Health status and severity of illness as predictors of outcomes in primary care

Med Care. 1995 Jan;33(1):53-66. doi: 10.1097/00005650-199501000-00005.

Abstract

Two measures of health status and severity of illness were tested as indicators of patient case-mix to predict health-related outcomes in a rural primary care community health clinic, using a convenience sample of 413 ambulatory adults (mean age = 40.4 years: 58.6% women, and 47.2% black). At baseline; patients completed the Duke Health Profile, and providers completed the Duke Severity of Illness Checklist. During the 18-month follow-up study, patients experienced the following outcomes: at least one follow-up visit (74.3%), more than six visits (20.6%), at least one referral or hospital admission (17.3%), upper tertile severity scores (24.9%), and upper tertile office charges (24.9%). Baseline physical health, perceived health, and severity scores were statistically significantly predictive of all five outcomes. Predictive accuracy (i.e., area under the receiver operating characteristic curves) for outcome probabilities estimated from a case-mix model of physical health, severity, age, gender, and race was 72.3% for follow-up, 69.7% for frequent follow-up, 70.5% for referral and/or hospital stay, 65.7% for high follow-up severity of illness, and 67.6% for high follow-up charges. These data support health status and severity of illness as case-mix indicators and outcome predictors of follow-up utilization, severity of illness, and cost in the primary care setting.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Community Health Services / statistics & numerical data
  • Direct Service Costs
  • Female
  • Health Care Costs / statistics & numerical data
  • Health Status*
  • Humans
  • Male
  • Medical Records / statistics & numerical data
  • Middle Aged
  • North Carolina
  • Odds Ratio
  • Outcome Assessment, Health Care / statistics & numerical data*
  • Primary Health Care* / statistics & numerical data
  • Prospective Studies
  • ROC Curve
  • Regression Analysis
  • Rural Health*
  • Severity of Illness Index*
  • Surveys and Questionnaires