Using An Economic Model Of Diabetes To Evaluate Prevention And Care Strategies In Australia

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1 Using An Economic Model Of Diabetes To Evaluate Prevention And Care Strategies In Australia A prevention modeling exercise for diabetes suggests that the greatest priority should be on reaching the population ages by Stephen Colagiuri and Agnes E. Walker ABSTRACT: The health benefits and costs of a national diabetes screening and prevention scenario are estimated among Australians ages The Australian Diabetes Cost- Benefit Model is used to compare baseline and scenario outcomes from 2000 to Those newly diagnosed in 2000 receive intensive care, resulting in lower complication rates. People at high risk of developing diabetes are offered lifestyle intervention, reducing the numbers developing diabetes. A total of 115,000 people became newly diagnosed. Among those deemed at high risk, 53,000 avoided developing diabetes by Average yearly intervention and incremental treatment cost was AU$179 million, with a cost per disability-adjusted life-year of AU$50,000. [Health Affairs 27, no. 1 (2008): ; /hlthaff ] The world health organization and International Diabetes Federation (IDF) have drawn attention to the current global diabetes epidemic, which is expected to escalate in coming years. 1 The most recent data from the IDF indicate that diabetes affects 246 million people worldwide and is expected to affect 380 million by In Australia as elsewhere, type 2 diabetes is of particular concern, because many people who have it are undiagnosed; with early treatment, the debilitating complications can be avoided or delayed; and with identification of the high-risk population (for example, people with impaired glucose tolerance, or IGT), the development of the disease itself can be avoided or delayed by lifestyle changes. 3 Early detection of undiagnosed type 2 diabetes has been proposed as an important strategy in reducing the personal, public, and economic costs of diabetes. 4 Australia s National Health and Medical Research Council has endorsed national Stephen Colagiuri is a professor at the Institute of Obesity, Nutrition, and Exercise, University of Sydney, in Australia. Agnes Walker (Agnes.Walker@anu.edu.au) is a fellow at the Australian Centre for Economic Research on Health, Australian National University, in Canberra. 256 January/February 2008 DOI /hlthaff Project HOPE The People-to-People Health Foundation, Inc.

2 Diabetes Prevention guidelines for Case Detection and Diagnosis of Type 2 Diabetes. 5 They advocate assessment of individual risk of having undiagnosed diabetes, measurement of fasting plasma glucose in those at risk, and further testing if the result is equivocal. The AusDiab study (Australian Diabetes, Obesity, and Lifestyle Study) was the first comprehensive biochemical Australian national prevalence study for diabetes and cardiovascular risk factors. 6 The AusDiab baseline study examined 11,247 people age twenty-five and older, each of whom completed a health questionnaire and had an oral glucose tolerance test, unless they had medicationtreated diabetes. The prevalence of diabetes was 7.4 percent; half of those found with diabetes were known to have it. In addition, 10.6 percent had IGT, 5.7 percent had impaired fasting glucose (IFG), and more than half had at least one risk factor for cardiovascular disease or undiagnosed diabetes. These findings have focused attention on the feasibility of implementing a national diabetes screening and prevention program. Information on the resource implications and potential benefits of such a program would assist decisionmakers in determining its value. Because these data are not available from longterm intervention studies, modeling is a well-accepted alternative. We used the Diabetes Cost-Benefit Model to examine the benefits and costs of a nationwide diabetes screening and prevention program in Australia, as compared with a no-intervention default scenario. Modifications to the intervention program are presented as sensitivity analyses. Study Data And Methods The Diabetes Cost-Benefit Model projects the incidence and progression of diabetes and its complications in the whole Australian population. Age, sex, diabetes status, and treatment costs are considered. The model estimates the monetary costs of intervention scenarios and health outcomes measured as disabilityadjusted life-years (DALYs). 