ASTHMA AND OCCUPATION: A POPULATION-BASED STUDY...
Transcript of ASTHMA AND OCCUPATION: A POPULATION-BASED STUDY...
ASTHMA AND OCCUPATION: A POPULATION-BASED STUDY AMONG YOUNG
DANISH ADULTS
Jesper Rasmussen, MD1, Jesper Bælum, MD 2, Lars R Skadhauge, MD 3, Maria C Mirabelli,
PhD 4, 4a, 4b, Jan-Paul Zock, PhD 4, 4a, 4b, Søren Dahl, MD 3, Øyvind Omland, MD 5, David
Sherson, MD 6, Hans C. Siersted, MD 7, Gert Thomsen, MD 8, Torben Sigsgaard, MD 9
1Department of Occupational and Environmental Medicine, Hospital of Southern Jutland,
Haderslev, Denmark; 2 Department of Occupational and Environmental Medicine, Odense
University Hospital, Odense, Denmark; 3 Department of Occupational and Environmental
Medicine, Hospital of Southern Jutland, Haderslev, Denmark; 4 Centre for Research in
Environmental Epidemiology (CREAL), Barcelona, Spain; 4a Municipal Institute of Medical
Research (IMIM-Hospital del Mar), Barcelona, Spain; 4b CIBER Epidemiología y Salud
Pública (CIBERESP), Spain; 5 Department of Occupational Medicine, Hospital of Aalborg,
Denmark; 6 Department of Occupational and Environmental Medicine, Vejle Hospital;
Denmark; 7 Department of Respiratory Medicine, Odense University Hospital, Denmark; 8
Department of Occupational Medicine, Hospital of South West Jutland, Denmark;
9Department of Environmental and Occupational Medicine, Aarhus University, Aarhus,
Denmark.
Corresponding author and requests for reprints
Jesper Rasmussen, Department of Occupational and Environmental Medicine, Hospital of
Southern Jutland, Simmerstedvej 1-3, DK-6100 Haderslev, Denmark, Telephone: 0045
73530222, Fax: 0045 74520373, E-mail: [email protected]
Running Title: Asthma and Occupation Word count: 4,237
Number of tables: 7 tables Number of figures: 2 figures
ABSTRACT
Objectives: To describe occupations and occupational exposures associated with the
prevalence of asthma among men and women in Denmark.
Methods: Information about occupation and asthma symptoms was collected from 7,271 men
and women, aged 20 to 44 years, who participated in a population-based, cross-sectional
survey of respiratory health in Denmark. Three respiratory health outcome definitions were
used: current wheeze, current asthma and doctor-diagnosed adult-onset asthma. The current
or last held job was coded according to ISCO-88 and linked to an asthma-specific job
exposure matrix. Associations between occupational exposures and the respiratory health
outcome definitions were evaluated using log-binomial regression analysis, stratified by
gender and adjusted for county, age and smoking status.
Results: Occupation as a cleaner or caretaker was associated with an increased prevalence of
current asthma compared to non-manual workers. These associations were most pronounced
among women (prevalence ratio (PR) 2.17; 95% confidence interval (CI) 1.47-3.21 and PR
1.50; 95% CI: 1.02-2.18) and were not modified by nasal allergy. Among men, exposure to
high-molecular-weight agents was associated with an increased prevalence of current asthma.
A greater prevalence of current asthma was found among women exposed to industrial
cleaning agents than in respondents unlikely to be exposed to any asthmagenic compounds.
Conclusions: Our data indicate a consistently increased asthma risk in female cleaners and
caretakers and in men with workplace exposure to high-molecular-weight agents.
