Abstract
Work disability due to respiratory disease, especially asthma, is common and costly among working age adults. The goal of this analysis was to characterize the risk factors for such disability. We analyzed data from the Swedish part of the European Community Respiratory Health Survey (ECRHS), a random population-based sample of adults age 20 to 44, enriched with symptomatic subjects at increased likelihood of having asthma. We analyzed structured interview data available for 2,065 subjects and further analyzed methacholine challenge and skin prick test data for 1,562 of these. We defined respiratory work disability as reported job change or work loss due to breathing affected by a job. We used binary generalized linear modeling with a log link to estimate disability risk. Eighty-four subjects (4%) reported such work disability. This increased to 13% among those with asthma (45 of 350 subjects). Adjusting for covariates, occupations at high risk for asthma were associated with disability (prevalence ratio [PR] 1.8; 95% confidence interval [CI] 1.1 to 3.0), as was self-reported regular exposure to environmental tobacco smoke (ETS) at work (PR 1.8; 95% CI 1.1 to 3.1) and self- reported job exposure to vapors, gases, dust, or fumes (VGDF) (PR 4.3; 95% CI 2.2 to 8.6). Workplace ETS exposure was also associated with methacholine challenge–positive asthma reported to be symptomatic at work among male subjects (PR 4.2; 95% CI 1.8 to 9.8), whereas high asthma-risk occupations were associated with this outcome among female subjects (PR 2.7; 95% CI 1.05 to 7.1). Respiratory work disability, defined as breathing-related job change due to work loss, was associated with workplace exposures themselves, even after taking into account other covariates. Better control of workplace exposures, including workplace ETS, may reduce work disability caused by respiratory conditions, especially adult asthma. Blanc PD, Ellbjär S, Janson C, Norbäck D, Norrman E, Plaschke P, Torén K. Asthma-related work disability in Sweden: the impact of workplace exposures.
Disability from respiratory disease is common and costly. Because asthma is a chronic disease with frequent exacerbations, this condition in particular is a major cause of disability among those of working age (1). In the United States, for example, asthma is the leading nonmusculoskeletal diagnosis associated with disability among those age 18 to 44, far surpassing conditions such as diabetes and hypertension (2). Data from other industrialized countries demonstrate a similar pattern (1).
Work disability among adults with asthma is common by whatever measure is used to assess it. Asthma-related lost work days are frequent among those with the condition. Other work disability measures relevant to asthma include changes in job duties or work hours resulting from the condition, job change brought about by asthma (due either to symptom aggravation by the job or simply to intolerable work absences), or complete cessation of all employment because of disabling disease (3). Not surprisingly, international economic estimates have found that the cost impact of asthma due to work loss is enormous (4, 5).
Despite its importance, there has been relatively little study of the predictors of work disability caused by respiratory disease among adults of working age. The purpose of this study was to analyze data from the Swedish part of a multinational study, the European Community Respiratory Health Survey (ECRHS) (6-8). This population-based study was limited to adults age 20 to 44 yr of age and included in its survey instrument items assessing work status, lung health (including asthma history), and job change attributed to workplace exposures. It also included measurement of airway responsiveness. For these reasons, we believed the ECRHS would be particularly well-suited to our primary study objective, which was to test a multifactorial risk model of the predictors of respiratory work disability.
We analyzed data collected in the Swedish part of the international ECRHS. The overall study design and, specifically, details of the Swedish component, have been published previously (6-9). In brief, the data were obtained through a two-step survey design based on a random population sample. The initial sampling was carried out in three regions of Sweden: Västerbotten, a large county in the north of Sweden; Uppsala, a university city close to Stockholm; and Göteborg, the second largest city in Sweden, with more industries that in the other study areas.
The initial sampling step, 9,281 adult subjects age 20 to 44 (86% of those contacted) completed a brief, self-administered screening questionnaire. The 10-question battery included three items on self-reported asthma attack, asthma medication use, or being woken “by an attack of shortness of breath” in the prior 12 mo.
