Abstract
Background
The Diabetes Prevention Program (DPP) demonstrated that interventions can delay or prevent the development of type 2 diabetes.
Objective
To estimate the lifetime cost–utility of the DPP interventions.
Design
Markov simulation model to estimate progression of disease, costs, and quality of life.
Data Sources
The DPP and published reports.
Target Population
Members of the DPP cohort 25 years of age or older with impaired glucose tolerance.
Time Horizon
Lifetime.
Perspectives
Health system and societal.
Interventions
Intensive lifestyle, metformin, and placebo interventions as implemented in the DPP.
Outcome Measures
Cumulative incidence of diabetes, microvascular and neuropathic complications, cardiovascular complications, survival, direct medical and direct nonmedical costs, quality-adjusted life-years (QALYs), and cost per QALY.
Results of Base-Case Analysis
Compared with the placebo intervention, the lifestyle and metformin interventions were estimated to delay the development of type 2 diabetes by 11 and 3 years, respectively, and to reduce the absolute incidence of diabetes by 20% and 8%, respectively. The cumulative incidence of microvascular, neuropathic, and cardiovascular complications were reduced and survival was improved by 0.5 and 0.2 years. Compared with the placebo intervention, the cost per QALY was approximately $1100 for the lifestyle intervention and $31 300 for the metformin intervention. From a societal perspective, the interventions cost approximately $8800 and $29 900 per QALY, respectively. From both perspectives, the lifestyle intervention dominated the metformin intervention.
Results of Sensitivity Analysis
Cost-effectiveness improved when the interventions were implemented as they might be in routine clinical practice. The lifestyle intervention was cost-effective in all age groups. The metformin intervention did not represent good use of resources for persons older than 65 years of age.
Limitations
Simulation results depend on the accuracy of the underlying assumptions, including participant adherence.
Conclusions
Health policy should promote diabetes prevention in high-risk individuals.
During the past half century, the number of persons with diagnosed diabetes in the United States has increased 4- to 6-fold (1). Recent large clinical trials from Asia, Europe, and North America have demonstrated that behavioral and medication interventions can delay or prevent the development of type 2 diabetes in persons with impaired glucose tolerance, which is defined by a plasma glucose level between 7.77 mmol/L (140 mg/dL) and 11.04 mmol/L (199 mg/dL) 2 hours after a 75-g oral glucose load (2–6). The Diabetes Prevention Program (DPP) randomly assigned 3234 nondiabetic persons 25 years of age or older with impaired glucose tolerance and fasting glucose levels between 5.27 mmol/L (95 mg/dL) and 6.94 mmol/L (125 mg/dL) to placebo; a lifestyle-modification program with the goals of at least a 7% weight loss and 150 minutes of physical activity per week; or metformin, 850 mg twice daily (4). The mean age of participants was 51 years, and the mean body mass index was 34.0 kg/m2; 68% were women and 45% were members of minority groups (4). The average follow-up was 2.8 years. Compared with the placebo intervention, the lifestyle intervention reduced the incidence of type 2 diabetes by 58% and the metformin intervention reduced the incidence of type 2 diabetes by 31% (4). We have previously described the costs of the DPP interventions and their cost-effectiveness within the 3-year trial period (7, 8). In this analysis, we project the costs, health outcomes, and cost-effectiveness of the DPP lifestyle and metformin interventions over a lifetime relative to the placebo intervention.
Context
The Diabetes Prevention Program (DPP) showed that lifestyle changes or metformin effectively decreased the development of type 2 diabetes in adults with impaired glucose tolerance. The economics of these interventions is important to policymakers.
Contribution
This cost-effectiveness model estimates that the DPP lifestyle intervention would cost society about $8800 and metformin would cost about $29 900 per quality-adjusted life-year saved. While lifestyle intervention had a favorable cost-effectiveness profile at any adult age, metformin was not cost-effective after age 65 years.
Implications
The cost-effectiveness of lifestyle intervention to prevent type 2 diabetes in high-risk individuals is within the range that American society typically finds acceptable for health care interventions.
-The Editors
METHODS
Clinical Trial
The lifestyle intervention involved a healthy, low-calorie, low-fat diet and moderate physical activity, such as brisk walking. The lifestyle intervention was implemented with a 16-lesson core curriculum covering diet, exercise, and behavior modification that was taught by case managers on a one-on-one basis, followed by individual sessions (usually monthly) and group sessions with case managers (9). At the end of the study, 38% of participants in the lifestyle intervention group had lost at least 7% of their initial body weight. The metformin and placebo interventions were initiated at a dosage of 850 mg once a day. At 1 month, the dosage of metformin or placebo was increased to 850 mg twice daily. Case managers reinforced adherence during individual quarterly sessions (10). At the end of the study, 72% of participants in the metformin intervention group and 77% of participants in the placebo intervention group took at least 80% of the prescribed dose. All participants received standard lifestyle recommendations through written information and an annual 20- to 30-minute individual session that emphasized the importance of a healthy lifestyle (10).
Simulation Model
We assessed the progression from impaired glucose tolerance to onset of diabetes to clinically diagnosed diabetes to diabetes with complications and death by using a lifetime simulation model originally developed by the Centers for Disease Control and Prevention and Research Triangle Institute International. The model has a Markov structure and includes annual transition probabilities between disease states (11). In addition to disease progression, the model tracks costs and quality-adjusted life-years (QALYs). The model has been described elsewhere (11). For our analyses, we modified the model to include data from the DPP on progression, costs, and quality of life associated with impaired glucose tolerance, data from the United Kingdom Prospective Diabetes Study (UKPDS) on diabetes progression and complications, and new data on cost and quality of life associated with diabetes. A technical report (available at www.annals.org) describing the model is available.
Disease Progression, Complications, and Comorbid Conditions
Impaired Glucose Tolerance to Onset of Type 2 Diabetes
We analyzed data from the DPP to assess the annual hazard of diabetes onset in the lifestyle, metformin, and placebo intervention groups. For patients receiving the placebo intervention, the annual hazard of diabetes onset was 10.8 per 100 person-years. At 3 years of follow-up, the risk reductions for the lifestyle and metformin interventions were 55.8% and 29.9%, respectively. In the base-case analysis, we assumed that the lifestyle and metformin interventions would be applied until diabetes onset and that the health and quality-of-life benefits associated with the interventions persisted until diabetes onset.
Complications and Comorbid Conditions Associated with Impaired Glucose Tolerance
We analyzed data from the DPP and other published sources to assess the prevalence of complications and co-morbid conditions in participants with impaired glucose tolerance. At baseline, 6.0% of DPP participants had microalbuminuria and 0.4% had nephropathy. The DPP did not measure peripheral neuropathy, but previous studies found that the prevalence of neuropathy in persons with impaired glucose tolerance was 74% of that in persons with newly diagnosed type 2 diabetes (12) and 12.3% of persons with newly diagnosed type 2 diabetes have neuropathy (13). Therefore, we assumed that at baseline, 8.5% of DPP participants had clinical neuropathy. At baseline, 28% of DPP participants had hypertension, 45% had dyslipidemia, 7% were smokers, 1.1% had a history of cerebrovascular disease, and 2.0% had a history of myocardial infarction. No other complications were present.
We assumed that during impaired glucose tolerance, microvascular or neuropathic complications would not progress. We assumed that hypertension and dyslipidemia developed at the rates observed in the DPP. On the basis of 2 large studies (14, 15), we assumed that the incidences of coronary heart disease and cerebrovascular disease in patients with impaired glucose tolerance were 58% and 56%, respectively, of those observed in patients with type 2 diabetes. We further assumed that non—diabetes-related mortality for persons with impaired glucose tolerance was the same as for persons with diabetes (16).
Onset of Type 2 Diabetes to Clinical Diagnosis of Type 2 Diabetes
In the DPP, participants were tested for diabetes every 6 months; diabetes was diagnosed at onset. In routine clinical practice, type 2 diabetes is estimated to develop 8 to 12 years before its clinical diagnosis (17, 18). In our base-case analysis, we therefore assumed that a 10-year delay occurred between the onset and clinical diagnosis of diabetes.
