Diagnostic, Pharmacy-Based, and Self-Reported Health Measures in Risk Equalization Models
Medical Care , Volume 48 - Issue 5 p. 448- 457
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. RESEARCH DESIGN: 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. SUBJECTS: 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. DATA: 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. CONCLUSION: 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.
|*Health Status, Adolescent, Adult, Aged, Aged, 80 and over, Chronic Disease, Diagnosis-Related Groups/*statistics & numerical data, Disability Evaluation, Female, Health Expenditures/*statistics & numerical data, Health Surveys, Humans, Insurance Claim Review/statistics & numerical data, Life Style, Male, Middle Aged, Models, Statistical, Pharmaceutical Services/*statistics & numerical data, Risk Adjustment/*methods, Socioeconomic Factors, Young Adult|
|Organisation||Erasmus School of Health Policy & Management (ESHPM)|
Stam, P.J.A, van Vliet, R.C.J.A, & van de Ven, W.P.M.M. (2010). Diagnostic, Pharmacy-Based, and Self-Reported Health Measures in Risk Equalization Models. Medical Care, 48(5), 448–457. doi:10.1097/MLR.0b013e3181d559b4