We compared single‐ and multi‐item measures of general self‐rated health (GSRH) to predict mortality and clinical events a large population of veteran patients.
We analyzed prospective cohort data collected from 21,732 patients as part of the Veterans Affairs Ambulatory Care Quality Improvement Project (ACQUIP), a randomized controlled trial investigating quality‐of‐care interventions.
We created an age‐adjusted, logistic regression model for each predictor and outcome combination, and estimated the odds of events by response category of the GSRH question and compared the discriminative ability of the predictors by developing receiver operator characteristic curves and comparing the associated area under the curve (AUC)/‐statistic for the single‐ and multi‐item measures.
Data Source/Study Setting
All patients were sent a baseline assessment that included a multi‐item measure of general health, the 36‐item Medical Outcomes Study Short Form (SF‐36), and an inventory of comorbid conditions. We compared the predictive and discriminative ability of the GSRH to the SF‐36 physical component score (PCS), the mental component score (MCS), and the Seattle index of comorbidity (SIC). The GSRH is an item included in the SF‐36, with the wording: “In general, would you say your health is: Excellent, Very Good, Good, Fair, Poor?”
The GSRH, PCS, and SIC had comparable AUC for predicting mortality (AUC 0.74, 0.73, and 0.73, respectively); hospitalization (AUC 0.63, 0.64, and 0.60, respectively); and high outpatient use (AUC 0.61, 0.61, and 0.60, respectively). The MCS had statistically poorer discriminatory performance for mortality and hospitalization than any other other predictors (<.001).
The GSRH response categories can be used to stratify patients with varying risks for adverse outcomes. Patients reporting “poor” health are at significantly greater odds of dying or requiring health care resources compared with their peers. The GSRH, collectable at the point of care, is comparable with longer instruments.
Data Collection/Extraction Methods