Impact of Type 2 diabetes prevention programmes based on risk identification and lifestyle intervention intensity strategies: a cost-effectiveness analysis
Breeze, PR; Thomas, C; Squires, H; et al.Brennan, A; Greaves, CJ; Diggle, PJ; Brunner, E; Tabak, A; Preston, L; Chilcott, J
Date: 17 November 2015
Aim To develop a cost-effectiveness model to compare Type 2 diabetes prevention programmes that target different at-risk population subgroups through lifestyle interventions of varying intensity. Methods An individual patient simulation model simulated the development of diabetes in a representative sample of adults without diabetes ...
Aim To develop a cost-effectiveness model to compare Type 2 diabetes prevention programmes that target different at-risk population subgroups through lifestyle interventions of varying intensity. Methods An individual patient simulation model simulated the development of diabetes in a representative sample of adults without diabetes from the UK population. The model incorporates trajectories for HbA1c, 2-h glucose, fasting plasma glucose, BMI, systolic blood pressure, total cholesterol and HDL cholesterol. In the model, patients can be diagnosed with diabetes, cardiovascular disease, microvascular complications of diabetes, cancer, osteoarthritis and depression, or can die. The model collects costs and utilities over a lifetime horizon. The perspective is the UK National Health Service and Personal Social Services. We used the model to evaluate the population-wide impact of targeting a lifestyle intervention of varying intensity to six population subgroups defined as at high risk for diabetes. Results The intervention produces 0.0020 to 0.0026 incremental quality-adjusted life-years and saves £15 to £23 per person in the general population, depending on the subgroup targeted. Cost-effectiveness increases with intervention intensity. The most cost-effective options were to target South-Asian people and those with HbA1c levels > 42 mmol/mol (6%). Conclusion The model indicates that diabetes prevention interventions are likely to be cost-saving. The criteria for selecting at-risk individuals differentially has an impact on diabetes and cardiovascular disease outcomes, and on the timing of costs and benefits. The model is not currently able to account for potential differential uptake or efficacy between subgroups. These findings have implications for deciding who should be targeted for diabetes prevention interventions.
Institute of Health Research
College of Medicine and Health
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