Factor Analysis of Gulf War Illness: What Does It Add to Our Understanding of Possible Health Effects of Deployment?

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1 American Journal of Epidemiology Copyright 2002 by the Johns Hopkins Bloomberg School of Public Health All rights reserved Vol. 156, No. 6 Printed in U.S.A. DOI: /aje/kwf087 Factor Analysis of Gulf War Illness: What Does It Add to Our Understanding of Possible Health Effects of Deployment? Susan E. Shapiro 1, Michael R. Lasarev 2, and Linda McCauley 2 1 School of Nursing, Oregon Health and Science University, Portland, OR. 2 Center for Research on Occupational and Environmental Toxicology, Oregon Health and Science University, Portland, OR. Received for publication August 16, 2001; accepted for publication May 24, The authors conducted factor analysis on survey data from 1,779 Persian Gulf War veterans. Their purposes were to: 1) determine whether factor analysis identified a unique Gulf War syndrome among veterans potentially exposed to chemical warfare agents; 2) compare the findings of factor analysis with those from an epidemiologic analysis of symptom prevalence; and 3) observe the behavior of factor analysis when performed on dichotomous data. The factor analysis identified three factors, but they were not unique to any particular deployment group. A unique pattern of illness was not found for the larger group of veterans potentially exposed to chemical warfare agents; however, veterans who had witnessed the demolition of chemical warfare agents at the Khamisiyah site in Iraq had a greater prevalence of dysesthesia. An analysis of the performance of dichotomous variables in factor analysis showed that the standard criteria used to determine the number of relevant factors and the dominant variables within them may be inappropriate. While Gulf War veterans appear to suffer an increased burden of illness, there is insufficient evidence to identify a unique syndrome in this population of deployed servicemen and women. Furthermore, the results provide evidence that factor analysis may make a limited contribution in this area of research. Am J Epidemiol 2002;156: chemical warfare agents; factor analysis, statistical; military medicine; Persian Gulf syndrome; veterans For more than 10 years, scientists have debated the nature and etiology of illness among veterans of the Persian Gulf War. Investigators have repeatedly observed an increased prevalence of many health symptoms among troops deployed in the conflict (1 3); however, a central question has been whether these symptoms represent a distinct medical entity that can be labeled Gulf War syndrome. Multiple investigators have used a statistical technique called factor analysis to determine whether the patterns of reported symptoms represent a unique illness (1 6). In these analyses, the central question has been whether the pattern of illness in deployed Gulf War troops differs from patterns of illness reported in troops not deployed to the region. Haley et al. (5) also applied factor analysis to determine whether veterans with a particular pattern of symptoms had had unique environmental exposures to organophosphate chemical warfare agents. Although debate about the significance of findings from epidemiologic analysis and factor analysis is ongoing (1, 6 12), investigators continue to explore the relation between possible exposure to chemical warfare agents and the presence of self-reported symptoms among veterans of the Gulf War (3, 13 16). Bieliauskas and Turner (7) have recommended that research on the current health of Gulf War veterans focus on those most likely to have been exposed to environmental toxins such as chemical warfare agents. One group that fits this criterion is veterans who were deployed in the Khamisiyah area of coalition-occupied Iraq, particularly those who witnessed a controlled detonation of Iraqi munitions later determined to have contained known chemical warfare agents. In 1996, the US Department of Defense disclosed that on March 4, 1991, US personnel destroyed munitions containing 8.5 metric tons of sarin/cyclosarin housed in bunker 73 at Khamisiyah and that on March 10, 1991, additional sarin/cyclosarin rockets were destroyed in a pit at Khamisiyah (17, 18). In 1997, the Defense Department notified all of the approximately 20,000 individuals who had been operating within a 50-km radius of the Khamisiyah site between March 4 and March 13, 1991, of their possible exposure to low levels of chemical warfare agents and the Reprint requests to Dr. Linda McCauley, Center for Research on Occupational and Environmental Toxicology, Oregon Health and Science University, Mailcode L-606, 3181 SW Sam Jackson Park Road, Portland, OR ( mccauley@ohsu.edu). 578