Participants in the DPP had a mean hemoglobin A1c level of 6.4% at the onset of diabetes. Participants in the UKPDS had a mean hemoglobin A1c of 7.1% after a dietary run-in period but before randomization (13). Both DPP placebo participants and UKPDS participants received standard lifestyle recommendations. Accordingly, we assumed that during the 10-year interval between onset and clinical diagnosis of diabetes, patients were treated for type 2 diabetes and that hemoglobin A1c level increased at 0.07% per year from 6.4% to 7.1%.
Complications and Comorbid Conditions Associated with Undiagnosed Diabetes
We further assumed that between onset and clinical diagnosis of diabetes, microvascular and neuropathic complications progressed slowly, such that by clinical diagnosis of type 2 diabetes, their prevalence reached the level observed in the UKPDS cohort at randomization (13, 19, 20). We assumed that blood pressure and lipid levels progressed as they did in DPP participants and that cardiovascular complications occurred as they would in type 2 diabetes according to risk factors and hemglobin A1c level (21, 22).
Clinical Diagnosis of Type 2 Diabetes to Diabetes with Complications and Death
We assumed that after clinical diagnosis, all persons with type 2 diabetes received intensive glycemic management as described in the UKPDS (13). We modeled changes in hemoglobin A1c and diabetes treatments to reflect those observed in the UKPDS intensive therapy group. We based risk for retinopathy progression on UKPDS 38 (23), risk for nephropathy progression on UKPDS 64 (20), and risk for neuropathy progression on UKPDS 33 (13). We based risk for cerebrovascular disease on UKPDS 60 (22) and risk for coronary heart disease on UKPDS 56 (21).
Costs
Costs of Impaired Glucose Tolerance
To estimate the total direct medical costs of impaired glucose tolerance, we considered the costs of the DPP interventions (the cost of identifying participants, implementing and maintaining the interventions, and monitoring and treating the side effects of the interventions) and the costs of the medical care outside the DPP (7). In analyses from the perspective of society, we included both direct medical costs and direct nonmedical costs. We did not include indirect costs because they are captured in the assessment of QALYs (24).
Table 1 shows the total direct medical costs by treatment group, sex, and year in the DPP (7). Costs were higher in the lifestyle and metformin interventions than in the placebo intervention and higher in women than in men. Costs decreased over time in all 3 intervention groups but after year 1 tended to decrease more in the lifestyle than the metformin intervention group.
Table 1.
Total Direct Medical Costs and Health Utility Scores of Impaired Glucose Tolerance *
| Variable | Lifestyle Intervention |
Metformin Intervention |
Placebo Intervention |
|||
|---|---|---|---|---|---|---|
| Men | Women | Men | Women | Men | Women | |
| Costs per treatment year, $ | ||||||
| First year | 2600 | 2800 | 2400 | 2500 | 1400 | 1600 |
| Second year | 1900 | 2100 | 2200 | 2200 | 1300 | 1600 |
| Third and subsequent years | 1900 | 2100 | 2100 | 2200 | 1300 | 1600 |
| Health utility score | 0.72 | 0.68 | 0.70 | 0.66 | 0.70 | 0.66 |
Costs include direct medical costs of the Diabetes Prevention Program interventions and direct medical costs of care outside the Diabetes Prevention Program.
To estimate the future costs of impaired glucose tolerance, we constructed a multiplicative cost model with the same structure as that for diabetes. We used the cost of the interventions from the DPP. We used the costs of medical care outside the DPP as baseline and applied the multipliers from the model to account for the incremental costs associated with incident hypertension and cardiovascular disease.
Costs of Type 2 Diabetes
To estimate the costs of type 2 diabetes, we applied a multiplicative prediction model that estimates annual direct medical costs according to demographic characteristics, diabetes treatment, cardiovascular risk factors, and microvascular and macrovascular complications (25). Table 2 shows the annual direct medical costs of type 2 diabetes. The baseline cost of $1684 is the annual direct medical cost for a nonobese white man with type 2 diabetes who is treated with diet and exercise and has no cardiovascular risk factors or microvascular, neuropathic, or cardiovascular complications (25). If a participant has any characteristic or complication listed in Table 2, the annual direct medical cost is then estimated as the product of the baseline cost and the multipliers corresponding to each participant’s characteristics or complications.
Table 2.
Annual Direct Medical Costs and Health Utility Scores of Type 2 Diabetes*
| Variable | Multiplier (Baseline Cost, $1684†) |
Penalty Score (Baseline Health Utility Score, 0.689§) |
|---|---|---|
| Women | 1.25 | -0.038 |
| Age | § | § |
| African-American ethnicity | 0.82 | § |
| Duration | ||
| Every year after onset | § | § |
| Body mass index | ||
| Every unit over 30 kg/m2 | 1.01 | § |
| Obese | § | -0.021 |
| Diabetes intervention | ||
| Oral antidiabetic agents | 1.10 | -0.023 |
| Insulin | 1.59 | -0.034 |
| High blood pressure | 1.24 | -0.011 |
| Retinopathy | ||
| Nonproliferative retinopathy | § | § |
| Proliferative retinopathy | § | § |
| Macular edema | § | § |
| Blindness in 1 eye | § | -0.043 |
| Blindness in 2 eyes | § | -0.170 |
| Nephropathy | ||
| Microalbuminuria | 1.17 | § |
| Proteinuria | 1.30 | -0.011 |
| Renal failure with dialysis | 10.53 | -0.078 |
| Neuropathy | ||
| Clinical neuropathy | § | -0.065 |
| History of amputation | § | -0.105 |
| Tingling and burning | § | -0.060 |
| Sores | § | -0.099 |
| Cardiovascular disease | ||
| Angina | 1.73 | § |
| History of myocardial infarction | 1.90 | § |
| Congestive heart failure | § | -0.052 |
| Stroke | ||
| Transient ischemic attack or stroke | § | -0.044 |
| Stroke with residual | 1.30 | -0.072 |
Annual direct medical cost is the baseline cost ($1684) multiplied by the multipliers for the combination of characteristics, treatments, and complications. For each variable, only the multiplier associated with most severe level should be used. Health utility score is the baseline health utility score minus the penalty scores for the combination of characteristics, treatments, and complications. For each variable, only the penalty score associated with the most severe level should be used. Adapted from references 25 and 26 with permission from the American Diabetes Association.
The baseline cost represents the median annual direct medical cost for a dietcontrolled white man with type 2 diabetes, body mass index of 30 kg/m2, and without microvascular, neuropathic, or cardiovascular risk factors or complications.
The baseline health utility represents the mean health utility score for a dietcontrolled white man with type 2 diabetes, body mass index of 30 kg/m2, and without microvascular, neuropathic, or cardiovascular risk factors or complications.
Variables did not enter into the model.
Combining the data from Tables 1 and 2, the approximate annual direct medical costs for a man progressing from impaired glucose tolerance to diabetes with complications would be as follows: impaired glucose tolerance treated with placebo, $1400; diabetes treated with diet and exercise, $1684; diabetes treated with an oral agent, $1900; diabetes treated with an oral agent and complicated by microalbuminuria, $2200; and diabetes treated with an oral agent and complicated by microalbuminuria and high blood pressure, $2700.
Health Utilities
Health utility scores are a measure of health-related quality of life, in which optimal health is assigned a value of 1.0 and the worst health, judged equivalent to death, is assigned a value of 0.0. In economic analysis, the quality adjustment weight for each health state is multiplied by the time in the state and then summed to calculate the number of QALYs (24).
Health Utilities Associated with Impaired Glucose Tolerance
We assessed the health utility scores associated with impaired glucose tolerance by using the self-administered Quality of Well-Being Index, a widely used multiattribute utility model (8). The instrument was administered to DPP participants annually. Table 1 shows the health utility scores of DPP participants with impaired glucose tolerance by treatment group and sex. In general, health utility scores were higher in the lifestyle intervention group than in the metformin or placebo intervention groups and higher in men than in women.
