Article

Predicting Mortality and Healthcare Utilization with a Single Question

University of Washington Seattle, Seattle, Washington, United States
Health Services Research (Impact Factor: 2.78). 08/2005; 40(4):1234-46. DOI: 10.1111/j.1475-6773.2005.00404.x
Source: PubMed
ABSTRACT
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)/c-statistic for the single- and multi-item measures.
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 (p<.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.

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Available from: ncbi.nlm.nih.gov
Predicting Mortality and Healthcare
Utilization with a Single Question
Karen B. DeSalvo, Vincent S. Fan, Mary B. McDonell, and
Stephan D. Fihn
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 ( po.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.
Key Words. Quality of life, mortality, hospitalization, outpatient, risk assessment
1234
r Health Research and Educational Trust
DOI: 10.1111/j.1475-6773.2005.00404.x
Page 1
Health administrators, researchers, and policymakers use prediction models
to forecast patient outcomes including morbidity, mortality, and health system
utilization. Traditionally, administratively derived predictors have been used
for such purposes, however, their limitations have led to the development of
alternatives (Romano et al. 1993; Iezzoni et al. 1996; Iezzoni 1999;
Schneeweiss and Maclure 2000; Schneeweiss et al. 2001, 2003). Measures of
self-rated health are robust risk predictors that have gained in popularity as a
substitute for administratively derived tools. These self-rated health measures
are patient centered and predictive of subsequent health outcomes, even in
patients without prior health problems. In several studies, patient self-rated
health status has predicted such important patient outcomes as mortality and
health system utilization (Miilunpalo et al. 1997; Curtis et al. 2002; Fan et al.
2002a, b; Spertus et al. 2002; Knight et al. 2003). These measures remain
consistent predictors of hospitalizations and mortality rates even after
adjustment for clinically relevant factors (Clarke and Oxmann 2002; Lowrie
et al. 2003).
Routine use of self-rated health measures for health care planning and
delivery is partially limited by burdens associated with collection of health
status information. Many self-rated health measures are multi-item scales that
are often onerous to collect in routine practice settings. Single-item general
self-rated health status (GSRH) measures may serve as a reasonable substitute
for multi-item measures of self-rated health (Balkrishnan and Anderson 2001).
They have the advantage of being less expensive and less burdensome to
collect, and could be conceivably collected at the point of care with relative
ease. In a health care setting that uses a relational, electronic database, this
collection could occur as part of routine intake in the primary care setting.
They are easy to score and interpret and, like the longer multi-item scales,
these single-item measures have predictive validity for mortality and health
care utilization in some populations (Idler and Benyamini 1997; Bierman et al.
1999; Balkrishnan et al. 2000). GSRH measures are relatively stable (Eriksson
et al. 2001) and sensitive to change (Rodin and McAvay 1992; Diehr et al.
2001).
Address correspondence to: Karen B. DeSalvo, M.D., M.P.H., M.Sc., Section of General Internal
Medicine, Tulane University School of Medicine, 1430 Tulane Avenue, SL-16, New Orleans, LA
70112. Karen B. DeSalvo is with the Department of Medicine, Tulane University School of
Medicine and also with the Department of Epidemiology, Tulane University School of Public Health
and Tropical Medicine. Vincent S. Fan, M.D., M.P.H., Mary B. McDonell, M.Sc., and Stephan D.
Fihn, M.D., M.P.H are with the VA Puget Sound Health Care System, NW, HSR & D Center of
Excellence. Drs. Fan and Fihn are also with Department of Medicine, University of Washington.
GSRH Prediction of Outcomes 1235
Page 2

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    • "SRH is easily measured in large population surveys, and is a useful " opener " in interview situations that allow interviewers to seek more nuanced and complex responses about people's perceptions of their health [3] . Poor SRH status has also been shown to be independently predictive of subsequent morbidity and higher health care utilization [4, 5]. The concept of health-related quality of life is that an individual's or group's perceived physical and mental health over time [6]. "
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    • "Tomblin Murphy et al. identified four categories of need constructs—measures of health risk, measures of morbidity, measures of mortality and measures of subjective health status [19]. Self-reported health status is known to correlate with a range of health outcomes and socio-economic variables [20][21][22][23] and has been shown to be a predictor of mortality and visits to the doctor or hospital [16]. Tomblin Murphy and colleagues [18, 24, 25] used self-reported health status as a measure of need when estimating the required number of health professionals including primary care professionals. "
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