Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models.
BACKGROUND
Current research on the added value of self-reported health measures for risk equalization modeling does not include all types of self-reported health measures; and/or is compared with a limited set of medically diagnosed or pharmacy-based diseases; and/or is limited to specific populations of high-risk individuals.


OBJECTIVE
The objective of our study is to determine the predictive power of all types of self-reported health measures for prospective modeling of health care expenditures in a general population of adult Dutch sickness fund enrollees, given that pharmacy and diagnostic data from administrative records are already included in the risk equalization formula.


METHODS
We used 4 models of 2002 total, inpatient and outpatient expenditures to evaluate the separate and combined predictive ability of 2 kinds of data: (1) Pharmacy-based (PCGs) and Diagnosis-based (DCGs) Cost Groups and (2) summarized self-reported health information. Model performance is measured at the total population level using R2 and mean absolute prediction error; also, by examining mean discrepancies between model-predicted and actual expenditures (ie, expected over- or undercompensation) for members of potentially "mispriced" subgroups. These subgroups are identified by self-reports from prior-year health surveys and utilization and expenditure data from 5 preceding years.


METHODS
Subjects were 18,617 respondents to a health survey, held among a stratified sample of adult members of the largest Dutch sickness fund in 2002, with an overrepresentation of people in poor health.


METHODS
The data were extracted from a claims database and a health survey. The claims-based data are the outcomes of total, inpatient, and outpatient annualized expenditures in 2002; age, gender, PCGs, DCGs in 2001; and health care expenditures and hospitalizations during the years 1997 to 2001. The SF-36, Organization for Economic Cooperation and Development items, and long-term diseases and conditions were collected by a special purpose health survey conducted in the last quarter of 2001.


RESULTS
Out-of-sample R2 equals 17.2%, 2.6%, and 32.4% for the models of total, inpatient and outpatient expenditures including PCGs, DCGs, and self-reported health measures. Self-reported health measures contribute less to predictive power than PCGs and DCGs. PCGs and DCGs also predict better than self-reported health measures for people with top 25% total expenditures or hospitalizations in each year during a 5-year period. On the other hand, self-reported health measures are better predictors than PCGs and DCGs for people without any top 25% expenditures during the 5-year period, for switchers, and for most subgroups of relatively unhealthy people defined by self-reported health measures. Among the set of self-reported health measures, the SF-36 adds most to predictive power in terms of R2, mean absolute prediction error, and for almost all studied subgroups.


CONCLUSIONS
It is concluded that the self-reported health measures make an independent contribution to forecasting health care expenditures, even if the prediction model already includes diagnostic and pharmacy-based information currently used in Dutch risk equalization models.
Stam
Pieter J A
PJ
Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands. piet.stam@sirm.nl
van Vliet
René C J A
RC
van de Ven
Wynand P M M
WP
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D004185:Disability Evaluation; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D008019:Life Style; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010593:Pharmaceutical Services / Q000706:statistics & numerical data*; D020379:Risk Adjustment / Q000379:methods*; D012959:Socioeconomic Factors; D055815:Young Adult