1 Department of Internal Medicine, The University of Iowa College of Medicine, Iowa City, IA.
2 Department of Medicine, The University of Alabama, Birmingham, Birmingham, AL.
3 Pulmonary and Critical Care Medicine, Duke University Medical Center, Durham, NC.
4 Department of Epidemiology, The University of Iowa College of Public Health, Iowa City, IA.
5 Department of Biostatistics, The University of Iowa College of Public Health, Iowa City, IA.
6 Veterans' Affairs Medical Center, Iowa City, IA.
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ABSTRACT |
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health status; health surveys; military personnel; models, statistical; Persian Gulf syndrome; quality of life; risk factors
Abbreviations: HRQL, health-related quality of life; MCS, mental component summary; PCS, physical component summary; SE, standard error; SF-36, Medical Outcome Study Short Form 36
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INTRODUCTION |
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When Gulf War troops returned home, concern mounted that some health problems experienced by veterans and their spouses and children were a result of exposures encountered in the Persian Gulf (1). Despite multiple investigations (2
8
) and evidential review by scientific panels (1
, 9
12
), few clear clinical or physiologic consequences of deployment have been identified or etiologic explanations for illness confirmed. However, epidemiologic studies have consistently shown increased symptomatology among those deployed (13
, 14
), and the impact of the Gulf War on veterans' health-related quality of life (HRQL) remains unclear.
The use of HRQL measures in medical research has grown in the past decade (1517
). Theoretical models are useful in defining health dimensions and in identifying potential contributors to HRQL (18
). Wilson and Cleary specified a hierarchic pathway of health outcomes leading from biologic and symptom variables to functional status and overall quality of life (15
); a variety of factors including individual and environmental characteristics were identified as influencing health outcomes. Similarly, Aday and Anderson classified predisposing, enabling, and need factors as influencing health services utilization (19
). Again, medical as well as nonmedical factors have been identified as contributing to health outcomes (20
).
Current medical illness is an influential contributor to HRQL (2123
), explaining 1224 percent of the variance (24
). However, additional cofactors such as sociodemographic, psychological, and treatment characteristics as well as illness duration and severity must be considered (25
). Collectively, available factors have explained up to 40 percent of the variance in HRQL scores (26
).
The Iowa Gulf War Study was initiated to determine the prevalence of symptoms and illnesses 5 years postconflict among military personnel deployed to the Gulf War Theater (subsequently referred to as deployed) compared with active-duty, but not Gulf War-deployed (referred to as nondeployed) controls. We reported previously that Medical Outcome Study Short Form 36 (SF-36) scores were poorer among Gulf War-deployed veterans compared with nondeployed controls (13). We also reported that participants meeting the criteria for multiple chemical sensitivity syndrome reported impaired HRQL (27
). The objective of this analysis was to further characterize the HRQL of military personnel, comparing deployed with nondeployed, and to identify potential pre- and perideployment risk factors for poorer HRQL.
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MATERIALS AND METHODS |
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A stratified random sample was drawn equally from four domains based on deployment to the Gulf War Theater ("deployed" vs. "nondeployed") and military status (regular military vs. National Guard or Reserve). The survey design was further stratified, with proportional allocation and oversampling of small strata, by service branch (Army, Air Force, Marines, or Navy/Coast Guard), rank (enlisted or officer), gender, race (White or Black/Other), and age in years (25 or >25).
A total of 4,886 eligible subjects were randomly selected and were surveyed to assess the prevalence of health problems and exposures. Interviews were conducted from September 1995 through May 1996, approximately 5 years after the conflict. Trained personnel administered the study survey via two structured telephone interviews. An introductory interview obtained subject consent and demographic information (mean length, 10 minutes). The health and exposure assessments were conducted during the main interview (mean length, 60 minutes).
Study procedures and instruments were approved by the institutional review board, and a Public Health Service Certificate of Confidentiality was obtained. In addition, reliability interviews were conducted with a random subsample of participants to assess test-retest reliability. Proxy interviews were also conducted when warranted. Further details regarding the survey methods and descriptive results are reported elsewhere (13, 28
).