To estimate health utility scores associated with impaired glucose tolerance beyond the 3-year timeframe of the DPP, we constructed an additive health utility model with the same structure as that for diabetes. We used the health utility scores at 3 years as baseline and applied the penalty scores from the model to account for the decreased quality of life associated with hypertension and cardiovascular disease.
Health Utilities Associated with Type 2 Diabetes
To estimate health utility scores associated with type 2 diabetes, we applied an additive prediction model to estimate health utility scores according to demographic, treatment, and disease state variables (26). Table 2 shows the health utility scores in type 2 diabetes. The baseline health utility score of 0.69 is the health utility score for a nonobese man with type 2 diabetes who is treated with diet and exercise and who has no cardiovascular risk factors or microvascular, neuropathic, or cardiovascular complications. The penalty scores represent the decrement in the health utility score associated with treatments, cardiovascular risk factors, and complications.
Combining the data from Tables 1 and 2, the health utility scores for a man progressing from impaired glucose tolerance to diabetes with complications can be described as follows: impaired glucose tolerance treated with placebo, 0.70; diabetes treated with diet and exercise, 0.69; diabetes treated with diet and exercise and complicated by neuropathy, 0.62; and diabetes treated with diet and exercise and complicated by neuropathy, high blood pressure, and stroke, 0.54.
Base-Case Analysis
In each analysis, we used the simulation model to assess disease progression, costs, and QALYs for the entire DPP cohort by age, sex, and race or ethnicity. In the base-case analysis, we modeled the interventions as they were implemented in the DPP and projected year 3 DPP intervention costs, health utility scores, and intervention effectiveness into the future. We assessed the simulated cumulative incidence of diabetes, microvascular and macrovascular complications, and life expectancy. We then assessed simulated lifetime costs and QALYs and the incremental costs and QALYs of the lifestyle and metformin interventions relative to the placebo intervention. We calculated cost-effectiveness ratios by dividing incremental costs by incremental QALYs. In these analyses, we adopted a health system perspective that considered only direct medical costs and discounted both costs and QALYs at 3% per year. Clinical outcomes were not discounted. We expressed costs in U.S. dollars (year 2000).
Sensitivity Analyses
In sensitivity analyses, we first modeled the interventions by age group. We then modeled the interventions as they might be implemented in routine clinical practice and assessed the effect of reducing the costs by modifiying the lifestyle and metformin interventions. Specifically, we recalculated the cost of the lifestyle intervention, assuming that the core curriculum, supervised activity sessions, and lifestyle group sessions were administered as a closed group of 10 participants and that costs were reduced accordingly (8). Studies have shown that group intervention programs are at least as effective as individual programs (27, 28). Similarly, we recalculated the cost of the metformin intervention by using generic metformin priced at 25% the cost of Glucophage (Bristol-Myers Squibb, Princeton, New Jersey). Third, we evaluated the effect of future participant adherence by reducing the effectiveness of the lifestyle and metformin interventions by 20% and 50%, respectively, after year 3. Fourth, we evaluated the effect of both reduced costs (as would be observed with a group lifestyle intervention and a metformin intervention using generic metformin) with reduced effectiveness (a 20% and 50% reduction of the effectiveness of both the lifestyle and metformin interventions, respectively) on lifetime cost-effectiveness. Fifth, we evaluated the effect of lower and higher discount rates. Sixth, to assess cost—utility from a societal perspective, we included the direct nonmedical costs of the interventions (costs of participant time, exercise classes, exercise equipment, food and food preparation items, and transportation) (24). We performed additional sensitivity analyses to assess the effect of variation in the hazard of diabetes and in the delay from onset to diagnosis of diabetes.
We also conducted a probabilistic sensitivity analysis for 50-year-old participants, for which 81 model parameters were simultaneously varied over probability distributions based on published 95% CIs or normal or logistic normal distributions (Appendix and Appendix Tables 1 to 7, available at www.annals.org) (29). We also applied uniform and triangular distributions where appropriate. We generated parameter values from the distributions by using @Risk software (Palisade Corp., Newfield, New York). We computed the cost-effectiveness of the lifestyle and met formin interventions for each of 500 iterations and examined the distribution of cost-effectiveness ratios across iterations.
Role of the Funding Sources
This study was supported by the Diabetes Prevention Program, National Institutes of Health, through the National Institute of Diabetes and Digestive and Kidney Diseases, Office of Research on Minority Health, National Institute of Child Health and Human Development, and National Institute on Aging; Centers for Disease Control and Prevention; Indian Health Service; General Clinical Research Program; National Center for Research Resources; American Diabetes Association; Bristol-Myers Squibb; and Parke-Davis. Corporate sponsors had no role in the design, conduct, or reporting of this analysis or in the decision to submit this manuscript for publication. The Centers for Disease Control and Prevention was involved in the design, conduct, and reporting of the analysis.
RESULTS
Base-Case Analysis
In the base-case analysis, we asked: What are the simulated lifetime costs and health consequences of identifying persons with impaired glucose tolerance and intervening with a lifestyle, metformin, or placebo intervention until they develop type 2 diabetes and intervening with a program of intensive glycemic management after clinical diagnosis?
The Figure illustrates the simulated lifetime cumulative incidence of type 2 diabetes by DPP intervention. If the entire DPP cohort were treated with the placebo intervention, approximately 50% of individuals would develop diabetes within 7 years. In contrast, it would take approximately 18 years for 50% of lifestyle-treated participants to develop diabetes and 10 years for 50% of metformintreated participants to develop diabetes. Thus, compared with the placebo intervention, the lifestyle intervention delays the onset of diabetes by 11 years and metformin delays the onset of diabetes by 3 years. Over a lifetime, 83% of participants treated with the placebo intervention would develop diabetes, as compared with 63% of those treated with the lifestyle intervention and 75% of those treated with the metformin intervention. Thus, compared with the placebo intervention, the lifestyle intervention reduces the absolute risk for developing diabetes by 20% and the metformin intervention reduces the risk for developing diabetes by 8%. The relative risk reductions are 24% and 10%, respectively.
Table 3 summarizes the simulated lifetime cumulative incidence of diabetes and microvascular and macrovascular complications and life expectancy. These results represent averages for the entire DPP cohort. Individual predictions depend on patient characteristics such as age. In general, health outcomes were best for the lifestyle intervention, intermediate for the metformin intervention, and worst for the placebo intervention. We estimate that the lifestyle intervention increases life expectancy by 0.5 year and reduces the cumulative incidence of blindness by 39%, end-stage renal disease by 38%, amputation by 35%, stroke by 9%, and coronary heart disease by 8%. The metformin intervention increases life expectancy by 0.2 year and reduces the cumulative incidence of blindness by 16%, end-stage renal disease by 17%, amputation by 16%, stroke by 3%, and coronary heart disease by 2%.
Table 3.
Lifetime Impaired Glucose Tolerance Intervention: Simulated Clinical Outcomes in the Diabetes Prevention Program Cohort*
| Outcome | Lifestyle Intervention |
Metformin Intervention |
Placebo Intervention |
|---|---|---|---|
| Diabetes, % | 62.6 | 74.9 | 82.8 |
| Life expectancy, y | 24.7 | 24.3 | 24.1 |
| Blindness, % | 3.4 | 4.7 | 5.6 |
| Nephropathy, % | 3.7 | 4.7 | 5.5 |
| Renal failure, % | 0.6 | 0.8 | 1.0 |
| Neuropathy, % | 23.1 | 27.0 | 30.1 |
| Amputation, % | 1.3 | 1.6 | 1.9 |
| Stroke, % | 19.3 | 20.6 | 21.3 |
| Coronary heart disease, % | 38.9 | 41.3 | 42.1 |
Undiscounted.
