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April 26, 2005Health Services Research682 citationsOpen Access

Predicting Mortality and Healthcare Utilization with a Single Question

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KDKaren B. DeSalvoVFVincent S. FanMMMary B. McDonell

Key Points

  • To compare the ability of a single-item general self-rated health question versus multi-item survey instruments to predict mortality, hospitalizations, and healthcare utilization.
  • Analyzed prospective cohort data from 21,732 veteran patients participating in the Veterans Affairs Ambulatory Care Quality Improvement Project (ACQUIP) randomized controlled trial.
  • Assessed predictors including the single-item general self-rated health (GSRH) question, the SF-36 physical component score (PCS), the SF-36 mental component score (MCS), and the Seattle index of comorbidity (SIC).
  • Fitted age-adjusted logistic regression models and compared discriminatory performance using receiver operating characteristic curves and the area under the curve (AUC).
  • The single-item GSRH demonstrated predictive accuracy comparable to PCS and SIC for mortality (AUC: 0.74, 0.73, and 0.73), hospitalization (AUC: 0.63, 0.64, and 0.60), and high outpatient utilization (AUC: 0.61, 0.61, and 0.60).
  • The MCS showed significantly lower discriminatory ability for mortality and hospitalization compared to all other predictors (p < 0.001).

Abstract

OBJECTIVE: 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. DATA SOURCE/STUDY SETTING: 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. STUDY DESIGN: 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)/c-statistic for the single- and multi-item measures. DATA COLLECTION/EXTRACTION METHODS: 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?" PRINCIPAL FINDINGS: 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 (p<.001). CONCLUSIONS: 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.

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Cite This Study

DeSalvo et al. (2005) studied this question.

synapsesocial.com/papers/6a001ca66018b8d0892daa1bhttps://doi.org/10.1111/j.1475-6773.2005.00404.x
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