Pharmacy- and diagnosis-based risk adjustment for children with Medicaid

Published: November 1, 2005
Category: Bibliography > Papers
Authors: Davis RB, Ferris TG, Iezzoni LI, Kuhlthau K, Perrin JM
Countries: United States
Language: null
Types: Population Health
Settings: Health Plan, Hospital

Med Care 43:1155-1159.

Center for Child and Adolescent Health Policy, MassGeneral Hospital for Children, Department of Pediatrics, Harvard Medical School, Boston, MA, USA

BACKGROUND: Risk adjustment is useful for adjusting health care payments based on patients’ health status.

OBJECTIVE: This work seeks to examine how well pharmacy- and diagnosis-based risk adjusters predict child health expenditures in Medicaid populations.

RESEARCH DESIGN: We used 1994-1995 Medicaid claims files for all children ages 0-18 years who were not covered by managed care in 3 states: Georgia, New Jersey, and Wisconsin. We examined separately 6 risk adjustment methods, 2 pharmacy-based and 4 diagnosis-based. We compared predictive accuracy of the methods for the whole sample and stratified by state and Medicaid enrollment category.

FINDINGS: Models with risk adjustment (either diagnosis- or pharmacy-based) had better predictive accuracy than demographic models. The pharmacy and diagnosis-based models had similar predictive accuracy. Risk adjuster performance differed by Medicaid enrollment category and state. Risk-adjusted models generally underpredict expenditures in populations with worse health status (eg, those in the Supplemental Security Income program [SSI]). The pharmacy-based models performed well for children in SSI relative to children in foster care.

CONCLUSIONS: Both pharmacy- and diagnosis-based risk adjustment improved the prediction of health expenditures compared models without risk adjustment. No single risk adjuster performed best in all situations, suggesting that optimal choices of risk adjusters may differ by purpose and context.

PMID: 16224310

Prescription Drug Use and Expenditures,Predictive Risk Modeling,Age,United States,Adolescent,Child,Preschool,Clinical Pharmacy Information Systems,Forecasting,Foster Home Care,Infant,Newborn,Gender,Models,Econometric,Social Security

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