievement of set
goals. Many patients observed a decrease in HbA1C and blood glucose levels over
the service adding to this sense of accomplishment. “I am very proud of myself. I am down to my goal weight and feel great to have
achieved something”
Patients also reported participation in the DMAS leading to increased motivation to
better manage their diabetes.
“The DMAS gave me the motivation to be in control… I looked forward to going to see
how I was improving”
“The service really made me behave and want to do the right thing to manage my
diabetes”
Provider Preference
The majority of patients preferred receiving the DMAS from their pharmacist in a
community pharmacy due to the convenience and ease of appointment. A minority
reported no preference for provider and only one of the 14 patients had a preference
to receive the service from their GP. “Pharmacy location is convenient”
“The service in the pharmacy was easy”
Need for the service
Many patients approved the service for all patients with type 2 diabetes and
recognised the need for the service to continue on a long-term basis. Commonly
reported reasons as to why the service should continue included: provision of
120
support, to answer patient questions, maintain motivation to better manage diabetes
and increased awareness of diabetes. “It would continue to keep me motivated and thinking about diabetes”
“It provides terrific support and should definitely be available”.
There were differing opinions as to the kind of service (informal or structured visits)
that should be available long-term and also as to what intensity of service is required
long-term. Suggested intensities included; when patient feels they need it, monthly,
every 2 months and every 6 months.
Patient satisfaction
Patients reported satisfaction with the relationship quality and the therapeutic
relationship that developed with the pharmacist during the service period. Patients
really enjoyed the personal one on one communication with the pharmacist. Patients
reported feeling like they had the full attention of the pharmacist and the pharmacist
was friendly, easy to understand and genuinely interested in their progress. “I really liked the one on one with the pharmacist, he was very approachable”
“I could really tell the pharmacist was genuinely interested in how I was doing”
Patients were also satisfied with the competence of the pharmacist and pharmacist’s
knowledge of diabetes. Patients trusted the pharmacist and believed their interests
were being served. “I trust the pharmacist”
“The pharmacist is trained enough to answer any questions on diabetes”
Patients reported satisfaction with the location of the DMAS in the pharmacy. Many
believed it was easy and convenient to make an appointment and the environment
was non-threatening. Patients felt comfortable receiving the service from their
pharmacist in the pharmacy. “I felt great receiving the service from the pharmacist, I felt relaxed and at ease in the
pharmacy”
“The pharmacist was non judgmental and great to deal with; I liked going to the
pharmacy”
Many patients reported the DMAS being better than expected. Most patients
expected to receive information about diabetes but were pleasantly surprised with the
patient centered approach of the service. Some patients had no prior expectations of
the service so found it hard to say if the service met their expectations.
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“Service was far better than I expected”
“The service was better than expected; I thought it would ratify things I didn’t understand
but it did much more”
Patients also expressed appreciation for the DMAS. Many were very grateful for the
time the pharmacist dedicated to deliver the service and the results that service
participation provided. “It is nice to know someone else cares about my diabetes and that someone
understands”
“The pharmacist was so supportive; I couldn’t have done it if it wasn’t for the support”
DMET item responses revealed that a majority of patients experienced a high level of
satisfaction with the usefulness and extent of information provided and their
knowledge of diabetes management and relevant lifestyle factors (including diet and
exercise) upon completion of the service. The weighted mean satisfaction ratings for
understanding of diabetes management and service location were 4.9 ±0.2 and 4.9
±0.5, out of a possible 5, respectively. The weighted mean satisfaction with
relationship quality with the pharmacist during the service was 4.9 ±0.2.
4.3.8 DMAS program - Pharmacist Satisfaction Focus groups for the intervention pharmacists were held in NSW on 6th March 2005
and in Victoria on 1st April 2005 to obtain feedback on pharmacists’ experiences of
the DMAS. Verbatim transcripts were produced from the tape recordings of focus
groups and were thematically analysed. The themes emerging from the data are
reported below along with illustrative verbatim quotations.
Pharmacists’ perception of impact on patients
Pharmacists reported having received appreciation and very positive feedback from
their patients. Improvements in patient’s knowledge, self-efficacy and confidence
were observed by the pharmacists. The following verbatim quotations illustrate some
of these impacts. “They just seemed glad that you took the time out to do it with them.”
“Most of the patients were very happy with the improvement in their health, such as loss of
weight.”
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“I had a number of comments from people that they had learned more about their diabetes
and what affects their diabetes and information about their medications and about exercise
than they had ever learned before.”
“Someone cares…..”
“Support during illness gives the patient confidence”
“The service received by clients, they loved it”
Motivating factors for patient
A key motivator for patients was the regular visits to the pharmacy and in particular
the pie chart of blood glucose readings within and out of range they were given at
each visit to the pharmacy. “The motivating factor is that they know they are coming back to see you next time and
they want to see some improvement. The patient likes to see changes to the numbers and
the graphs (pie chart)”
“Seeing patients motivated them to try to improve their health”
“DMAS – gives control back to the patient and raises public awareness of what
pharmacists can actually do”
“Blood glucose monitoring and printing results with explanations to patients worked well”
“Even though the service is over, we tell people to bring in their machines and we will
download their BGL and give them that data which they can take back to their GPs.”
“People are impressed with what pharmacists can do and this is good for pharmacists’
morale”
Effecting behaviour change Some pharmacists remarked on the challenges of effecting behaviour change in their
patients and the need to set small achievable goals.
“It’s very difficult for a lot of people to make those changes as they might be ingrained
parts of their life”
“Yes. Some of them just found it a motivating thing for them – often they knew the right sort
of things to be doing but they just needed someone to give them a bit of a push along the
way”
“I think anything that they do is worthwhile”
“Too hard for them to remember everything, need to change one thing at each visit”
“They are terrified of insulin – made such an enormous difference to one man’s life, the
main fear was insulin - now he feels 10 times better.”
“Goal setting is good”
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Impacts on Communication with the GPs and Endocrinologists
Pharmacists reported both positive and negative experiences with respect to
communication and interactions with GPs and endocrinologists. Overall, once the
benefit to the patient was realised by the GP or endocrinologist they became more
supportive.
Examples of positive experiences
“I’m sure if we show appropriate knowledge they’ll respect it. Three or 4 times, I called
doctors while the patient was there and said I’d like to do this and GPs were more than
prepared to adjust the medications over the phone.”
“When they saw some of the results they were quite enthusiastic about it so I got some
good feedback from the GP.”
“The GPs were very active in helping me recruit the patients but he did give me all the
hard ones – the ones that he couldn’t do anything with and said ‘good luck’”
“Some GPs are happy that their patients are involved in trying to do something about their
diabetes.”