2 Factor Analysis of Gulf War Illness 579 availability of clinical examinations by the Department of Defense or the Department of Veterans Affairs (19, 20). In a recent study, McCauley et al. (16) reported on the health status of veterans who had been deployed within a 50- km radius of Khamisiyah. They noted no increased risk of current self-reported symptoms among veterans deployed in the Khamisiyah area compared with those who had been deployed to the Gulf region but not to Khamisiyah. Within the Khamisiyah group, however, veterans close enough to witness the demolition reported significantly more of 16 different symptoms within 2 weeks of the demolition than nonwitnesses, and all but three of these symptoms were consistent with exposure to organophosphate agents. Eight years after the demolition, these same witnesses reported a significant excess of eight health-related symptoms, some of which could plausibly be related to long-term effects of lowdose exposure to chemical warfare agents. This paper presents the results of further analysis of the McCauley et al. (16) data. Our first purpose was to subject the self-reported health data of Khamisiyah troops to a factor analysis to determine whether a unique pattern of symptoms or factors was present and, if so, whether the pattern differed from that of non-khamisiyah troops and troops not deployed to the Gulf region. Our second purpose was to compare the results of the factor analysis with those reported by McCauley et al., especially as they related to the apparent increase in the presence of certain current symptoms among veterans who witnessed the detonation. Our last purpose was to examine how factor analysis behaves when the data are dichotomous. MATERIALS AND METHODS Study population The study population consisted of veterans who were on active or reserve duty in the US Army or National Guard during the combat period of the Gulf War and the weeks immediately thereafter (January 1, 1991 March 31, 1991). The sampling frame for the study was obtained from a Gulf War database maintained by the Defense Department. We pulled random samples from three groups of veterans: 1) those who were on active duty during the study period but were not deployed to the Gulf region (the nondeployed group); 2) those who were deployed to southwestern Asia but were not within the 50-km radius around Khamisiyah (the deployed non-khamisiyah group); and 3) those who were deployed within the 50-km radius around Khamisiyah (the Khamisiyah group). All potential subjects entered into our sampling pool had to have telephone numbers that could be located by common search mechanisms. The sampling pool for the telephone interview consisted of 3,219 veterans. We contacted 2,918 of these (90.6 percent), but 530 were found to be ineligible, primarily because they were not veterans of the Gulf War or were not members of the designated military branches (the Army or National Guard) during the Gulf War. Of the 2,918 telephone contacts made, 1,833 interviews were completed, and we were able to use 1,779 of these interviews in our analyses. Details on the sampling procedure, the location and recruitment of participants, and the characteristics of nonrespondents have been provided elsewhere (16). The 1,779 Gulf War veterans in the study included 516 who were on active duty during the study period but were not deployed to the Gulf region, 610 who were deployed to southwestern Asia but not within the 50-km radius around Khamisiyah, and 653 who were deployed within the 50-km radius around Khamisiyah. Within the Khamisiyah group, 162 veterans reported that they had witnessed the munitions detonations (the witness subgroup) and 405 reported that they had not (the nonwitness subgroup). Study instrument We adapted an existing survey instrument used in a population-based study of Gulf War veterans in the northwestern United States (21 24). We adapted the questionnaire to obtain more information on troop movements in the Khamisiyah area, including exposure to detonation of ammunition bunkers. In addition, we modified the health symptom questionnaire to include more questions on specific neurologic symptoms, as explored by Haley et al. in 1997 (5). The reliability of the instrument is reported elsewhere (21 23). Study participants completed two checklists: one of health symptoms they had experienced within 2 weeks of the Khamisiyah detonations and one of current health symptoms that had been present within the past month. We used only the current health symptoms in our