7 The model uses the cost-benefit method, which has the advantage of using monetary amounts for both costs and benefits. A disadvantage is that not all costs or benefits can be expressed in monetary terms. However, the cost-benefit approach can be complemented with cost-utility analysis to allow for consideration of hard-to-measure factors such as disability-free life-years gained. 8 We achieved this complementarity by making DALYs available in our model. Here the findings for the target population are disaggregated by age group only. A complex chronic-disease model system (including diabetes) is being developed to allow disaggregation across many demographic, socioeconomic, and healthrelated variables. 9 Elements of the model. The model considers fifteen stages of diabetes and its complications: (1) people without diabetes low risk of developing diabetes; (2) without diabetes high risk of developing diabetes; (3) with undiagnosed diabetes; (4) diagnosed with diabetes without complications poorly controlled; (5) diag- HEALTH AFFAIRS ~ Volume 27, Number 1 257

3 nosed with diabetes without complications moderately controlled; (6) diagnosed with diabetes without complications well controlled; (7) diagnosed with diabetes with early-stage eye complications (retinopathy); (8) diagnosed with diabetes with early-stage renal complications (microalbuminuria, macroalbuminuria); (9) diagnosed with diabetes with early-stage limb complications (neuropathy, peripheral vascular disease); (10) diagnosed with diabetes with early-stage cardiovascular complications (angina, transient ischemic episodes); (11) diagnosed with diabetes with advanced eye complications (vision-threatening retinopathy, blindness); (12) diagnosed with diabetes with advanced renal complications (dialysis, transplantation); (13) diagnosed with diabetes with advanced limb complications (foot ulcer, amputation); (14) diagnosed with diabetes with advanced cardiovascular complications (myocardial infarction, bypass surgery, stenting, stroke); and (15) death (based on Australian Bureau of Statistics, or ABS, mortality data). Simulations. Details of the model have been described elsewhere. 10 Our simulations have a time frame. They concern screening for undiagnosed diabetesandprediabetes(igtandifg)ofaustraliansages55 74andthosewhowere ages and were obese (body mass index of 30 or higher), had a family history of diabetes, or had hypertension, or some combination. Population projections come from published ABS data. 11 The simulated interventions involve screening at AU$ per person and an unspecified lifestyle program at AU$500 per person per year. This latter amount is seen by Australian health authorities as an affordable annual expenditure on such a program. Direct health care costs in Australian dollars for the year 2000 are held constant. Costs include visits to doctors (general practitioners and specialists), visits to other health professionals (educator, dietitian, podiatrist), in-hospital costs, prescription medications, and diabetes-related supplies (such as self-monitoring of blood glucose levels). The Diabetes Cost-Benefit Model s estimates of total treatment costs were in line with estimates by other researchers who used different sources. DALY estimates were within 1 percent of published estimates by other researchers. 12 Although the base simulation assumes that the 2000 proportions across the population diagnosed with diabetes (groups 4 14) remain constant, it allows for increasesindiabetesprevalencethroughatrendfactor.thisfactorreflectsthenationwide historical annual rate of increase in the prevalence of diabetes. In base simulations, the model s trend factor is computed from the changes in prevalence rates across the two latest National Health Surveys (2001 and ). 13 Prevalence of diagnosed diabetes increased from 3.0 percent to 3.5 percent, defining the default rate as 5 percent per year (that is, 5 percent is the product of two factors: theinverseofthetimeperiodinyearsbetweenthetwosurveys 1/3.5years;and thepercentagechangebetweenthetwosurveys [ ]/0.03).Inthebase case, this trend arises mainly from increased prevalence of risk factors such as obesity and inactivity. 14 It is lower under the scenario because the lifestyle program re- 258 January/February 2008