Study instruments
The structured interview was developed to assess a broad array of health concerns. Emphasis was placed on using standardized and validated questions or instruments. Investigator-derived questions were also used based on peer-reviewed data, interviews with Department of Veterans Affairs Gulf War Registry participants, pilot studies, and input from public and scientific advisory committees. Participants were asked about potential risk factors for poor health, such as sociodemographic and behavioral factors and current and predeployment medical and psychiatric health history. Military preparedness was characterized by using six items that asked how prepared or trained participants were in August 1990 to do their job. The number of positive responses indicated their level of preparedness, as follows: 03, least prepared; 45, moderately prepared, 6, most prepared.
The SF-36, a widely used general-health-profile questionnaire with established reliability and validity, was used to assess HRQL (29). The SF-36 has demonstrated sensitivity to health differences in the general population and in patients with chronic diseases (30
). Questions are allocated to the following eight scales: 1) limitations in physical activities because of health (physical functioning), 2) limitations in social activities because of physical or emotional problems (social functioning), 3) limitations in role activities because of physical health problems (role-physical), 4) bodily pain (bodily pain), 5) general mental health (mental health), 6) limitations in role activities because of emotional problems (role-emotional), 7) vitality (vitality), and 8) general health perceptions (general health). Two SF-36 summary scales, the physical component summary (PCS) score and the mental component summary (MCS) score, have been identified and account for more than 80 percent of the variance in the subscales (31
). The PCS score is a single measure of physical health derived primarily from the physical functioning, role-physical, bodily pain, and general health subscales. The MCS score is a single measure of mental health derived primarily from the mental, role-emotional, social functioning, and vitality subscales. Scoring was performed according to recommended guidelines and ranged from 0 to 100; lower scores reflect poorer health (32
).
Analytical methods
The statistical program SUDAAN (33) was used to account for the complex sample design. Standard errors were calculated according to standard techniques for estimation under stratified random sampling without replacement (34
). Means and standard errors were reported for continuous variables. Tests of association were calculated by using the SUDAAN linear regression procedure. Coefficients of the independent variables provided estimates of the adjusted mean differences in outcome. Ninety-five percent confidence intervals for these coefficients tested the hypotheses of no mean difference. The alpha value was set at 0.05, and all p values were two tailed.
Cronbach's alpha assessed internal consistency of the SF-36 scales (35). Test-retest agreement was assessed by using the weighted kappa statistic (36
). Construct validity, or whether measures relate to other variables as expected, was evaluated by hypothesizing and then identifying the relations between various SF-36 scales and specific current medical conditions using t tests (37
).
The clinical significance of observed differences in HRQL across groups was evaluated by comparison with values published from other study samples and by evaluating effect size. The effect size was calculated as the difference between mean scores for nondeployed versus deployed participants divided by the subscale's standard deviation for the general US population (32).
To control for potential confounders in the relation between deployment and HRQL and to identify potential risk factors, multivariate linear regression methods were used (38). Modeling strategies outlined by Harrell et al. were adopted (39
). Independent variables were organized into theoretically related groups as follows: sociodemographic and behavioral factors, predeployment medical conditions, mental health history, and exposures. Dependent variables were the SF-36 PCS and MCS scores, each modeled independently.
The association of potential risk factors with each summary measure was evaluated. Among those factors with univariate p values of 0.25 or less, stepwise regression identified independent risk factors for physical and mental health. The stepwise procedure was performed by using SAS software, version 6.12 (40). Initial selection was conducted within each variable group because of the potential advantages over single-variable selection methods (38
). All variables retained after the initial selection process were then considered in a final stepwise procedure, conducted independently for the PCS and MCS scores. Final model parameter estimates and standard errors were obtained by using SUDAAN software (33
). The R2 statistic was used to estimate each model's "goodness-of-fit."
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RESULTS |
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We predicted that certain medical conditions would be inversely associated with specific subscales, while others would not. For example, persons reporting arthritis were predicted to have low bodily pain scores, whereas low mental health scores were predicted for those with depressive symptomatology. Mean SF-36 subscale and summary scores varied as hypothesized, supporting the construct validity (41). Participants reporting current physical or mental health disorders also had significantly lower SF-36 scores than those without the specific disorder (t test p < 0.001).