“We already have a good working relationship with the GPs in the area so it wasn’t really a
problem and the bulk of the patients came from one practice.”
“Endocrinologist was impressed with the program. A patient with highly variable levels
dropped from 8.5 to below 7.”
“I had the endocrinologist ask the patient to bring the pie-graphs along to each
appointment as it gave him a good idea what was going on. He hadn’t seen one of these
before and thought it was a great idea.”
Examples of negative experiences
“It depends on the GP – some GPs we have a good relationship with and with others there
is no cooperation.”
“There was about 50:50 support from GPs”
“Doctors have a fear we are treading on their toes”
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Role of the Pharmacist vis a vis Diabetes Educators
Pharmacists suggested that they could fill in the gap with respect to diabetes
education created by lack of diabetes educators, especially in rural areas. “One of the problems with our area (rural) is that the diabetes educator is shared between
3 or 4 towns so they don’t have much of a chance to see their diabetic patients. So if they
know we are offering a service then they will certainly come in and seek advice. GPs are
very supportive of it too.”
“There certainly aren’t enough diabetes educators out there and so we fulfil a need there.”
“Patient will be more likely to be open with their pharmacist who they know rather than a
diabetes educator who they don’t know”
Most useful parts of the service
All pharmacists agreed that the pie charts were universally regarded by patients as
the most useful aspect of the DMAS service. “They are very keen to get the next download to see if there is a difference from the
previous one.”
“Patients love the charts – they can see what’s happening and they ask for copies to take
to their GP”. Remuneration for DMAS
All pharmacists acknowledged the need to receive appropriate remuneration for the
service to make it sustainable in the community pharmacy. They expressed
uncertainty about the willingness of their patients to personally pay for the service. “I guess it’s the payment as that enables you to spend the time”
“We need to be paid for the time that we’re spending because it seems like everyone got
good results for the patients”
“It is a service that should be charged for”
“Patients may have resistance to paying for these services at the pharmacy”
Impact on the Pharmacy and other Spin-offs
Pharmacists reported that participating in DMAS had improved their own knowledge
and self confidence in relation to diabetes. They also noted that it had very positive
impacts on their business, their relationships with clients and the overall image of the
pharmacy as a proactive contributor to patient health care.
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“I think it is a generation of goodwill and an increase in my knowledge, rather than about
dollars.”
“I think it’s a confidence thing for pharmacy and pharmacists, if we’ve done this program we
have a lot more confidence in the knowledge that we have.”
“Helps pharmacy to provide better quality healthcare to the patient. This helps to build trust
and loyalty and emphasizes that pharmacy is not a supermarket.”
”Pharmacy has changed and peoples expectations have changed” “There was a change in relationships with patients, a definite building up of trust.”
“From the pharmacy perspective, there is now more interest in diabetes and understanding
of problems than there was before it started.”
“The people who are still our customers will get informally followed up when they come in
to get their prescriptions. There is an undoubted benefit in that. From a customer
relationship point of view”
“People come into my pharmacy asking me to check what the doctor has prescribed for
them and what he has told them to do”
“I was asked to give a talk about diabetes at a community group”
“I was asked to organise a support group for people with Type 2 diabetes”
Potential modifications
Pharmacists expressed differing views on the most beneficial frequency of patient
follow-up. “Some people might only need a couple of visits and others you might need to hold their
hand”
“If you saw the patient quarterly, that would be a good thing”
“Seeing patients monthly would keep people motivated”
“Having an appointment at the pharmacy the same time each month is easier to
remember”
In summary, pharmacists were very satisfied with their participation in the DMAS and
reported many benefits both for themselves personally, for the professional profile of
the pharmacy and for their patients. Moreover, the data obtained from pharmacists
served to triangulate the data obtained from the in-depth interviews with DMAS study
participants.
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4.4 ECONOMIC ANALYSIS
4.4.1 Outcomes The main measures of effectiveness are expected life years and QALYs gained per
patient projected over their remaining lifetimes. After 10 years (i.e. when the DMAS is
assumed to finish) each group faces a similar hazard for the rest of the simulation.
Figure 24 shows the estimated difference in the proportion of patients alive in each year
of the simulation, using Scenario A (i.e. the effect of the DMAS is a 0.35% reduction in
HbA1C, when adjusted for differences at baseline). At any given year in the simulation a
higher proportion of those in the intervention group are alive than those in the control
group. After 10 years 0.018 more people were alive in the intervention group than in the
control.
Table 36 shows mean values for these outcomes by treatment allocation. Under
Scenario A, patients in the control group were estimated to live a further 15.80 ±5.87
(mean± SD) years, whereas, patients allocated to the DMAS intervention were estimated
to live a further 15.94 ±5.22 (mean± SD) years. A difference of 0.14 (95% CI: -0.23, 0.52)
years undiscounted, or 0.14 (95% CI: -0.05, 0.31) years when discounted at 5%. Adjusting
for the effect complications have on quality of life the expected QALYs for the two
groups were 11.37± 4.79 (mean± SD) and 11.48±4.53 (mean± SD) respectively, a
difference of 0.11 (95% CI: -0.18,0.41) undiscounted or 0.11 (95% CI: -0.04, 0.25) when
discounted at 5%. Scenario B (i.e. the effect of the DMAS is a 0.7% reduction in
HbA1C) produced a greater difference in outcomes between groups: 0.23 (95% CI: -0.10,
0.55) life years undiscounted or 0.19 (95% CI: 0.03, 0.34) life years when discounted at
5%; and 0.18 (95% CI: -0.08, 0.45) QALYs undiscounted or 0.15 (95% CI: -0.02, 0.31)
QALYs when discounted at 5%.
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Figure 24: Difference in the estimated proportion of patients surviving between the control and intervention groups
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Years from baseline
Diff
eren
ce in
Pro
port
ion
surv
ivin
g
4.4.2 Costs Table 37 shows the estimated mean cost per patient if the DMAS continues for a
period of 10 years. The average fixed costs associated with the DMAS intervention
were $8 per patient based on renewing software and counter displays every 3 years.
Over the 6 month follow-up in the present study each patient made up to four follow-
up visits to the pharmacy at an average cost of $271 per patient. If the DMAS
intervention continued over a period of 10 years the total cost of providing the DMAS
service would be $5,367 per patient. The effect of DMAS on other costs within the
health system is also reported in Table 37. During the period of the study the cost of
medications rose by $101 in the DMAS intervention and $242 in the control group,
mainly due to a higher proportion of patients converting to insulin in the control group.
The overall difference of $141 was observed over the period of the trial. In the main
analysis we assume that this difference is maintained while the DMAS continues.