factor analysis. Statistical analyses Factor analysis. For the first part of the study, an exploratory factor analysis of each deployment group (Khamisiyah, deployed non-khamisiyah, and nondeployed) was performed using SPSS, version 10.0 (25). Factors were extracted by principal components and subjected to a varimax rotation (26). Those factors having eigenvalues greater than 1 and individually accounting for at least 5 percent of the overall variance were retained. Symptoms with rotated loadings greater than 0.60 in absolute value were considered dominant and served as the defining symptoms for each specific factor. Ordinary least-squares (i.e., unweighted regression) analysis (26) was used to calculate factor scores. These decision rules regarding factor retention and identification of dominant and defining symptoms were empirically derived through a Monte Carlo simulation and are more fully described below. Comparison of factor analysis results with prevalence of self-reported symptoms. Although a factor analysis was performed on the entire Khamisiyah group (n = 653), the limited number of veterans who had witnessed the munitions detonation (n = 162) prevented an independent factor analysis of this subpopulation. Instead, we determined whether the distribution of factor scores differed significantly between veterans who had witnessed the demolition and their nonwitnessing counterparts (n = 405), using a Wald- Wolfowitz runs test (27). Eighty-six of the 653 veterans in the Khamisiyah group were dropped from this part of the analysis because we were unable to reliably ascertain their

3 580 Shapiro et al. TABLE 1. Distribution of witnesses and nonwitnesses to the munitions demolition activities at Khamisiyah, Iraq, during the Gulf War, according to combinations of the three symptom factors included in a factor analysis Factor 1 Factor 2 Factor 3 Nonwitnesses (n = 405) Witnesses (n = 162) Estimated expected odds (witness/ nonwitness) No No No 331 (328.80)* 122 (121.87) 0.37 Yes 11 (11.18) 2 (4.15) 0.37 Yes No 16 (17.17) 12 (13.16) 0.77 Yes 4 (2.64) 3 (2.03) 0.77 Yes No No 26 (29.42) 12 (10.91) 0.37 Yes 7 (5.60) 3 (2.07) 0.37 Yes No 7 (5.47) 5 (4.20) 0.77 Yes 3 (4.71) 3 (3.61) 0.77 * Numbers in parentheses are expected cell counts determined from a loglinear fit. status as an observer/nonobserver of the detonation. We then classified veterans as possessing a particular factor on the basis of their endorsing all of the dominant symptoms within the factor. Using this rule, a veteran could possess several factors simultaneously, a single factor, or none at all. The number of veterans within each combination of factors was then stratified by the veterans status as witnesses or nonwitnesses to the demolition activities. Within this stratification (table 1), associations between combinations of factors and the witness/nonwitness status of veterans were examined using log-linear models (28). Unlike logistic regression, this technique can be used to investigate the structure of dependencies within a multiway table without having to specify a particular dependent outcome variable. TABLE 2. Percentages of Gulf War veterans in three different study populations reporting health symptoms used in a factor analysis Symptom Khamisiyah (n = 653) Deployed non- Khamisiyah (n = 610) Nondeployed (n = 516) Tingling, burning sensation of pins and needles Numbness or lack of feeling Loss of muscle strength in arms/legs Loss of balance/coordination Dizzy spells Changes in memory Difficulty sleeping Persistent fatigue, tiredness, or weakness Depression Unusual irritability/anger Mood swings Problems following directions/instructions Difficulty concentrating Cramping, aches, pains, or muscle stiffness Problems breathing Increased sensitivity to everyday chemicals Effects from confined spaces Continuous eye irritation/sensitivity Unexplained weight gain of more than 10 pounds (>4.5 kg)

4 Factor Analysis of Gulf War Illness 581 TABLE 3. Factors extracted from three different populations of Gulf War veterans Symptom Factor 1 (cognitive psychological) Khamisiyah Deployed non-khamisiyah Nondeployed Factor 2 (dysesthesia) Factor 3 (vestibular dysfunction) Factor 1 (cognitive psychological) Factor 2 (dysesthesia) Factor 3 (vestibular and other) Factor 1 (cognitive psychological) Factor 2 (neuromuscular) Unusual irritability/ anger X X X Depression X X X Difficulty concentrating X X X Difficulty following directions X Difficulty sleeping X Fatigue X X Memory changes X X Mood swings X X X Loss of strength X Numbness X X X Tingling X X X Chemical sensitivity X X Dizziness X X X Loss of balance X X Shortness of breath X Factor 