4 Diabetes Prevention duces obesity and inactivity. In both simulations, the costs were discounted at 3 percent per annum. Our default scenario. To select the default scenario, we examined two Diabetes Prevention Program trials: one for Finland and the other for the United States. 15 These concluded that programs to modify overweight, physical inactivity, and poor diet could prevent or delay the development of diabetes by 58 percent. Our default intervention scenario involves screening, in the year 2000, of Australians ages The default lifestyle program cost was also analyzed at AU$1,000, since the Finnish trial, with frequent one-on-one encounters, probably cost more than the default of AU$500. Another analysis assumed that the intervention reduced the risk of diabetes by only three-fourths of the reported 58 percent, or 43.5 percent. A follow-up study of the Finnish trial showed a 43 percent reduction in diabetes at seven years, after a mean intervention of four years. 16 In our study, such an outcome is approximated in scenario 4 (described later), with key differences being that our results are ten years after screening with a lifestyle program uptake of ten years, compared with the Finnish follow-up study s seven years after screening with mean lifestyle program uptake of four years. The screening commences with risk-factor assessment followed by measurement of fasting plasma glucose in people at high risk, followed by an oral glucose tolerancetestinpeoplewithanequivocalresultbetween5.5and6.9mmol/1. 17 Using the criteria from the National Health and Medical Research Council guideline, all people age fifty-five and older are at high risk of undiagnosed diabetes. People ages with obesity, hypertension, or family history of diabetes, or some combination, are also considered at high risk, making up half of the population in this age group. Besides identifying people with undiagnosed diabetes, to be offered diabetes care, this procedure also identifies people with IGT or IFG, to be offered a lifestyle intervention program (diet, weight reduction, and exercise) and people at high risk but with normal glucose tolerance, offered general lifestyle advice. Such people are not considered further here. Thebenefitsaftertenyearsareexpectedtobelowerratesofcomplications among the newly diagnosed and avoidance of diabetes by those successful with the lifestyle intervention program. Savings on treatment may partly offset the costs of the screening and lifestyle programs. Exhibit 1 presents a flowchart of the default intervention program and its effects. The target population is modeled in two age groups of people at high risk of undiagnosed diabetes: ages (with obesity, hypertension, or family history of diabetes or some combination) and ages 55 74; half of each group underwent screening. 18 BasedondatafromtheU.K.ProspectiveDiabetesStudy(UKPDS),we modeled earlier detection of diabetes to reduce death by 47 percent; myocardial infarction by 36 percent; eye and kidney disease by 61 percent; progression of eye disease by 50 percent; and progression of kidney disease by 28 percent. 19 The Finnish and U.S. studies showed that type 2 diabetes can be prevented or HEALTH AFFAIRS ~ Volume 27, Number 1 259

5 EXHIBIT 1 Overview Of The Default Scenario, Diabetes Screening And Prevention, Australia Screen for undiagnosed diabetes the following high-risk groups: All people ages (N = 3,007,339) Those ages with one or more risk factors (N = 1,277,235) Assume 50 percent screened (N = 2,142,287) Newly diagnosed diabetes N = 115,403 IGT N = 187,025 IFG N = 163,567 Complication reduction: Death by 47% Myocardial infarction by 36% Eye and kidney disease by 61% Progression of eye disease by 50% Progression of kidney disease by 28% Lifestyle modification will reduce development of diabetes by 60% No data on the effects of programs to prevent development of diabetes; can model different effects but initially assume half of the benefit among those with IGT (30%) Lifestyle modification program: 50% take up and persevere with program; $500 a year cost for intervention program Improved outcomes Reduced diabetes SOURCE: Diabetes model simulations. NOTES: IGT is impaired glucose tolerance. IFG is impaired fasting glucose. delayed by 58 percent in people with IGT; we rounded this to 60 percent. For absent data on preventing diabetes with lifestyle intervention in people with IFG, we assumed that half the benefit of IGT would accrue to those with IFG. Exhibit 2 describes the settings of the default scenario and the key variables common to the base and scenario simulations. These are the trend factor and the discount rate (5 percent and 3 percent per year, respectively). Other common model variables are described elsewhere. 