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

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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

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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

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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

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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

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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

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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.

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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.

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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.

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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”

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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.

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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).

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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

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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).

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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

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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.

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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

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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

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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”

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“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

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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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

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Figure 25: Life Years - Scenario A

-$2,000

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Figure 26: Life Years - Scenario B

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Figure 27: Quality Adjusted Life Years - Scenario A

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Figure 28: Quality Adjusted Life Years - Scenario B

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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.

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10%

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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.

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Scenario B

Scenario A

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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

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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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