3 (mixed) Behavior of dichotomous variables. We performed a simulation (29) study for each of the three deployment groups to investigate how factor analytical methods perform using dichotomous data. All simulations used 500 replications of the following procedure. Within each deployment group and for each replication, an artificial set of 19 separate dichotomous variables was randomly generated. This generation of simulated data resulted in independently created expressions of symptom patterns corresponding to the frequencies of the 19 symptoms of interest (table 2). The number of rows, corresponding to individual veterans, varied according to the deployment group under consideration. Factor analysis was then performed on the artificial data (extraction of principal components followed by varimax rotation), and the resulting eigenvalues and rotated loadings were stored. The distribution of eigenvalues and rotated loadings observed across these 500 replications show how principal-components factor analysis accompanied by varimax rotation behaves when correlation among 19 dichotomous variables is determined by a random process. Simulations were performed using the statistical language R and the associated multivariate analysis package (30). RESULTS Factor analysis A correlation matrix with 25 symptom variables reported by the entire sample of 1,779 veterans was constructed; six health symptoms had a correlation with all other symptoms that was less than 0.3 and were removed from further analysis (31). The 19 remaining health symptoms were included in all reported analyses and are listed in table 2. Factor analysis using principal-components extraction with varimax rotation was initially conducted on the Khamisiyah veteran sample (table 3). Three factors with eigenvalues greater than 1.0 were identified, and together they accounted for 46.7 percent of the total variance. Using a cutoff of 0.60 for factor loadings, the first factor contained six symptoms: unusual irritability/anger; mood swings; changes in memory; persistent fatigue, tiredness, or weakness; difficulty concentrating; and depression. This was labeled a cognitive/psychological factor. Factors 2 and 3 each contained two variables. The second factor, dysesthesia, consisted of a tingling, burning sensation of pins and needles and numbness or lack of feeling. The third factor, labeled vestibular dysfunction, contained loss of balance or coordination and dizzy spells. Factor analysis for the nondeployed veterans and the deployed non-khamisiyah veterans was performed using the same procedure (table 3). Three factors were also identified among the nondeployed veterans, and they accounted for 52.2 percent of the overall variance. Symptoms contained in the first factor for the nondeployed group were persistent fatigue, tiredness, or weakness; depression; unusual irritability/anger; mood swings; difficulty following directions; and difficulty concentrating. As with the Khamisiyah group,

5 582 Shapiro et al. this was named a cognitive/psychological factor. The second factor, identified as neuromuscular, contained three symptoms: a tingling, burning sensation of pins and needles; numbness or lack of feeling; and loss of muscle strength in the arms or legs. The third factor had two symptoms as well: dizzy spells and increased sensitivity to everyday chemicals. This factor was named vestibular and other. Factor analysis of the deployed non-khamisiyah veterans yielded three factors accounting for 49.8 percent of the total variance (table 3). The first factor, again identified as cognitive/psychological, consisted of changes in memory, difficulty sleeping, depression, unusual irritability/anger, mood swings, and difficulty concentrating. The second factor, also called dysesthesia, contained numbness or lack of feeling and a sensation of pins and needles. The third factor, mixed, contained loss of balance or coordination, dizzy spells, shortness of breath, and chemical sensitivities. Factor analysis results versus epidemiologic analysis of symptom prevalence Results from the nonparametric Wald-Wolfowitz test indicated no significant differences in the distributions of factor scores between the witness and nonwitness subgroups (factor 1, p = 0.88; factor 2, p = 0.19; factor 3, p = 0.28). Loglinear analysis revealed that factors 1 and 3 ( cognitive/ psychological and vestibular dysfunction ) were unrelated to a veteran s status as a witness (χ 2 (2 df) = 0.66, p = 0.72) and that factor 2 ( dysesthesia ) was significantly associated with whether a veteran had witnessed the demolition (χ 2 (1 df) = 6.10, p = 0.013). When factor 2 was treated