title: Risk-adjusted capitation rates for children: how useful are the survey-based measures?
abstract: OBJECTIVE
  Despite the recognition by some experts that survey measures have the potential to improve capitation rates for those with chronic conditions, few studies have examined risk-adjustment models for children, and fewer still have focused on survey measures. This study evaluates the performance of risk-adjustment models for children and examines the potential of survey-based measures for improving capitation rates for children.
  
  
  METHODS
  The study sample includes 8,352 Medicaid children who were followed up for 2 years by the Medical Expenditure Panel Survey in 2000-2005.
  
  
  METHODS
  Children's information in 1 year was used to predict their expenditures in the next year. Five models were estimated, including one each that used demographic characteristics, subjectively rated health status, survey measures about children with special health care needs (CSHCN), prior year expenditures, and Hierarchical Condition Category (HCC), which is a diagnosis-based model. The models were tested at the individual level using multiple regression methods and at the group level using split-half validation to evaluate their impact on expenditure predictions for CSHCN.
  
  
  RESULTS
  The CSHCN information explained higher proportion of the variance in annual expenditures than the subjectively rated health status, but less than HCC measures and prior expenditures. Adding the CSHCN information into demographic factors as adjusters would remarkably increase capitation rates for CSHCN.
  
  
  CONCLUSIONS
  Survey measures, such as the CSHCN information, can improve risk-adjustment models, and their inclusion into capitation adjustment may help provide appropriate payments to managed-care plans serving this vulnerable group of children.
authors.affiliation: RAND Corporation, 4570 Fifth Avenue, Pittsburgh, PA 15213, USA. hao_yu@rand.org
authors.forename: Hao
authors.initials: H
authors.lastname: Yu
authors.forename: Andrew W
authors.initials: AW
authors.lastname: Dick
mesh_terms: D002204:Capitation Fee / Q000458:organization & administration* / Q000706:statistics & numerical data; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease / Q000628:therapy*; D003365:Costs and Cost Analysis; D005260:Female; D006304:Health Status; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000191:economics / Q000458:organization & administration* / Q000706:statistics & numerical data; D020379:Risk Adjustment / Q000191:economics / Q000458:organization & administration* / Q000706:statistics & numerical data; D014481:United States
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