Factor Analysis of Self-reported Symptoms: Does It Identify a Gulf War Syndrome?

James D. Knoke1,2, Tyler C. Smith1, Gregory C. Gray1, Kevin S. Kaiser1 and Anthony W. Hawksworth1

1 Emerging Illness Division, Health Sciences and Epidemiology Department, Naval Health Research Center, San Diego, CA.
2 Division of Epidemiology, Department of Family and Preventive Medicine, School of Medicine, University of California, San Diego, La Jolla, CA.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Active duty US Naval mobile construction battalion personnel (Seabees) were surveyed in 1994 for the presence of a variety of symptoms. Questions were drawn from the Hopkins Symptom Checklist and from a collection of symptoms either defining clinical depression or commonly reported by Persian Gulf War veterans. Of those surveyed, 524 were Gulf War veterans and 935 were nondeployed Gulf War-era veterans. Factor analysis applied to Gulf War veterans yielded five factors, three deriving from the Hopkins Symptom Checklist, one suggesting clinical depression, and one containing symptoms commonly reported by Gulf War veterans. Factor analysis applied to nondeployed veterans yielded five similar factors. Three of the factors yielded statistically significantly greater standardized factor scores for Gulf War veterans than for nondeployed veterans. Four of the factors resembled factors resulting from a previous analysis on a sample of similar Gulf War veterans. Gulf War veterans and nondeployed era veterans reported similar clusters of symptoms and illnesses. However, Gulf War veterans reported these same clusters with greater frequencies than did nondeployed veterans. The authors conclude that, in contrast to a previous report, factor analysis did not identify a unique Gulf War syndrome. Am J Epidemiol 2000;152:379–88.

factor analysis; statistical; military medicine; Persian Gulf syndrome; symptoms and general pathology; veterans

Abbreviations: GWV, Gulf War veteran; NDV, nondeployed veteran.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Thousands of US veterans of the Persian Gulf War have reported varied symptoms and illnesses since the cessation of hostilities in March 1991 (1GoGo–3Go). The diversity of symptoms reported has complicated the diagnosis of many of these veterans' conditions (4GoGoGo–7Go). A recent report (8Go) (hereafter termed the previous report) used factor analysis on a selected group (n = 249) of Gulf War veterans (GWVs) to search for factors (there termed "syndromes") that may be indicative of neurotoxic exposures. The conclusion of the previous report, that 25 percent of ill veterans studied have "symptoms that may reflect generalized neurologic injuries" (8Go, p. 222), has raised public attention (9GoGo–11Go) and has spawned additional studies (12Go) designed to investigate this assertion. The purpose of the present study was to further investigate the usefulness of factor analysis in characterizing a Gulf War syndrome.

We performed a factor analysis similar to that of the previous report, using survey data from a similar group of GWVs. We also performed a factor analysis using data from a control group of comparable nondeployed Gulf War-era veterans (NDVs) and performed a third analysis using the combined GWV and NDV data. We compared the results of these three factor analyses with each other and with those of the previous report. Finally, we performed a discriminant analysis to determine the efficacy of these factors in separating the two groups.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Population
The study population consisted of US Naval mobile construction battalion personnel (Seabees) who were on active duty in September 1990 and remained on active duty through 1994. This population included all 14 major Seabee commands that were based at either Port Hueneme, California, or Gulfport, Mississippi. Since Seabees have frequent foreign deployments, extending up to 6 months a year, we made three visits to each of these sites (in late 1994 and early 1995) to recruit subjects. This study is described in detail elsewhere (13Go).

Data
All data used for the present report were collected from a self-completed, computer-scanned survey questionnaire. This survey included, among other information, whether the respondent was deployed to the Gulf War and whether he or she experienced one or more of 98 symptoms. These symptoms included 57 questions (one question was inadvertently omitted) detailed in a classic report (hereafter termed the Hopkins Symptom Checklist) (14Go) and 41 questions compiled by the survey authors. These 41 questions included symptoms commonly reported by Gulf War veterans and several questions relating to depression. In addition, one validity symptom, "earlobe pain," which is thought not to have a physiologic basis, was included.

As in the Hopkins Symptom Checklist, the 57 questions were grouped into six categories. Five of these categories resulted from "clinical clustering" and factor analyses, discussed in that report (14Go), and the sixth contained those questions that did not cluster with or load on any of the five factors. The 41 questions compiled by the survey authors were considered a seventh category (table 1).


