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.
The study sample includes 8,352 Medicaid children who were followed up for 2 years by the Medical Expenditure Panel Survey in 2000–2005.
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.
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.
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.