title: Risk-adjusted capitation payments: how well do principal inpatient diagnosis-based models work in the German situation? Results from a large data set.
abstract: Five models of risk adjusters were tested as a (proxy) measure for health status with data from a large German sickness fund. The first two models use standard demographic and socio-demographic variables. One model incorporates a simple binary indicator for hospitalization and the last two are based on the hierarchical coexisting conditions (HCCs: DxCG Risk Adjustment Software Release 6.1) using in-patient diagnoses. Special investigations were done on the subgroups of insurees who left, joined or stayed with the fund over the observation period. Age and gender grouping accounted for 3.2% of the variation in total expenditure for concurrent as well as prospective models. The current German risk adjusters age, sex, and invalidity status account for 5.1 and 4.5% of the variance in the concurrent and prospective models, respectively. Age, gender, invalidity status and in-patient HCC covariates explain about 37% of the variations of the total expenditures in a concurrent model and roughly 12% of the variations of total expenditures in a prospective model. Only modest improvement can be achieved with the long-term-care (LTC) indicator. For high-risk (cost) groups, substantial under-prediction remains; conversely, for the low-risk group, represented by enrolees who did not show any health care expense in the base year, all of the models over-predict expenditure. Special investigations were done on the subgroups of insurees who left, joined or stayed with the fund over the observation period.
authors.affiliation: Institute of Health Economics, GSF National Research Center for Environment and Health, Munich, Germany.
authors.forename: Corinne
authors.initials: C
authors.lastname: Behrend
authors.forename: Florian
authors.initials: F
authors.lastname: Buchner
authors.forename: Michael
authors.initials: M
authors.lastname: Happich
authors.forename: Rolf
authors.initials: R
authors.lastname: Holle
authors.forename: Peter
authors.initials: P
authors.lastname: Reitmeir
authors.forename: Jürgen
authors.initials: J
authors.lastname: Wasem
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D002204:Capitation Fee; D015897:Comorbidity; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D005260:Female; D005858:Germany; D006760:Hospitalization / Q000191:economics* / Q000706:statistics & numerical data*; D006801:Humans; D038801:International Classification of Diseases / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D020379:Risk Adjustment / Q000379:methods*; D012737:Sex Factors; D012959:Socioeconomic Factors; D055815:Young Adult