Similarly, the cost of GP visits during the study was also extrapolated over a 10 year
period based on the difference in rates of attendance achieved during the trial. Based
on the expected rates of complications under Scenario A the cost of hospitalisation
over the patients remaining lifetime was $5,554 ±281 (mean± SD) for the control
group and $5,398 ±294 (mean± SD) for the intervention group, a difference of $156
(95% CI: -989, 623).
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Table 36: Modelled outcomes based on a DMAS of 10 years duration
Outcome Life expectancy QALYs
Control
mean (SD)
Intervention
mean (SD)
Difference
(95% CI)
Control
mean (SD)
Intervention
mean (SD)
Difference
(95% CI)
Scenario A
Undiscounted 15.80 (5.87) 15.94 (5.22) 0.14 (-0.23, 0.52) 11.37 (4.79) 11.48 (4.53) 0.11 (-0.18, 0.41 )
3% discount rate 11.70 (3.52) 11.85 (3.14) 0.15 (-0.09, 0.39) 8.45 (3.00) 8.56(2.83) 0.11 (-0.07, 0.31)
5% discount rate 9.85 (2.62) 9.99 (2.33) 0.14 (-0.05, 0.31) 7.12 (2.30) 7.21 (2.25) 0.11 (-0.04, 0.25)
10% discount rate 6.91 (1.42) 7.02 (1.25) 0.10 (-0.01, 0.20) 5.03 (1.35) 5.11 (1.12) 0.08 (-0.01, 0.16)
Scenario B
Undiscounted 15.80 (5.87) 16.03 (5.16) 0.23 (-0.10, 0.55) 11.37 (4.79) 11.55(4.59) 0.18(-0.08, 0.45 )
3% discount rate 11.70 (3.52) 11.91 (3.09) 0.21 (0.00, 0.41) 8.45 (3.00) 8.62(2.78) 0.17(-0.00, 0.33)
5% discount rate 9.85 (2.62) 10.04 (2.29) 0.19 (0.03, 0.34) 7.12 (2.30) 7.27(2.10) 0.15(-0.02, 0.31)
10% discount rate 6.91 (1.42) 7.04 (1.22) 0.13 (0.04, 0.23) 5.03 (1.35) 5.13(1.05) 0.10(-0.02, 0.16)
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Table 37: Modelled costs in 2004 A$ based on a DMAS of 10 years duration
Mean Cost (SD) Category
Control Intervention Difference
(95% CI) Pharmacy based costs
Fixed Costs 0 8 8
Variable costs 0 5,367 5,367
Health System Costs
Medication Costs 2,423 (835) 1,009 (749) -1,414 (-3615, 788)
GP Costs 1,356 (135) 1,371 (157) -14 (-429, 397)
Hospital costs due to complications:
Scenario A 5,554 (281) 5,398 (294) -156 (-989, 623)
Scenario B 5,512 (282) 5,362 (294) -150 (-955, 655)
Scenario A
Total Costs (No discounting) 9,335 (951) 13,153 (785) 3,819 (1409, 6229)
Total costs (3% Discounting) 7,919 (874) 11,541 (778) 3,622 (1301, 5943)
Total costs (5% Discounting) 7,148 (818) 10,512 (730) 3,364 (1188, 5540)
Total costs (10% Discounting) 5,808 (707) 8,652 (635) 2,845 (961, 4729)
Scenario B
Total Costs (No discounting) 9,292 (950) 13,118 (786) 3,825 (1414, 6236)
Total costs (3% Discounting) 7,901 (876) 11,535 (777) 3,634 (1312, 5957)
Total costs (5% Discounting) 7,134 (820) 10,507 (728) 3,373 (1196, 5549)
Total costs (10% Discounting) 5,800 (707) 8,649 (634) 2,849 (965, 4732)
Under scenario A the average total 10 year cost was $9,335 ±951 (mean±SD) for
each patient in the control group and $13,153 ±785 (mean±SD) for each patient in
intervention group, a difference of $3,819 (95% CI:1,409, 6,229). Hence the
incremental net cost of the DMAS was approximately $382 per annum. When
discounted at 5% the total incremental cost over 10 years was $3,364 (95% CI:
1,188, 5,540) or approximately $336 per annum. The comparative incremental cost
under scenario B was $3,373 (95% CI: 1,196, 5,549) over the 10 year period or $337
per annum.
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4.4.3 Cost-effectiveness One way of representing the combinations of cost and effect differences reported
above for each intervention is to plot the changes on a cost-effectiveness plane,
which simultaneously represents the difference in the mean costs (on the y-axis) and
life expectancy (on the x-axis). Figures 25 and 26, show cost-effectiveness planes in
terms of life expectancy for Scenarios A and B respectively. The comparative cost-
effectiveness planes in terms of QALYs are shown in Figures 27 and 28. The vertical
I-bars show the 95% confidence interval for the cost-difference (from Table 37) and
the horizontal I-bars show the 95% confidence interval for the difference in QALYS
(from Table 36). The two I-bars cross at the point estimates of cost and effect and the
slope of the line joining that point to the origin of the plane represents the point
estimate of cost per life year/QALY. Under Scenario A the 5% discounted cost of
DMAS was on average $3,364 more per patient and the discounted benefits gained
were 0.14 life years and 0.11 QALYs, giving a cost per life year gained of $24,029
and a cost per QALY gained of $30,582. Under Scenario B the incremental 5%
discounted cost was similar ($3,373), but the expected incremental benefit was
greater (0.19 life years; 0.15 QALYs) and so the cost per life year/QALY for DMAS
intervention was $17,752/ $22,486.