The second stage of the survey was based on a subsample of the initial study group. There were two sampling frames for this stage of the study. In the main sampling frame, a 20% random sample of the entire initial screening questionnaire group was selected. This was supplemented with a second sampling frame from among the 80% not selected as part of the random sample. It was designed to enrich the study population for adults with asthma. This supplemental frame recruited subjects who responded affirmatively to one of the three asthma-related questions from the screening questionnaire noted previously. The target was approximately 6% of the remaining group not already selected in the random subsample, yielding approximately 20% of the follow-up group overall.
Subjects selected in the second stage were invited to complete a face-to-face, interviewer-administered questionnaire and undergo allergen skin prick testing, pulmonary function testing, and measurement of airway responsiveness through methacholine challenge. However, subjects were allowed to complete the questionnaire by telephone and forgo the laboratory component of the study. In total, questionnaires for the second stage were obtained in 2,400 subjects (25.9% of those with screening questionnaires completed).
The questionnaire included items covering respiratory symptoms, asthma and atopy history, medication usage, smoking, diet, indoor air quality, and occupation. Asthma-related questions included self-report of the following: any history of asthma, physician diagnosis of asthma, age of first asthma attack, and use of asthma medicines (9).
All subjects were asked whether or not they were full-time students. Only those who were not full-time students were then asked a series of occupational questions. The current or recent job was ascertained, but prior employment was not systematically elicited. However, two other questions did allow for specification of prior occupations. Those answering affirmatively to the question, “Have you ever had to change or leave your job because it affected your breathing?” were asked to specify the job involved. Subjects responding affirmatively to the question, “Have you ever worked in a job which exposed you to vapors, gas, dust or fumes?” were also asked to specify that job. In addition to these questions, subjects were also asked, “Does being at work ever make your chest tight or wheezy?”, with no allowance to specify a job. There was also a specific question about exposure to environmental tobacco smoke (ETS) on the job, “Do people smoke regularly in the room where you work?”
All jobs reported in the second-stage questionnaire were noted in an open-ended format allowing for job title and industry of employment. These responses were later coded in accordance with ECRHS protocol using the European Commission Alphabetical Index for Classifying Occupations (10). This 3-digit coding scheme allows for 348 different occupations and two additional classifications, “Inadequately described occupations” and “Occupations not stated, including housewife and student.” Industry of employment, as opposed to occupation, was not coded from the ECRHS questionnaire.
We used a hierarchical approach to identify subjects' employment of greatest respiratory risk. In this hierarchy, preference was given to the job specified as changed because it affected breathing > job with exposure to vapors, gas, dust, or fumes (VGDF) > current or most recent job. Based on this hierarchy, each subject had one job code extracted from the questionnaire responses that we employed as a measure of the occupation of greatest risk. We then used this job code to establish two exposure matrices: one for asthma risk (“asthma” matrix) and a second for dusty trades (organic and inorganic dusts), with or without inherent asthma risk (“dust” matrix). We reviewed the entire list of 348 occupational classifications and for each matrix assigned the classification a code of “1” for low risk, “2” for intermediate risk, and “3” for high risk.
For asthma, there were 162 of 348 classifications (47%) that we designated a priori as intermediate-risk and 61 that we designated as high-risk jobs (18%). Examples of intermediate-risk occupational codes included those for nurses, vocational trainers, photographers, garage proprietors, launderers, shoe repairers, printers, motor mechanics, sheet metal workers, construction workers, and stevedores. Examples of high-risk occupational codes include those for laboratory technicians, medical technicians, farmers, firefighters, welders, cleaners, bleachers, bakers, spray painters, and cabinet makers and carpenters.
For the dust matrix there were 65 of 348 classifications (19%) that we designated a priori as intermediate and 62 (18%) that we designated as high-risk. Examples of intermediate-risk classifications included those for building inspectors, land surveyors, firefighters, leather cutters, bakers, wood processors, carpet fitters, machine tool operators, boatmen, and refuse collectors. Examples of high-risk codes include those for mine engineers, farmers, chimney sweeps, textile weavers and spinners, carpenters, ceramic makers, molders and foundry workers, stone cutters, and miners.