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TABLE 1. Sources for and categorization of symptom clusters, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995

 
The 57 questions from the Hopkins Symptom Checklist asked whether the respondent experienced the symptom during the "past several days." The answers were assigned the following scores: as "not at all" (score 0), "a little bit" (score 1), "quite a bit" (score 2), or "extremely" (score 3). The questions compiled by the survey authors asked whether the symptom had persisted for "one month or longer since July 1990." (The Gulf War deployment began in August 1990.) These answers were simply recorded as "no" (score 0) or "yes" (score 1). Although the wording and the time period queried for symptoms were different, there was overlap between the two collections of questions. For example, the first symptom on the Hopkins Symptom Checklist is "headache," while the first symptom compiled by the survey authors is "severe headache." (A copy of the questionnaire used can be obtained upon request from the corresponding author.)

Factor analysis
We strove to make our approach to factor analysis as similar as possible to that of the previous report, while maintaining generally accepted methodological principles. To that end, we performed a principal factor analysis, used Kaiser's measure of sampling adequacy to quantify the goodness-of-fit of the principal factor model, used the squared multiple correlations as the prior communality estimates, included only symptoms with loadings of at least 0.40 on at least one factor, and performed varimax rotations (which are orthogonal). We used a five-step approach for determining the number of factors: a scree plot of the eigenvalues against the corresponding factor numbers, the Kaiser-Guttman rule for eigenvalue magnitude, the cumulative proportion of the variance, the magnitude of the residual correlations, and the internal consistency or interpretability of the factors. The number of variables (symptoms) in each factor was determined by backwards stepwise elimination, removing the variable with the smallest loading until all included variables had loadings of at least 0.40 on at least one factor. Perhaps the most controversial aspect of our approach was using orthogonal varimax rotations. While many analysts prefer orthogonal rotations, other analysts prefer oblique rotations. However, it is not really possible to say that one type of rotation is more correct than the other. A discussion of the relative merits of the two methods of rotation, as well as other subjective details inherent in factor analysis, can be found in a standard reference (15Go).

Other statistical analyses
The factors were compared between the GWV and NDV groups using factor scores computed from the regression coefficients of the GWV factor analysis, standardized for both groups by subtracting the median and dividing by the semi-interquartile range of the score for the GWV group. A similar comparison of standardized factor scores was performed based on the analysis of the combined GWV and NDV groups. The results of this comparison were similar to the one based on the GWV group alone and consequently are not reported. The distributions of the standardized factor scores were then compared by histograms, the Wilcoxon rank sum test, and discriminant analysis (16Go). Finally, the ability of the factors to separate the two groups was assessed by estimating the probabilities of misclassification by cross-validation (17Go) and by visualizing the (discriminant analysis-based) posterior probabilities of being a Gulf War veteran. All data management and statistical analyses were performed using the SAS system (18Go). The analysis routines used included the UNIVARIATE, FACTOR, and DISCRIM procedures.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Ascertainment and demographics
In September 1990, early in the Gulf War deployment period, there were approximately 15,400 active-duty Seabees. About 31 percent (n = 4,700) of these Seabees were deployed. Approximately 55 percent of the active-duty Seabees (n = 8,500) remained on active duty through 1994 and therefore were eligible for the present study. About 2,900 of these remaining Seabees were in residence at either Port Hueneme or Gulfport during one of the three visits made to each of these sites. Approximately 50 percent of the available Seabees agreed to participate, resulting in data on 528 GWVs and 968 NDVs. Since there were only four women among the 528 GWVs, and anticipating that sex might be an important variable in the subsequent statistical analyses, we restricted attention to those subjects known to be men, resulting in 524 GWVs and 935 NDVs. There were differences in the participation rates of GWVs and NDVs; 65 percent of eligible male GWVs participated, compared with 46 percent of eligible male NDVs. There were no significant demographic differences (age, race, marital status, and service-entry aptitude scores) between those who agreed to participate and those who did not. There may be other differences between participants and nonparticipants, however. For example, individuals with symptoms may have been more likely to participate than those without. It is also possible that such self-selection was greater for GWVs, who participated at a higher rate, than for NDVs.

The GWVs studied were younger (by 1 year, on average), were more likely to be unmarried (27 percent, as compared with 22 percent), had completed less education (62 percent had only a high school education, as compared with 51 percent), and were more likely to be of enlisted rank (97 percent, as compared with 93 percent) than the NDVs studied. Race (76 percent White, 10 percent Black, and 14 percent "other") was not significantly different between the GWV and NDV groups.