20 Exhibit 2 also shows default settings for the proportions with undiagnosed diabetes and with IFG/IGT in each population group; the accuracy of the diagnostic process; the rate at which people with IFG/IGT are expected to develop diabetes; the costs of screening and the lifestyle program; the proportions taking up the lifestyle program and their success in avoiding diabetes; the proportions of newly diagnosed with and without complications; and the rates of reductions in the development of complications. Study Results Diabetes diagnoses. Exhibit 3 summarizes the findings for the base case and the default scenario. In 2000 the population of Australians ages was 5.6 million, of whom 252,000 had diagnosed diabetes and 357,000 had undiagnosed diabetes under the base case. As noted earlier, the undiagnosed proportions that is, 5.4 percent of the population who are obese, have a family history of diabetes, or have hypertension, or some combination, and 7.3 percent of the population 260 January/February 2008

6 Diabetes Prevention EXHIBIT 2 Input Variables, Default Scenario Of Reducing Diabetes Incidence And Costs In Australia Age group (years) Population-level characteristics Trend increase per year in diabetes prevalance over study period a Discount rate (on cost streams) Percent with diagnosed diabetes, no complications b Well-controlled treatment Moderately controlled treatment Poorly controlled treatment High-risk groups with undiagnosed diabetes c Diagnosed with IGT/IFG developing diabetes each year c Screening and lifestyle program d 5% 3% 60% % 10% 5% 3% 60% % 10% Percent of population screened 25% 50% Diagnostic accuracy For undiagnosed diabetes Fot IGT For IFG Per person screening cost (AU$) Percent uptake lifestyle program d Costperyearoflifestyleprogram(AU$) Of people screened with at least 1 risk factor c,e Percent with IGT Percent with IFG Percent other Success rate of lifestyle program Success rate in people with IFG taking up lifestyle program Success rate in people with IGT taking up lifestyle program d (rounded) People with newly diagnosed diabetes b No complications Early complications No complications who develop some by 2005 No complications who develop some by 2010 Prevalence of heart complications in newly diagnosed b Early complications in 2005 Late complications in 2005 Early complications in 2010 Late complications in 2010 Complication reductions under scenario e Death Myocardial infarction Eye and kidney disease Progression of eye disease Progression of kidney disease 80% $ % $ % % 60 60% % % % $ % $ % % 60 60% % % SOURCES: Diabetes model input data, plus others indicated below (full citations are available in the text endnotes). NOTES: IFG is impaired fasting glucose. IGT is impaired glucose tolerance. a Australian Bureau of Statistics, National Health Surveys (Note 13 in text). b A. Walker et al. (Note 10 in text). c D.W. Dunstan et al. and the related AusDiab database (Note 6 in text). d J. Tuomilehto et al. and Diabetes Prevention Program Research Group (Note 15 in text). e S. Colagiuri et al. (Note 19 in text). HEALTH AFFAIRS ~ Volume 27, Number 1 261

7 EXHIBIT 3 Base Case And Default Scenario Results, 2000 And 2010 Ages Ages Ages Population (millions) Number diagnosed, base case Number undiagnosed, base case , , , , , , , , , , , ,186 Number screened, scenario (millions) Number with newly diagnosed diabetes, scenario , , ,403 Number diagnosed with IGT, scenario Number diagnosed with IFG, scenario 48,271 48, , , , ,567 Number of others at risk, scenario (millions) Number taking up lifestyle program, scenario , , ,296 Total diagnosed with diabetes, scenario Number IGT/IFG prevented from developing diabetes, scenario 116,068 14, ,322 38, ,390 52, year treatment cost in scenario (more than in base) (AU$ millions) 10-year cost of screening plus lifestyle program, scenario (AU$ millions) $184 $279 $618 $710 $801 $ year intervention cost less saving in treatment cost (AU$ millions) Reduction in DALYs: base to scenario Cost per DALY avoided (over 10 years) (AU$) $463 8,577 $53,955 $1,327 27,432 $48,386 $1,790 36,009 $49,713 SOURCE: Diabetes model simulations. NOTES: IGT is impaired glucose tolerance. IFG is impaired fasting glucose. DALY is disability-adjusted life-year. (Exhibit 2) were obtained from AusDiab data. Under the default scenario, 2.1 million were screened, and 115,000 were newly diagnosed with diabetes. The 351,000 screened who were found to have IGT or IFG were offered access to the lifestyle program.ofthese,175,000accepted,andby2010,52,500remainedfreeofdiabetes. Without the intervention, these 52,500 people would have developed diabetes. By 2010, the DALYs lost due to diabetes were estimated to be 36,000 lower under the default scenario than under the base case. In both 2000 and 2010, more people were diagnosed under the scenario, putting upward pressure on treatment costs. However, the number with complications was lower, pushing treatment costs down. The net effect, shown in Exhibit 3, is higher treatment costs under the scenario, by AU$801 million over ten years. The ten-year total intervention cost was AU$989 million. As expected, the total number with diabetes in 2000 was the same for both the base case and the scenario. However, screening resulted in a proportion of the undiagnosed becoming newly diagnosed. We also found that the benefits of the scenario became apparent in 2010, with a lower total number of people with diabetes (808,000 versus 913,000 in the base case) and a smaller proportion with 262 January/February 2008