as the dependent outcome in a logistic regression model, the estimated odds of having witnessed the demolition activities among veterans reporting both symptoms related to dysesthesia were 0.77 (table 1). This estimate was 2.08 times higher (95 percent confidence interval: 1.16, 3.68) than the corresponding odds for veterans who did not report any of the symptoms related to dysesthesia (odds = 0.37). Performance of factor analysis with dichotomous variables The Monte Carlo simulations produced similar results for all three populations (Khamisiyah, deployed non- Khamisiyah, and nondeployed), so we report only those from the Khamisiyah group in detail. Again, the purpose of this portion of the study was to investigate how traditional criteria for selecting factors (eigenvalues in excess of 1) and subsequently determining which components dominate a given factor (loadings in excess of 0.40) are affected when factor analysis is performed on random dichotomous data. Simulations revealed that eigenvalues for the first eight factors were always in excess of 1; the ninth factor satisfied this criterion 84 percent of the time, and factors never produced eigenvalues greater than 1. Additionally, the proportion of variance explained by each of the individual factors ranged between 3.4 percent and 7.6 percent, with an average of 5.3 percent. This may be regarded as the typical amount of variance explained, per factor, when variables are uncorrelated. These data further indicated that for 19 randomly generated dichotomous variables, five factors were sufficient to account for an average of 32 percent of the total variance. Additionally, the 90th percentile for the rotated loadings varied from 0.60 to 0.62, with approximately 95 percent of the rotated loadings exceeding DISCUSSION Results of factor analysis The initial factor analysis of the McCauley et al. data was performed to determine whether one or more discrete factors emerged in troops deployed in the Gulf War in comparison with nondeployed troops and to determine whether a unique pattern of symptoms emerged among veterans with possible exposure to chemical warfare agents. While the factors identified in the three separate groups were not identical, there was substantial overlap in the symptoms within each factor. These findings are consistent with those of other investigators who performed factor analysis using groups of deployed and nondeployed veterans (1, 2, 4, 6); that is, factors could be identified, but they were not unique to a particular deployment group. Although our findings are largely consistent with those of most other investigators, direct comparisons are not possible, for several reasons. First, different investigators have used different lists of symptoms in their studies. Second, the sizes, sources, and compositions of the samples used in other reports have varied considerably. Third, investigators have used different factor extraction and rotation techniques and different thresholds for factor loadings to identify their syndromes. While these analytical variations are entirely acceptable (31), this has resulted in disparate findings. Table 4 summarizes these differences. The one consistent theme among these studies is that no investigators other than Haley et al. (5) have identified a unique Gulf War syndrome based on the results of a factor analysis. Our findings concur with those of most other investigators. While we were able to identify clusters of symptoms that appeared to form plausible syndromes, they were not unique to any deployment group, even among veterans who had the greatest acknowledged likelihood of having been exposed to chemical warfare agents. Comparison of factor analysis with epidemiologic analysis In their earlier paper, McCauley et al. (16) found some evidence of increased symptoms among Khamisiyah veterans who had witnessed the detonation of munitions containing known chemical warfare agents compared with other veterans who had been within 50 km of Khamisiyah but had not witnessed the detonations. These symptoms are shown in table 5. The earlier analysis did not isolate a particular cluster of persistent symptoms that differentiated the Khamisiyah group from either the deployed non-khamisiyah group or the nondeployed group, and neither did the factor analysis reported here. However, when we applied the results of the

