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(Chest. 2004;126:81-89.)
© 2004 American College of Chest Physicians

The Veterans Short Form 36 Questionnaire Is Predictive of Mortality and Health-Care Utilization in a Population of Veterans With a Self-Reported Diagnosis of Asthma or COPD*

Mark D. Sprenkle, MD, MS; Dennis E. Niewoehner, MD; David B. Nelson, PhD and Kristin L. Nichol, MD, MPH, MBA

* From the Division of Pulmonary and Critical Care Medicine (Dr. Sprenkle), Hennepin County Medical Center; and the Department of Medicine (Drs. Nichol and Nelson) and Division of Pulmonary and Critical Care (Dr. Niewoehner), Veterans Affairs Medical Center, Minneapolis, MN.

Correspondence to: Mark D. Sprenkle, MD, MS, Pulm Med 865B, Hennepin County Medical Center, 701 Park Ave, Minneapolis, MN 55415; e-mail spren005{at}umn.edu


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Study objective: Measures of health-related quality of life (HRQL) correlate with disease stage in persons with COPD. However, as their predictive capacity for mortality or medical utilization is less well defined, we sought to examine the relationship of a general measure of HRQL and outcomes in persons with obstructive lung disease.

Design: Prospective cohort study.

Setting: Upper Midwest Veterans Integrated Service Network (VISN)-13.

Participants: All veterans in VISN-13 (n = 70,017) were surveyed with the Veterans Short Form 36 (SF-36V). Persons with reported asthma or COPD who completed the SF-36V formed the study cohort (n = 8,354).

Measurements and results: For purposes of analysis, individuals were divided into quartiles of HRQL according to their physical component summary (PCS) and mental component summary (MCS), values derived from the SF-36V. Outcomes of mortality, hospitalization, and outpatient visits were recorded for 12 months after the survey. Outpatient utilization was dichotomized into high vs low use, with high use being defined as the upper quartile of visits in the 12 months prior to survey mailing. The study cohort had a mean age of 65 years and was largely male (95%), both consistent with a veteran population. After correcting for potential confounding factors through multivariable regression, the PCS was independently predictive of death, hospitalization, and high outpatient utilization. When using the first quartile of PCS as the reference population, those in the fourth quartile of PCS had a hazard ratio for death of 5.47 (95% confidence interval [CI], 3.63 to 8.26). Similarly, the odds ratios for hospitalization, high primary care visits, and high specialty medicine visits in the fourth quartile of PCS were 1.82 (95% CI, 1.51 to 2.19), 1.54 (95% CI, 1.26 to 1.87), and 1.46 (95% CI, 1.21 to 1.78), respectively. The MCS, through multivariable regression, was predictive of death but unassociated with subsequent hospitalization or high outpatient utilization.

Conclusion: HRQL, as assessed by the SF-36V, is an independent predictor of mortality, hospitalization, and outpatient utilization in persons with self-reported obstructive lung disease.

Key Words: COPD • health status • hospitalization • mortality


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
The manner in which patients perceive, manage, and adapt to chronic respiratory diseases varies significantly. An individual’s personal experience with a disease is modified by many factors, including disease severity, emotional coping skills, social support systems, comorbid disease, and global functional capacity.123 Numerous disease-specific45 and general measures6789 of health-related quality of life (HRQL) have been developed to quantify the experience individuals have with their health. Indeed, the past 2 decades have seen a growing body of literature on the relationship between HRQL and obstructive lung disease.

Given the prognostic importance of lung function in obstructive lung disease, many studies3101112 examining HRQL in obstructive lung disease have focused on their relationship to physiologic measures of pulmonary or exercise capacity. In general, these studies have shown a statistically significant correlation. However, typically this correlation is not strong, suggesting that these physiologic tests and measures of HRQL may quantify different aspects of disease. While studies131415 examining impaired functional status of persons with COPD have found an association with mortality, fewer studies1617 have attempted to validate HRQL measures regarding clinically important outcomes such as mortality or medical utilization.

We postulated that a general measure of HRQL, the short form 36-item questionnaire,18 would be independently predictive of mortality and health-care utilization. To evaluate this hypothesis, we utilized existing data from a large population-based survey performed through a Veterans Affairs (VA) medical network. After identifying individuals with a self-reported diagnosis of obstructive lung disease and quantifying their HRQL, we prospectively examined outcomes of mortality, hospitalization, and outpatient visits.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
This study was approved by the institutional review board of the Minneapolis Veterans Affairs Medical Center.