Figure 1 illustrates the distribution of the PCS (deployed mean = 51.4, standard error (SE), 0.2 and nondeployed mean = 53.1, SE, 0.2) and MCS (deployed mean = 51.3, SE, 0.2 and nondeployed mean = 53.4, SE, 0.2) scores by deployment status. Regardless of deployment status, health states ranged from dysfunction to positive well-being, with a slight skew toward poorer health. Deployed veterans tended to have lower values than nondeployed controls.
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Each mean SF-36 subscale and summary score, adjusted for the stratification variables (figure 2), was significantly lower for deployed veterans compared with nondeployed controls. The unadjusted standardized differences, or effect sizes, across deployment status were as follows: physical functioning, 0.07; role-physical, 0.13; bodily pain, 0.24; general health, 0.32; vitality, 0.34; social functioning, 0.12; role-emotional, 0.11; mental health, 0.21; PCS score, 0.17; and MCS score, 0.20. General health and vitality showed the largest differences, whereas physical and emotional functioning and role were least affected by deployment. An effect size of 0.20 is considered small, 0.50 is considered moderate, and 0.80 is considered large (42).
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HRQL correlates and multivariate modeling
Linear regression results are shown in table 2. Each variable's univariate association with the summary score (PCS and MCS) is shown, along with the mean score for the referent group. Deployment to the Persian Gulf was associated with a 1.7 (SE, 0.3)-point lower mean PCS score and a 2.0 (SE, 0.3)-point lower mean MCS score.
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Significant risk factors for a poorer PCS score included deployment, the Army branch of the military (vs. other services), unemployment, being married, lower military preparedness, cigarette smoking, prior mental health care, previous jail time, and several specific preGulf War medical conditions (hypertension, migraines, asthma, chronic sinusitis, chronic ear infection, ulcer disease, gastritis, colitis, arthritis/rheumatism, fibromyalgia, and lumbago). This model accounted for 19 percent of the common variance.
Significant risk factors for a poorer MCS score included deployment, non-White race, the Army branch of the military (vs. other services), divorce, shorter active-duty duration, cigarette smoking, prior mental health care, and several preGulf War medical conditions (seizures/convulsions, asthma, enteritis, kidney disease, arthritis/rheumatism, post-traumatic stress disorder, and depression). This model accounted for 22 percent of the common variance.
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DISCUSSION |
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Potentially modifiable factors such as smoking and military preparedness, and other nonmodifiable factors such as predeployment medical and mental health comorbidity, marital status, race, and service branch, were identified as risk factors for worse postdeployment HRQL. The identified variables may be important predisposing, precipitating, perpetuating, or prognostic factors and should be considered in health protection and promotion efforts (43).
Theoretical models provided a framework to examine contributors to and potential confounders in the relation between deployment and HRQL (15, 44
). Health status potentially resulting from deployment (postdeployment variables) was not included in the multivariate models. For example, considering health conditions present at the time of the survey would have increased the model variance explained but may be a consequence of deployment. In cross-sectional data, it is difficult to determine whether current health conditions are independent causes or confound the relation with altered HRQL. These data demonstrate the need for routine general health status assessments using standardized instruments prior to and during future deployments.
The health disparity associated with deployment changed very little after we adjusted for other factors. SF-36 PCS and MCS scores remained approximately two points lower for deployed compared with nondeployed military personnel. These summary measures are scored to have a mean of 50 and a standard deviation of 10 in the general US population. A difference of one point in the PCS score is roughly equivalent to the annual average decline in the summary score for people aged 65 years or more (31) and has been associated with increased health service utilization (45
).
The unadjusted mean difference across deployment status on the eight subscales ranged from 1.7 to 7.1. These differences, while statistically significant, are relatively small clinically, whereas a 10-point difference is considered moderate and 20 points very large (32). Comparatively, a meaningful difference among Hodgkin's disease patients was regarded as 710 points (46
). However, the bodily pain and vitality scores for deployed veterans were similar to those reported by patients with minor medical conditions (47
). Deployed veterans reported reduced health for all domains, much as medically unexplained physical symptoms often involve multiple organ systems (48
). The general health and vitality health domains were the most impacted, followed by bodily pain and mental health. In contrast, physical and emotional functioning and role subscales were impacted the least.
