Economic Modelling of the Prevention of Type 2 Diabetes in Australia Linc Thurecht, Laurie Brown and Mandy Yap

Transcription

1 National Centre for Social and Economic Modelling University of Canberra Economic Modelling of the Prevention of Type 2 Diabetes in Australia Linc Thurecht, Laurie Brown and Mandy Yap Presentation to the Technical Workshops of the 1 st General Conference of the International Microsimulation Association, Vienna, 22 August 2007

2 About NATSEM The National Centre for Social and Economic Modelling was established on 1 January 1993, and supports its activities through research grants, commissioned research and longer term contracts for model maintenance and development with the federal departments of Families, Community Services and Indigenous Affairs; Employment and Workplace Relations; Treasury; and Education, Science and Training. NATSEM aims to be a key contributor to social and economic policy debate and analysis by developing models of the highest quality, undertaking independent and impartial research, and supplying valued consultancy services. Policy changes often have to be made without sufficient information about either the current environment or the consequences of change. NATSEM specialises in analysing data and producing models so that decision makers have the best possible quantitative information on which to base their decisions. NATSEM has an international reputation as a centre of excellence for analysing microdata and constructing microsimulation models. Such data and models commence with the records of real (but unidentifiable) Australians. Analysis typically begins by looking at either the characteristics or the impact of a policy change on an individual household, building up to the bigger picture by looking at many individual cases through the use of large datasets. It must be emphasised that NATSEM does not have views on policy. All opinions are the authors own and are not necessarily shared by NATSEM. Director: Ann Harding NATSEM, University of Canberra 2007 National Centre for Social and Economic Modelling University of Canberra ACT 2601 Australia 170 Haydon Drive Bruce ACT 2617 Phone Fax natsem@natsem.canberra.edu.au Website

3 Abstract In late 2005 the Diabetes Prevention Working Party of the National Public Health Partnership commissioned the National Centre for Social and Economic Modelling, in association with the International Diabetes Institute, to build a quantitative econometric model that would facilitate economic analyses evaluating whether or not type 2 diabetes prevention programs are likely to be cost-effective in the Australian context. This paper outlines the development of the Diabetes Model that projects the number of Australian adults with pre-diabetes and type 2 diabetes from 2005 to 2050 in three year increments. The model produces a wide range of epidemiological and economic output to assess the current and projected impact of those with the disease. The model is then able to quantify the effect of hypothetical counter-factual "shocks" in the management of diabetes and associated trends in risk factor prevalence over the simulation period. By comparing the results of the base case to alternative simulations, it is possible to identify the extent to which short-run investments can reap long term benefits in both human and economic terms. Author note Dr Linc Thurecht is a Senior Research Fellow at the National Centre for Social and Economic Modelling (NATSEM) at the University of Canberra, Associate Professor Laurie Brown is Research Director (Health) and Ms Mandy Yap is a Research Fellow at NATSEM. Acknowledgments The author acknowledges the contributions of additional members of the Diabetes Model research team including: Associate Professor Philip Clarke, Senior Research Fellow, School of Public Health, University of Sydney; and Professor Paul Zimmet, Director, International Diabetes Institute (IDI) and Associate Professor Jonathon Shaw, Deputy Director, IDI. Members of the Diabetes Prevention Working Party of the National Public Health Partnership are also thanked for their input and advice.

4 Contents v Abstract Author note Acknowledgments iii iii iii 1 Introduction 1 2 Objective of the Diabetes Model 1 3 Overview of the Diabetes Model 2 4 Policy Environment 4 5 Technical Specifications 5 6 Conclusion 6 References 7

