MULTIVARIATE COST-EFFECTIVENESS ANALYSIS - AN APPLICATION TO OPTIMIZING AMBULATORY CARE FOR HYPERTENSION

Cost-effectiveness analysis (CEA) is being used increasingly to alloca te health resources efficiently. This paper develops an extension of C EA based on multivariate regression analysis and applies if to hyperte nsion treatment. After assembling clinic and patient characteristics, outcomes, and costs for 2,439 randomly chosen patients in the 32 speci al hypertension clinics of the Department of Veterans Affairs (VA), we identified 19 significant predictors of cost and diastolic blood pres sure (DBP) using multiple regression analysis. We classified these ind ependent 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 t hat fully implementing all unambiguous clinic changes would reduce cos ts by 33% while improving DBP. Multivariate CEA could help managed car e companies and government programs with cost and outcome data to redu ce costs and improve outcomes.