8 Diabetes Prevention undiagnosed diabetes (34 percent versus 49 percent in the base case). Intervention and treatment costs. The discounted intervention and treatment cost streams show that initially treatment costs are higher under the scenario because more people with diagnosed diabetes are being treated as a result of the screening program. However, scenario treatment costs gradually decrease relative to base-case costs, because fewer complications are developing among the newly diagnosed. By 2008, scenario treatment costs were estimated to fall below base-case costs. Intervention costs were highest in 2000 the year in which screening occurred (Exhibit 4). In following years, intervention costs comprise only the cost of the lifestyle program, which remains constant in nominal terms and declines only due to discounting. Total incremental costs intervention costs plus the base-toscenario treatment cost difference decline considerably over the period. The break-even point, at which the saving in health care costs accruing from earlier detection and treatment approximately equals the cost of the intervention, occurs in 2009, with cost savings relative to base starting in Considering the incremental cost associated with the intervention (AU$801 million plus AU$989 million), the cost per DALY saved was estimated at AU$49,713 (Exhibit 3). This is close to the threshold of what is considered cost-effective in Australia and in the United Kingdom. 21 Under the default scenario, comparison of the cost-per-daly estimates for the two age groups suggests a somewhat higher priority for screening those ages 55 74, since their avoided cost per year lived with disability is lower (AU$48,386) than that of those ages (AU$53,995). Sensitivity analyses. Nine sensitivity analyses were carried out and compared with the default scenario (1): 70 percent take-up of the lifestyle program (2); reducing the benefits arising from lower complication rates by half (3); reducing the EXHIBIT 4 Diabetes Intervention And Treatment Cost Streams Among Australians Ages 45 74, Millions of Australian dollars Intervention cost 400 Intervention cost plus treatment cost difference SOURCE: Diabetes model simulations. HEALTH AFFAIRS ~ Volume 27, Number 1 263

9 IFG/IGT-related diabetes avoidance benefit to three-quarters (4); increasing the cost of the lifestyle program to AU$1,000 per person per year (5); increasing the base-case proportion with undiagnosed diabetes by 50 percent (6); increasing the proportion of the population screened to 50 percent for those ages and 75 percent for those ages (7); combining the 100 percent lifestyle program uptake scenario with the double program cost scenario (the default program cost would double from AU$500 to AU$1,000) (8); increasing the prevalence trend rate to 8 percent per year (9); and increasing the discount rate to 6 percent per year (10). In each sensitivity test, one variable was changed relative to the default (except for scenario 8). Exhibit 5 presents summary results for the default scenario (1) and sensitivity scenarios 2 8. For scenarios 9 and 10, the cost-per-daly estimates were close to those of scenarios 2 and 1, respectively. A higher trend rate (scenario 9) resulted in a slightly greater reduction in DALYs and lower cost per DALY than in the default, and a higher discount rate (scenario 10) led to lower cost per benefit summed over ten years. Among the options beyond the control of policymakers, scenario 6 led to the greatest decline in cost per DALY (AU$42,137), and scenario 3, to the greatest increase in cost per DALY (AU$75,949). Among the options achievable by changing the default scenario settings, the most desirable outcome was estimated for scenario 7, with cost per DALY reduced to AU$43,967. This was achieved despite higher ten-year discounted total costs (AU$2.917 million) due to the cost of the additional screening. The least desirable outcome was estimated for scenario 5, with cost