Self-reported symptoms
The 98 symptoms queried and the percentages of subjects reporting these symptoms, by Gulf War deployment status, are enumerated in table 2. Significantly more GWVs than NDVs reported most symptoms. (For the purposes of this statistical comparison, any positive response to questions from the Hopkins Symptom Checklist was assigned a score as "present" (score 1), since the combined responses "quite a bit" (score 2) and "extremely" (score 3) were reported on average by less than 5 percent or the respondents.) GWVs also scored significantly higher than NDVs on all five Hopkins Symptom Checklist categories. These symptoms and their relation to psychologic morbidity have been reported elsewhere (13Go).


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TABLE 2. Percentages of respondents reporting symptoms, by source of symptom and Gulf War deployment status, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995

 
Factor analysis of GWVs
We first performed a factor analysis on the 524 GWVs. The final analysis is summarized in table 3 under the columns labeled GWV. The squared multiple correlations ranged from 0.34 to 0.68, suggesting that principal factor analysis was the more appropriate methodological approach than was principal components analysis. (Principal components analysis would have been the more appropriate had the squared multiple correlations all been near 1.0.) The final measure of sampling adequacy was 0.92, suggesting goodness-of-fit of the factor analysis model. (The measure of sampling adequacy cannot be greater than 1.0, and values less than 0.5 suggest a poor fit.) In the course of this analysis, we varied the factor-loading threshold but found that a threshold of 0.40 was optimal for factor interpretability. By factor interpretability we mean that factors emerged that tended 1) to aggregate questions associated with a single Hopkins Symptom Checklist category or one of the two collections of questions compiled by the survey authors and 2) to minimize the number of questions that loaded on multiple factors. We also modeled differing numbers of factors. Statistical criteria, as well as factor interpretability, suggested that five was the appropriate number of factors. The five factors accounted for 80 percent of the variance present in the symptoms retained in the model. The eigenvalue of the fifth factor was 1.32, while that of a sixth factor would have been 0.97. (The Kaiser-Guttman rule advocates continuing to add factors until the next eigenvalue will be less than 1.0.) Finally, the five factors appeared highly interpretable, had minimal overlap, and represented three of the five Hopkins Symptom Checklist factors and the two collections of symptoms included by the survey authors.


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TABLE 3. Summary of factor analyses for symptoms reported by Gulf War veterans (GWVs), nondeployed veterans (NDVs), and all veterans combined, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995

 
We termed the five factors that emerged from this analysis as follows:
  1. Insecurity or minor depression. The symptoms primarily come from Hopkins Symptom Checklist categories 3, 4, 5, and 6 (table 1).
  2. Somatization. The symptoms primarily come from Hopkins Symptom Checklist category 1 and have been given the same name.
  3. Depression. The symptoms primarily come from questions compiled by the survey authors and relate to depression.
  4. Obsessive-compulsive. The symptoms primarily come from Hopkins Symptom Checklist category 2 and have been given the same name.
  5. Malaise. The symptoms primarily come from questions compiled by the survey authors and relate to symptoms commonly reported by Gulf War veterans. They have been termed malaise because they include a variety of miscellaneous symptoms and the validity symptom, earlobe pain.

Factor analysis of NDVs
We then performed a factor analysis on the NDVs paralleling the analysis on GWVs. The final analysis is summarized in table 3 under the columns labeled NDV. Five factors also emerged from this analysis. The factors were similar to those for GWVs, and they were named the same. The squared multiple correlations ranged from 0.28 to 0.55. The measure of sampling adequacy was 0.90. The five factors accounted for 89 percent of the variance present in the symptoms retained in the model. The eigenvalue of the fifth factor was 1.1, while that of a sixth factor would have been 0.79. The primary difference between the GWV and NDV models was that not as many questions loaded on the factors in the NDV model as did in the GWV model, suggesting that the factors may not be as strongly present in the NDV group as in the GWV group.

Factor analysis of the combined GWV and NDV groups
We performed a third factor analysis on the combined groups paralleling the previous analyses. The final analysis is summarized in table 3 under the columns labeled All. Five factors also emerged from this analysis and were named the same. The squared multiple correlations ranged from 0.19 to 0.58. The measure of sampling adequacy was 0.95. The five factors accounted for 93 percent of the variance present in the questions retained in the model. The eigenvalue of the fifth factor was 1.0, while that of a sixth factor would have been 0.78. The difference between this model and the previous models was that an intermediate number of questions loaded on the factors as did in the NDV and GWV models, which is consistent with the pooling of the two populations.

Factor comparisons between veteran groups
The distributions of the standardized factor scores for the two groups are compared in table 4. Notice that the medians of the standardized factor scores for the GWV group are zero, by construction. Factor 1, although the strongest factor to emerge from our analyses, had standardized factor scores that were not significantly different between the two groups. For factors 2, 3, and 4, the standardized factor scores for the GWV group were strongly statistically significantly greater than for the NDV group. Conversely, for factor 5 the standardized factor scores for the NDV group were strongly statistically significantly greater than for the GWV group. Notice that factors 2, 3, and 4 bear a correspondence with the previous report's first three syndromes.