In the ECRHS, allergen skin prick testing was carried out using a standardized protocol including a battery of cat, house dust mite (D. pteronyssinus), timothy grass, Cladosporium herbarium, Alternaria alternata, and Parietaria judaica. Two “local” allergens were also allowed for, which in Sweden were dog and mugwort (8). As in the earlier published analysis of the skin prick test data from the Swedish component of the ECRHS, we defined atopics as those responding to one or more skin prick allergen test with a response of 3 mm or greater and excluded histamine nonresponders (n = 9). Although IgE levels were obtained as part of the ECRHS, we did not include those data in this analysis.
In addition to skin prick testing, all subjects completing the second-stage questionnaire were invited to complete a standardized pulmonary function protocol measuring airflow. All centers were required to use a Spiro-Medics system 2130 (Sensor Medics, Anaheim, CA) (10). Reference FEV1 values were calculated using the formulae: (males) 4.30 · height (m) − 0.029 · age (yr) − 2.49 or (females) 3.95 · height (m) − 0.025 · age (yr) − 2.60. Subjects with an FEV1 of greater than 70% predicted and greater than an absolute value of 1.5 L, absent medical exclusions (including heart disease, epilepsy, pregnancy, nursing) were eligible to participate in a methacholine inhalation challenge. We defined a methacholine-positive challenge as one in which there was a 20% or greater fall from baseline FEV1 before a maximal cumulative dose of methacholine (2 mg) was administered.
From the 2,400 potential subjects with second-stage interview data, we excluded from further analysis all respondents who reported that they were full-time students at the time of the interview (n = 250; 10.4%). We also excluded 30 subjects (1.3%) who did not appear to have any history of prior civilian labor force participation. There were an additional 55 subjects (2.3%) with missing data for key occupational items or whose most recent employment was coded as “inadequately described” or “not stated.” After all exclusions, 2,065 (86%) remained for analysis. Of these subjects, 778 (38%) were contributed by the Göteborg study site, 669 (32%) by the Uppsala site, and 618 (30%) by the Västerbotten site. Of the 2,065 subjects, 1,607 (78%) were from the random sample and 458 (22%) from the asthma-enriched sampling frame.
Because subjects could have completed the questionnaire without participating in the skin prick allergen or pulmonary function testing component of the study, these data were not available for all subjects. Data for methacholine and atopy were available for 1,562 of the 2,065 subjects otherwise included in the analysis.
Data analyses were performed using a standard computer statistical package (SAS, Cary, NC). We tested the association between job matrix and self-reported exposure using the kappa statistic. We tested differences in the frequencies of subjects' demographic variables and health conditions among the three Swedish study sites using the chi-square or, in the case of age, analysis of variance (ANOVA). We tested pairwise differences in age between sites using Tukey's modified t test. We calculated the 95% confidence intervals (95% CI) for observed proportions of the key work-related variables.
The primary outcome of study interest was respiratory work disability, which we defined as a positive response to the question, “Have you ever had to change or leave your job because it affected your breathing?” To test the hypothesis that occupational factors would be associated with respiratory work disability, we first carried out single predictor analyses with each variable of study interest (two variables when intermediate- and high-risk jobs were included in the same model, with the low-risk category as the reference category). We employed a binary generalized linear model with a log link to estimate prevalence ratios for the risk factors of the study interest (SAS). We then tested the same factors in multiple predictor models without and then with a group of other covariates. We also carried out further analyses stratified by subject-reported allergic rhinitis and/or hay fever history.
In addition to respiratory questions, the ECRHS questionnaire also asked, “Are you disabled from walking by a condition other than heart or lung disease?” We use this as a measure of nonrespiratory disability. We tested the same predictors as we had for respiratory disability in a multiple predictor model.
In addition to these analyses, we also used binary generalized linear modeling to estimate the association between occupational exposures and reactive airways disease symptomatic at work. We defined this as self-reported breathing symptoms at work (a positive answer to the question, “Does being at work ever make your chest tight or wheezy?”) in subjects with asthma who were also methacholine test positive for nonspecific airway hyperresponsiveness. We limited this analysis to subjects for whom there were both skin prick test and methacholine data along with all other variables (n = 1,562). We further tested this model stratifying by atopic skin prick status and gender.