Study Population
The study population was drawn from veterans who received care through the Upper Midwest Veterans’ Integrated Service Network (VISN)-13. VISN-13 was a network of VA hospitals in North Dakota, South Dakota, and Minnesota. VISN-13 subsequently merged with VISN-12, containing care centers in Iowa, to form VISN-23. Through VISN-13, veterans could receive care at five medical centers: Minneapolis, MN; St. Cloud, MN; Fargo, ND; Sioux Falls, SD; and Ft. Meade, SD. All veterans in VISN-13 with a medical encounter at a VA facility between October 1, 1997, and March 31, 1998, and valid mailing address were sent a survey containing the short form-36 and a checklist of six common medical conditions. The initial mailing took place on August 21, 1998. Those persons who had not responded to the initial mailing by October 1, 1998, were sent a second survey on October 30, 1998. Those persons who returned a completed survey with a self-reported diagnosis of obstructive lung disease formed our study cohort.

Questionnaire
HRQL was assessed by an adapted form of the short form-36, the Veterans Short Form 36 (SF-36V).19 This questionnaire was specifically adapted for use in veterans receiving care in an ambulatory setting. The SF-36V measures eight dimensions of health status including physical functioning, role limitations due to physical problems, bodily pain, general health, energy/vitality, social functioning, role limitations due to emotional problems, and mental health. These eight dimensions can be summarized numerically into the physical component summary (PCS) and the mental component summary (MCS). The PCS and MCS are standardized to the US population and range between 0 and 100, with higher scores indicated better HRQL. In addition, the questionnaire included a list of six medical conditions: (1) asthma, emphysema, or chronic bronchitis; (2) hypertension; (3) diabetes mellitus; (4) arthritis; (5) myocardial infarction or history of angina; and (6) depression. Certain demographic characteristics, including age, educational level, race, and smoking status, were also ascertained through the survey.

Data Collection and Outcome Variables
In addition to the mailed survey, certain demographic variables were ascertained through the VISN-13 patient database (ie, gender, employment status, marital status, and percentage of service connection). Outcomes were assessed for a 1-year period after the survey date. Medical utilization was measured as the number of hospitalizations, primary care visits, and specialty medicine visits. Death from any cause was determined from the Beneficiary Identification and Record Locator System for the year following the date of survey. Beneficiary Identification and Record Locator System data have been shown to capture 95 to 98% of deaths.2021

Statistical Analysis
All statistical analyses were performed using a statistical software package (SAS, version 7.0; SAS Institute; Cary, NC). For purposes of analysis, the study cohort was divided into quartiles of PCS and MCS. In simple univariate analyses, Pearson {chi}2 tests and a one-way analyses of variance were used to assess the ability of the PCS and MCS to predict subsequent categorical and continuous outcomes, respectively, where the population was partitioned into groups according to the PCS and MCS quartiles. Those persons with the best HRQL (ie, first quartile) served as the reference category. The Mantel test for trend was used to assess for a linear component to the trend across quartiles of PCS and MCS in the categorical outcome variable of any prior hospitalization in the year prior to survey mailing. Assuming the results of the F test in the one-way analysis were statistically significant, multiple comparisons were performed against the reference population using the Bonferoni multiple comparisons correction method.

In order to assess the independent predictive capacity of HRQL on mortality and medical utilization, multivariable regression analyses were performed for each outcome measure where the regression models included the PCS and MCS quartile groups and demographic variables comprising age (< 60 years, 60 to 70 years, 70 to 80 years, >80 years), gender, education level, employment status, marital status, percentage of service connection, and smoking status as explanatory variables. Comorbidity was modeled both as a count of the number of self-reported diseases, as well as by including individually the three disease states found to be associated with an increased risk of death and hospitalization by univariate logistic regression (ie, diabetes mellitus, depression, myocardial infarction, or history of angina). As both representations of comorbidity resulted in substantively similar results in the multivariable models, we elected to use the count of comorbidities in the final models. In addition, hospitalization, number of primary care visits, and number of specialty medicine visits in the year prior to the survey mailing were included in order to account for a potential association with prior utilization.