The joint uncertainty for costs and life expectancy/QALYs is shown by the elliptical
contour in four figures (Figures 25 to 28) which cover 95% of the integrated joint
density (assuming joint normality). In all four cases the 95% confidence surface
extends beyond a single quadrant of the cost-effectiveness plane and in these
situations the calculation of 95% confidence intervals for cost-effectiveness can be
problematic due to the inherent instability of ratio statistics. An alternative
presentation is given in Figure 29 (or Figure 30 for QALYs) which shows, for different
values of the maximum willingness to pay for a life year (or QALY), the probability
that the intervention under evaluation is cost-effective. These curves are known as
cost-effectiveness acceptability curves and have become an accepted way of
presenting uncertainty in cost-effectiveness information. These curves indicate the
probability the DMAS intervention is cost-effective for different amounts society would
131
Figure 25: Life Years - Scenario A
-$2,000
-$1,000
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
Incremental life-years
Incr
emen
tal c
osts
Figure 26: Life Years - Scenario B
-$2,000
-$1,000
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
Incremental life-years
Incr
emen
tal c
osts
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Figure 27: Quality Adjusted Life Years - Scenario A
-$2,000
-$1,000
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
-0.2 -0.1 0 0.1 0.2 0.3 0.4
Incremental QALYs
Incr
emen
tal c
osts
Figure 28: Quality Adjusted Life Years - Scenario B
-$2,000
-$1,000
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
Incremental QALYs
Incr
emen
tal c
osts
133
Figure 29: Cost effectiveness acceptability curves indicating the probability that the DMAS is cost effective (y axis) for different levels of willingness to pay for a life year.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
$0 $20,000 $40,000 $60,000 $80,000 $100,000
Ceiling value for incremental cost per Life year
Prob
abili
ty th
at in
terv
entio
n is
bel
ow
ceili
ng v
alue
Scenario B
Scenario A
Figure 30: Cost effectiveness acceptability curves indicating the probability that the DMAS is cost effective (y axis) for different levels of willingness to pay for a QALY.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
$0 $20,000 $40,000 $60,000 $80,000 $100,000
Ceiling value for incremental cost per QALY
Prob
abili
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at in
terv
entio
n is
bel
ow
ceili
ng v
alue
Scenario B
Scenario A
134
be willing to pay to extend life expectancy or QALYs. For example, if society were
willing to pay $50,000 for each extra year of life gained from a health care
intervention then there is a 78% chance that the DMAS is cost-effective under
scenario A and a 93% chance under scenario B (see Figure 29). Whereas if society
was willing to pay a higher amount, for example $100,000 per life year, then the
probability the DMAS is cost-effective increases to 88% and 97% respectively.
Similarly, if the maximum willingness to pay per QALY was $50,000 then there is a
71% chance under scenario A and an 88% chance under scenario B of the DMAS
intervention being cost-effective (Figure 30).
4.4.4 Sensitivity analyses Sensitivity analyses performed to examine whether the results in the main analysis
are robust to different assumptions are shown in Table 38. Under scenario A the
effect of an increase by 25% in the fee paid for the pharmacists time, was an
increase in the average cost per life year/QALY to $31,857/$49,545 while a 25%
reduction in this fee reduced the cost per life year/QALY to $16,314/$20,763. If there
were no savings from improved management of pharmaceutical regimens (i.e. no
saving in the cost of medication) then the cost per life year/QALY would increase to
$34,257/$43,600. If there were no savings in hospital costs due to reduced rates of
complications in the DMAS group the impact on the cost effective ratio would be
much smaller. If the lower discount rate of 3% was used for both the cost and
outcomes then the cost per life year/QALY would be $24,146/$32,927 respectively.
Using a higher discount rate of 10% the values would increase to $28,450 per life
year and $35,562 per QALY. A similar sensitivity analysis was conducted for
Scenario B and the results are also reported in Table 38.
The sensitivity analysis demonstrates that even if the net cost of implementing the
DMAS was higher than we have assumed in the main analysis, the cost
effectiveness ratios fall within a range generally considered cost-effective in an
Australian health care setting.
135
Table 38: Summary of results from the sensitivity analysis (2004 A$).
Aspect tested Scenario A Scenario B
Cost per
Life year
Cost Per
QALY
Cost per
Life year
Cost Per
QALY
Base Case (Used in Main analysis) 24,029 30,582 17,752 22,486
50% reduction in change in HbA1C 56,066 70,083 24,029 30,582
25% increase in Fee for Pharmacist:
31,857 40,545 23,474 29,733
25% decrease in Fee for Pharmacist:
16,314 20,764 12,021 15,227
No savings from improved management of medications
34,257 43,600 25,242 31,973
No saving from reduced rates of complications
22,714 28,909 16,737 21,200
3% disc costs; 3% disc outcomes 24,147 32,927 19,063 24,147
10% disc costs; 10% disc outcomes 28,450 35,563 14,974 18,967
136
5. DISCUSSION 5.1 SCREENING PROGRAM The screening arm of the Pharmacy Diabetes Care Program was designed to
investigate the capacity of Australian community pharmacies to identify and refer
people with risk factors for type 2 diabetes to the GP. The results of the trial clearly
demonstrated the feasibility of offering screening programs in the community
pharmacy setting. Over the 12 week implementation period of the screening service
a total of 1286 people were screened in 30 randomly selected pharmacies across
Australia, encompassing both rural and metropolitan settings. However, during the
period of screening only an estimated average of 8 out of every 1000 people entering
the store per week with apparent risk factors (age > 55 years and BMI ≥30 kg/m2 as
identified by observational exit surveys), were screened. This suggests that a
nationally implemented pharmacy screening program for undiagnosed diabetes
would have the potential to reach into a much larger population.
The literature suggests that risk assessment questionnaires, such as the Tick Test of
Diabetes Australia, have generally performed poorly as stand alone tests for
screening and that the inclusion of biochemical tests such as the capillary blood
glucose test improves the detection of diabetes 15. Moreover, if blood glucose tests
are to be used, FBG is considered the more reliable indicator 91. To test this
hypothesis, we designed our trial to compare two screening methods (tick test only
(TTO) and sequential screening (SS)). The TTO comprised a diabetes risk
assessment followed by referral to the GP for individuals with one or more risk
factors. The second method (SS) also involved an initial risk assessment which was
followed by a capillary blood glucose test using FBG as the preferred option, for
individuals found to have one or more risk factors for diabetes. Both methods were
designed in accordance with NHMRC guidelines for the case detection of type 2
diabetes 19.
Overall, as a result of the screening program offered in community pharmacy, 10
(0.8%) people were newly diagnosed with type 2 diabetes and 24 (1.9%) with
prediabetes. However, comparison of the two screening methods demonstrated
significant differences in the efficiency. The rate of diagnosis of diabetes using the
137
SS method was 1.7% compared with 0.2% for the TTO. After imputing for patients
lost to follow-up these rates were estimated to be 2.73% and 0.35% respectively.
Further, people screened by the SS protocol were seven times more likely to be
diagnosed with diabetes than those screened by the TTO protocol. The SS protocol
also identified a higher proportion of people with prediabetes (2.1%) compared with
the TTO protocol (1.7%) and after adjustment the rates were estimated to be 2.69%
for SS group and 2.44% for the TTO group. However this effect was not statistically
significant. These results suggest that whilst the risk assessment alone had the
capacity to predict prediabetes, the case detection of type 2 diabetes was
significantly enhanced by the inclusion of the capillary blood glucose test in the SS
screening protocol.
It is also interesting to note that the recommendation of the NHMRC guidelines
regarding the blood glucose testing of all individuals with one or more risk factors is
supported given that 30% of people diagnosed with diabetes in the study had only 1
risk factor. A pharmacy based SS program implemented in Swiss pharmacies in
2002 used the criterion of 2 or more risk factors as a basis for a blood glucose test
which resulted in a referral rate to the GP of 12.3% compared with 24.4% for the SS
method in the present study 92. However the rate of diagnosis of diabetes in the
Swiss study was unknown.
