title: SF-36 as a predictor of health states.
abstract: BACKGROUND
  There are a number of claims that Medical Outcomes Study Short Form 36 (MOS SF-36) mean scores can be used to discriminate between healthy and nonhealthy persons and determine various levels of health.
  
  
  OBJECTIVE
  The purpose of this study was to evaluate the ability of the SF-36 to predict whether or not respondents reported health problems.
  
  
  METHODS
  We used structural equation modeling (SEM) techniques to evaluate the SF-36 and its ability to discriminate between those who reported health problems or reported physician-determined illness and those who did not in a sample from the 1990 National Survey of Functional Health Status (NHS).
  
  
  RESULTS
  The correlation between physician-determined illness and Physical Health was -.404, resulting in 16.32% shared variance. The correlation between reported health problems and Physical Health was -.360, resulting in 12.96% shared variance. These correlations are markedly lower than those to the eight first-order scales or between Physical and Mental Health (r = .889). Mental Health could not predict physician-determined illness or reported health problems independent of Physical Health.
  
  
  CONCLUSIONS
  The SF-36 is relatively poor at accounting for the health status of respondents. There are significant paths but the variance accounted for in absolute and relative terms is small. Physical Health does a much better job of accounting for general mental health than it does for perceived health problems or physician-determined illness. These findings suggest that the SF-36 may not discriminate well between healthy and nonhealthy groups and that objective measures of health status may be required in conjunction with the use of the SF-36.
authors.affiliation: University of Tennessee Health Science Center, Memphis, TN, USA. pjreed@utmem.edu
authors.forename: P J
authors.initials: PJ
authors.lastname: Reed
authors.forename: D D
authors.initials: DD
authors.lastname: Moore
mesh_terms: D000787:Angina Pectoris / Q000175:diagnosis / Q000453:epidemiology; D016022:Case-Control Studies; D002908:Chronic Disease / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000453:epidemiology; D005191:Family Characteristics; D006305:Health Status Indicators; D006333:Heart Failure / Q000175:diagnosis / Q000453:epidemiology; D006801:Humans; D016013:Likelihood Functions; D008501:Medical Records, Problem-Oriented; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000175:diagnosis / Q000453:epidemiology; D009369:Neoplasms / Q000175:diagnosis / Q000453:epidemiology; D017064:Process Assessment, Health Care; D014481:United States / Q000453:epidemiology