Table 4 summarizes the simulated economic out-comes. Over a lifetime, the placebo intervention was associated with the lowest direct medical costs and the lifestyle intervention was associated with the most QALYs. Compared with the placebo intervention, the lifestyle intervention costs $635 more over a lifetime and produces a gain of 0.57 QALY. The cost per QALY (Δ cost/Δ QALY) is approximately $1100. Compared with the placebo intervention, the metformin intervention costs $3922 more over a lifetime and results in a gain of 0.13 QALY. Thus, compared with the placebo intervention, the metformin intervention costs approximately $31 300 per QALY. Compared with the metformin intervention, the lifestyle intervention costs $3287 less over a lifetime and results in a gain of 0.45 QALY. Thus, the lifestyle intervention dominates the metformin intervention.
Table 4.
Lifetime Impaired Glucose Tolerance Intervention: Simulated Economic Outcomes in the Diabetes Prevention Program Cohort*
| Outcome | Lifestyle Intervention |
Metformin Intervention |
Placebo Intervention |
|---|---|---|---|
| Lifetime intervention costs, $ | 9718 | 8801 | 2907 |
| Lifetime outcome costs, $ | 42 256 | 46 460 | 48 432 |
| Total lifetime direct medical costs, $ |
51 974 | 55 261 | 51 339 |
| Lifetime QALYs | 10.89 | 10.45 | 10.32 |
| Δ Cost vs. placebo, $ | 635 | 3922 | - |
| Δ QALY vs. placebo | 0.57 | 0.13 | - |
| Δ Cost/Δ QALY, $ | 1124 | 31 286 | - |
Costs and QALYs discounted at 3% per year. QALY = quality-adjusted life-year.
Sensitivity Analyses
We used sensitivity analyses to assess the effect of age and plausible changes in costs and treatment effectiveness on cost-effectiveness (Table 5). Compared with the placebo intervention, the lifestyle intervention was cost-saving in participants younger than 45 years of age and cost-effective in all age groups. In contrast, the metformin intervention was relatively cost-effective in the younger age groups but cost more than $100 000 per QALY in participants 65 years of age or older. The reduced benefit (Δ QALYs) of the metformin intervention in the older age groups may explain the dramatic increase in the incremental cost-effectiveness ratios with age.
Table 5.
Sensitivity Analyses*
| Variable | Lifestyle Intervention vs. Placebo Intervention |
Metformin Intervention vs. Placebo Intervention |
||||
|---|---|---|---|---|---|---|
| Δ Cost, $ | Δ QALY | Δ Cost/Δ QALY, $ | Δ Cost, $ | Δ QALY | Δ Cost/Δ QALY,$ | |
| Base-case analysis | 635 | 0.57 | 1124 | 3922 | 0.13 | 31 286 |
| Age 25-44 y | -395 | 0.63 | Cost-saving | 2574 | 0.27 | 9573 |
| Age 45-54 y | 489 | 0.63 | 781 | 4024 | 0.13 | 30 013 |
| Age 55-64 y | 1807 | 0.53 | 3409 | 4413 | 0.07 | 64 904 |
| Age 65-74 y | 2617 | 0.39 | 6646 | 4119 | 0.02 | 173 593 |
| Age ≥ 75 y | 2508 | 0.21 | 11 700 | 3255 | 0.01 | 273 207 |
| Reduced cost† | -3696 | 0.57 | Cost-saving | 220 | 0.13 | 1755 |
| 20% reduced effectiveness | 1417 | 0.46 | 3102 | 4084 | 0.11 | 38 145 |
| 50% reduced effectiveness | 2371 | 0.30 | 7886 | 4307 | 0.80 | 52 562 |
| Reduced cost† and 20% reduced effectiveness |
-2181 | 0.41 | Cost-saving | 635 | 0.10 | 6576 |
| Reduced cost† and 50% reduced effectiveness |
-348 | 0.23 | Cost-saving | 1198 | 0.06 | 20 994 |
| 0% discount rate | -1526 | 0.99 | Cost-saving | 4041 | 0.24 | 17 110 |
| 5% discount rate | 1382 | 0.42 | 3271 | 3784 | 0.09 | 42 686 |
| Societal perspective | 4967 | 0.57 | 8790 | 3748 | 0.13 | 29 900 |
QALY = quality-adjusted life-year.
Assumes that lifestyle intervention is implemented in a closed group of 10 patients and that metformin intervention is implemented with generic metformin.
If implementing the intervention in a closed group of 10 patients reduced the costs of the lifestyle intervention and the use of generic metformin reduced the cost of the metformin intervention, the lifestyle intervention would be cost-saving relative to the placebo intervention and the metformin intervention would cost approximately $1800 per QALY. If future adherence were less than that observed in the DPP and the effectiveness of the lifestyle and metformin interventions were 20% or even 50% less than that observed in the DPP, the lifestyle intervention would cost $3100 to $7900 per QALY compared with the placebo intervention and the metformin intervention would cost $38 000 to $52 600 per QALY. If both the lifestyle and metformin interventions were implemented at lower costs, reflecting group lifestyle classes and generic metformin pricing but with effectiveness reduced by 20% or 50% relative to that observed in the DPP, the lifestyle intervention would be cost-saving relative to the placebo intervention and the metformin intervention would cost approximately $6600 to $21 000 per QALY. Reducing or increasing the discount rate reduces or increases the cost per QALY. When a societal perspective is adopted and direct nonmedical costs are included, the lifetime costs of the lifestyle intervention increase by more than $4300 and the cost-effectiveness ratio increases to approximately $8800 per QALY. Adopting a societal perspective has little effect on the cost-effectiveness of the metformin intervention. Similarly, using the lower and upper bounds of 95% CIs for the hazard of diabetes and varying the duration of undiagnosed diabetes from 5 to 15 years had little effect on the cost-effectiveness of the lifestyle or metformin interventions (data not shown).
The probabilistic sensitivity analyses revealed that among participants 50 years of age, the median cost of the lifestyle intervention was $4137 per QALY (95% of the cost-effectiveness ratios were between -$587 and $9456 per QALY). The median cost of the metformin intervention was $36 327 per QALY (95% of the cost-effectiveness ratios were between $16 509 and $84 583 per QALY).
DISCUSSION
We previously demonstrated that the DPP lifestyle and metformin interventions were more expensive than placebo intervention (7). Yet, delaying or preventing type 2 diabetes delays or prevents the direct medical costs of diabetes, including the costs of diabetes education and nutritional counseling, glucose monitoring, treatment, surveillance for complications, and treatment of complications. It also improves quality of life and length of life. We recently demonstrated that from the perspective of a health system over 3 years and relative to the placebo intervention, the lifestyle and metformin interventions cost $16 000 and $31 000 per case of diabetes prevented and $32 000 and $100 000 per QALY, respectively (8). Adopting a 3-year time horizon overestimates treatment costs and underestimates the benefits of the lifestyle and metformin interventions (8). In this paper, we aimed to extend the results of our previous analyses and project the costs, health out-comes, and cost-effectiveness of the lifestyle and metformin interventions relative to the placebo intervention over a lifetime.
In these analyses, we used a previously developed and published simulation model (11). We modified this simulation model to include progression from impaired glucose tolerance to diabetes, recognize the 10-year delay between the onset and clinical diagnosis of type 2 diabetes, incorporate new data describing costs and health utility scores, and incorporate the most recently published data from the UKPDS on the development and progression of complications among intensively treated patients with type 2 diabetes.
Compared with the placebo intervention, the lifestyle and metformin interventions produced clinically meaningful reductions in type 2 diabetes and its microvascular, neuropathic, and cardiovascular complications. Our projections presented in the Figure indicate that the lifestyle and metformin interventions can delay and prevent type 2 diabetes by forestalling its onset by several years and reducing its cumulative incidence.
From the perspective of a health system, the lifestyle intervention was highly cost-effective, costing only $1100 per QALY, and the metformin intervention was in a generally cost-effective range, costing approximately $31 300 per QALY. The lifestyle intervention, compared with the metformin intervention, cost less and resulted in better health outcomes. In sensitivity analysis, we observed important differences by age. Whereas the lifestyle intervention was cost-saving in participants younger than 45 years of age and was cost-effective even in the oldest age groups, the metformin intervention was not cost-effective in participants older than 65 years of age. Otherwise, the results were robust to plausible changes in implementation strategy and participant adherence, as manifest by changes in both costs and treatment effectiveness. Under a scenario of both reduced costs and 20% to 50% reduced effectiveness, the lifestyle intervention was cost-saving relative to the placebo intervention and the metformin intervention cost only $6600 to $21 000 per QALY.