A Cox proportional-hazards survival analysis was used to model mortality and to assess the independent predictive capacity of PCS and MCS quartile for mortality. In an examination of a potential interaction between follow-up time and PCS/MCS quartile, we found no evidence against the assumption of proportional hazards. Logistic regressions were constructed to model hospitalization and outpatient visits. The Hosmer-Lemeshow statistic was used with the logistic regression models to assess model fit. Primary care and specialty medicine utilization was dichotomized into high vs low use, with high use defined as utilization surpassing the upper quartile of visits in the 12 months prior to the survey mailing. In the Cox proportional hazards and logistic regression analyses, backward elimination model selection procedures were implemented to generate parsimonious predictive models. Covariates were eliminated at a p value of 0.05 when using backward elimination. Given the established prognostic importance of age and gender in obstructive lung disease, these covariates were maintained in all multivariable regression models.


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Survey Response and Demographic Characteristics
The initial mailing to patients with a recent medical encounter at one of the VISN-13 medical centers encompassed a group of 70,674 veterans (Fig 1 ). A total of 40,605 individuals responded to the survey, a response rate of 58.0% after excluding those who had died or no longer had a valid mailing address. Of this population, 10,135 persons had a self-reported diagnosis of asthma, COPD, or chronic bronchitis. In this group of respondents, 1,781 persons did not complete the SF-36V questionnaire adequately to compute a PCS or MCS. These individuals were excluded, forming the final study cohort of 8,354 persons.



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  		Figure 1. Flowsheet of survey response and formation of the study cohort.

 
The demographic characteristics of the study population are consistent with a veteran population from the northern Midwest (Table 1 ). The mean age of the study cohort was 65.0 years with a large preponderance of men. Individuals reported a high number of comorbidities, with a mean value (including obstructive lung disease) of 2.79. The majority of our study population was white, educated to high school or beyond, and retired or not employed.


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  		Table 1. Demographics of the COPD Cohort*

 
Univariate Analysis
In univariate analysis, there were statistically significant associations between in almost all demographic characteristics and the PCS or MCS measures (Tables 2, 3 ). The age and number of comorbidities of the study population increased with worsening HRQL. In addition, a worse PCS or MCS was a marker for those who are not employed or have an educational level below graduation from high school. Prior health-care utilization was strongly associated with HRQL. Worsening quartile of PCS and MCS was associated, in a stepwise fashion, with a higher rate of hospitalization in the year prior to study entry. Similar associations of prior increased outpatient medical utilization with worse HRQL were present by quartile of both PCS and MCS.


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  		Table 2. Univariate Analysis of Demographic Characteristics and Previous Health-Care Utilization by PCS Quartile*

 

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  		Table 3. Univariate Analysis of Demographic Characteristics and Previous Health-Care Utilization by MCS Quartile*

 
During the period of follow-up, there were 429 deaths (5.1%) in the study cohort. Hospitalization occurred at least once in 1,407 of the entire population (16.8%). In unadjusted models, a statistically significant association was found between the quartile of PCS and all outcome measures (Tables 4, 5 ). The hazard ratio for mortality was 8.72 (95% confidence interval [CI], 5.81 to 13.1) in the lowest quartile of PCS when compared to those persons in the highest quartile. The risk of any hospitalization was also more likely with worsening PCS (odds ratio, 2.62; 95% CI, 2.21 to 3.11 when comparing fourth to first quartiles of PCS). Similarly, the odds ratios for high outpatient primary care and specialty medicine utilization increased in a stepwise fashion with worsening quartile of PCS. Persons with the worst mental health status, as assessed by quartile of MCS, had higher mortality, rate of hospitalization, and primary care utilization by univariate analyses (Tables 4, 5). When comparing the lowest quartile of MCS to the highest, the hazard ratio for mortality was 2.50 (95% CI, 1.88 to 3.33). Hospitalization occurred more frequently in those with lower MCS (odds ratio, 1.63; 95% CI, 1.38 to 1.92 when comparing fourth to first quartiles of MCS). While a statistically significant association was noted between the MCS and high primary care utilization, no association was found between MCS and high specialty medicine visits in univariate analyses.


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  		Table 4. Cox Proportional Hazards Model of the Relationship Between the PCS and MCS and Mortality

 

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  		Table 5. Univariate and Multivariate Logistic Regression Models of the Relationship Between the PCS and MCS and the Risk of Hospitalization or Primary Care/Specialty Medicine Visits

 
Multivariable Analysis
Analysis by a Cox proportional hazards model revealed the PCS and MCS to be independent predictors of mortality (Table 4). With PCS modeled as a continuous variable, a 5-point decrease in the PCS resulted in a 39% increased risk of death (p < 0.0001). When analyzed by PCS quartile, with the first quartile of PCS as the reference population, a hazard ratio of 5.47 (95% CI, 3.63 to 8.26) was found for the fourth quartile. Similarly, the hazard ratio for mortality increased with worsening quartile of MCS.