Key words: work-related asthma; job exposure matrix; prevalence
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INTRODUCTION
Asthma is the most common occupational respiratory disorder in Western industrialised
populations1, and 15-20% of the population burden of asthma may be attributable to
occupational exposures.2;3 The list of occupations and exposures reported to be associated
with asthma is expanding, and over 250 workplace agents have been identified as specific
causes of occupational asthma.4;5
Population-based studies have reported an increased risk of asthma among
agricultural workers, bakers and other food processors, cleaners, chemical processors,
farmers, laboratory technicians, nurses, painters, personal care workers, plastic and rubber
workers, spray painters, waiters and welders.2;6-9 Work as a domestic or professional cleaner
and frequent use of cleaning sprays have been reported to increase the risk of asthma.13-18
Healthcare workers and hospital technicians also experience increased risk of asthma,
possibly due to the use of specific products at work,10;11 and an increased risk of new-onset
asthma has been reported among nursing professionals with occupational exposure to
cleaning products and disinfectants.12
Estimates in population-based studies mainly report work-related asthma without
further characterisation as occupational asthma. Because work-related asthma includes
asthma caused by the workplace (i.e., occupational asthma) as well as pre-existing asthma
that is exacerbated by occupational exposures (i.e., work-exacerbated asthma), work-
exacerbated asthma is also an important area of research interest.19 Epidemiological research
indicates that work-exacerbated asthma is common.20 Particularly high percentages have
been reported among garment workers, janitors, restaurant workers and security guards,
possibly due to irritant exposure in the workplace.21 Participants exposed to either high-
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molecular-weight (HMW) or low-molecular-weight (LMW) agents, high peak irritant
inhalation accidents, biological enzymes, flour dust, latex, mite and other animal-derived
proteins, textiles, industrial cleaning agents and highly reactive chemicals have shown
increased asthma risk in epidemiologic studies conducted using an asthma- specific job
exposure matrix (JEM).2;9;22
Respiratory symptoms among men and women have been attributed not only to
occupation, but also to exposures that are likely to be relevant to the exposures encountered
in Denmark. A study of Dutch farmers and agricultural industry workers reported an
association between endotoxin exposure and respiratory effects, (such as wheezing and
wheezing with shortness of breath), among those with a farm childhood.24 This finding
suggests that growing up on a farm may increase, rather than decrease, the risk of adult
asthma symptoms when accounting for farming-related endotoxin exposure. Also, a higher
risk of occupational asthma has been reported among individuals having a parental history of
asthma.2 A positive association between rhinitis and asthma has been reported in both adult
men and women, but without accounting for occupational exposure.25 Finally, a study
reported that individuals who grew up in large families were also likely to experience adult
asthma, although occupational exposures were not accounted for.26 Given the potential for
the exposure-outcome relationships to vary, not only between men and women, but also
between individuals having these a priori suspected or known risk factors, it is important to
include these risk factors in the study of associations between occupational exposures and
asthma.
In Denmark, the prevalence of self-reported asthma among adults has increased from
2.9% in 1987 to 6.4% in 2005.27 In the Danish Risk Factor for Adult Asthma study (RAV),
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we found an asthma prevalence of 7.0% in 2004. 28 The aetiology of this increase is unknown
and may partially be explained by occupational exposure, although associations between
occupational exposure and asthma have rarely been studied in Denmark.
In 2005, Denmark was the European country with the highest percentage of female
workers, with women accounting for 47.5% of the labour force.29 Few studies have
investigated gender differences in the associations between occupational exposure and
asthma. One study reported that women were more exposed to asthmagenic agents than men
and that associations between asthmagenic agents and severe asthma were stronger among
women than men.9 Another study reported an increased incidence of respiratory symptoms
among only female woodworkers exposed to dry wood.23 In a study of trends in the Danish
work environment in 1990-2000, the prevalence of cleaning, clerk, military and textile jobs
decreased, while academic, computer, and managerial jobs increased. The number of part-
time cleaners has declined considerably, resulting in more hours of cleaning work per
individual. In addition, skin contact with cleaning agents among nurses has increased.30 With
the unusually high percentage of women in the Danish workforce overall, and in several
occupations having a high risk of asthma in particular, the prevalence of work-related asthma
may plausibly be expected to be higher than those reported in other European countries.
The purpose of this study was to investigate how gender influences the relationship
between occupation and asthma in a large population-based cohort of adults in Denmark. In
addition, we assessed the effects of growing up in a rural area or on a farm, parental asthma
history, nasal allergy and the number of siblings in modifying the relationship between
occupational exposures and asthma.