per DALY increasing to AU$70,476 and total ten-year cost to AU$2.538 million. If the increase in lifestyle program uptake from 50 percent to 100 percent occurred as a result of additional resources (an extra AU$500 per person per year) to get all people with IFG/IGT to accept the lifestyle program offer (scenario 8), then the estimated saving in DALYs would have more than doubled (from 36,009 to 76,209), and cost per DALY declined to AU$44,699. This would have been achieved at a higher total ten-year incremental cost (AU$3.406 million). Discussion The development of chronic disease models, including diabetes, has been on the increase worldwide, partly because of growing awareness of their potential usefulness in priority setting, and partly because the alternatives to modeling such as large-scale, long-term clinical trials are more costly and time-consuming. 22 Strengths and weaknesses. Like all models, ours has strengths and weaknesses. Strengths are that it is based on robust full population data and on recent Australian data on prevalence of diabetes and IGT/IFG and cost. 23 Also, the outcomesofthesimulationsarebasedonrandomizedcontrolledtrialsofdiabetesprevention programs in countries with health patterns similar to Australia s. Weaknessesincludethelackofdefinitivedataonsomeaspectsofthemodel; 264 January/February 2008

10 Diabetes Prevention EXHIBIT 5 Ten-Year Summary Results For Default And Sensitivity-Test Scenarios (2 8) Age group (years) Default scenario (1) 8,577 $ ,955 27,432 $ 710 1,327 48,386 36,009 $ 989 1,790 49,713 70% uptake of lifestyle program (2) 11,181 $ ,369 29,896 $ 926 1,402 46,886 41,077 $ 1,288 1,898 46,201 Complication benefits reduced to half (3) Cost per DALY gained over 10 years (AU$) 4,321 $ ,499 22,066 $ 710 1,501 68,008 26,388 $ 989 2,004 75,949 IFG/IGT benefits reduced to three-quarters (4) 8,009 $ ,929 26,531 $ 710 1,389 52,364 34,540 $ 989 1,877 54,350 Llifestyle program cost doubled (5) 8,577 $ ,111 27,432 $ 1,250 1,868 68,088 36,009 $ 1,736 2,538 70,476 Base-case percent with undiagnosed diabetes increased by 50% (6) Increasing percent screened (7) a 15,800 $ ,974 39,715 $ 693 1,739 43,793 55,516 $ 967 2,339 42,137 25,173 $ ,767 41,149 $ 1,064 1,991 48,386 66,322 $ 1,623 2,917 43, % lifestyle program uptake with double program costs (8) 30,562 $ ,630 45,647 $ 2,331 2,501 54,789 76,209 $ 3,232 3,406 44,699 SOURCE: Diabetes model simulations. NOTE: DALY is disability-adjusted life-year. a To 50 percent of those ages and 75 percent of those ages the group-based nature of the model, which does not track disease progression at the individual level; the difficulties surrounding quantification of health outcomes; and the uncertainties in predictions of future events, which might not match the model s assumption that past trends will continue. One data limitation is that trial data are available for Finland and the United States, but not for Australia. Also, trials concern only one scenario, while we ana- HEALTH AFFAIRS ~ Volume 27, Number 1 265

11 lyzed ten scenarios. As a result, the trial outcomes in the simulations only provide approximations to the net effects. For example, the Finnish follow-up trial s diabetes risk reduction estimate of 43 percent in scenario 4 might have been too high, because our time frame was ten years compared with the seven years of the followup, or too low, because in scenario 4 the lifestyle program was continued throughout the ten-year period, compared with four out of seven years in the Finnish follow-up study. Although the net effect of these opposing influences is unclear, 43 percent (or more) might be appropriate for the scenario 4 simulation. An important finding is that our cost-per-daly estimates for the default scenario and for scenario 4 are similar (around $50,000 per DALY). Weaknesses also arise from limitations of the diabetes model, such as underestimation resulting from the use of national averages for computing mortality outcomes and for the populationwide trend factor for increases in the prevalence of diabetes; the relative simplicity of the sensitivity tests that the model allows; and the extent to which the overall results can be disaggregated by demographic, socioeconomic, and health-related variables. Most of these weaknesses will be attenuated in the complex chronic disease model system (which includes diabetes) that is being developed. 