Many recent studies have assessed the relative cost-effectiveness of interventions in diabetes. Few interventions in diabetes are cost-saving, that is, the experimental intervention is both more effective and less expensive than the comparator. Cost-saving interventions in diabetes include preconception counseling for women with type 1 diabetes (30), angiotensin-converting enzyme inhibitor and angiotensin-receptor blocker therapy for patients with clinical nephropathy (31, 32), and intensive blood pressure control for patients with hypertension (33). A recent report estimated that intensive glycemic control for patients with newly diagnosed type 2 diabetes costs approximately $41 000 per QALY over a lifetime (11) and statin therapy in patients with type 2 diabetes, no coronary disease, and total cholesterol levels greater than 5.18 mmol/L (>200 mg/dL) costs approximately $52 000 per QALY (11).
Cost-saving interventions present no difficulty with respect to policy implications. They should be rapidly and widely implemented since they are more effective and less expensive than existing therapies. However, most new treatments in diabetes are more effective and costly, requiring incremental resources per QALY. There is no universally accepted rule to evaluate such treatments (34). Laupacis and colleagues (35) have proposed a system to rate interventions on the basis of the likely magnitude of the net benefit associated with their application (cost per QALY). They argue that interventions that cost less than $20 000 per QALY are an appropriate way to use resources and those that cost $20 000 to $100 000 per QALY are probably appropriate, but those that cost greater than $100 000 per QALY may not be a good use of resources.
We based our model on the DPP. No simulation model can perfectly represent reality, and all models have inherent limitations (36). Participants in the DPP, while broadly representative of the population with impaired glucose tolerance, were volunteers and may have been more highly motivated than nonparticipants. The DPP could not study all clinical relevant interventions or measure disease progression and intervention effects over a lifetime. When long-term information is not available, models may be used to integrate evidence from clinical trials to make inferences about future economic, quality of life, and health outcomes and to provide data for decision making. The predictions provided by a model depend on the clinical trial itself and the assumptions made in the simulation. In our base-case analyses, we assumed that the DPP interventions were applied as they were in the DPP for as long as an individual remained nondiabetic. We further assumed that the costs were the same as those in the DPP, the interventions would remain as effective as they were in the DPP, and participants developing diabetes would receive intensive therapy. Higher costs or lower effectiveness of interventions for diabetes prevention would tend to reduce the cost-effectiveness of diabetes prevention.
In summary, compared with the placebo intervention, the DPP lifestyle and metformin interventions provided substantial health benefits at an attractive cost. The lifestyle intervention, compared with the metformin intervention, provided greater health benefits at lower costs and, from the perspective of a fiscally prudent policymaker, represents the intervention of choice. Investment in DPP lifestyle and metformin interventions in high-risk individuals with impaired glucose tolerance may help stem the current epidemic of diabetes.
Figure.
Simulated cumulative incidence of diabetes among adults with impaired glucose tolerance by the Diabetes Prevention Program treatment group.
Acknowledgments
Grant Support: By the Diabetes Prevention Program, National Institutes of Health through the National Institute of Diabetes and Digestive and Kidney Diseases, Office of Research on Minority Health, National Institute of Child Health and Human Development, and National Institute on Aging; Centers for Disease Control and Prevention; Indian Health Service; General Clinical Research Program; National Center for Research Resources; American Diabetes Association; Bristol-Myers Squibb; and Parke-Davis.
Appendix: Parameter Values for Base-Case Analysis and Distributions Applied in Probabilistic Sensitivity Analysis
Appendix Tables 1 to 7 include the parameter values used in the diabetes screening and disease progression model. We applied values in the “Base-Case Analysis” columns in all model runs unless otherwise specified (in 1-way and probabilistic sensitivity analyses). The “Probabilistic Sensitivity Analysis Distribution” columns list the distributions from which we randomly sampled parameter values in the probabilistic sensitivity analyses. We report CIs for those parameters for which CIs were available or could be calculated without any named distribution. Normal distributions based on those CIs were applied in the analysis. The ranges for parameters without published variability data followed the guidelines in Appendix Table 1. We applied relevant limits to all ranges (for example, quality of life and probabilities must be between 0 and 1).
Appendix Table 1.
Guidelines for Distributions around Variables without Published Variability Data in Probabilistic Sensitivity Analysis*
| Variable Types | Distribution | Decreased Variation, % |
Increased Variation, % |
|---|---|---|---|
| Small costs (<$300) | Triangular | 0.25 | 0.25 |
| Large costs | Logistic normal |
0.15 | NA |
| Probabilities and hazard rates |
Logistic normal |
0.25 | NA |
| Discounts | Triangular | 0.3333333 | 0.6666667 |
NA = not applicable.
Appendix Table 2.
Variables Specific to Prediabetes or the Diabetes Prevention Program*
| Variable | Value |
Base Source (Author, Reference) |
Distribution Notes | |
|---|---|---|---|---|
| Base-Case Analysis |
Probabilistic Sensitivity Analysis Distribution† |
|||
| Prediabetes | ||||
| Annual probability of onset of diabetes | 0.108 | Normal (0.089 to 0.127) | DPP data | - |
| Hazard rates | ||||
| Hypertension | 0.0506 | Normal (0.006 to 0.098) | DPP data | - |
| High cholesterol level | 0.0375 | Normal (0.000 to 0.084) | DPP data | - |
| Normal to microalbuminuria | 0 | Not varied | Assumed | - |
| Normal to peripheral neuropathy | 0 | Not varied | Assumed | - |
| CHD risk factor, UKPDS | 0.58 | Normal (0.54 to 0.62) | Qureshi et al., 14 | Calculated by using logit transforms; reported SEs |
| Stroke risk factor, UKPDS | 0.56 | Normal (0.50 to 0.63) | Qureshi et al., 14 | Calculated by using logit transforms; reported SEs |
| DPP | ||||
| Cost of oral glucose tolerance test | 17.80 | Triangular (13.35 to 22.25, 17.80) | HCFA, 37 | Assumed |
| Diabetes onset risk reduction | ||||
| Metformin | ||||
| Years 1-3 | 29.9 | Normal (16 to 41) | DPP data | Reported CI |
| Years 4-95 | 29.9 | Normal (16 to 41) | Assumed | Assumed |
| Lifestyle | ||||
| Years 1-95 | 55.3 | Normal (45 to 63) | DPP data | Reported CI |
| Years 4-95 | 55.3 | Normal (45 to 63) | Assumed | Assumed |
| Hypertension onset risk reduction | ||||
| Metformin | ||||
| Years 1-3 | 0 | Not varied | DPP data | - |
| Years 4-95 | 0 | Not varied | Assumed | - |
| Lifestyle | ||||
| Years 1-3 | 100 | Not varied | DPP data | - |
| Years 4-95 | 100 | Not varied | Assumed | - |
| High cholesterol onset risk reduction | ||||
| Metformin, years 1-95 | 0 | Not varied | DPP data | - |
| Lifestyle | ||||
| Years 1-3 | 22.6 | Log. normal (22.6, 16.95) | DPP data | Assumed |
| Years 4-95 | 22.6 | Log. normal (22.6, 16.95) | Assumed | Assumed |
| Intervention cost, $ | ||||
| Placebo | ||||
| Year 1 | 43 | Triangular (32 to 43, 54) | Herman et al., 7 | Assumed |
| Years 2-3 | 18 | Triangular (14 to 18, 23) | Herman et al., 7 | Assumed |
| Years 4-95 | 18 | Triangular (14 to 18, 23) | Assumed | Assumed |
| Metformin | ||||
| Year 1 | 1019 | Log. normal (1019, 866) | Herman et al., 7 | Assumed |
| Year 2 | 772 | Log. normal (772, 656) | Herman et al., 7 | Assumed |
| Year 3 | 751 | Log. normal (751, 638) | Herman et al., 7 | Assumed |
| Years 4-95 | 751 | Log. normal (751, 638) | Assumed | Assumed |
| Lifestyle | ||||
| Year 1 | 1399 | Log. normal (1399, 1189) | Herman et al., 7 | Assumed |
| Year 2 | 679 | Log. normal (679, 577) | Herman et al., 7 | Assumed |
| Year 3 | 702 | Log. normal (702, 597) | Herman et al., 7 | Assumed |
| Years 4-95 | 702 | Log. normal (702, 597) | Assumed | Assumed |
| Impact of intervention on medical costs, $ | ||||
| Placebo | ||||
| Women, years 1-3 | 53 | Log. normal (53, 45) | Herman et al., 7 | Assumed |
| Women, years 4-95 | 0 | Not varied | Assumed | - |
| Men, years 1-3 | 23 | Log. normal (23, 20) | Herman et al., 7 | Assumed |
| Men, years 4-95 | 0 | Not varied | Assumed | - |
| Metformin | ||||
| Women, years 1-3 | -20 | Log. normal (-20, -17) | Herman et al., 7 | Assumed |
| Women, years 4-95 | 0 | Not varied | Assumed | - |
| Men, years 1-3 | 83 | Log. normal (83, 71) | Herman et al., 7 | Assumed |
| Men, years 4-95 | 0 | Not varied | Assumed | - |
| Lifestyle | ||||
| Women, years 1-3 | -33 | Log. normal (-33, -28) | Herman et al., 7 | Assumed |
| Women, years 4-95 | 0 | Not varied | Assumed | - |
| Men, years 1-3 | -105 | Log. normal (-105, -89) | Herman et al., 7 | Assumed |
| Men, years 4-95 | 0 | Not varied | Assumed | - |
CHD = coronary heart disease; DPP = Diabetes Prevention Program; HCFA = Health Care Financing Administration; UKPDS = United Kingdom Prospective Diabetes Study.