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METHODS
STUDY DESIGN AND POPULATION
The RAV study used a cross-sectional study design following the protocol used for the
European Community Respiratory Health Survey (ECRHS).31 Between December 2002 and
January 2004, an extended ECRHS screening questionnaire was mailed to a gender- and age-
stratified random sample of 10,000 adults aged 20 to 44 years. Participants were drawn from
a general population-based sample of residents in five counties in western Denmark, a
geographic region having a total population of 1,800,000. The study was approved by local
ethical committees.
QUESTIONNAIRE
The questionnaire included items about asthma symptoms, asthma and suspected asthma risk
factors, including the current or last held job, smoking, parental asthma, growing up in a rural
environment or on a farm, the number of siblings26 and nasal allergy.
OCCUPATIONAL EXPOSURE ASSESSMENT
Employment information was obtained using the following question that defined the current
or last held job: ‘What is your current or last held job?’ The current or last held job was
coded according to the Danish version of the International Labour Office system, ISCO-88
(International Standard Classification of Occupations).32 Two estimates of exposure were
used. First, jobs were grouped into 26 categories of potentially high-risk jobs and a reference
group consisting of participants who worked in professional, clerical, or administrative jobs,
as previously defined by the ECRHS.2 Second, an asthma-specific JEM33 was used to
estimate exposure to high- and low-risk asthmagenic agents. For each ISCO-88 job code, the
asthma-specific JEM classified the job as exposed to or not exposed to 18 high-risk
asthmagenic agents or settings, which we further subdivided into four groups: HMW agents,
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LMW agents, mixed environments or high irritant peaks. The four low-risk asthmagenic
agents or settings were engine exhaust, environmental tobacco smoke, possible irritants and
low- risk antigens. Furthermore, we grouped the JEM exposure categories into two groups:
exposure to any high- risk agents or any low- risk agents. The low-risk group was not
exposed to any high-risk agent, but in each of the two separate groups, participants may have
had more than one specific high-risk or low-risk exposure. Overall, 754 participants could
not be classified into any exposure category using the asthma-specific JEM. To avoid
excluding these participants, we considered this group more likely to be unexposed than
exposed, so in our analysis, they are included in the reference group. The expert step,
recommended when using the asthma-specific JEM, was not applied in this study.
ASTHMA DEFINITION
We used three definitions of asthma based on diagnosis or symptoms that are highly
suggestive of asthma, ranging from a relatively high sensitivity to a relatively high
specificity. Current wheeze was defined by an affirmative response to the question ‘Have
you had wheezing or whistling in the chest at any time during the last 12 months when you
did not have a cold?’ Current asthma was defined by an affirmative response to any of the
following questions: ‘Have you had an attack of asthma in the last 12 months?’, ‘Have you
been woken by an attack of shortness of breath at any time in the last 12 months?’ and ‘Are
you currently taking any medicine for asthma?’ The third group, doctor-diagnosed adult-
onset asthma, comprised participants who had their first asthma attack after the age of 16
and answered affirmatively to both of the following questions, ‘Have you ever had asthma?’
and ‘Was it confirmed by a doctor?’ These definitions are consistent with those used in
previous ECRHS studies.2;13
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POTENTIAL EFFECT MODIFIERS
Gender is considered the main potential effect modifier. Smoking status was classified as
follows: non-smokers answered ‘Have you ever smoked for as long as a year?’ in the
negative. Current smokers were defined by an affirmative response to both of the following
questions: ‘Have you ever smoked for as long as a year?’ and ‘Do you smoke now?’ Ex-
smokers were defined by an affirmative response to the question ‘Have you ever smoked for
as long as a year?’ and a negative response to the question ‘Do you smoke now?’ Parental
asthma was defined by an affirmative response to either of the questions: ‘Did your mother
ever have asthma?’ or ‘Did your father ever have asthma?’ Growing up in a rural area was
defined by an affirmative response to the question ‘Did you grow up in a rural area?’
Growing up on a farm was defined by an additional affirmative response to the question ‘Did
you live on a farm?’ Growing up in a rural area and on a farm with animals was defined by
an affirmative response to all three questions: ‘Did you grow up in a rural area?’, ‘Did you
live on a farm?’ and ‘Were there animals on the farm?’ The number of siblings was
determined by asking the question ‘How many siblings do you have or did you have?’ Nasal
allergy was defined by an affirmative response to the question ‘Do you have nasal allergies
(e.g. hay fever)?’