The Pharmacy Diabetes Care Screening program represents a combination of
opportunistic and selective screening. In other words the community pharmacy
setting offers a chance encounter for high risk individuals to be screened by a health
care professional. Since there are no screening programs in the literature that
precisely match the protocol or setting used in this screening program it is difficult to
make direct comparisons. However, the effectiveness of the SS protocol compares
favourably with other studies which have implemented selective and opportunistic
screening programs in health care settings such a general practice surgeries. For
example an opportunistic screening program for diabetes delivered in routine clinical
practice in the US between 1998 and 2000, of people aged 45 and over, yielded a
diagnosis rate of 0.6% 93. A screening program for people aged 40 and over,
implemented in Canada (DIASCAN) in family physicians’ offices reported diagnosis
rates of 2.2% for diabetes and 3.5% for prediabetes respectively 94. The detection
138
rates from the SS protocol also compare favourably with those detected by
population screening programs. For example, a population based stepwise screening
program conducted in Denmark between 2001 and 2002 yielded a diagnosis rate of
diabetes of 0.8% 95.
There is strong evidence that the success of any screening program in terms of
diagnosis of prediabetes and type 2 diabetes appears to be dependent on a number
of factors. Firstly, capturing a population with a high prevalence of risk factors for
type 2 diabetes appears to be important and there are a number of ways to do this.
Opportunistic and selective screening approaches applied during routine visits of
patients to healthcare professionals achieve this. Indeed, the use of risk assessment
procedures based on validated predictors of diabetes such as age and BMI 96 was
supported by the results of the pharmacy screening program. Among the 34
participants diagnosed with either prediabetes or diabetes, the most common risk
factors were being over 55 years of age (85%), followed by being over 45 with a BMI
greater than 30 (53%). However, in this study, the proportion of people screened
with one or more risk factors was substantially higher (>75%) than that observed in
other screening programs (~50%) 53, 95, 97, 98 which suggests that the pharmacy
population may already be a high risk population.
Another persistent barrier to success in screening programs is the dropout rate of
participants at every step of the process 95. A recent large scale study of
opportunistic screening in routine general practice in the US found that follow-up of
patients with abnormal blood glucose results is uncommon and yield of screening is
low 93. Hence, screening programs that utilise strategies that maximise retention of
participants on the diagnostic path e.g., where high risk people are immediately
offered a biochemical test or which include procedures for systematic follow-up, are
more likely to yield higher rates of diagnosis. For example the protocol used in the
SS method adopted a flexible approach for blood glucose testing of people with one
or more risk factors. While the preferred option was to ask them to return for a fasting
blood glucose test, if they were unable to do so a random blood glucose test was
conducted straight away. This ensured that people with undiagnosed diabetes (i.e.,
RBG >11 mmol/L) who were unable to return could be immediately identified and
referred and retained on the diagnostic path. Another strategy and crucial
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component of the screening program was to follow up all the people who had positive
screening test results and who received a referral to the GP, to remind them to visit
the GP.
In spite of the extensive follow-up undertaken in the Pharmacy Diabetes Care
Screening program we nevertheless experienced retention problems. Although 77%
of people screened using the TTO method had one or more risk factors for type 2
diabetes and therefore qualified for a referral to the GP only 20% took up the referral
to the GP and a further 49% declined the GP referral from the pharmacist. One
possible reason for this may be related to how people perceive their personal risk of
diabetes. A Dutch study found that participants in a screening program for
undiagnosed diabetes perceived that their risk of diabetes was low, despite the
presence of risk factors, and had poor understanding of quantitative risk information 99. It is likely that the mere act of completing a risk assessment e.g., TTO, did not
influence participants’ perceived risk.
However, with the SS method there were significantly fewer dropouts at each step of
the screening pathway. Although there was a small percentage of people (15%) who
declined the fingerprick test in the pharmacy, no one declined the GP referral form
from the pharmacist and the rate of uptake of the GP referral was substantially higher
(42%) than for the TTO method. It is plausible that the interventional nature of the
capillary blood glucose test, which requires a lancet device to draw blood from the
patient’s finger which is then applied to the test strip for an instant reading, impacted
on participants’ perception of risk and subsequent behaviour. The patient no doubt
felt that an actual medical test had been performed and hence the result may have
been more meaningful.
5.1.1 Consumer perceptions of the Pharmacy Diabetes Care
Screening Program The results of the follow-up survey of participants in the screening program suggest
that they were very satisfied and highly approved of the diabetes screening service
being available in community pharmacy. Approval ratings were higher on average in
TAS and NSW where patients received the SS process. This difference may again
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be due the consumers’ perception that a service which involved a capillary blood
glucose test in the pharmacy was a more substantial service. These findings support
consumer acceptance of community pharmacists as service providers and are
consistent with many studies which demonstrate positive attitudes towards
pharmacist’s involvement in the provision of extended services 100, 101 including
screening services for diabetes.
Increased awareness of diabetes was the most common reason given for approval of
the screening service amongst both protocols. However, the TTO group also stated
that it was a reminder to be tested while the SS group cited convenience and easy
access. This may explain why the SS group preferred to have the screening service
performed in community pharmacy. Again these findings are supported by other
studies that indicate screening is well suited to the community pharmacy environment
due to its easily accessibility 102.
A third of respondents reported making lifestyle changes such as a change in diet or
an increase in engaging in exercise as a result of the health information received
during the screening service. These results also concord with findings of a
community pharmacy screening and health promotion program for cardiovascular
disease where participants also self reported increases in physical activity following
advice from the pharmacist 103. The impact of this type of intervention may be
substantial but is difficult to measure.
5.1.2 Economic Analysis of the Screening Program The economic results show that when SS is compared with TTO there is no
significant difference in the overall costs, but a higher proportion of the screened
population were diagnosed with diabetes using SS. The additional proportion
detected using the SS method greatly reduces the average cost per case detected
from over A$6,000 to A$788, which is comparable to previous estimates of costs of
screening programs 53, 91. We would therefore regard the SS method to be the
superior method both from a cost as well as an efficacy perspective and it should be
considered as the preferred option for screening if community based pharmacy
screening was to be funded in Australia.
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If government funding for screening in community pharmacy were unavailable, there
is some evidence from the results of the follow-up survey that consumers may be
willing to pay directly for this service. On the basis of a small sample of participants
who nominated an amount, the overall median WTP was A$10.00. This would not be
sufficient to cover the minimum basic cost of service delivery (an estimated A$11.43
per screening for SS method which included consumables and salaries only). The
majority also preferred to receive the service in community pharmacy rather than GP
surgery but the mean incremental WTP for the community preference was A$1.43.