Log. normal (a, b) = logistic normal distribution (mean, lower bound); normal (a to b) = normal distribution (95% CI); triangular (a to b, c) = triangular distribution (minimum to maximum, mode).
Appendix Table 3.
Variables Specific to Coffey and Colleagues’ Additive Quality-of-Life Model*
| Variable | Value |
Base Source (Author, Reference) |
Distribution Notes | |
|---|---|---|---|---|
| Base-Case Analysis |
Probabilistic Sensitivity Analysis Distribution† |
|||
| Intercepts | ||||
| Diabetes | 0.6890 | Normal (0.662 to 0.716) | Coffey et al., 26 | Based on reported SEs |
| Prediabetes | 0.7302 | Normal (0.713 to 0.748) | DPP data | Calculated based on reported SEs for base model intercept and coefficients associated with BMI < 30 kg/m2 and male sex |
| Coefficients associated with characteristics | ||||
| Women | -0.0380 | Normal (-0.052 to -0.024) | Coffey et al., 26 | Based on reported SEs |
| Hypertension | -0.0110 | Normal (-0.025 to 0.003) | Coffey et al., 26 | Based on reported SEs |
| Blindness | -0.1700 | Normal (-0.192 to -0.148) | Coffey et al., 26 | Based on reported SEs |
| Nephropathy | -0.0110 | Normal (-0.029 to 0.007) | Coffey et al., 26 | Based on reported SEs |
| End-stage renal disease | -0.0780 | Normal (-0.129 to -0.027) | Coffey et al., 26 | Based on reported SEs |
| Peripheral neuropathy | -0.0650 | Normal (-0.081 to -0.049) | Coffey et al., 26 | Based on reported SEs |
| Foot ulcer | -0.0990 | Normal (-0.124 to -0.074) | Coffey et al., 26 | Based on reported SEs |
| Lower-extremity amputation | -0.1050 | Normal (-0.144 to -0.066) | Coffey et al., 26 | Based on reported SEs |
| History of cardiac arrest or myocardial infarction | -0.0520 | Normal (-0.074 to -0.030) | Coffey et al., 26 | Based on reported SEs |
| Stroke | -0.0720 | Normal (-0.103 to -0.041) | Coffey et al., 26 | Based on reported SEs |
| DPP lifestyle intervention | 0.0189 | Normal (0.012 to 0.026) | DPP data | Based on reported SEs |
| DPP drug intervention | 0.0031 | Normal (-0.004 to 0.010) | DPP data | Based on reported SEs |
| BMI ≥ 30.0 kg/m2 | -0.0210 | Normal (-0.035 to -0.007) | Coffey et al., 26 | Based on reported SEs |
BMI = body mass index; DPP = Diabetes Prevention Program.
Normal (a to b) = normal distribution (95% CI).
Appendix Table 4.
Variables Specific to Brandle and Colleagues’ Multiplicative Cost Model*
| Variable | Value |
Base Source (Author, Reference) |
Distribution Notes | |
|---|---|---|---|---|
| Base-Case Analysis |
Probabilistic Sensitivity Analysis Distribution† |
|||
| Diabetes costs | ||||
| Base | $1684 | Not varied | Brandle et al., 25 | - |
| Multiplier | ||||
| Female | 1.2500 | Normal (1.112 to 1.394) | Brandle et al., 25 | Based on reported SEs |
| White | 1.0000 | Not varied | Assumed | - |
| African American | 0.8200 | Normal (0.694 to 0.96) | Brandle et al., 25 | Based on reported SEs |
| Hispanic | 1.0000 | Not varied | Assumed | - |
| Asian | 1.0000 | Not varied | Assumed | - |
| Native American | 1.0000 | Not varied | Assumed | - |
| BMI ≥ 30 kg/m2 | 1.0100 | Normal (1.001 to 1.019) | Brandle et al., 25 | Based on reported SEs |
| Oral antidiabetic agents | 1.1000 | Normal (0.852 to 1.412) | Brandle et al., 25 | Based on reported SEs |
| Insulin | 1.5900 | Normal (1.221 to 2.061) | Based on reported SEs | |
| Microalbuminuria | 1.1700 | Normal (0.941 to 1.451) | Brandle et al., 25 | Based on reported SEs |
| Nephropathy | 1.3000 | Normal (1.103 to 1.526) | Brandle et al., 25 | Based on reported SEs |
| End-stage renal disease with dialysis | 10.5300 | Normal (4.612 to 24.059) | Brandle et al., 25 | Based on reported SEs |
| History of stroke | 1.3000 | Normal (1.107 to 1.519) | Brandle et al., 25 | Based on reported SEs |
| Angina | 1.7300 | Normal (1.316 to 2.282) | Brandle et al., 25 | Based on reported SEs |
| History of cardiac arrest or myocardial infarction | 1.9000 | Normal (1.663 to 2.16) | Brandle et al., 25 | Based on reported SEs |
| Peripheral vascular disease | 1.3100 | Normal (1.15 to 1.481) | Brandle et al., 25 | Based on reported SEs |
| Hypertension (treated) | 1.2400 | Normal (1.089 to 1.402) | Brandle et al., 25 | Based on reported SEs |
| Prediabetes costs | ||||
| Base | $1296 | Not varied | DPP data | - |
| Multiplier | ||||
| Female | 1.1420 | Normal (1.02 to 1.278) | DPP data | Assumed same relative variance as for diabetes |
| White | 1.0000 | Not varied | Assumed | - |
| African American | 0.8200 | Normal (0.694 to 0.96) | Assumed same as diabetes |
Based on reported SE |
| Hispanic | 1.0000 | Not varied | Assumed | - |
| Asian | 1.0000 | Not varied | Assumed | - |
| Native American | 1.0000 | Not varied | Assumed | - |
| BMI ≥ 30 kg/m2 | 1.0100 | Normal (1.001 to 1.019) | Assumed same as diabetes |
Assumed same as diabetes |
| Oral antidiabetic agents | 1.0000 | Not varied | Assumed | - |
| Insulin | 1.0000 | Not varied | Assumed | - |
| Microalbuminuria | 1.0000 | Not varied | Assumed | - |
| Nephropathy | 1.0000 | Not varied | Assumed | - |
| End-stage renal disease with dialysis | 1.0000 | Not varied | Assumed | - |
| History of stroke | 1.3000 | Normal (1.107 to 1.519) | Assumed same as diabetes |
Assumed same as diabetes |
| Angina | 1.7300 | Normal (1.316 to 2.282) | Assumed same as diabetes |
Assumed same as diabetes |
| History of cardiac arrest or myocardial infarction | 1.9000 | Normal (1.663 to 2.16) | Assumed same as diabetes |
Assumed same as diabetes |
| Peripheral vascular disease | 1.0000 | Normal (1.15 to 1.481) | Assumed same as diabetes |
Assumed same as diabetes |
| Hypertension (treated) | 1.2400 | Normal (1.089 to 1.402 | Assumed same as diabetes |
Assumed same as diabetes |
| Acute event costs | ||||
| Acute myocardial infarction | $24 500 | Normal ($15 000 to $50 000) | Brandle et al., 25 | Reported IQR |
| Stroke | $26 600 | Normal ($15 400 to $44 900) | Brandle et al., 25 | Reported IQR |
| Lower-extremity amputation | $37 600 | Normal ($23 300 to $62 200) | Brandle et al., 25 | Reported IQR |
| Other | ||||
| BMI | 30 | Not varied | Assumed | - |
| Peripheral vascular disease prevalence | 39 | Normal (36.4 to 41.6) | Brandle et al., 25 | Calculated using binomial distribution |
BMI = body mass index; DPP = Diabetes Prevention Program; IQR = interquartile range.