STATISTICAL ANALYSIS
Differences in the distributions of gender and other characteristics between the study
population and the excluded population were assessed using the chi-squared or Student’s t-
test. We used log-binomial regression models to assess associations between 26 separate
categories of potential high-risk jobs, 22 separate JEM high-risk asthmagenic agent
exposures or 5 separate JEM low-risk asthmagenic agent exposures and current wheeze,
current asthma and doctor-diagnosed adult-onset asthma. Associations for each of the
exposure-outcome relationships stratified by gender were estimated using separate models.
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Referent categories for the job and JEM exposure categories were the job reference group
and the unexposed JEM group, respectively. Confounders were selected a priori. Therefore,
all models were adjusted for age (in 5-year strata), smoking status (non-smoker, ex-smoker,
current smoker), and county (five counties). All associations are presented as prevalence
ratios (PRs)34 with 95% confidence intervals (CIs).
We evaluated potential modification of effects using a stratified analysis to assess the
associations of any JEM high-risk asthmagenic or any JEM low-risk asthmagenic exposure
with current asthma and compared these associations with those of unexposed individuals;
these associations were conducted among the entire study population and for the strata of
gender, smoking status, nasal allergy, parental asthma, growing up in a rural area or on a
farm and the number of siblings. Each separate model was adjusted for age, gender and
county (in analyses other than those grouped by gender), and for smoking status (in analyses
other than those grouped by smoking status). We assessed differences in stratum-specific
associations using a Wald test for the statistical significance of an interaction term introduced
into a Poisson regression model with robust error estimation. We chose the Poisson
regression model in this portion of the analyses to resolve problems of non-convergence with
the log-binomial regression models. Analyses using the JEM were repeated after the
exclusion of jobs with uncertain exposure estimates (nexcluded=1,771). For all tests, p≤0.05
was considered statistically significant. All analyses were performed using Stata version SE
10.1 (Stata Corporation, College Station, Texas, USA).
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RESULTS
A total of 7,271 participants (response rate, 73%) returned the questionnaire. Overall, the
participation rates were higher among women (p<0.001) and among the older age groups
(p<0.001). We excluded 642 respondents who were classified as occupationally inactive,
including homemakers (n=27), unemployed (n=43), individuals not working because of poor
health (n=19), full-time students (n=302), retirees (n=58) and respondents with missing
occupational history information (n=193). We also excluded 302 respondents with missing
information regarding asthma outcomes or any of the potential confounders and effect
modifiers considered in our analysis. There were differences between those who were
excluded because of occupational inactivity and missing data (n=944) and those participants
eligible for analysis in the study. The excluded population was younger (p<0.0001) and had a
higher percentage of females (p=0.0006) than males. The prevalences of current wheeze and
current asthma were higher among the excluded persons than in the study population
(p<0.0001 for both), but there was no difference in the prevalence of doctor-diagnosed, adult-
onset asthma between the two populations. There were fewer non-smokers (p=0.0390) and a
higher percentage of ex-smokers (p=0.0263) in the excluded population.
After these exclusions, 6,327 participants remained eligible for inclusion in our
occupational analysis. Table 1 summarises the descriptive statistics for the participants
stratified by sex. The participants averaged 33 years old, and the response rates were higher
in the older age groups (30-44 years) than in the younger age groups (20-29 years). The
prevalences of current asthma and doctor-diagnosed, adult-onset asthma were higher in
women than in men (p=0.043 and p=0.0001, respectively), and wheeze was more common in
men, although not significantly so (p=0.094). There were no differences by county.
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Table 2 shows the gender-specific associations between occupation and current
asthma. These analyses generated significantly increased prevalences of current asthma for
female cleaners and caretakers. The increased PR of current asthma for cleaners and
caretakers was similar in those with and without nasal allergy (data not shown). A small but
borderline-significant increase in the PR of current asthma was observed for female medical
and pharmacy workers other than nurses. Fully adjusted PRs for occupational categories of
potential interest such as hairdressers, welders, plastics and rubber workers, and paper
workers could not be generated because of the small number of current asthma cases in each
occupational group. The results of all analyses for current wheeze and doctor-diagnosed
adult-onset asthma are presented in Supplementary Tables 1 and 2.