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TABLE 4. Distribution of standardized factor scores, by Gulf War deployment group, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995

 
Figures 1GoGoGo5 present cumulative frequencies of the standardized factor scores, which illustrate the mean shift of the GWV group scores in the positive direction relative to the NDV group for factors 2, 3, and 4; the mean shift in the negative direction for factor 5; and the coincidence of the distributions for factor 1.



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FIGURE 1. Cumulative frequencies of standardized scores for factor 1, insecurity, by Gulf War deployment status, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995. GWV, Gulf War veteran; NDV, nondeployed Gulf War-era veteran.

 


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FIGURE 2. Cumulative frequencies of standardized scores for factor 2, somatization, by Gulf War deployment status, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995. GWV, Gulf War veteran; NDV, nondeployed Gulf War-era veteran.

 


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FIGURE 3. Cumulative frequencies of standardized scores for factor 3, depression, by Gulf War deployment status, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995. GWV, Gulf War veteran; NDV, nondeployed Gulf War-era veteran.

 


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FIGURE 4. Cumulative frequencies of standardized scores for factor 4, obsessive-compulsive, by Gulf War deployment status, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995. GWV, Gulf War veteran; NDV, nondeployed Gulf War-era veteran.

 


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FIGURE 5. Cumulative frequencies of standardized scores for factor 5, malaise, by Gulf War deployment status, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995. GWV, Gulf War veteran; NDV, nondeployed Gulf War-era veteran.

 
To assess the multivariate significance of the difference in standardized factor scores between the two groups, we performed a discriminant analysis on the five factor scores, with backward elimination. Factors 1 and 5 were eliminated, while factors 2, 3, and 4 remained in the model with p values < 0.0001. Consequently, the results of the multivariate discriminant analysis were similar to those of the univariate Wilcoxon analyses.

We then estimated the probabilities of misclassification by cross-validation, using a discriminant function including factors 2, 3, and 4 and prior probabilities equal to the population proportions (524/1,459 = 0.36 for GWVs and 935/1,459 = 0.64 for NDVs). The estimated probabilities of misclassification were 76.5 percent for the GWV group and 7.4 percent for the NDV group, suggesting that the factors satisfactorily classified most of the NDVs but only a small proportion of the GWVs. Additionally, the overall misclassification rate was estimated to be 0.32, compared with 0.36, the misclassification rate that would have resulted from knowing only the prior probabilities. The estimated misclassifications based on factors 2, 3, and 4 suggested that a minority of the GWVs (about 20 percent) stand out from the NDVs and the remaining GWVs.

Finally, we estimated the (discriminant analysis-based) posterior probabilities of being a GWV. The cumulative frequencies of these posterior probabilities (figure 6) illustrate the separation between the groups based on factors 2, 3, and 4. Noting that 30 percent of the posterior probabilities for GWVs are greater than the 90th percentile for NDVs (0.45) suggested that approximately three times the number of GWVs had elevated factor scores than did NDVs.



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FIGURE 6. Cumulative frequencies of posterior probabilities of being a Gulf War veteran, by Gulf War deployment status, Port Hueneme, California, and Gulfport, Mississippi, 1994–1995. GWV, Gulf War veteran; NDV, nondeployed Gulf War-era veteran.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The previous report identified six factors (there termed "syndromes") from its analysis. It noted that only the first three syndromes "represented strongly clustered symptoms" and that the last three syndromes "involved weaker clustering and mostly overlapped syndromes 2 and 3" (8Go, p. 215). It named them as follows:
  1. Impaired cognition
  2. Confusion-ataxia
  3. Arthro-myo-neuropathy
  4. Phobia-apraxia
  5. Fever-adenopathy
  6. Weakness-incontinence

