Volume 54, Issue 4 p. 667-673

Use of Health-Related, Quality-of-Life Metrics to Predict Mortality and Hospitalizations in Community-Dwelling Seniors

David A. Dorr MD, MS

David A. Dorr MD, MS

From the *Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon†Intermountain Health Care, Salt Lake City, Utah‡Department of Medical Informatics, University of Utah, Salt Lake City, Utah§HealthInsight, Inc., Salt Lake City, Utah.

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Spencer S. Jones MStat

Spencer S. Jones MStat

From the *Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon†Intermountain Health Care, Salt Lake City, Utah‡Department of Medical Informatics, University of Utah, Salt Lake City, Utah§HealthInsight, Inc., Salt Lake City, Utah.

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Laurie Burns PT, MS

Laurie Burns PT, MS

From the *Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon†Intermountain Health Care, Salt Lake City, Utah‡Department of Medical Informatics, University of Utah, Salt Lake City, Utah§HealthInsight, Inc., Salt Lake City, Utah.

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Steven M. Donnelly PhD

Steven M. Donnelly PhD

From the *Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon†Intermountain Health Care, Salt Lake City, Utah‡Department of Medical Informatics, University of Utah, Salt Lake City, Utah§HealthInsight, Inc., Salt Lake City, Utah.

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Cherie P. Brunker MD

Cherie P. Brunker MD

From the *Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon†Intermountain Health Care, Salt Lake City, Utah‡Department of Medical Informatics, University of Utah, Salt Lake City, Utah§HealthInsight, Inc., Salt Lake City, Utah.

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Adam Wilcox PhD

Adam Wilcox PhD

From the *Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon†Intermountain Health Care, Salt Lake City, Utah‡Department of Medical Informatics, University of Utah, Salt Lake City, Utah§HealthInsight, Inc., Salt Lake City, Utah.

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Paul D. Clayton PhD

Paul D. Clayton PhD

From the *Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon†Intermountain Health Care, Salt Lake City, Utah‡Department of Medical Informatics, University of Utah, Salt Lake City, Utah§HealthInsight, Inc., Salt Lake City, Utah.

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First published: 06 April 2006
Citations: 94
Address correspondence to David A. Dorr, 3181 SW Sam Jackson Park Road, Mailcode: BICC, Portland, OR 97239. E-mail: address: [email protected]

Abstract

OBJECTIVES: To investigate whether health-related quality-of-life (HRQoL) scores in a primary care population can be used as a predictor of future hospital utilization and mortality.

DESIGN: Prospective cohort study measuring Short Form 12 (SF-12) scores obtained using a mailed survey. SF-12 scores, age, and a comorbidity score were used to predict hospitalization and mortality rate using multivariable logistic regression and Cox proportional hazards during the ensuing 28-month period for elderly patients.

SETTING: Intermountain Health Care, a large integrated-delivery network serving a population of more than 150,000 seniors.

PARTICIPANTS: Participants were senior patients who had one or more chronic diseases, were community dwelling, and were initially treated in primary care clinics.

MEASUREMENTS: SF-12 survey Version 1.

RESULTS: Seven thousand seventy-six surveys were sent to eligible participants; 3,042 (43%) were returned. Of the returned surveys, 2,166 (71%) were complete and scoreable. For the respondent group, a multivariable analysis demonstrated that older age, male sex, higher comorbidity score, and lower mental and physical summary measures of SF-12 predicted higher mortality and hospitalization. On average, nonresponders were older and had higher comorbidity scores and mortality rates than responders.

CONCLUSION: The SF-12 survey provided additional predictive ability for future hospitalizations and mortality. Such predictive ability might facilitate preemptive interventions that would change the course of disease in this segment of the population. However, nonresponder bias may limit the utility of mailed SF-12 surveys in certain populations.