Using self-reported health measures to predict high-need cases among Medicaid-eligible adults

Health Serv Res. 2014 Dec;49 Suppl 2(Suppl 2):2147-72. doi: 10.1111/1475-6773.12222. Epub 2014 Aug 15.


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.

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

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Diagnostic Self Evaluation*
  • Eligibility Determination*
  • Female
  • Health Services Needs and Demand / statistics & numerical data*
  • Humans
  • Logistic Models
  • Male
  • Medicaid / statistics & numerical data*
  • Self Report*
  • United States