6 Factor Analysis of Gulf War Illness 583 TABLE 4. Characteristics of studies that have used factor analysis to evaluate reported health symptoms among Gulf War veterans Study characteristic Sample population Haley et al. (5) Active and retired* (Navy) Fukuda et al. (1) Active and reserve (Air Force) Ismail et al. (4) Bourdette et al. (3) * Sample contained deployed veterans only. Veterans from the United Kingdom. Total sample size of all groups studied. Model-based; computed as the weighted sum of ordinal items first-stage factor weights. Doebbeling et al. (2) Active Active and reserve* Active and reserve Knoke et al. (6) Active (Navy) Sample size 249 3,255 3, ,695 1,459 1,779 Type of data Interval Ordinal Ordinal Dichotomous Ordinal and dichotomous Ordinal and dichotomous Shapiro et al. (current study) Active Dichotomous Method Principal axis Principal Principal factors Principal components Unknown Principal factors Principal components components Rotation Varimax Promax and procrustes Factor loading cutoff Varimax Varimax Varimax and promax Varimax >0.40 >0.40 >0.40 >0.40 >0.35 >0.40 >0.60 Factors isolated Percentage of variance explained 71 ~31 ~20 ~ Factors identified Impaired cognition; Fatigue; confusion-ataxia; moodcognition; arthro-myoneuropathy; musculoskeletal pain phobia-ataxia; feveradenopathy; weaknessincontinence Mood-cognition; respiratory system; peripheral nervous system Cognitive/psychological; mixed somatic; musculoskeletal Somatic distress; psychological distress; panic Varimax Insecurity; somatization; (See table 2.) depression; obsessive-compulsive; malaise factor analysis to the current symptoms reported by the witness and nonwitness subgroups, we did find that the witness group had a significantly greater prevalence of the second factor, dysesthesia. One of the two symptoms in this factor a tingling, burning sensation of pins and needles was identified as significant in the earlier work of McCauley et al. (16). Because the witness group had had suspected exposure to known chemical warfare agents, this finding suggests a need to examine these veterans more closely in order to document the presence of physiologic findings consistent with such reported symptoms. TABLE 5. Self-reported current symptoms found by McCauley et al. (16) to be significantly higher among Gulf War veterans who witnessed the munitions detonations at Khamisiyah, Iraq, than among veterans who did not witness the detonations Symptom p value Tingling, burning sensation of pins and needles Changes in memory Difficulty sleeping Persistent fatigue, tiredness, or weakness Depression Bloody diarrhea* * Removed from the factor analysis because its correlation with all other symptoms was less than Although the epidemiologic evidence in McCauley et al. s earlier paper clearly demonstrated an increase in bloody diarrhea in the witness group compared with the nonwitness group (odds ratio = 3.1, 95 percent confidence interval: 1.6, 6.0), we did not include this symptom in our factor analysis, because it failed to correlate with any other symptoms. This was the only significant symptom in the witness group that was not included in our factor analysis. This illustrates another limitation of using factor analysis for epidemiologic purposes: Factor analysis only identifies joint associations among variables and the latent structures that may describe them; it provides no information about individual variables, thereby making it possible to miss an isolated symptom that significantly adds to the burden of illness in a population. Performance of factor analysis with dichotomous variables Factor analysis has been used extensively to test measurement scales and to refine and test constructs such as intelligence and self-concept (31, 32). These analyses have been used traditionally with ordinal and interval-level data, and at least one statistical program specifies interval-level data as the minimum requirement for factor analysis (25); yet many of the data used in previously reported factor analyses of Gulf War veterans operate below this level. Knoke et al. (6) addressed this issue briefly in their paper; otherwise, previous investigators have had little to say about the ways

7 584 Shapiro et al. in which factor analysis might perform on non-interval-level variables and how that might have affected their results. Our work has shown that application of standard rules to 19 randomly generated and independently created dichotomous variables could result in models containing five factors, which explained approximately 30 percent of the total variance. Even more troubling is the realization that rotated loadings in excess of 0.40, the traditional cutoff used by investigators, occurred more than 95 percent of the time in our randomly generated data set. If, as our simulation demonstrated, similar results can be obtained using randomly generated data, we are forced to reconsider the existence of