Further, 62% of respondents indicated that they would be willing to pay, the major
reason being that the service is presently funded by Medicare through GPs who
bulkbill. This means that the demand for a consumer funded service is likely to be
low.
5.1.3 Limitations of Study A number of factors contributed to under recruitment in the screening study. In NSW,
the first to commence the Screening Program, a procedural problem was identified
early in the trial where a number of risk assessment forms (Appendix 3) were taken
away by customers and not returned. Hence the proportion with risk factors could not
be determined. However, this problem was rectified in the other states by numbering
the forms.
There were also some difficulties in retrieving data on outcomes of referrals to GPs
for some patients. In some instances, when the patient had been referred to the GP
and had taken up the referral, the GP did not fax the triplicate form back to the
project officer as requested on the form. To address this, the pharmacists were
asked to phone the patients as a reminder to visit their GP and to determine if they
had already taken up the referral. Not all pharmacists completed the reminder phone
calls. Hence project staff also directly contacted GPs who had not responded to
determine the outcomes of patient referrals. Finally, outcomes of referrals were
collected by means of the follow-up phone survey of participants.
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After extensive follow-up of all the people involved in the process, including the
patient, pharmacist and the GP, there still remained a number of people who had
been screened but for whom we did not know the outcome, 7.3% for the TTO method
and 8% for the SS method.
These procedural and other difficulties may have implications for the broader
implementation of a pharmacy based screening service. They highlight the need for
effective inter professional communication and patient follow-up procedures.
Another limitation of the study is that the response rate of participants in the
screening service was not ideal (21%). Many patients were unable to be contacted
after several attempts. Therefore patient responses may not be representative of the
entire study population. In the TTO only those who had more than 1 risk factor and
agreed to take up a referral to the GP were asked to supply contact information.
Further, for both methods many patients were unable to be contacted after several
attempts, especially in TAS and VIC.
5.1.4 Conclusion In conclusion the SS method was significantly more efficient and cost-effective than
the TTO method and could be successfully implemented in community pharmacies
resulting in fewer unnecessary referrals to the GP while resulting in a higher rate of
diagnosis. Therefore, the benefits of conducting the capillary blood glucose testing in
the pharmacy appear to be twofold: it eliminates those people with risk factors whose
blood glucose levels are normal, i.e. < 5.5mmol/L, and people who receive the
fingerprick test in the pharmacy take the screening service more seriously than those
who receive the TTO method and are more likely to act upon a referral to the GP.
The higher rate of detection using the SS method also greatly reduces the average
cost per case detected. On average it costs A$6241 per case detected using the
TTO and A$788 using the SS method.
Consumers were very satisfied with and strongly approved the diabetes screening in
community pharmacy. Community pharmacies provide an ideal environment for the
provision of extended pharmacy services. Over time patients have become more
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accepting and welcoming of extended services in community pharmacy, largely due
to convenience and increased likelihood of service participation. Future provision of
extended services, including diabetes screening, would be adopted and supported by
patients in Australian community pharmacies.
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5.2 DMAS The Diabetes Medication Assistance Service (DMAS), a specialised model for
disease state management for patients with type 2 diabetes, which comprised an
initial consultation followed by ongoing monitoring of the patient with type 2 diabetes
at four follow-up visits to a community pharmacy over a six month period, was
successfully implemented in 28 intervention pharmacies in New South Wales,
Victoria, Western Australia and Tasmania. In excess of 400 patients expressed
interest in taking part in both arms of the study. However, of these approximately
25% didn’t meet the eligibility criteria. Thus, a total of 335 eligible patients (176
interventions and 159 controls) were fully recruited into the study and 84% of
intervention and 88% of control patients completed the study. Overall, the
intervention and control pharmacies and pharmacists were well matched in terms of
pharmacy and personal demographics. Study participants were also well matched
with respect to demographics, diabetes history and most clinical parameters with the
exception of baseline HbA1C. The reasons for the difference in baseline HbA1C are
uncertain and may have been due to chance. Alternately, the difference may have
been due to the awareness of intervention pharmacists of the objective of the study
causing them to select patients whom they felt would benefit most from the service
i.e., patients with poorer glycaemic control.
Implementation of the DMAS resulted in better diabetes control for intervention
patients based on improvements in mean blood glucose levels and significant
reductions in HbA1c. In fact, the change in the main outcome variable ie HbA1c was
both clinically and statistically significantly greater in the intervention group. Better
blood pressure control was also achieved by the intervention group, based on
reduction in mean systolic BP readings measured at each visit to the pharmacy.
Other significant benefits of the intervention included improvements in understanding
of long term management of diabetes and adherence to medications. There were
also trends to improvement in well being, quality of life, self-care ability, problems
accessing mediations and BMI in the intervention group that were not seen in the
control group.
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It is interesting to note that there was a small, but statistically significant, reduction in
HbA1c observed in the control group. This was not observed in the Sugar Care
study41 and may be attributed to the use of a different protocol for recruitment which
involved communication with the GP. In the DMAS, eligibility for enrolment (in both
intervention and control groups) was established on the basis of the most recent
clinical data (HbA1c, lipids, BP) obtained by request from the GP. This request may
have prompted the GP to review the diabetes management plan for the patient and
thus contributed to a small improvement in glycaemic control. There were also
significant reductions in total cholesterol and triglycerides in both the intervention and
control patients. Again, these improvements observed in the control patients might be
due to the “usual care” of the pharmacist and the process of requesting clinical data
from the GP prompting additional care.
The success of the DMAS model supports findings in similar community pharmacy
based, pharmacist-delivered diabetes programs, where improvements in clinical
outcomes have been reported 104-106. In our study, we observed a large improvement
of 11% (1.0/8.9) from baseline in final HbA1c after 6 months in patients receiving the
community pharmacy service, which is very similar in magnitude to the findings of
Cranor et al.,104 and Wermeille et al.105. However, it was double the improvement in
HbA1c reported by Clifford et al. 106 a 12 month pharmacist delivered pharmaceutical
care program for type 2 diabetes and double the 6% improvement (0.5/7.9) observed
in the SugarCare study 67.
An important reason for this difference in effects between studies is undoubtedly due
to differences in baseline HbA1c of study participants. Choe et al.107 reported that the
response to a pharmacist’s delivered clinic intervention varied according to baseline
HbA1c, the higher the baseline, the greater the percentage reduction. In the DMAS,
the intervention group had a baseline HbA1C of 8.9% compared to 7.5% in the Clifford
et al.105 study and 7.9% in the SugarCare study67. Patients with a higher HbA1c at
baseline have more potential for improved outcomes. Indeed we took account of this
in designing our eligibility criteria.
