A Matter of Classes: Stratifying Health Care Populations to Produce Better Estimates of Inpatient Costs

David B. Rein
2005 Health Services Research  
Objective. To stratify traditional risk-adjustment models by health severity classes in a way that is empirically based, is accessible to policy makers, and improves predictions of inpatient costs. Data Sources. Secondary data created from the administrative claims from all 829,356 children aged 21 years and under enrolled in Georgia Medicaid in 1999. Study Design. A finite mixture model was used to assign child Medicaid patients to health severity classes. These class assignments were then
more » ... to stratify both portions of a traditional two-part risk-adjustment model predicting inpatient Medicaid expenditures. Traditional model results were compared with the stratified model using actuarial statistics. Principal Findings. The finite mixture model identified four classes of children: a majority healthy class and three illness classes with increasing levels of severity. Stratifying the traditional two-part risk-adjustment model by health severity classes improved its R 2 from 0.17 to 0.25. The majority of additional predictive power resulted from stratifying the second part of the two-part model. Further, the preference for the stratified model was unaffected by months of patient enrollment time. Conclusions. Stratifying health care populations based on measures of health severity is a powerful method to achieve more accurate cost predictions. Insurers who ignore the predictive advances of sample stratification in setting risk-adjusted premiums may create strong financial incentives for adverse selection. Finite mixture models provide an empirically based, replicable methodology for stratification that should be accessible to most health care financial managers. Key Words. Risk adjustment, health care financing, inpatient costs, adverse selection Traditional models of health care utilization, such as ordinary least squares (OLS) regression models and the two-part model, restrict the effect of predictive covariates to be equal for all patients in the data. This may be unrealistic as the effects of predictive variables such as gender, age, or diagnosis r Health Research and Educational Trust
doi:10.1111/j.1475-6773.2005.00393.x pmid:16033501 pmcid:PMC1361179 fatcat:pjzwnu4lknh6nj2twpsai6y5xa