5 Economic Modelling of the Prevention of Type 2 Diabetes in Australia 1 1 Introduction Type 2 diabetes is a common, chronic and costly health condition that imposes a significant burden on affected individuals, their families and the community at large. The World Health Organisation (WHO) recently estimated that in 2000 there were 171 million people world wide with diabetes with this number estimated to rise to 366 million people by 2030 (WHO 2004). 1 This represents an increase in the world wide prevalence of the disease from 2.8% to 4.4%. Within Australia, it is estimated that more than 940,000 Australians aged 25 years and over (7.2% of that population) had type 2 diabetes in 2000 (Dunstan et al 2002). A follow up study indicated that in 2005 around 275 Australian adults are developing diabetes every day, implying an increase in the diabetic population of more than 100,000 each year (Barr et al 2006). WHO estimates that direct health care costs of diabetes range from 2.5% to 15% of annual health care budgets within individual countries, depending on the prevalence and sophistication of the treatment available. 2 Within Australia, it was estimated that in around 1.7% of recurrent health expenditure was spent on diabetes (AIHW 2005). Colagiuri et al (2003) estimated that the annual cost for people with type 2 diabetes aged over 40 years in 2001 was $2.2 billion, rising to $6 billion when the cost of carers and benefits paid by the Commonwealth government are included. Yet despite the significant burden and cost associated with the disease, type 2 diabetes largely occurs as a result of modifiable lifestyle factors. Excess weight and physical inactivity have been identified as the two most important risk factors for diabetes. Abnormal blood pressure and serum cholesterol levels are also typically associated with these risk factors. Programs that seek to moderate individuals' lifestyle habits to limit the onset of the disease have the potential to significantly limit the growth in diabetic numbers with the associated improvements of quality of life and reduced strain on the health system. However, the initial investment will not bear fruit until many years into the future creating the problem of justifying government funding for benefits that may not accrue until many budget cycles hence. 2 Objective of the Diabetes Model The Diabetes Model aims to address the issue of demonstrating long term benefits from near term investments by providing the infrastructure for testing scenarios that may be developed for the purpose of making broad policy decisions about public health 1 The estimated number of diabetics and prevalence rates in WHO (2004) is based on people 20 years of age and older. For those under 20 years of age, type 1 diabetes was also estimated. 2 (accessed on 2 August 2007).

6 2 NATSEM IMA Technical Workshop Paper investment. Two specific research questions are addressed by the Diabetes Model. First, what are the relative benefits of investing in primary prevention compared to secondary prevention. 3 Second, what is the optimal balance of investment across strategies aimed at the whole population versus those targeting high risk groups. The Diabetes Model achieves this by quantifying the costs and benefits of an investment in a program aimed at reducing the prevalence of diabetes risk factors and the concomitant diabetic population. A base case is first developed against which a counter-factual "shock" is then imposed. The difference between the base case and the specified scenario measures the benefit that may be achieved and the period over which it will emerge. As a policy development tool, the Diabetes Model therefore enables the identification of which types of intervention are likely to be the most cost-effective and the long term gains that can be achieved from far-sighted investment in diabetes prevention programs. 3 Overview of the Diabetes Model The Diabetes Model is a complex cell based population projections model that generates a time-series of cross-sectional prevalence based snap shots of the Australian 25 year and older population over the simulation period. The prevalence of both pre-diabetes and diabetes are determined for each population cell based on its specific set of characteristics (known as a population-share based structure). The model comprises 3,456 cells representing the combination of eight key diabetes risk factors: sex (two groups); age (six groups); income (four categories); waist circumference (two categories); blood pressure (two categories); abnormal cholesterol (two categories); physical activity (three categories); and smoking history (three categories). Prevalence of pre-diabetes and diabetes can be simulated over a 45 year period comprising 15 three-year cycles. The model s population base is updated every three years to reflect population ageing and changes in the prevalence of the modifiable risk factors. Two logistic regression models are used to project the proportion of a cell population that is likely to have pre-diabetes or diabetes. The cell-based structure of the model is underpinned by unit record data from the Australian Diabetes, Obesity and Lifestyle (AusDiab) Study (Dunstan et al 2002). These data are used to model changes in individual biometric characteristics of waist 3 Guidance provided by the Diabetes Prevention Working Party was that a "primary prevention" should be interpreted as programs that aim to limit the incidence of disease and disability in the population by measures that eliminate or reduce causes or determinants of departures from good health, control exposure to risk, and promote factors that are protective of health. A "secondary prevention" should be interpreted as programs that aim to reduce progression of disease through early detection, usually by screening at an asymptomatic stage, and early intervention.