Table 1 shows the demographic and clinical variables for the study population, and Table 2 presents frequencies and their 95% CI for the key occupational variables of study interest. Altogether 4% of those analyzed reported respiratory work disability defined by ever changing jobs or leaving work because it affected the respondent's breathing. Of those in the study with asthma (n = 350), 45 (12.9%; 95% CI 9.3 to 16.4%) reported work disability by this measure. Among those without self-report of asthma (n = 1,715), 39 (2.3%) reported respiratory work disability. Because the cohort was enriched for those with asthma, we also calculated the frequency of respiratory work disability among the general population random sampling frame alone (n = 1,607). Of this group, 41 (2.6%; 95% CI 1.8 to 3.3%) reported respiratory work disability. As Table 2 also shows, the a priori classification of potential job risks by the two exposure matrices we employed yielded differing frequency patterns for asthma risk compared with likely dusty trades. Nonetheless, there was a statistically significant association between the two (kappa = 0.12, 95% CI 0.09 to 0.15). Self-reported exposure to VGDF was strongly associated with both intermediate to high a priori risk dusty jobs (kappa = 0.39, 95% CI 0.35 to 0.41) and, to a lesser extent, with intermediate or high asthma job risk (kappa = 0.12, 95% CI 0.09 to 0.14). Of note, even among 1,337 subjects classified as of low risk for exposure to dust, 595 (45%) reported exposure to VGDF although only 48 of 362 (13%) in the most likely exposed group denied such exposure. Similarly 399 of 673 (59%) in the low-risk asthma category reported exposure to VGDF, whereas only 69 of 509 (14%) in the most likely exposed group denied exposure.
| Subject Characteristic | Study Center | p Value | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| G (n = 778) | V (n = 618) | U (n = 669) | Total (n = 2,065) | |||||||
| n (%) | n (%) | n (%) | n (%) | |||||||
| Female sex | 406 (52) | 319 (52) | 327 (49) | 1,052 (51) | > 0.4 | |||||
| Cigarette status | ||||||||||
| Ex-smoker | 164 (21) | 153 (25) | 155 (23) | 472 (23) | > 0.2 | |||||
| Current smoker | 292 (38) | 157 (25) | 186 (28) | 635 (31) | < 0.001 | |||||
| Self-supported asthma | ||||||||||
| Childhood onset | 53 (7) | 75 (12) | 72 (11) | 200 (19) | 0.002 | |||||
| Adult onset | 41 (5) | 60 (10) | 49 (7) | 150 (7) | 0.006 | |||||
| Allergic rhinitis/hay fever | 230 (30) | 229 (37) | 241 (36) | 700 (34) | 0.005 | |||||
| Age, yr, mean ± SD | 33 ± 7 | 34 ± 7 | 33 ± 7 | 34 ± 7 | 0.02 | |||||
| Work-related Variables | n (%) | 95% CI* | ||
|---|---|---|---|---|
| Currently employed | 1,939 (94) | 93–95 | ||
| Ever changed or left job because it affected breathing | 84 (4) | 3–5 | ||
| Changed or left job and not currently employed | 12 (0.6) | 0.2–0.9 | ||
| Work ever makes chest tight or wheezy | 308 (15) | 13–16 | ||
| Ever exposed on job to VGDF | 1,245 (60) | 58–62 | ||
| Regular workplace ETS exposure | 223 (11) | 9–12 | ||
| Job exposure asthma risk matrix classification | ||||
| Low risk | 673 (33) | 31–35 | ||
| Intermediate risk | 883 (43) | 41–45 | ||
| High risk | 509 (25) | 23–27 | ||
| Job exposure dusty trades matrix classification | ||||
| Low risk | 1,337 (65) | 63–67 | ||
| Intermediate risk | 366 (18) | 16–19 | ||
| High risk | 362 (18) | 16–19 |
The results of binary generalized linear analyses are presented in Table 3. Once again, in these analyses respiratory- related work disability, which is the primary dependent outcome, is defined as reported job change or work cessation because the employment affected the subject's breathing. In separate analyses without covariates, high-risk asthma and dusty occupations classified by job matrix were both associated with statistically increased prevalence ratios (PRs) for work disability, as were workplace ETS exposure and exposure to VGDF.
