OBJECTIVES: Health economic models of type 2 diabetes are increasingly being used to guide formulary decision making in the US. Models informing such decisions must be clinically credible and valid for the populations of interest. The aim of the present analysis was to evaluate the impact of using risk equations derived from different populations on modeled long-term outcomes for a US population with type 2 diabetes.

METHODS: Long-term projections of clinical outcomes were made for a US cohort with type 2 diabetes receiving two hypothetical interventions to reduce HbA1c and BMI (treatment A was superior to treatment B in terms of HbA1c and BMI improvements). In the first scenario, the risk of complications and mortality was modeled using equations from the BRAVO model (derived from a US population) and in a second scenario United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS OMS2, derived from a UK population) equations were used. All other simulation settings and parameter inputs were identical. Future clinical benefits were discounted at 3% annually.

RESULTS: Differences in mortality were observed with incremental life expectancy of approximately 0.09 years using BRAVO equations (11.91 versus 11.82 years) compared with 0.03 years using UKPDS OM2 (12.56 versus 12.53 years). Incremental quality adjusted life expectancy estimates were 0.16 QALYs with BRAVO equations (7.52 versus 7.36 QALYs) versus 0.08 QALYs with UKPDS OM2 (7.36 versus 7.28 QALYs). Small differences between treatments were projected in the cumulative incidence of most diabetes-related complications, but overall complication rates were lower with UKPDS OM2 than with BRAVO equations.

CONCLUSIONS: Different risk equations in an economic model of a US cohort with type 2 diabetes produces different long-term outcomes, which could have a notable influence on the cost-effectiveness of interventions.