7 Economic Modelling of the Prevention of Type 2 Diabetes in Australia 3 circumference, blood pressure and hdl cholesterol levels, in addition to exercise and smoking status, of individuals simulated to participate in the secondary prevention program. The results are then aggregated to the cell level. The impact of changes in the size and age-sex structure of the Australian population on diabetes prevalence is automatically captured in the model through the use of Australian Bureau of Statistics (ABS) population projections for each age-sex group (ABS 2006). Modelling a prevention strategy involves changing the number of people that have a particular set of risk factors and moving them into another cell reflecting their new set of characteristics following participation in the prevention program. The population in each risk factor combination in each age-sex group is represented by a weight. This weight is the proportion of individuals in the age-sex group that has a specific set of risk characteristics that are used to apportion the number of people in each age-sex group (derived from the ABS population projections) across all the risk factor combinations within each age-sex specific group. When the weights are summed across the risk factor combinations within the age-group, they add to 100% thus representing all the persons within that particular age-sex group. These weights are systematically changed for each three year cycle depending upon base line trends or the likely effects of a prevention strategy. Changes in the weights lead to new cell proportions, reflecting the new prevalence of the different risk factor combinations within each age-sex group. Applying the new cell proportions to the age-sex group population counts (from the ABS population projections) generates the estimated number of persons at the end of each cycle who now have each risk factor combination. Specific prevention programs will target only a proportion of the population eg those at high risk of diabetes, and only a proportion of these individuals may participate in a lifestyle intervention program. The model first identifies which unit records fall into at-risk groups. A random number generator is then used to select records to represent those participating in the program. The model then makes changes in line with the expected outcomes of the program to the biometric and other risk characteristics of the selected records. Cell weights are then recalculated based on the new distribution of values in the unit record dataset. New random numbers are generated for each of the three-year cycles. A record may be selected in more than one cycle over the course of the simulation with the impact of the interventions being cumulative until a record achieves normal status for individual risk factors. Having derived the expected number of people in each cell at the end of each cycle, the number of persons expected to have pre-diabetes or diabetes is then calculated. This is done by assigning each cell (ie every combination of age, sex, income and the modifiable risk factors), a probability of having diabetes or pre-diabetes. The proportion of a cell population that is likely to have pre-diabetes or diabetes is determined from two logistic regression models derived from the AusDiab survey. Multiplying the probability of

8 4 NATSEM IMA Technical Workshop Paper pre-diabetes or diabetes by the cell population produces the number of persons expected to occupy this state. The total number of persons with type 2 diabetes is then divided between undiagnosed or diagnosed cases depending on the initial ratio at the start of the cycle which is then adjusted for the estimated number of new cases detected through the screening activities of the prevention program in that cycle. 4 A prevention strategy is modeled by specifying the type of prevention program (either a primary, secondary or combination prevention strategy) and entering the parameter values for the strategy into the model. These include: general parameters such as discount rates or the ratio of diagnosed to undiagnosed diabetes; risk factor parameters defining the likely impact of the program on the prevalence of different risk factors; components of the prevention strategy such as recruitment, screening, and intervention activities (eg mass media campaigns, mail-outs, doctor visits, diagnostic testing, group or one-on-one sessions); and the costs associated with the prevention program. The model generates basic epidemiological results on the prevalence of pre-diabetes and type 2 diabetes, DALYs and diabetes related deaths, in addition to economic results in terms of the cost effectiveness, cost utility and cost benefit of the prevention program. Non-discounted and discounted results are produced, with the discount rate being a user specified parameter. The costs and benefits associated with a particular prevention strategy can be assessed against a chosen comparator which is typically set as the base case. The base case simulates current trends in diabetes prevalence, risk factors, current screening and detection practices, and diabetes care ie the status quo. A User Guide to Diabetes Model can be found at Brown et al (2006). 4 Policy Environment The current version of the Diabetes Model was constructed for the Diabetes Prevention Working Party of the National Public Health Partnership (NPHP) to address the two research questions identified in Section 2. The potential application of this model to scenario simulation is illustrated through a number of worked examples. These examples are a demonstration of the utility of the model and the capacity to evaluate likely outcomes of different prevention strategies and test assumptions about diabetes prevention. They also serve to highlight the importance of further work to better scope the nature and cost of diabetes prevention programs in order to provide a more robust basis for economic analyses. Some of the scenarios developed for the NPHP involved mass media based primary 4 The separate identification of diagnosed and undiagnosed diabetics is important as an undiagnosed diabetic will typically place less demands on the health system than a diagnosed diabetic. Of course, when an undiagnosed diabetic is eventually diagnosed, they will, in general, have more severely progressed symptoms and complications associated with the disease.