24 Benefits not in the model. Some major benefits are not accounted for in the model. First, the 1.7 million Australians ages who were screened but found not to have diabetes or IGT/IFG (the other at risk group in Exhibit 3) are well placed to receive information during screening on lifestyle changes to reduce their chances of developing chronic diseases (such as diabetes, heart disease, cancer, and arthritis). Thus, although hard to quantify, the potentially considerable additional health benefits arising from such people attending screening should also be considered during the decision-making process. Second, we could not include the non-diabetes-related health benefits expected from participation in the lifestyle program. These benefits arise because lifestyle-related risk factors are common to most chronic diseases. Including such benefits in the model would have shown reductions in the incidence of other chronic illnesses such as heart disease (without diabetes), cancer, and arthritis. Because we excluded these factors, the scenario-related benefits are almost certainly underestimated. Once completed, the chronic disease model system will allow simultaneous cost-benefit analyses of multiple chronic diseases in Australia. Another consideration is the study s relatively short ten-year time frame, since the benefits of the scenario would clearly extend well beyond one decade. A longer study period is likely to indicate greater end-period cost savings, since the savings in total costs (the intervention plus treatment cost difference relative to base) observed in 2010 would have increased in following years. However, some of this increase would be offset by costs of repeating the screening and lifestyle program. Strengths of modeling approaches. The major general strengths of modeling approaches, including this one, are the coherent framework that modeling imposes 266 January/February 2008

12 Diabetes Prevention on the study of intervention options; the highlighting of data gaps, which may influence future data collections; and the ability to quickly investigate and rank a broad range of intervention options under several possible future worlds (for example, more or less rapid increases in the overweight/obese population). The sensitivity tests reported here are only simple examples of questions that the Australian model could address. Examining the effects of changing some broad background variables (for example, using the upper or lower population projections by the ABS); analyzing a set of quite different interventions; and decomposing the relative cost-benefit contributions of the newly diagnosed and of people with IFG/IGT are examples that could be studied in the future. Although considerable judgment needs to be exercised when one is selecting the most appropriate scenario for implementation, our paper demonstrates the usefulness of models in informing and influencing policy choices. If implemented, the scenario we modeled would result in considerable health improvements in a relatively short period. In terms of value for money (indicated by the cost-per-daly measure), it would be similar to Australian health interventions that have already been implemented. This is despite the fact that the population-level benefits are likely to be considerable underestimates. In a real-life situation, outcomes should be considered over a time frame longer than a decade, in a program that includes repeated screening. In such a situation, more favorable cost-benefit estimates are expected, because of the improved cost savings and greater health benefits over the longer term. The dilemma for governments and policymakers is that realizing the longer-term benefits would require sizable initial financial resources and ongoing investment. A prototype of the Diabetes Cost-Benefit Model was jointly developed by the Diabetes Centre, Prince of Wales Hospital, Sydney; and the National Centre for Social and Economic Modelling, University of Canberra. In 1999 it was finalized and validated, thanks to funding by the then Commonwealth Department of Health and Aged Care. Guidance under a Steering Committee of experts set up by that department was greatly appreciated. In 2000, members of the Steering Committee unanimously endorsed the model. Since then, the model has been updated with new data and further developed by the authors. This application of the model was funded in part by Australian Research Council Grant no. DP NOTES 1. S. Wild et al., Global Prevalence of Diabetes: Estimates for the Year 2000 and Projections for 2030, Diabetes Care 27, no. 5 (2004): International Diabetes Federation, Diabetes Facts and Figures, node=6 (accessed 9 October 2007). 3. Australian Institute of Health and Welfare, Australia s Health 2006 (Canberra: AIHW, 2006). 4. S. Colagiuri, R. Colagiuri, and J. Ward, TheNationalDiabetesStrategyandImplementationPlan (Canberra: Diabetes Australia, 1998). 5. S. Colagiuri et al., Evidence Based Guideline for Case Detection and Diagnosis of Type 2 Diabetes, December 2001, (accessed 8 November 2007). HEALTH AFFAIRS ~ Volume 27, Number 1 267