| Binary Generalized Linear Modeling | PR | 95% CI | ||
|---|---|---|---|---|
| Analyses without inclusion of exposure covariates | ||||
| Asthma risk by job matrix | ||||
| Low asthma risk job (referent) | 1.0 | — | ||
| Intermediate asthma risk job | 0.9 | 0.5–1.7 | ||
| High asthma risk job | 2.3 | 1.4–3.9 | ||
| Dust exposure risk by job matrix | ||||
| Low dust exposure risk job (referent) | 1.0 | — | ||
| Intermediate dust exposure risk job | 1.6 | 0.9–2.7 | ||
| High dust risk job | 2.5 | 1.6–4.0 | ||
| Regular workplace ETS exposure | 2.1 | 1.3–3.5 | ||
| Exposed to VGDF | 4.9 | 2.5–9.4 | ||
| Multiple predictor analysis | ||||
| Asthma risk by job matrix | ||||
| Low asthma risk job (referent) | 1.0 | — | ||
| Intermediate asthma risk job | 1.1 | 0.6–1.9 | ||
| High asthma risk job | 1.8 | 1.05–3.0 | ||
| Regular workplace ETS exposure | 1.7 | 1.01–2.8 | ||
| Exposed to VGDF | 4.1 | 2.0–9.4 | ||
| Multiple predictor analysis including other covariates | ||||
| Asthma risk by job matrix | ||||
| Low asthma risk job (referent) | 1.0 | — | ||
| Intermediate asthma risk job | 1.1 | 0.6–1.9 | ||
| High asthma risk job | 1.8 | 1.1–3.0 | ||
| Regular workplace ETS exposure | 1.8 | 1.1–3.1 | ||
| Exposed to VGDF | 4.3 | 2.2–8.6 |
Because the pattern of associations was similar for the two matrices, Table 3 presents the results of further multiple predictor analyses for the asthma risk classification only. Including all of the work exposure variables in the same model reduced the estimated PRs for each factor somewhat, although not dramatically so. Further adding to the multiple predictor model a number of additional covariates did not have a meaningful impact on the point estimates of job-associated or vapor exposure–associated PRs (Table 3). Neither current smoking (PR 1.2; 95% CI 0.7 to 2.0) nor former smoking (PR 0.9; 95% CI 0.5 to 1.6) (not shown in Table 3) was statistically associated with breathing-related job change. Age was marginally predictive of disability (per 10 yr, PR = 1.3, 95% CI 1.0 to 1.8). None of the other factors included were statistically associated with disability.
We reestimated the multiple predictor model (without other covariates) stratified by subject-reported allergic rhinitis and/ or hay fever history. Among those with allergic rhinitis (n = 700; 55 [7.9%] with disability), the PR point estimates were greater for high asthma risk jobs (PR = 2.2) and workplace ETS (PR = 1.9), and less for VGDF (PR = 3.4) (p < 0.05 in all cases). For those without allergic history (n = 1,365; 29 [2.1%] with disability), the point estimate for exposure to VGDF increased to 4.1 (p < 0.05), whereas the other risk factors did not achieve statistical significance.
There were 45 of 2,065 survey respondents (2.2%) who reported noncardiopulmonary disability manifest by limitation walking. When tested in the multiple predictor model with all the covariates shown in Table 3, none of the occupational factors was statistically associated with this disability measure. Increasing age (odds ratio [OR] 1.7 per 10 yr of age; 95% CI 1.1 to 2.6) was a significant predictor in this model.
Table 4 presents an assessment of the key risk factors as predictors of work-associated, symptomatic asthma. We defined this as the presence of airway hyperresponsiveness by methacholine challenge coupled with a history of asthma and reported chest tightness or wheezing at work. We limited this analysis to subjects with both methacholine and allergen skin prick testing data. Of the 1,562 subjects included in this analysis, 160 (10%) reported asthma and airway hyperresponsiveness. Of these, 61 (4% of the entire group) reported chest tightness at work, meeting our study definition of work-associated symptomatic asthma. Respiratory work disability was strongly associated with these characteristics, being reported by 12 (22%) of the 61 compared with only 43 (3%) of the remaining subjects (p < 0.001).