9 Economic Modelling of the Prevention of Type 2 Diabetes in Australia 5 prevention campaigns with three alternative levels of investment and three associated attributable affects. Four secondary intervention strategies were also developed which considered progressively more intensive lifestyle interventions with correspondingly greater attributable effects. The secondary interventions were based on the landmark Laaksonen et al (2005) study. These scenarios demonstrated both the utility of the Diabetes Model in addition to its sensitivity to parameter values used in the simulation. Further development of the Diabetes Model is proposed to better understand how adherence to good diabetes control (eg maintenance of blood pressure, glycaemic and lipid levels) affects progression of the disease and the associated costs to the health system. The research questions to be addressed here relate to the potential impact that strategies to improve adherence to diabetes control may have on reduced or delayed progression of diabetes complications. To capture these effects, a Diabetes Management Module will be constructed to model the impact of diabetes control and associated onset of complications arising from diabetes. It is also intended that the methodological framework developed for the Diabetes Model be applied to other chronic diseases such as dementia, cardiovascular disease or cancers associated with modifiable risk factors. Given that the underlying methodology is based on the prevalence based movement of people among different risk factor states, there is potential to apply this framework where the cumulative impact of lifestyle factors contribute to the onset of a particular disease. 5 Technical Specifications The Diabetes Model was developed using Microsoft Excel. All aspects of the simulation are controlled by programs written in Microsoft Visual Basic for Applications (VBA). Policy developers interact with the model through a User Interface that enables the various model parameters to be individually set by the user for each simulation. The User Interface uses standard Microsoft Windows dialogue boxes. While the choice of platform was appropriate in the early stages of developing the Diabetes Model, certain aspects of the way the methodology was ultimately implemented has resulted in somewhat slow runtimes. While considerable effort has been made to optimise the flow of data and to remove redundant processing (within the Excel spreadsheet environment), the size of the model and future development proposals suggest that the Diabetes Model should be redeveloped using an application more suited to the size of the basefile and the amount of data manipulation being performed during a simulation.

10 6 NATSEM IMA Technical Workshop Paper 6 Conclusion The Diabetes Model provides an important tool for policy developers to evaluate the potential costs and benefits of a given intervention to mitigate the growing burden associated with the disease. A particular strength of the Diabetes Model is the way long term benefits are identified from near term investments, something that is not always possible when considered over conventional government budgeting cycles. There remains, of course, much additional work that could be done on improving the underlying methodology of the model and more effectively modelling the risk of diabetes based on the underlying AusDiab survey. However, the model currently provides a useful platform from which to investigate the potential cost-benefits from implementing a diabetes prevention program and assessing among a range of alternatives.

11 References Economic Modelling of the Prevention of Type 2 Diabetes in Australia 7 Australian Bureau of Statistics (ABS) "Population Projections, Australia, 2004 to 2101". Catalogue Number , Australian Bureau of Statistics, Canberra. Australian Institute of Health and Welfare (AIHW) "Cost of Diabetes in Australia, ". Bulletin 26. AIHW, Canberra. Barr E L M, Magliano D J, Zimmet P Z, Polkinghorne K R, Atkins R C, Dunstan D W, Murray SG and Shaw J E "AusDiab 2005: The Australian Diabetes, Obesity and Lifestyle Study. Tracking the Accelerating Epidemic: Its Causes and Outcomes". International Diabetes Institute. Brown L, Thurecht L and Yap M "Diabetes Model (v06.1) User Guide". NATSEM, University of Canberra. Colagiuri S, Colagiuri R, Conway B, Grainger D and Davey P "DiabCo$t Australia: Assessing the Burden of Type 2 Diabetes in Australia". Diabetes Australia, Canberra. Dunstan D W, Zimmet P Z, Welborn T A, DeCourten M P, Cameron A J, Sicree R A, Dwyer T, Colagiuri S, Jolley D, Knuiman M, Atkins R, Shaw J E on behalf of the AusDiab Steering Committee "The Rising Prevalence of Diabetes and Impaired Glucose Tolerance The Australian Diabetes, Obesity and Lifestyle Study". Diabetes Care, 25(2), pp Laaksonen D, Lindstrom J, Lakka T, Eriksson J, Niskanen L, Wikstrom K, Aunola S, Keinanen-Kiukaanniemi S, Laakso M, Valle T, Ilanne-Parikka P, Louheranta A, Hamalaninen H, Rastas M, Salminen V, Cepaitis Z, Hakumaki M, Kaikkonen H, Harkonen P, Sundvall J, Tuomilehto J, Uustipa M "Physical Activity in the Prevention of Type 2 Diabetes: The Finnish Diabetes Prevention Study". Diabetes, 54, Wild S, Roglic G, Green A, Sicree R and King H "Global Prevalence of Diabetes". Diabetes Care, 27(5), pp