An independent relationship between HRQL, as assessed by PCS, and health-care utilization was noted in multivariable logistic regression (Table 5). The risk of hospitalization was significantly increased for all quartiles of PCS in comparison to the reference population. When examining PCS as a continuous variable, a 5-point decrease results in a 13% increased risk of hospitalization. The risk of high outpatient utilization was, in general, statistically significant. With the first quartile serving as the reference population, those individuals in the fourth quartile of PCS had odds ratios of 1.54 (95% CI, 1.26 to 1.87) and 1.46 (95% CI, 1.21 to 1.78) for high primary care and specialty medicine utilization, respectively. We found no association between MCS and health-care utilization after correcting for potential confounding factors (Table 5).

Nonresponders
Of the entire eligible population of veterans mailed both surveys, a total of 29,412 persons did not respond. Demographic characteristics of the entire population of responders and nonresponders are presented in Table 6 . Nonresponders, in general, were younger and less likely to be married. In the year prior to survey mailing, nonresponders had fewer outpatient visits but more hospitalizations than respondents. In addition, mortality during the year following survey mailing was higher in nonresponders.


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  		Table 6. Demographic Characteristics of Responders and Nonresponders*

 

   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Our results indicate that HRQL is a useful predictor of outcomes in persons with a self-reported diagnosis of obstructive lung disease. Our study cohort included an elderly, predominantly male population of 8,354 veterans. In this study population, we found a statistically significant, stepwise association between the quartile of PCS and all outcome measures. In multivariable analysis, HRQL was found to be an independent predictor of mortality and medical utilization. Of the outcome measures we analyzed, we found mortality to have the strongest association with HRQL (odds ratio, 5.47, fourth quartile vs first quartile). However, a significant association was also present between risk of hospitalization and HRQL, with 82% greater odds of hospitalization in the fourth quartile of PCS compared to the reference population after adjustment for confounding factors. Somewhat smaller, though significantly increased, risks were present when examining high outpatient primary care and specialty medicine utilization. Interestingly, while both the PCS and MCS were independently predictive of mortality, the MCS did not prove to be associated with increased risk of hospitalization or high outpatient utilization.

There is little information in the medical literature on HRQL and clinical outcomes in obstructive lung disease. There have been a few investigations examining the predictive capacity of functional status measures in persons with COPD.131415 These studies all found that functional status was independently predictive of clinically important end points such as mortality or weaning from mechanical ventilation. However, tools to assess functional status and measures of HRQL assess different aspects of daily living as it relates to health.1 Functional capacity measures are focused on the ability of an individual to perform activities of daily living, while measures of HRQL are more subjective measures of the multiple ways (ie, physical and emotional) that health impacts daily life. While one study16 did find an increased risk of mortality in those with poor quality of life, this study measured quality of life using a global rating scale of "excellent" to "poor."

Fan et al17 performed the only previous study to examine a validated, systematic measure of HRQL to assess the risk mortality and medical utilization in persons with obstructive lung disease. They examined a population of persons enrolled at general internal medicine clinics at seven VA hospitals geographically distributed across the United States. Their primary analysis involved the predictive capacity of a disease-specific HRQL measure, the Seattle Obstructive Lung Disease Questionnaire, in determining subsequent mortality or hospitalization over a 1-year period of follow-up. Additionally analyses were performed based on participant response to the SF-36V.

The present study both supports and expands on the study performed by Fan et al.17 Both studies found a statistically significant, independent association between HRQL and mortality or hospitalization. Though both studies enrolled a population of veterans, some differences in the study populations exist. Though our study was geographically limited to Northern-Midwest states, we surveyed a broader sample of veterans, enrolling all persons seen recently throughout a Veterans’ Integrated Service Network. This difference in survey administration allowed for inclusion of persons outside of general internal medicine clinics. In addition, we were able to survey a larger number of persons, resulting in a larger number of persons available for follow-up (n = 3,282 vs n = 8,354). Finally, we were able to examine not only mortality and hospitalization, but also outpatient primary and specialty medicine care as outcome variables.