| Binary Generalized Linear Modeling | PR | 95% CI | ||
|---|---|---|---|---|
| Analyses without inclusion of covariates | ||||
| Asthma risk by job matrix | ||||
| Low asthma job (referent) | 1.0 | — | ||
| Intermediate asthma job | 1.6 | 0.8–3.2 | ||
| High asthma risk job | 1.9 | 0.99–3.8 | ||
| Workplace ETS exposure | 1.8 | 0.95–3.4 | ||
| Exposure to VGDF | 1.4 | 0.8–2.4 | ||
| Multiple predictor analysis including covariates | ||||
| Asthma risk by job matrix | ||||
| Low asthma job (referent) | 1.0 | — | ||
| Intermediate asthma job | 1.6 | 0.8–3.1 | ||
| High asthma risk job | 1.7 | 0.9–3.4 | ||
| Workplace ETS exposure | 1.7 | 0.9–3.3 | ||
| Exposure to VGDF | 1.3 | 0.7–2.4 |
The high asthma risk job category was associated with the greatest estimated risk of symptomatic asthma at work, although its 95% CI did not exclude no association (PR = 1.9; 95% CI 0.99 to 3.8). Reestimating the risk in a multiple predictor model including all of the occupational exposure factors as well as covariates, did not appreciably change the point estimates of the PRs.
We further stratified the analysis by both gender and skin prick test status as shown in Table 5. Both among men and among the atopic by skin prick testing, reported workplace ETS exposure was statistically associated with increased risk of occupational chest tightness. Among the substratum of males with atopy (n = 335), 31 (9.3%) reported work chest tightness. Among these subjects, the estimated PR for ETS was 6.0 (95% CI 2.5 to 14.9). Among women, high-risk asthma jobs were associated with a statistically increased PR for chest tightness at work (Table 5).
| Risk Factors | Stratified Analyses | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Women (n = 740) | Men (n = 822) | Atopic (n = 593) | Nonatopic (n = 969) | |||||||||||||
| PR | 95% CI | PR | 95% CI | PR | 95% CI | PR | 95% CI | |||||||||
| Intermediate-risk job | 1.7 | 0.7–4.1 | 1.0 | 0.4–2.9 | 2.1 | 0.98–4.4 | 0.9 | 0.3–3.0 | ||||||||
| High-risk job | 2.7 | 1.05–7.1 | 1.0 | 0.4–2.7 | 2.0 | 0.9–4.4 | 1.3 | 0.4–4.4 | ||||||||
| Workplace ETS | 0.6 | 0.1–2.3 | 4.2 | 1.8–9.8 | 2.5 | 1.3–4.8 | 0.5 | 0.1–3.7 | ||||||||
| VGDF | 1.7 | 0.9–3.3 | 2.1 | 0.5–9.3 | 1.2 | 0.6–2.2 | 1.6 | 0.5–5.4 | ||||||||
This is the first published analysis of the ECRHS focusing on disability risk in asthma. We found that respiratory work disability, defined broadly on the basis of job change resulting from affected breathing, was not rare in this cohort. Although the study group was, by design, enriched for adults with respiratory symptoms, even among the random population sample, 26 per thousand reported such job changes. Participation rates in the Swedish ECRHS sites were excellent (and the highest of any centers participating in the ECRHS) (11), indicating that selection bias is unlikely to be a major confounder in this analysis. Among those with asthma, 13% reported work disability measured by breathing-related job change. Among those with asthma, confirmed hyperresponsiveness, and reported work symptoms, 22% reported such disability. These estimates are similar to the work disability prevalence of 17% estimated with a comparable measure among a cohort of adults under specialist treatment for asthma from the United States (3).
If respiratory work disability is a relatively common problem, what are its predictors? Our analysis suggests that the risk factors associated with such disability are multifactorial. Even after taking demographic factors into account, many of which could have introduced confounding bias, working conditions themselves were potent predictors of disability. This is consistent with other models of work disability in asthma, and of disability in chronic disease generally (1, 3, 12, 13). In this analysis we found that both risk factors assigned on the basis of an exposure matrix linked to occupational classification and risk factors based on self-report of exposure were associated with respiratory work disability.
