Multivariate cost-effectiveness analysis: an application to optimizing ambulatory care for hypertension.

Cost-effectiveness analysis (CEA) is being used increasingly to allocate health resources efficiently. This paper develops an extension of CEA based on multivariate regression analysis and applies it to hypertension treatment. After assembling clinic and patient characteristics, outcomes, and costs for 2,439 randomly chosen patients in the 32 special hypertension clinics of the Department of Veterans Affairs (VA), we identified 19 significant predictors of cost and diastolic blood pressure (DBP) using multiple regression analysis. We classified these independent variables as "unambiguous" if a given change was associated with both lower cost and better DBP, or as "trade-off" variables if any change improving DBP entailed higher costs. The results suggest that fully implementing all unambiguous clinic changes would reduce costs by 33% while improving DBP. Multivariate CEA could help managed care companies and government programs with cost and outcome data to reduce costs and improve outcomes.