A matter of classes: stratifying health care populations to produce better estimates of inpatient costs.
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.


METHODS
Secondary data created from the administrative claims from all 829,356 children aged 21 years and under enrolled in Georgia Medicaid in 1999.


METHODS
A finite mixture model was used to assign child Medicaid patients to health severity classes. These class assignments were then used 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.


RESULTS
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.
Rein
David B
DB
RTI International, Division of Health Economics Research, Atlanta, GA 30341, USA.
D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D005260:Female; D005845:Georgia; D017721:Hospital Costs / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D015273:Insurance Selection Bias; D016013:Likelihood Functions; D008297:Male; D008484:Medicaid / Q000191:economics*; D017059:Models, Econometric; D011907:Rate Setting and Review / Q000379:methods / Q000706:statistics & numerical data; D012051:Reimbursement Mechanisms / Q000191:economics / Q000706:statistics & numerical data; D020379:Risk Adjustment / Q000191:economics / Q000379:methods / Q000706:statistics & numerical data; D020414:Risk Sharing, Financial; D012720:Severity of Illness Index; D014481:United States