Other reasons for differences in clinical impact between studies may be due to the
nature and intensity of the pharmacist delivered intervention. The majority of trials of
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diabetes care models delivered by pharmacists either in clinic or community settings
have utilised varied and complex interventions comprising a combination of the
following: diabetes self-management education and coaching to assist in
empowerment of the patient 41, 46, 48, 49, 52; monitoring and promoting patient
adherence with medication and other components of self-management (e.g.,
prescription refills) 41, 51; monitoring and documenting easily measurable key clinical
outcome measures, such as blood glucose levels 41, 46-48, 51, 52; blood pressure 46, 48,
52; lipid levels 46-48, 52; reminding patients of the importance of regular examinations
for the presence of diabetic complications, e.g., eye and feet examinations 41, 52; and
ensuring the quality and evidence-based use of medications 41, 45-49, 51. However, it is
unclear to what extent individual elements of the intervention made a contribution to
the effectiveness of the service.
The DMAS service implemented in this study incorporated most of the above
elements. Collectively it represents a complex service model that involves the
community pharmacist in the care of patients with type 2 diabetes. The rationale for
this model resides in the implementation of processes designed to address some
previously identified barriers to care 108 such as the lack of communication between
health care practitioners involved in the care of patients with type 2 diabetes and the
need to provide more intensive support to improve patients’ self-management skills.
These have previously been shown to improve glycaemic control, patient satisfaction
and quality of life 109.
In the community pharmacy, all intervention patients were given a MediSense™
meter and instructed on its use. Subsequently, the pharmacist downloaded the blood
glucose readings and gave feedback to the patient at each of the four visits. Other
aspects of the service were tailored to the individual patient’s needs. These included
providing adherence support, advice about medication issues and instruction on
lifestyle issues. From the pharmacists’ documentation, it is clear that they delivered
a range of interventions targeting these various aspects of the management of
diabetes.
Strict control of diabetes can result in significant risk reduction in terms of the onset
of complications 110, 111. Intensive blood glucose and blood pressure control in
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patients with type 2 diabetes have also been shown to be cost effective in terms of
managing these complications 54. The fact that the mean blood glucose values fell
steadily in the intervention patients, suggests they also achieved better blood glucose
control; as do the figures for the proportion of readings outside the normal range,
which also decreased in this group of patients.
Good diabetes control depends on a variety of factors, the most important of which is
achieving the right balance between medication, energy expenditure (physical
activity) and energy inputs (diet). Much of this depends on appropriate self-care
behaviours being undertaken by the patient. Psychological factors such as
depression, negative attitudes and poor self-efficacy can also impact on diabetes
care and critically influence adherence to the required self care behaviours 109. The
results showed that intervention patients had improvements in humanistic outcomes.
For example the improvements in understanding long-term management scores
reflected improvement in knowledge and understanding of diabetes by intervention
patients as a result of the DMAS. The improvement in self care ability scores
reflected an increased self confidence in managing diabetes observed in the
intervention and not the control group. At the end of the trial, they had significant
improvements in their understanding of long-term management of diabetes
compared with controls. They also had improvements in their quality of life reflected
in the EQ-5D scores; improvements in well-being reflected by less negative attitudes
towards their diabetes and higher energy; and greater confidence in their self-care
ability.
There was an increase in adherence to medication in DMAS patients. This indicates
a positive impact of the pharmacists’ interventions on medication taking and may
have contributed to the improvements in glycaemic control. All intervention patients
had some documented adherence interventions. Overall, the results suggest that
patients developed a better understanding of their medications and the importance of
medication adherence as a result of the service. An increase in the mean number of
antidiabetic medications and the defined daily doses of metformin were also seen in
the intervention patients and this too may have contributed to the achievement of
better control.
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Overall, the results of the DMAS study reinforce the notion that community
pharmacists, as highly trained and accessible health care professionals, can be very
effective in delivery of disease state management services for diabetes. However, a
number of unanswered questions remain in relation to the nature of services to be
delivered by pharmacists. Further research is needed to clarify the type and intensity
of interventions that are most clinically and cost-effective and also to elucidate the
amount each component of the DMAS service contributes to the improved outcomes.
Qualitative research with participants in previous pharmacy diabetes trials and in the
present study, suggests that an important and appreciated components of the
pharmacist delivered service are, the regular review with the pharmacist of blood
glucose readings (produced as a print-out from computer software); the ongoing
support and motivation provided by the pharmacist at each visit; and the
reinforcement of critical information about the disease and its management.
The DMAS study is the first large scale randomised clustered controlled trial to
evaluate the clinical, humanistic and economic impact of community pharmacist
delivered disease state management services for type 2 diabetes. As such it
represents a milestone in community pharmacy practice research and considerably
strengthens the evidence base for the value of diabetes disease state management
services delivered in community pharmacy.
5.2.1 DMAS Program - Patient Satisfaction Patient responses to the DMET questionnaire demonstrated a high level of
satisfaction with their interactions with the pharmacists, the location of the service
provider and the service impacts, such as understanding of certain aspects of
diabetes management (e.g. medications, diet, exercise and how to deal with
diabetes). Patients reported increased self efficacy and gratitude for the opportunity
to receive the DMAS. They also indicated greater confidence in their ability to self-
manage their condition. Goal setting was seen to be a significant motivation for a
patient to work harder or modify their behaviour. In short, the DMAS service provides
an ideal environment to promote improvements in self regulation, self efficacy and
motivation as it is patient centred and involves realistic goal setting and pharmacist
feedback over the course of service provision.
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5.2.2 DMAS Program - Pharmacist satisfaction As elucidated by the focus groups, community pharmacists recognise that they are in
an ideal position to provide extended services and welcome the opportunity to be
included and appreciated as a valuable member of the diabetes health care team.
They have demonstrated an ability to form relationships and collaborate with GPs,
specialists and other health care professionals and understand that everyone in the
team has a valuable contribution to make. This is consistent with the view that a
collaborative approach to health care for the patient with type 2 diabetes is more
likely to result in improved outcomes. Most pharmacists who were interviewed in the
focus groups reported a positive impact on their professional image and were
confident that the screening and DMAS services could be successfully implemented
in community pharmacies.
5.2.3 Economic Analysis of the DMAS Program In the Sugarcare Care study 41, the intervention subjects received the specialised
service for type 2 diabetes in a community pharmacy at 4-6 weekly intervals over 9
months while control patients received no service. At the end of the 9 months there
was a significant reduction in mean HbAIC of 0.5% in the intervention compared to
0.03% in the control group. An incremental cost effectiveness analysis demonstrated
that to obtain the reduction in HbA1c achieved by the specialised service, the cost to
the health care sector was Aus $383 per patient per 9 months 41.
