LAUSR

OBJECTIVES Risk adjustment is a widely used tool for health expenditure prediction and control. Early approaches for estimating health expenditure were based on patient demographic variables alone, whereas more recent models incorporate patient information, such as chronic medical conditions, clinical diagnoses, and self-reported health status. Many studies have investigated the health expenditure predictive capacity of single demographic, morbidity, or health-related quality of life measures, but the best models prove to be those that include them all. The aim of this study was to develop an index that combines measures of perceived health and disease severity and to compare its efficacy in predicting health expenditure with that of the measures taken individually. STUDY DESIGN This is a linked cross-sectional study. METHODS In 2009 and 2010, the health-related quality of life questionnaire SF-36 (8 scales, two indices: Physical Component Summary [PCS] and Mental Component Summary [MCS]) was distributed to 886 patients of general practitioners in the Province of Siena, Italy. Severity of diseases was calculated for each patient using the Charlson Index (CH-I) and Cumulative Illness Rating Scale Severity Index (CIRS-SI). Siena Local Health Unit 2012 data on health expenditure were obtained for each patient. Multivariate linear regression was applied to test the performance of severity (CH-I, CIRS-SI) and perceived health (PCS and MCS) measures in predicting health expenditure. The indexes that predicted health expenditure best were then combined in a new tool, and its expenditure predictive capacity was tested. RESULTS The best health expenditure predictors proved to be PCS and SI (R2 = 0.15 and R2 = 0.17, respectively). When combined in a new index (PCS-SI), better predictive capacity of health expenditure was obtained than with the two single measures separately (R2 = 0.19). CONCLUSIONS A multidimensional indicator proved to be a better predictor of healthcare expenditure than single health measures.