A New Type 2 Diabetes Microsimulation Model to Estimate Long-Term Health Outcomes, Costs, and Cost-Effectiveness

Abstract

Objectives: This study aimed to develop a microsimulation model to estimate the health effects, costs, and cost-effectiveness of public health and clinical interventions for preventing/managing type 2 diabetes.

Methods: We combined newly developed equations for complications, mortality, risk factor progression, patient utility, and cost-all based on US studies-in a microsimulation model. We performed internal and external validation of the model. To demonstrate the model's utility, we predicted remaining life-years, quality-adjusted life-years (QALYs), and lifetime medical cost for a representative cohort of 10 000 US adults with type 2 diabetes. We then estimated the cost-effectiveness of reducing hemoglobin A1c from 9% to 7% among adults with type 2 diabetes, using low-cost, generic, oral medications.

Results: The model performed well in internal validation; the average absolute difference between simulated and observed incidence for 17 complications was < 8%. In external validation, the model was better at predicting outcomes in clinical trials than in observational studies. The cohort of US adults with type 2 diabetes was projected to have an average of 19.95 remaining life-years (from mean age 61), incur $187 729 in discounted medical costs, and accrue 8.79 discounted QALYs. The intervention to reduce hemoglobin A1c increased medical costs by $1256 and QALYs by 0.39, yielding an incremental cost-effectiveness ratio of $9103 per QALY.

Conclusions: Using equations exclusively derived from US studies, this new microsimulation model achieves good prediction accuracy in US populations. The model can be used to estimate the long-term health impact, costs, and cost-effectiveness of interventions for type 2 diabetes in the United States.

Keywords: diabetes; microsimulation; probabilistic sensitivity analysis; risk equations.

Figure 1.

Overview of the model and simulation flow. Once a scenario has been defined, the input population is initialized at t = 0. During initialization, baseline values for all risk factors and complication histories are calculated and any selected basic intervention is applied. After initialization, the simulation runs t time steps of the model and loops over all living individuals (i) every time step (t). Within each time step, risk factors are updated first in random order except for the update to an individual’s smoking state, which is always updated last. The update of risk factors at t = 1 accounts for baseline values of the risk factors. Next, all previous history of complication variables are updated, again in random order except for CVD. CVD state is updated after the set of macrovascular complications has been updated. Mortality is updated last, depending on the CVD state of an individual. Not shown in the figure are time-invariant risk factors: age at entry, diabetes duration at entry, accord, postsecondary education status. Black, Hispanic, other race. bmi indicates body mass index; CHF, congestive heart failure; CVD, cardiovascular disease; EGFR 30 (60), estimate glomerular filtration rate < 30(60) mL/min/1.73m²; hba1c, glycated hemoglobin A1c; hdl, HDL cholesterol; ldl, LDL cholesterol; MI, myocardial infarction; sbp, systolic blood pressure.

Figure 2.

Simulated versus observed complications at year 13 (internal validation).

Figure 3.

Observed versus predicted diabetes complication rates by study.