Table 3 shows the associations between occupational exposures categorised using the
asthma-specific JEM and current asthma. In these analyses, exposure to HMW agents was
associated with current asthma in men, whereas the PR for women was borderline-
significant. Among men, the elevated PR was driven by elevated prevalences of several
specific HMW agents, namely, flour, plants and mites (data not shown). Exposure to LMW
agents was less clearly associated with current asthma. However, the nested category of
cleaning agents was related to current asthma in women but not in men. A significantly
increased current asthma PR was associated with exposure to possible irritants only among
women. The results of all analyses for current wheeze and doctor-diagnosed adult-onset
asthma are presented in Supplementary Tables 3 and 4.
The main jobs identified with possible irritant exposure in women were cleaners and
caretakers (71%), food and tobacco processing (9%), and construction and mining (16%).
Figures 1 and 2 show the PRs for current asthma due to any JEM high- or low-risk asthma
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agent exposure in separate groups as defined by potential effect modifiers, including gender.
Figure 1 shows that the PRs of current asthma related to any high-risk asthma agent exposure
were significantly lower for non-smokers (p=0.048 for interaction) compared to ex-smokers
or current smokers. No significant differences were observed for gender, nasal allergy,
parental asthma or having grown up in a rural or farm environments. While dichotomising
the number of siblings (0-2 siblings vs. 3 or more), a significant interaction (p=0.04) was
found between having three or more siblings and exposure to high-risk asthma agents
regarding the presence or absence of current asthma.
Figure 2 shows that the PRs of current asthma that were related to low-risk asthma
agent exposure were significantly higher for those growing up on a farm and those growing
up on a farm with animals (p=0.027 and p=0.041, respectively, for interaction) compared
with those who did not grow up on a farm or in a rural area. No significant differences were
observed for gender, nasal allergy, smoking status or parental asthma. While dichotomising
the number of siblings (0-2 siblings vs. 3 or more), a significant interaction (p=0.0023) was
found between having three or more siblings and exposure to low-risk asthma agents
regarding the presence or absence of current asthma.
After excluding jobs with uncertain JEM (n=1,771) (data not shown) the PRs among
men were slightly increased for current asthma and high- and low-risk asthma agents, but the
PR for HMW was no longer significant (PR=1.67, 95% CI: 0.95-2.92). Among women,
slightly decreased PRs for current asthma and HMW agents, mixed environments and low-
risk asthma agents were observed, and the PR for current asthma and LMW agents was
modestly elevated. Cleaning agent exposure remained significantly elevated (PR=1.38, 95%
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CI: 1.02-1.86). Possible irritant exposure was no longer significantly associated with current
asthma (PR=0.87, 95% CI: 0.23-3.28).
After exclusion of jobs with uncertain JEM, the stratified analysis no longer showed
significant interactions for non-smokers between current asthma and high-risk asthma agents
or between growing up on a farm with or without animals and current asthma and low-risk
asthma agents. When dichotomising the number of siblings (0-2 siblings vs. 3 or more), a
significant interaction remained between having three or more siblings and exposure to both
high- and low-risk asthma agents in relation to current asthma (p=0.0137 and p=0.0278,
respectively).
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DISCUSSION
This population-based study among young adults in five Danish counties showed a
consistently increased risk of current asthma among female cleaners and caretakers and
among males exposed to high-molecular-weight agents. Workplace exposure to irritants was
associated with an increased risk of current asthma in women. Those who grew up on a farm
or had three or more siblings showed increased current asthma risk associated with
occupational exposures. The increased PR of current asthma for cleaners and caretakers was
similar in those with and without nasal allergy.