Normal (a to b) = normal distribution (95% CI).
Appendix Table 5.
Discount Rate Variables*
| Variable | Value |
Base Source | Distribution Notes | |
|---|---|---|---|---|
| Base-Case Analysis |
Probabilistic Sensitivity Analysis Distribution† |
|||
| Discount rate applied to costs | 3.00 | Triangular (2.00 to 5.00, 3.33) | Assumed | Assumed |
| Discount rate applied to QALYs | 3.00 | Triangular (2.00 to 5.00, 3.33) | Assumed | Assumed |
QALY = quality-adjusted life-year.
Triangular (a to b, c) = triangular distribution (minimum to maximum, mode).
Appendix Table 6.
Variables Specific to Disease Progression*
| Variable | Value |
Base Source (Author, Reference) |
Distribution Notes | |
|---|---|---|---|---|
| Base-Case Analysis |
Probabilistic Sensitivity Analysis Distribution† |
|||
| Nephropathy hazard rates | ||||
| Normal to microalbuminuria | ||||
| Baseline | 0.0202 | Normal (0.0192 to 0.0222) | Adler et al., 20 | Based on reported CIs |
| Hypertension with moderate control | 0.0202 | Normal (0.0192 to 0.0222) | Adler et al., 20 | Based on reported CIs |
| Microalbuminuria to nephropathy | ||||
| Baseline | 0.0284 | Normal (0.0253 to 0.0325) | Adler et al., 20 | Based on reported CIs |
| Hypertension with moderate control | 0.0284 | Normal (0.0253 to 0.0325) | Adler et al., 20 | Based on reported CIs |
| Nephropathy to end-stage renal disease | ||||
| Baseline | 0.02327 | Normal (0.0151 to 0.0305) | Adler et al., 20 | Based on reported CIs |
| Hypertension with moderate control | 0.02327 | Normal (0.0151 to 0.0305) | Adler et al., 20 | Based on reported CIs |
| Neuropathy hazard rates | ||||
| Normal to peripheral neuropathy | 0.036 | Log. normal (0.036, 0.027) | DCCTRG, 38 | Assumed |
| Peripheral neuropathy to lower-extremity amputation |
0.0067 | Log. normal (0.028, 0.021) | UKPDS 38, 23 | Assumed |
| Probability of additional amputations, % | 11 | Log. normal (11, 8) | Reiber et al., 39 | Assumed |
| Probability of diabetes foot ulcer, % | 4.00 | Log. normal (4.00, 3.00) | Reiber et al., 39; Moss et al., 40 | Assumed |
| Probability of death from amputation, % | 10.5 | Log. normal (10.5, 8) | Reiber et al., 39 | Assumed |
| Retinopathy hazard rates | ||||
| Normal to photocoagulation | ||||
| Baseline | 0.011 | Log. normal (0.011, 0.008) | DCCTRG, 38 | Assumed |
| Hypertensive with moderate control | 0.0166 | Log. normal (0.017, 0.012) | UKPDS 33, 13 | Assumed |
| Photocoagulation to blindness | ||||
| Baseline | 0.1065 | Log. normal (0.107, 0.080) | UKPDS 33, 13 | Assumed |
| Hypertensive with moderate control | 0.1065 | Log. normal (0.107, 0.080) | UKPDS 33, 13 | Assumed |
| Cardiovascular heart disease hazard rates | ||||
| None | - | - | ||
| Stroke hazard rates | ||||
| Stroke to death | ||||
| Immediate | 0.142 | Log. normal (0.142, 0.107) | Sacco et al., 41 | Assumed |
| 1 y | 0.092 | Log. normal (0.092, 0.069) | Sacco et al., 41 | Assumed |
DCCTRG = Diabetes Control and Complications Trial Research Group; UKPDS = United Kingdom Prospective Diabetes Study.
Log. normal (a, b) = logistic normal distribution (mean, lower bound); normal (a to b) = normal distribution (95% CI).
Appendix Table 7.
Variables Specific to Moderate Hypertension Control*
| Variable | Value |
Base Source (Author, Reference) |
Distribution Notes | |
|---|---|---|---|---|
| Base-Case Analysis |
Probabilistic Sensitivity Analysis Distribution† |
|||
| Treatment effect, % | ||||
| Relative risk reduction of CHD | 13 | Log. normal (13, 10) | UKPDS 38, 23 | Assumed |
| Relative risk reduction of stroke | 17 | Not varied | UKPDS 38, 23 | - |
CHD = cardiovascular heart disease; UKPDS = United Kingdom Prospective Diabetes Study.
Log. normal (a, b) = logistic normal distribution (mean, lower bound).
Footnotes
Current author addresses and author contributions are available at www.annals.org.
Potential Financial Conflicts of Interest: None disclosed.