This study builds on this previous work by undertaking an evaluation of the cost-
effectiveness if DMAS was implemented as part of Pharmacy Diabetes Care Program in
community pharmacy. To produce results that can be compared with other programs we
have used a computer simulation model to estimate the likely long term benefits in terms
of improvements in life expectancy and quality adjusted life expectancy if the DMAS
continued for a period of ten years. In order to extrapolate future outcomes we have had
to make assumptions regarding long-term improvements in HbA1c for patients
participating in the DMAS program. For the main analysis, we have adopted two
scenarios that involve a continuation of the difference in HbA1c achieved in this study.
Previous randomized trails of interventions to intensify blood-glucose control (UKPDS 33
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112) indicate that the difference in metabolic control between groups achieved early in the
study is a good predictor of long term difference between groups. In the extremely
conservative scenario A we assumed a 0.35% reduction in HbA1c which adjusted for the
higher mean HbA1c in the DMAS group at baseline. This scenario produced an increase
in life expectancy of 0.14 years and 0.11 QALYs when these outcomes were discounted
at 5% per year. These figures become even more favourable if we assume a more
realistic and less conservative reduction in HbA1c of 0.7%. The net cost (taking into
account wide impacts on the health care system) of a ten year DMAS intervention was
around $3,400 per patient (or $340 per annum) and so the cost per life year gained was
$24,029 and the cost per QALY gained was $30,582. If a DMAS program were
implemented it is likely that the benefits of the service could be maintained with fewer
visits following the initial intensive 6 months, therefore the cost per patient and the cost
per life year/QALY gained would be even lower.
It is important to note that DMAS program also produces savings that can offset
some the costs of its implementation. In particular, based the pattern of drug usage
within the study we assumed that better medication management would lead to
savings of around $1500 for those enrolled DMAS program over the 10 year
evaluation period. Similarly lower rates of complications in the DMAS group would
also produce some savings. Taking all of the potential savings into account we
estimate the net cost to be in the order of $3,800 over a 10 year period.
While there may be other potential saving (e.g. fewer days off work in the DMAS
group due to reduced rates of complications) we have not taken these into account in
the current report to ensure comparability of our methodology with current standards
for drug evaluation in Australia.
In recent years an increasing variety of strategies available for the management of
diabetes are becoming available, such as new therapies (e.g. Thiazolidinediones )
and new modes of delivering existing therapies (e.g. insulin administered by
inhalation). Given resource constraints within the health care sector, choices need to
be made between alternative interventions. Further, it is important to consider
whether it is more cost-effective to improve the management of existing therapies
before adopting new ones. This study has shown that a community pharmacy-based
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service can significantly reduce HbA1c in high risk patients with diabetes to a greater
degree than achieved in some randomized trials of newer pharmacological therapies
(Acarbose Paper 113) that are listed on the PBS. Further, based on our economic
evaluation, the cost-effectiveness of DMAS is likely to compare favourably with other
accepted uses of health care resources in Australia. For example, the cost-effectiveness
of the DMAS is similar to other prevention programs such as for breast cancer that are
currently funded by the Australian Government 114. Furthermore an analysis of decisions
by the Pharmaceutical Benefits Advisory Council (PBAC) on the cost-effectiveness of
new drugs shows that interventions below $37,000-$69,000 per life year have been
funded by the Australian Government 88. If decision makers select a similar ceiling or
maximum acceptable ratio then our analysis indicates the DMAS is cost-effective.
5.2.4 DMMR- Domiciliary medication management review The DMMR component of the PCDP was optional for the individual patient and was
dependent on a request from their GP. During the study no DMMRs were requested
for any enrolled patients. The reasons for this are unclear.
5.2.5 Study Limitations It should be noted that there were certain limitations associated with this study.
Whilst the intervention and control groups were well matched on most clinical
parameters, HbA1c was higher at baseline in the intervention group compared with
control (8.9% versus 8.3%). To address the uncertainty that may arise as a result of
this, we used appropriate statistical methods to control for this baseline difference.
Hence we reported a very conservative estimate of the impact of DMAS on
glycaemic control. As far as possible we used an intention-to-treat approach in the
analysis, however there were difficulties in retrieving final clinical data from GPs for
some patients; it should be noted that the proportion of patients for whom final clinical
data were missing was similar for intervention (20%) and control patients (24%).
Measurement of adherence relied on administration of an instrument, the BMQ.
Although pharmacists were instructed on the administration of the BMQ, it is quite
sensitive to prompting on behalf of the interviewer and the extent of prompting by the
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pharmacist may have affected the level of reporting of medications and problems
with medications. Since the intervention pharmacists were aware that they needed
to address adherence and had strategies to deal with any problems, the instrument
may have been used differently in their hands compared to the control pharmacists.
In order to undertake the economic analysis we have had to make a number of
assumptions regarding the long-term impact of DMAS on outcomes and costs. In
particular, we have assumed that the incremental difference in HbA1C achieved
during the study is sustained for those patients continuing to receive the intervention.
Given that we have used a computer simulation model in which changes in HbA1C
are an important determinant of outcome, it would be very useful to conduct longer
term trials of pharmacy based interventions, such as DMAS, to establish effects on
metabolic control over time. This would also bring the evaluation of this type of health
service research intervention on a comparable footing with the evaluation of
pharmaceutical therapies where there has been an increasing emphasis on
conducting long term studies of effectiveness (e.g. the CARDS study 115).
It is also important to note that this evaluation has not considered the overall cost of
implementing DMAS in Australia. This type of analysis would require data on the
likely take-up and long-term compliance with the program. While these factors are
important, they are more likely to impact on the overall program cost than the cost-
effectiveness (i.e. patients who don’t participate or withdraw from DMAS accrue
neither costs or benefits and so there is likely to be little net impact on cost-
effectiveness).
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6. CONCLUSION
The Pharmacy Diabetes Care Program is a clinically and cost effective professional
service which may be implemented in a wide range of community pharmacies in
Australia. The patients and pharmacists involved in the Pharmacy Diabetes Care
Program were very satisfied with and approved the diabetes screening and DMAS
services in community pharmacy. Community pharmacies provide an ideal
environment for the provision of extended pharmacy services. Over time patients
have become more accepting and welcoming of extended services in the community
pharmacy setting, largely due to convenience and increased likelihood of service
participation. Therefore, it appears likely that future provision of extended services,
including screening for undiagnosed diabetes and the DMAS, would be adopted and
supported by patients in Australian community pharmacies. Moreover, subsidisation
by the Australian Government would enable widespread uptake of the Pharmacy
Diabetes Care Program by community pharmacy.
In conclusion, the screening and DMAS services implemented in this study have the
potential to contribute to improved health outcomes for patients with type 2 diabetes,
to enhance the contribution of the community pharmacist in the care of patients with
type 2 diabetes and provide significant cost savings to the health care system.
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