Estimating the Risk-Based Value of the Diabetes Prevention Program: How well does clinical trial-based cost-effectiveness apply to the real world?

ObjectivesMany economic evaluations rely on clinical trial data that may not represent real world populations and intervention effectiveness. We compare risk and cost-effectiveness for the original Diabetes Prevention Program (DPP) clinical trial population and a real world population eligible for the National Diabetes Prevention Program (NDPP). MethodsWe identified National Health and Nutrition Examination Survey (NHANES) subjects eligible for the NDPP and adjusted projections using survey weights to produce real world (US population) representative results. We used clinical predictive models to estimate individual diabetes risk and microsimulation to estimate lifetime costs, benefits, and net monetary benefits (NMB) for lifestyle intervention and metformin. We compared results across the original DPP clinical trial and real world populations. ResultsOnly 20% of the NHANES population eligible for NDPP met inclusion/exclusion criteria for the DPP trial. Three-year risk of diabetes onset for trial population (mean 19.7%, median 10.3%) exceeded corresponding risk for the NHANES population (mean 14.6%, median 4.8%). The proportion of individuals with < 10% three-year diabetes risk for the trial population (49%) was less than the corresponding proportion for NHANES (67%). Lifestyle intervention had mean NMB $34,889 for the DPP trial population and $28,652 for NHANES. ConclusionsReal world populations eligible for the NDPP include a greater proportion of low-risk individuals, for whom prevention programs may confer smaller benefits. Using individualized diabetes risk estimates to inform referrals and prioritization for diabetes prevention can maximize benefit and is expected to have greater impact on real world populations than the clinical trial cohort. Key PointsReal world populations eligible for the National Diabetes Prevention Program include a greater proportion of low-risk individuals than the original clinical trial, and for these people, prevention programs may confer smaller benefits. Using individualized diabetes risk estimates to inform referrals and prioritization for diabetes prevention can maximize benefit and is expected to have greater impact on real world populations than the clinical trial cohort.