References
- 1.Engelgau MM, Geiss LS, Saaddine JB, Boyle JP, Benjamin SM, Gregg EW, et al. The evolving diabetes burden in the United States. Ann Intern Med. 2004;140:945–50. doi: 10.7326/0003-4819-140-11-200406010-00035.[PMID: 15172919]
- 2.Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20:537–44. doi: 10.2337/diacare.20.4.537.[PMID: 9096977]
- 3.Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344:1343–50. doi: 10.1056/NEJM200105033441801.[PMID: 11333990]
- 4.Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393–403. doi: 10.1056/NEJMoa012512.[PMID: 11832527]
- 5.Buchanan TA, Xiang AH, Peters RK, Kjos SL, Marroquin A, Goico J, et al. Preservation of pancreatic beta-cell function and prevention of type 2 diabetes by pharmacological treatment of insulin resistance in high-risk hispanic women. Diabetes. 2002;51:2796–803. doi: 10.2337/diabetes.51.9.2796.[PMID: 12196473]
- 6.Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laakso M, et al. Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet. 2002;359:2072–7. doi: 10.1016/S0140-6736(02)08905-5.[PMID: 12086760]
- 7.Herman WH, Brandle M, Zhang P, Williamson DF, Matulik MJ, Ratner RE, et al. Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program. Diabetes Care. 2003;26:36–47. doi: 10.2337/diacare.26.1.36.[PMID: 12502656]
- 8.Within-trial cost-effectiveness of lifestyle intervention or metformin for the primary prevention of type 2 diabetes. Diabetes Care. 2003;26:2518–23. doi: 10.2337/diacare.26.9.2518.[PMID: 12941712]
- 9.The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care. 2002;25:2165–71. doi: 10.2337/diacare.25.12.2165.[PMID: 12453955]
- 10.The Diabetes Prevention Program. Design and methods for a clinical trial in the prevention of type 2 diabetes. Diabetes Care. 1999;22:623–34. doi: 10.2337/diacare.22.4.623.[PMID: 10189543]
- 11.Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA. 2002;287:2542–51. doi: 10.1001/jama.287.19.2542.[PMID: 12020335]
- 12.Herman WH, Aubert RE, Engelgau MM, Thompson TJ, Ali MA, Sous ES, et al. Diabetes mellitus in Egypt: glycaemic control and microvascular and neuropathic complications. Diabet Med. 1998;15:1045–51. doi: 10.1002/(SICI)1096-9136(1998120)15:12<1045::AID-DIA696>3.0.CO;2-L.[PMID: 9868980]
- 13.UK Prospective Diabetes Study (UKPDS) Group Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) Lancet. 1998;352:837–53.[PMID: 9742976]
- 14.Qureshi AI, Giles WH, Croft JB. Impaired glucose tolerance and the likelihood of nonfatal stroke and myocardial infarction: the Third National Health and Nutrition Examination Survey. Stroke. 1998;29:1329–32. doi: 10.1161/01.str.29.7.1329.[PMID: 9660382]
- 15.Barzilay JI, Spiekerman CF, Kuller LH, Burke GL, Bittner V, Gottdiener JS, et al. Prevalence of clinical and isolated subclinical cardiovascular disease in older adults with glucose disorders: the Cardiovascular Health Study. Diabetes Care. 2001;24:1233–9. doi: 10.2337/diacare.24.7.1233.[PMID: 11423508]
- 16.Saydah SH, Loria CM, Eberhardt MS, Brancati FL. Subclinical states of glucose intolerance and risk of death in the U.S. Diabetes Care. 2001;24:447–53. doi: 10.2337/diacare.24.3.447.[PMID: 11289466]
- 17.Harris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis. Diabetes Care. 1992;15:815–9. doi: 10.2337/diacare.15.7.815.[PMID: 1516497]
- 18.Thompson TJ, Engelgau MM, Hegazy M, Ali MA, Sous ES, Badran A, et al. The onset of NIDDM and its relationship to clinical diagnosis in Egyptian adults. Diabet Med. 1996;13:337–40. doi: 10.1002/(SICI)1096-9136(199604)13:4<337::AID-DIA71>3.0.CO;2-A.[PMID: 9162609]
- 19.Kohner EM, Aldington SJ, Stratton IM, Manley SE, Holman RR, Matthews DR, et al. United Kingdom Prospective Diabetes Study, 30: diabetic retinopathy at diagnosis of non-insulin-dependent diabetes mellitus and associated risk factors. Arch Ophthalmol. 1998;116:297–303. doi: 10.1001/archopht.116.3.297.[PMID: 9514482]
- 20.Adler AI, Stevens RJ, Manley SE, Bilous RW, Cull CA, Holman RR, et al. Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64) Kidney Int. 2003;63:225–32. doi: 10.1046/j.1523-1755.2003.00712.x.[PMID: 12472787]
- 21.Stevens RJ, Kothari V, Adler AI, Stratton IM. The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56) Clin Sci (Lond) 2001;101:671–9.[PMID: 11724655]
- 22.Kothari V, Stevens RJ, Adler AI, Stratton IM, Manley SE, Neil HA, Holman RR. UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke. 2002;33:1776–81. doi: 10.1161/01.str.0000020091.07144.c7.[PMID: 12105351]
- 23.UK Prospective Diabetes Study Group Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ. 1998;317:703–13.[PMID: 9732337]
- 24.Gold MR, Siegel JE, Russell LB, Weinstein MC, editors. Cost-Effectiveness in Health and Medicine. Oxford Univ Pr; New York: 1996. [Google Scholar]
- 25.Brandle M, Zhou H, Smith BR, Marriott D, Burke R, Tabaei BP, et al. The direct medical cost of type 2 diabetes. Diabetes Care. 2003;26:2300–4. doi: 10.2337/diacare.26.8.2300.[PMID: 12882852]
- 26.Coffey JT, Brandle M, Zhou H, Marriott D, Burke R, Tabaei BP, et al. Valuing health-related quality of life in diabetes. Diabetes Care. 2002;25:2238–43. doi: 10.2337/diacare.25.12.2238.[PMID: 12453967]
- 27.Kingsley RG, Wilson GT. Behavior therapy for obesity: a comparative investigation of long-term efficacy. J Consult Clin Psychol. 1977;45:288–98. doi: 10.1037//0022-006x.45.2.288.[PMID: 850013]
- 28.Renjilian DA, Perri MG, Nezu AM, McKelvey WF, Shermer RL, Anton SD. Individual versus group therapy for obesity: effects of matching participants to their treatment preferences. J Consult Clin Psychol. 2001;69:717–21.[PMID: 11550739]
- 29.Doubilet P, Begg CB, Weinstein MC, Braun P, McNeil BJ. Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach. Med Decis Making. 1985;5:157–77. doi: 10.1177/0272989X8500500205.[PMID: 3831638]
- 30.Scheffler RM, Feuchtbaum LB, Phibbs CS. Prevention: the cost-effectiveness of the California Diabetes and Pregnancy Program. Am J Public Health. 1992;82:168–75. doi: 10.2105/ajph.82.2.168.[PMID: 1739141]
- 31.Rodby RA, Firth LM, Lewis EJ. An economic analysis of captopril in the treatment of diabetic nephropathy. The Collaborative Study Group. Diabetes Care. 1996;19:1051–61. doi: 10.2337/diacare.19.10.1051.[PMID: 8886549]
- 32.Herman WH, Shahinfar S, Carides GW, Dasbach EJ, Gerth WC, Alexander CM, et al. Losartan reduces the costs associated with diabetic end-stage renal disease: the RENAAL study economic evaluation. Diabetes Care. 2003;26:683–7. doi: 10.2337/diacare.26.3.683.[PMID: 12610022]
- 33.UK Prospective Diabetes Study Group Cost effectiveness analysis of improved blood pressure control in hypertensive patients with type 2 diabetes: UKPDS 40. BMJ. 1998;317:720–6.[PMID: 9732339]
- 34.Ubel PA, Hirth RA, Chernew ME, Fendrick AM. What is the price of life and why doesn’t it increase at the rate of inflation? Arch Intern Med. 2003;163:1637–41. doi: 10.1001/archinte.163.14.1637.[PMID: 12885677]
- 35.Laupacis A, Feeny D, Detsky AS, Tugwell PX. How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. CMAJ. 1992;146:473–81.[PMID: 1306034]
- 36.Herman WH. Diabetes modeling [Editorial] Diabetes Care. 2003;26:3182–3. doi: 10.2337/diacare.26.11.3182.[PMID: 14578260]
- 37.Health Care Financing Administration. 1998. 1999 Medicare Clinical Diagnostic Laboratory Fee Schedule. Baltimore, MD:
- 38.The Diabetes Control and Complications Trial Research Group Lifetime benefits and costs of intensive therapy as practiced in the diabetes control and complications trial. JAMA. 1996;276:1409–15.[PMID: 8892716]
- 39.Reiber GE, Boyko EJ, Smith DG. Diabetes in America. National Diabetes Data Group, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; Bethesda, MD: 1995. Lower extremity foot ulcers and amputations in diabetes; pp. 409–428. [Google Scholar]
- 40.Moss SE, Klein R, Klein BE. The prevalence and incidence of lower extremity amputation in a diabetic population. Arch Intern Med. 1992;152:610–6.[PMID: 1546925]
- 41.Sacco RL, Shi T, Zamanillo MC, Kargman DE. Predictors of mortality and recurrence after hospitalized cerebral infarction in an urban community: the Northern Manhattan Stroke Study. Neurology. 1994;44:626–34. doi: 10.1212/wnl.44.4.626.[PMID: 8164815]