Objective: The goal of this study was to develop unbiased risk-assessment models to be used for paying health plans on the basis of enrollee health status and use propensity. We explored the risk structure of adult employed HMO members using self-reported morbidities, functional status, perceived health status, and demographic characteristics.
Data sources/study setting: Data were collected on a random sample of members of a large, federally qualified, prepaid group practice, hospital-based HMO located in the Pacific Northwest.
Study design: Multivariate linear nonparametric techniques were used to estimate risk weights on demographic, morbidity, and health status factors at the individual level. The dependent variable was annual real total health plan expense for covered services for the year following the survey. Repeated random split-sample validation techniques minimized outlier influences and avoided inappropriate distributional assumptions required by parametric techniques.
Data collection/extraction methods: A mail questionnaire containing an abbreviated medical history and the RAND-36 Health Survey was administered to a 5 percent sample of adult subscribers and their spouses in 1990 and 1991, with an overall 44 percent response rate. Utilization data were extracted from HMO automated information systems. Annual expenses were computed by weighting all utilization elements by standard unit costs for the HMO.
Principal findings: Prevalence of such major chronic diseases as heart disease, diabetes, depression, and asthma improve prediction of future medical expense; functional health status and morbidities are each better than simple demographic factors alone; functional and perceived health status as well as demographic characteristics and diagnoses together yield the best prediction performance and reduce opportunities for selection bias. We also found evidence of important interaction effects between functional/perceived health status scales and disease classes.
Conclusions: Self-reported morbidities and functional health status are useful risk measures for adults. Risk-assessment research should focus on combining clinical information with social survey techniques to capitalize on the strengths of both approaches. Disease-specific functional health status scales should be developed and tested to capture the most information for prediction.