Why is it important to examine HRQL measures in obstructive lung disease when measures of pulmonary capacity can already provide useful prognostic information in this patient population? It appears that HRQL measures and spirometry measure different characteristics of chronic lung disease. Studies examining the relationship between FEV1 and general measures of HRQL have found only a weak-to-moderate correlation (correlation coefficients, 0.20 to 0.25).310 A smaller study by Mahler and Mackowiak12 also found FEV1 and five of nine components of the short form-36 to be statistically correlated, though the correlation between a composite score (ie, PCS) and FEV1 was not presented.12 As HRQL measures examine other consequences of disease on daily living besides exertional capacity, it is possible that these tests could provide significant prognostic information, independent of individual pulmonary function measures. Furthermore, it is possible that HRQL measures and pulmonary function tests could provide additive prognostic information in persons with obstructive lung disease. Unfortunately, given the size of our study cohort, we were unable to systematically perform pulmonary function studies to examine these questions.

Validation of HRQL measures, both general and disease specific, is a challenging task.12223 As noted above, validation of these measures in obstructive lung disease can be performed through their comparison to well-established measures of disease severity, such as spirometry. In addition, validation can be supported by significant change in measured HRQL after administration of therapies, such as inhaled bronchodilators.242526 This study provides an additional evidence, in the form of independent prognostic capacity, to support the value of HRQL measures in obstructive lung disease. Whether this type of patient information could be used to target at-risk populations for medical interventions including vaccination, smoking cessation, or pulmonary rehabilitation requires further investigation.

Our study has a number of limitations. First, diagnostic misclassification may have occurred. We relied on a self-reported diagnosis of obstructive lung disease in order to identify individuals for our study cohort. Other investigators have examined the concordance between physician-diagnosed chronic lung disease and patient self-report272829; these studies have found very good agreement, with {kappa} coefficients ranging from 0.56 to 0.73. While the size of our study population is a strength of this investigation, it made definitive diagnosis of their obstructive lung disease prohibitive. In addition, our study population included both asthmatics and persons with COPD. Because of concerns of diagnostic misclassification, study participants were not asked whether they had asthma or COPD. Instead, they were asked an inclusive question: "have you been told by a physician that you have asthma, emphysema, or chronic bronchitis?" It is possible that the relationship of HRQL to mortality and medical utilization is different in persons with asthma compared with COPD. Given the elderly nature of our veteran population, it is likely that the majority of our study population had COPD. However we would be unable to distinguish differences in the prognostic capacity of HRQL depending on the type of obstructive lung disease present.

We do not have any data regarding study participants receiving medical care outside of the VA medical centers enrolled in this study. As a consequence, confounding by differential non-VA hospitalization or outpatient utilization among our study cohort may be present. VA hospitals often do not provide emergent medical care, largely due to issues of poor proximity or lack of emergency medical facilities. As a consequence, veterans can receive medical care, if necessary, at community hospitals. It is probable that persons with the worst HRQL would be at greater risk of emergent hospitalization or ambulatory medical evaluation than those with better HRQL. If we accept that persons with impaired HRQL would be more likely to receive care at a non-VA hospital or clinic, this ascertainment bias would lessen the statistical association between HRQL and the risk of hospitalization or outpatient visit. As death statistics were collected regardless of the place of death, the relationship between HRQL and mortality is unaffected by this potential bias.

Next, residual confounding by inadequate assessment of patient comorbidities could be present. Diagnostic misclassification could have occurred as a consequence of patient self-report of comorbidities. In addition, only a limited number of comorbidities were assessed through the study questionnaire. Finally, the results of these analyses are valid only for those persons who were willing to participate in this survey. While our response rate was reasonably high at 58.0%, there are clear demographic differences between responders and nonresponders that preclude generalization of these finding to the entire eligible veteran population.

In conclusion, we have shown that the assessment of HRQL in persons with obstructive lung disease can independently identify populations at risk for death and increased medical resource utilization. This information provides yet more evidence to validate the use of general measures of HRQL in chronic lung disease. While tools to assess HRQL are being used widely to assess the therapies in COPD, further research is needed to discover whether impaired HRQL can be used as a marker to identify individuals who would benefit from medical intervention or pulmonary rehabilitation.


   Footnotes
 
Abbreviations: CI = confidence interval; HRQL = health-related quality of life; MCS = mental component summary; PCS = physical component summary; SF-36V = Veterans Short Form 36; VA = Veterans Affairs; VISN-13 = Upper Midwest Veterans’ Integrated Service Network

Presented, in part, in abstract form at the American Thoracic Society International Meeting in Seattle, WA, May 18, 2003.

Funding was provided by the Veterans Affairs Upper-Midwest Veterans Service Network.

Received for publication September 18, 2003. Accepted for publication February 10, 2004.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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