The observed association between reported occupational ETS exposure and job change is particularly interesting. Moreover, consistent with this association, workplace ETS exposure was also associated with asthma symptoms on the job among two major groups: males and those atopic by skin prick testing. Recent data among adults with asthma suggest that ETS can be associated with increased morbidity (14). We did not observe a strong association between direct smoking and respiratory work disability. This lack of association may be due in part to the relatively young age range of the subjects studied.
Work disability, estimated by whatever measure used, is typically dependent on respondent self-report. We do not have independent verification that the reported job changes took place, or if so, their true reason. To assess whether the same risk factors would be evident for nonrespiratory disability, we did exploit the one relevant measure available in the ECRHS, impairment that was not attributed to cardiopulmonary disease. At least in terms of that measure, none of the occupational variables demonstrated the association to nonpulmonary disability that we observed for job change reportedly caused by affected breathing. Such an association might have been expected as a marker of recall because it might be linked to generic disability. Moreover, the assigned job matrix, as opposed to subject report of exposure, is an independent measure. Nonetheless, we cannot exclude the possibility that recall bias may have impacted our estimates.
Subjects who incorrectly report their asthma status may introduce measurement error. In particular, many of the subjects with mild disease or with adult onset disease may not appreciate that they do indeed have asthma. The ECRHS includes a physiologic determination of airway responsiveness, along with allergy skin prick testing to determine atopy. We had such data available for approximately three-quarters of our subjects. We exploited those data to examine risk factors for asthma with hyperresponsiveness that way symptomatic at work. Broadly defined, this is consistent with occupational asthma, although it does not differentiate between asthma of work-related origin and preexisting asthma aggravated by work.
The core questionnaire of ECRHS is not structured in a way that allows a comprehensive assessment of past occupations. Several analyses have studied the association between various occupational categories and the prevalence of asthma in the ECRHS (15-17). Studies from Spain and New Zealand have adopted a similar analytic approach, defining the “at risk” job as either the occupation reported in association with respiratory job change or, if no such change was reported, the current or most recent job held (15, 16, 18). Our approach differed in two important ways. First, we also used the job associated with exposure to VGDF, given that such exposures were reported by the majority of our subjects. Second, we then used these jobs to assign broad categories of risk.
Unlike the Spanish and New Zealand analyses, we observed a statistical association between a high-risk job classification and asthma only among female subjects, using those symptomatic at work as a surrogate for occupational asthma. This difference may be explained by limited generalizability of the Swedish findings to other centers, or by the particular analytic approach that we employed (which was not primarily intended to explore asthma etiology), or it may simply reflect limitations in the ECRHS study design in the assessment of past occupations. The Uppsala component of the Swedish study did administer a separate, supplemental questionnaire with a complete work history. Analysis of those data, published elsewhere (19, 20), found that among 562 subjects from Uppsala (men and women combined), the asthma risk from cumulative job exposures, assigned on the basis of occupation and industry of employment rather than subject report, was elevated (OR 1.7; 95% CI 1.1 to 2.9) (20). With 28% of the group so exposed, this yielded a population attributable risk (PAR) estimate of 16%, a higher estimate than those from either the Spanish (9%) or New Zealand study (3%) (15, 16). Other studies have also estimated that the occupationally related PAR% is > 10% in Sweden (21, 22).
The ECRHS provides a rich, multinational data base from which the impact of respiratory disease on health can be assessed. Work disability is a critical measure of morbidity among adults of working age. Work disability is particularly relevant in asthma, a chronic condition that is frequent precisely in this age group. Our findings indicate that job changes resulting from respiratory symptoms are linked not only to asthma, occurring among more than one in 10 with that condition, but to working conditions themselves. Working conditions may be changed, thus potentially preventing disability. Many engineering changes to reduce workplace exposures may be complicated and expensive. Other changes may be more straightforward, for example, substitution with a less hazardous material or, as our findings particularly indicate, control of cigarette smoking in the workplace. Further analyses of ECRHS data from other centers may help further elucidate the frequency and risk factors for work disability associated with asthma and other respiratory diseases.
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Supported in part (Dr. Blanc) by a Research Career Investigator Award (K04 HL03225) and by the Swedish Council for Worklife Research (Dr. Torén and Dr. Norbäck), Swedish Medical Research Council, the Swedish Heart and Lung Foundation, and the Swedish Association Against Asthma and Allergy.
