SF-36 as a predictor of health states

Value Health. 2000 May-Jun;3(3):202-7. doi: 10.1046/j.1524-4733.2000.33005.x.

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.

Objectives: 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.

Publication types

  • Validation Study

MeSH terms

  • Angina Pectoris / diagnosis
  • Angina Pectoris / epidemiology
  • Case-Control Studies
  • Chronic Disease / epidemiology
  • Diabetes Mellitus / diagnosis
  • Diabetes Mellitus / epidemiology
  • Family Characteristics*
  • Health Status Indicators*
  • Heart Failure / diagnosis
  • Heart Failure / epidemiology
  • Humans
  • Likelihood Functions
  • Medical Records, Problem-Oriented
  • Models, Statistical*
  • Myocardial Infarction / diagnosis
  • Myocardial Infarction / epidemiology
  • Neoplasms / diagnosis
  • Neoplasms / epidemiology
  • Process Assessment, Health Care
  • United States / epidemiology