Comparing cost-of-illness estimates from alternative approaches: an application to diabetes

Abstract

Objective: To compare disease cost estimates from two commonly used approaches.

Data source: Pooled Medical Expenditure Panel Survey (MEPS) data for 1998-2003.

Study design: We compared regression-based (RB) and attributable fraction (AF) approaches for estimating disease-attributable costs with an application to diabetes. The RB approach used results from econometric models of disease costs, while the AF approach used epidemiologic formulas for diabetes-attributable fractions combined with the total costs for seven conditions that result from diabetes.

Data extraction: We used SAS version 9.1 to create a dataset that combined data from six consecutive years of MEPS.

Principal findings: The RB approach produced higher estimates of diabetes-attributable medical spending ($52.9 billion in 2004 dollars) than the AF approach ($37.1 billion in 2004 dollars). RB model estimates may in part be higher because of the challenges of implementing the two approaches in a similar manner, but may also be higher because they capture the costs of increased treatment intensity for those with the disease.

Conclusions: We recommend using the RB approach for estimating disease costs whenever individual-level data on health care spending are available and when the presence of the disease affects treatment costs for other conditions, as in the case of diabetes.