13 6. D.W. Dunstan et al., The Rising Prevalence of Diabetes and Impaired Glucose Tolerance: The Australian Diabetes, Obesity, and Lifestyle Study, Diabetes Care 25, no. 5 (2002): ; and the related AusDiab database. 7. S. Begg et al., The Burden of Disease and Injury in Australia 2003, AIHW Cat. no. PHE 82 (Canberra: AIHW, 2007); C. Murray and A. Lopez, eds., The Global Burden of Disease (Boston: Harvard University Press, 1996); andc. Mathers, T. Vos, andc. Stevenson, TheBurdenofDiseaseandInjuryinAustralia (Canberra: AIHW, 1999). 8. R. O Connor, Measuring Quality of Life in Health (London: Churchill Livingstone, Elsevier, 2004). 9. A. Walker, Multiple Chronic Diseases and Quality of Life: Patterns Emerging from a Large National Sample, Australia, Chronic Illness (forthcoming); and A. Walker and S. Colagiuri, Cost-Benefit Model Systems of Chronic Diseases in Australia: To Assess and Rank Prevention and Treatment Options (Paper presented at International Microsimulation Association conference, Vienna, Austria, August 2007). 10. A. Walker, S. Colagiuri, and M. McLennan, Cost-Benefit Model of Diabetes Prevention and Care: Model Construction, Assumptions, and Validation, Technical Paper no. 28, natsem/index.php?mode=download&file_id=569 (accessed 9 October 2007). 11. Australian Bureau of Statistics, Projections of the Populations of Australia, States, and Territories: , Cat. no (Canberra: Australian Government Publishing Service, 1996). For the diabetes model, the bureau s No overseas migration scenario was chosen, which simplified the tracking of cohorts. 12. See Walker and Colagiuri, Cost-Benefit Model Systems. 13. ABS, National Health Survey 2001, Summary of Results, Cat.no (Canberra: ABS, 2002); andabs, National Health Survey , Summary of Results, Cat.no (Canberra:ABS,2006). 14. D. McCarty et al., The Rise and Rise of Diabetes in Australia (Canberra: Diabetes Australia, 1996). 15. J. Tuomilehto et al., Prevention of Type 2 Diabetes Mellitus by Changes in Lifestyle among Subjects with Impaired Glucose Tolerance, New England Journal of Medicine 344, no. 18 (2001): ; and W.C. Knowler et al., Reduction in the Incidence of Type 2 Diabetes with Lifestyle Intervention or Metformin, New England Journal of Medicine 346, no. 6 (2002): J. Lindström et al., Sustained Reduction in the Incidence of Type 2 Diabetes by Lifestyle Intervention: Follow-up of the Finnish Diabetes Prevention Study, Lancet 368, no (2006): S. Colagiuri et al., Screening for Type 2 Diabetes and Impaired Glucose Metabolism: The Australian Experience, Diabetes Care 27, no. 2 (2004): Colagiuri et al., Evidence Based Guidelines. 19. S. Colagiuri et al., Are Lower Fasting Plasma Glucose Levels at Diagnosis of Type 2 Diabetes Associated with Improved Outcomes? U.K. Prospective Diabetes Study 61, Diabetes Care 25, no. 8 (2002): Walker et al., Cost-Benefit Model of Diabetes. 21. L. Brown et al., Funding of High Cost Biotechnology and Other Innovative Targeted Therapies under the Pharmaceutical Benefits Scheme, NATSEM Position Paper for Collaborative Forum, Sydney, Australia, 7 March 2002; A. Kennedy et al., The Effectiveness and Cost Effectiveness of a National Lay-Led Self Care Support Programme for Patients with Long-Term Conditions: A Pragmatic Randomised Controlled Trial, Journal of Epidemiology and Community Health 61, no. 3 (2007): ; and C. Griffiths et al., How Effective Are Expert Patient (Lay Led) Education Programmes for Chronic Disease? British Medical Journal 334, no (2007): R. Eastman et al., Model of Complications of NIDDM, I. Model Construction and Assumptions, Diabetes Care 20, no. 5 (1997): ; R. Eastman et al., Model of Complications of NIDDM, II. Analysis of the Health Benefits and Cost-Effectiveness of Treating NIDDM with the Goal of Normoglycemia, Diabetes Care 20, no. 5 (1997): ; and P. Clarke et al., A Model to Estimate the Lifetime Health Outcomes of Patients with Type 2 Diabetes: The United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68), Diabetologia 47, no. 10 (2004): Dunstan et al., The Rising Prevalence of Diabetes ; and S. Colagiuri et al., DiabCo$t Australia: Assessing the Burden of Diabetes in Australia (Canberra: Diabetes Australia, 2004). 24. Walker, Multiple Chronic Diseases. 268 January/February 2008

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