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Using Self-Reported Health Measures to Predict High-Need Cases among Medicaid-Eligible Adults 

Keywords: Medicaid;prediction models;self-rated health measurement;risk assessment

Objective: To assess the ability of different self-reported health (SRH) measures to prospectively identify individuals with high future health care needs among adults eligible for Medicaid.

Data Sources: The 1997–2008 rounds of the National Health Interview Survey linked to the 1998–2009 rounds of the Medical Expenditure Panel Survey (n = 6,725).

Study Design: Multivariate logistic regression models are fitted for the following outcomes: having an inpatient visit; membership in the top decile of emergency room utilization; and membership in the top cost decile. We examine the incremental predictive ability of six different SRH domains (health conditions, mental health, access to care, health behaviors, health-related quality of life [HRQOL], and prior utilization) over a baseline model with sociodemographic characteristics. Models are evaluated using the c-statistic, integrated discrimination improvement, sensitivity, specificity, and predictive values.

Principal Findings: Self-reports of prior utilization provide the greatest predictive improvement, followed by information on health conditions and HRQOL. Models including these three domains meet the standard threshold of acceptability (c-statistics range from 0.703 to 0.751).

Conclusions: SRH measures provide a promising way to prospectively profile Medicaid-eligible adults by likely health care needs.

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