The health profile of deployed veterans generally was slightly below US norms, although the mean scores of these veterans remained higher than those for medically and mentally ill populations (32). However, for these veterans, the MCS score was higher than age-matched US norms. Perhaps the emotional burden of service is less severe than the more subtle physical consequences, which potentially lead to medically unexplained physical symptoms. Additionally, veterans may be reluctant to acknowledge impaired mental health. Other potential explanations such as age distribution, media effect, reporting bias, and regional trends need to be explored.
Our results are consistent with reports from other studies. Similar health status distributions were found among a small group of Air Force Gulf War veterans from Pennsylvania seeking medical evaluation (14). British researchers found very similar SF-36 scores and also noted a larger difference in general health relative to physical functioning for Gulf War veterans versus controls (49
). Compared with controls, Canadian Gulf War veterans have reported a greater reduction in activities because of poor health and a higher number of bed days (50
). Proctor et al. reported significantly lower SF-36 scores for Gulf War veterans (n = 291) compared with those deployed to Europe (n = 50); however, the prevalence of fair or poor health status was twice that seen here, perhaps because of these authors' use of a more highly selected group (51
). More recently, Proctor et al. reported SF-36 scores for Gulf War-deployed veterans (n = 141) and Germany-deployed controls (n = 46) 4 years postconflict (52
). Gulf War veterans were also shown to have poorer health than the general US population. In contrast to our study, these authors identified current medical and psychological conditions associated with lower physical functioning.
Our study has a number of unique strengths. A 76 percent participation rate is one of the highest among comparable studies and minimizes the likelihood of participation bias (53). Inclusion of all personnel, regardless of whether they were discharged from military service, reduces concerns of bias due to unequal follow-up or a healthy worker effect. The deployed sample served throughout the Gulf War Theater, and, importantly, a comparable nondeployed control group was included. The large sample was important for the analytical methods used. Finally, we considered a broad range of health, personal, and environmental characteristics as potential risk factors in these analyses.
Generalizability may be limited, since the population was restricted to subjects reporting Iowa as the home of record at enlistment. Ascertainment of health status and risk factor data might have been influenced by recall or reporting bias, especially given the extensive media coverage of a potential "Gulf War syndrome." Disability claim status may also have influenced reporting. However, only 9 percent of respondents reported receiving Department of Veterans Affairs disability compensation, and there was no difference by deployment status. Of note, this study assessed HRQL 5 years postconflict; thus, we could not address the immediate impact of deployment on HRQL. Given the cross-sectional design, observed relations between variables are descriptive, not causal. Although a broad range of potential risk factors was assessed, other factors not assessed, such as health behavior or social support, might have obscured the true relation between HRQL and deployment.
Although our study identified that slightly poorer HRQL was associated with deployment, much of the variability in scores remains unexplained. It is not clear whether the poorer health status of Gulf War veterans is specifically related to deployment, unmeasured factors, or perhaps recall or reporting bias. Even though we have identified important trends and risk factors for HRQL, further studies of the determinants of postdeployment health are needed.
Clearly, there are health consequences of military service other than obvious war injuries. Complaints similar to those reported by Gulf War veterans have historically been common among veterans following major conflicts (54). While much research to date has focused on detecting a novel Gulf War illness (14
, 55
, 56
), we and others believe it is essential to evaluate HRQL thoroughly. A recent report highlighted the need for broad-based health status assessment and compilation of personal and environmental health correlates (53
). Efforts are also under way to improve medical surveillance and record keeping for future conflicts. The Department of Veterans Affairs has adopted an outcomes management system that includes ongoing SF-36 data collection (57
). There has also been a call to further explore methods to prevent or at least mitigate deployment-related health effects (58
). The determinants of HRQL we identified may be useful in designing preventive and therapeutic interventions aimed at helping both Gulf War veterans and future military personnel successfully adapt to life after war.
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ACKNOWLEDGMENTS |
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The authors appreciate the contributions of all research personnel in coordinating and administrating the original study, Statistical Laboratory Survey Section of the Iowa State University Statistics Department personnel in carefully conducting the telephone interview and data collection, and members of the Iowa Gulf War Study Group for their advice concerning development and analysis of the original telephone survey. The authors also acknowledge Mike Dove and the Defense Manpower Data Center for their assistance in making data available to draw the sample. The contributions of the Scientific Advisory Committee and Public Advisory Committee in providing advice, input, and review during development of the project are also greatly appreciated.
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NOTES |
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REFERENCES |
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