Although the previous report used a unique questionnaire, studied a different population, used a two-stage method for symptom definition, and coined unique names for the resulting factors, a correspondence between its factors and ours can be noted. In particular, its factor 1 resembled our factor 4 (and the Hopkins Symptom Checklist's corresponding factor), obsessive-compulsive, and its name for this factor, impaired cognition, describes it well. The previous report's factor 2 resembled our factor 3, depression, with a portion of our factor 4, obsessive-compulsive, mixed in. Its factor 3 resembled our factor 2 (and the Hopkins Symptom Checklist's corresponding factor), somatization, and its factor 5 resembled our factor 5, malaise (common Gulf War symptoms). In addition, the three factors for which we found an excess of elevated scores among GWVs resembled the first three factors, said to contain "strongly clustered symptoms," of the previous report (8Go, p. 215). Although the previous report did not publish its survey instrument, comparison of that report's table 2 with our table 3 suggests some overlap between instruments and some differences. In particular, it appears that both instruments contained some questions that were not present on the other, perhaps explaining why the previous report's factors 4 and 6 did not correspond with any of our factors, and our factor 1, insecurity, did not correspond with any of the previous report's factors. Finally, there were differences in the populations studied. The previous report studied members of a specific reserve battalion of Seabees, while we studied all available active-duty Seabees, who may have been generally healthier than the reserves.

The five factors found in our analyses were not unexpected, given that three were from the established, validated Hopkins Symptom Checklist and two were the categories of questions especially targeted by the survey authors. The five factors that emerged from the GWV and NDV groups were similar, but the factors were generally stronger and involved more questions for the GWV group. The greater proportion of high positive factor scores among the GWVs for three of the factors was consistent with the factors being stronger for the GWV group, with the many veterans who have reported a variety of symptoms and illnesses since returning from the Persian Gulf War, and with the higher participation rate of GWVs. The elevated factor scores affected only about 20 percent of the GWVs, however.

Other investigators have performed factor analyses on different groups of GWVs and NDVs and, while differing in their methodological approaches and using different survey instruments, have seen similar factors emerge from their GWV group as from their NDV group, but with an excess of positive factor scores for the GWVs compared with the NDVs (19GoGo–21Go). Also noteworthy is a recent report that found factors similar to those found in ill GWVs in a civilian population reporting severe fatigue (22Go). Historically, the Hopkins Symptom Checklist investigators also found generally the same factors in a variety of populations, including hospitalized mentally ill patients and noninstitutionalized healthy subjects, but with differences between populations in the magnitudes of the factor scores (14Go).

While factor analysis is commonly used in psychologic research, it is seldom seen in the epidemiologic literature. There are many variations on how a factor analysis can be performed, all potentially leading to different conclusions. Consequently, there is a strong, necessarily subjective component to any factor analysis. The skill with which this subjective component is executed materially influences the ultimate usefulness of a factor analysis. We placed the goal of maximizing factor interpretability high in our subjective decision-making process. It is interesting to note that the developers of the Hopkins Symptom Checklist, in 1974, used clinical judgment as well as factor analysis in evolving their categories of symptom clusters (14Go).

As one of the reviewers pointed out, classical factor analysis assumes that the manifest variables, the observed variables entered into the analysis, are continuous. Symptom data, as used for the Hopkins Symptom Checklist, the previous report, references 19–22, and the present analysis, are necessarily discrete. The reviewer's suggestion that latent structure analysis (23Go, 24Go) would be more appropriate in this situation is certainly germane. We used classical factor analysis to be consistent with previous work in this area and, especially, with the previous report that motivated the present analysis. There is a pragmatic justification for using classical factor analysis with discrete manifest variables; that is, since factor analysis includes a strong subjective component, statistical optimality is of reduced importance.

Despite the similarity of our results to those of the previous report, we differ in our interpretation of the results. Specifically, we believe that the symptoms and illnesses of GWVs closely reflect the symptoms and illnesses reported by NDVs; GWVs simply participate at a higher rate and report more of the same symptoms and illnesses. In addition, relating the symptoms and illnesses to specific exposures is conjectural at this time, since available exposure data are self-reported and imprecise. Notably, previous reports of self-reported exposures and self-reported symptoms have generally found that almost all exposures are related to almost all symptoms (4Go, 13Go). Our analysis indicates that the conclusion of the previous report, that its factor analysis establishes unique Gulf War syndromes "that appear to reflect a spectrum of neurologic injury" (8Go, p. 215), was premature and would not have been reached had a corresponding analysis on an appropriate control group been performed. Identifying a new syndrome such as the putative Gulf War syndrome is a difficult task (6Go) and is unlikely to be accomplished by factor analysis, or any other statistical methodology, performed on a small, selected group of Gulf War veterans.


    ACKNOWLEDGMENTS
 
This is report no. 98-6, supported by the Department of Defense (Health Affairs) and the Naval Medical Research and Development Command, Bethesda, Maryland, Department of the Navy, under work unit no. 63738DP4464.001-6423.


    NOTES
 
Reprint requests to Dr. Gregory C. Gray, DoD Center for Deployment Health Research, Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186 (e-mail: henry{at}nhrc.navy.mil).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication November 30, 1998. Accepted for publication October 28, 1999.