syndromes found in earlier studies, especially those discovered through factor analysis of dichotomous variables. One of the advantages of factor analysis is that it allows investigators to explore their data rather liberally. Because there are few rules governing the choices of factor extraction, rotation, or factor loading cutoffs, factor analysis is often used as a last resort when other analyses fail to yield significant results (31). Since the publication of Haley et al. s initial paper (5), investigators in this field seem compelled to provide results of factor analysis as a necessary component of analyses related to unexplained Gulf War illnesses. In the absence of more robust decision rules for these kinds of data, the resulting factors may be a rich mixture of randomness, which could lead investigators down uninformative paths. While we do not intend to imply that the hard work of previous investigators has been inadequate, we feel that there has been a rush to use this technique to try to identify a unique Gulf War syndrome when more classic epidemiologic methods have failed to do so. Given the limitations cited above, it seems that factor analysis has also failed in this task. Our study was subject to several limitations, most of which are fully detailed in the paper by McCauley et al. (16). These include all of the issues related to self-reporting of symptoms, such as recall and selection bias, and to the sampling biases inherent in the use of telephone surveys. Our sample was limited to individuals who had served in the Army or National Guard and whose telephone numbers could be tracked using common search mechanisms; therefore, our sample may not be representative of the entire population of troops serving in the Gulf War. Although we have compared the results of our factor analysis with those reported by other investigators, it is important to realize that the methods used to sample the veterans in these studies differed substantially, and data were collected using different approaches. The results of our simulation are unique to this data set and cannot be taken to establish guidelines for other studies in which factor analysis is performed on dichotomous variables. We urge other investigators performing factor analysis with dichotomous variables to determine how their analytical techniques would perform using simulated data sets of an appropriate size, with individual variables randomly generated to mimic the observed symptom frequencies. Comments Research on whether a unique Gulf War syndrome exists tends to be of three types: studies that rely solely on standard epidemiologic analyses; studies that rely solely on factor analyses; and studies that use a combination of these techniques. Taken together, this body of research indicates that Gulf War veterans have reported an increased burden of illness as long as 8 years after their participation in the Gulf War. However, the majority of studies have not disclosed a unique Gulf War syndrome that is, a set of symptoms appearing in these veterans which is different from any other identified disease entity, which appeared only after participation in the Gulf War, and which does not appear with similar frequency in other populations of veterans or nonveterans. This is not to minimize the health problems encountered by veterans of the Gulf War. We now recognize that many of those veterans were possibly exposed to known chemical warfare agents, and it is reasonable to expect that some will experience physical symptoms even after more than 10 years time. Veterans suffering from war-related symptoms deserve to be thoroughly evaluated and treated, regardless of the fact that a unique Gulf War syndrome has yet to be described. We agree with Steele (33) that problems reported by Gulf War veterans are complex and that investigators in this field need to consider many possible causes and combinations of causes as the basis for these symptoms. These complexities make it unlikely that any single analytical tool, such as factor analysis, will be the sole source of answers in this continuing controversy. ACKNOWLEDGMENTS The US Army Medical Research and Material Command (grant DAMD ) provided funding for this research. REFERENCES 1. Fukuda K, Nisenbaum R, Stewart G, et al. Chronic multisymptom illness affecting Air Force veterans of the Gulf War. JAMA 1998;280: Doebbeling BN, Clark WR, Watson D, et al. Is there a Persian Gulf War syndrome? Evidence from a large population-based survey of veterans and nondeployed controls. Am J Med 2000; 108: Bourdette DN, McCauley LA, Barkhuizen A, et al. 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