title: Using self-reported health measures to predict high-need cases among Medicaid-eligible adults.
abstract: 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.
  
  
  METHODS
  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).
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
authors.affiliation: University of Michigan School of Public Health, Ann Arbor, MI.
authors.forename: Laura R
authors.initials: LR
authors.lastname: Wherry
authors.forename: Marguerite E
authors.initials: ME
authors.lastname: Burns
authors.forename: Lindsey Jeanne
authors.initials: LJ
authors.lastname: Leininger
mesh_terms: D000328:Adult; D059026:Diagnostic Self Evaluation; D004608:Eligibility Determination; D005260:Female; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data*; D057566:Self Report; D014481:United States
keywords: Medicaid; prediction models; risk assessment; self-rated health measurement