A high risk of occupational asthma in cleaners has been reported in other
populations.2;6;8 Among cleaners, chemicals such as bleach, ammonia-containing compounds
and disinfectants including glutaraldehyde and formaldehyde have been identified as specific
causes of respiratory disorders, including asthma. Increased risk of asthma has also been
related to specific job tasks, such as cleaning floors, windows, mirrors, ovens and dishes.35
Some studies have identified specific professional cleaning products associated with asthma,
including bleach and sprays.14;18 Still, it remains unclear how much of cleaning-related
asthma is related to specific sensitisation and how much is due to airway inflammation
induced by a mixture of irritants.35
The positive association between HMW agent exposure and the prevalence of asthma
is consistent with results from other population-based studies of asthma prevalence and
incidence conducted using the asthma-specific JEM.2;9 We found that exposure to LMW
agents was less clearly associated with asthma, although the nested category of cleaning
agents was related to asthma in women but not in men. These results are partly consistent
with those of the ECRHS study and the French Pollution Atmosphérique et Affections
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Respiratoires Chroniques (PAARC) survey, where increased asthma risk was found in
subjects exposed to industrial cleaning agents and reactive chemicals.2;9 Industrial cleaning
agents could be considered as LMW according to the JEM, but some of them are also
irritants.2
Workplace exposure to irritants (not high peak-exposure) showed an increased PR of
asthma in women. Evidence is growing for the importance of repeated moderate irritant
exposures in the development of asthma.21;36 Evidence is also accumulating about the effects
of irritant exposure on the occurrence of asthma in specific occupations, such as cleaning or
the pulp and paper industries.2;37 Recent population-based studies suggest an increased
relative asthma risk in occupations with low-to-moderate respiratory irritant exposure, and
that these exposures are common.37 Additional studies are needed to determine the airway
effects of such exposures.
Women were more exposed to asthmagens in the present survey than men, but less
exposed to other agents. The associations appear to be different between men (a significant
association with HMW in general) and women (a significant association with industrial
cleaning agents). This is probably explained by gender differences in job exposures, job
choice and suscepbtibility.9;23;38;39 However, further research is needed to elucidate whether
these findings reflect differences in gender susceptibility or in gender-specific occupational
exposures.
The observed increase in asthma risk associated with occupational exposures in
participants who grew up on a farm is consistent with a study of Dutch farmers and
agricultural industrial workers, which showed slightly higher associations between endotoxin
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exposure and respiratory effects, such as wheezing and wheezing with shortness of breath.24
The increased prevalence of asthma associated with occupational exposures in those with
more siblings is partly supported by the literature. In an analysis of ECRHS data, it was
found that participants who grew up in large families experienced more asthma in
adulthood.26 However, in our study we showed a possible protective effect against asthma in
participants with more siblings and a farm childhood, without accounting for occupational
exposures.40 Whether the increased prevalence of asthma among participants growing up on
a farm or with more siblings and occupational exposures reflects different susceptibility,
occupational exposures or job choice in these two groups needs to be studied further.
In contrast to results from previous studies, we were not able to confirm elevated PRs
for established high-risk occupations and exposures, such as farmers, bakery workers,
painters, laboratory technicians, plastic and rubber workers, welders, latex, highly reactive
chemicals and textiles.6-8 This may be explained by a lack of statistical power for some
groups and occupational exposures. Alternatively, a lower-than-expected prevalence of
asthma in these risk occupations may reflect differences in occupational risk factors or job
selection tendencies in Denmark compared with other European countries.29;30 We examined
the effect of the current job or last held job, not the job occupied at the time of the
exacerbation of respiratory symptoms. This might have lead to a healthy-worker effect in our
results that is, the over-representation of participants who are more resistant to occupational
exposures and an underestimation of associations between asthma and occupational
exposures.6;7 Differing exposures and awareness of symptoms in different social classes may
also be a factor leading to over-or under-estimation of possible associations between
symptoms and exposures. We believe that the participants’ general knowledge about
occupational asthma and its causes are limited, and therefore it is unlikely that participants in
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certain jobs were more likely to report symptoms or become diagnosed with asthma than
those in other jobs. The application of JEM may reduce reporting or recall bias, thus
minimising potential overestimation of the prevalence of asthma in relation to occupational
exposure. However, the JEM has the limitation that exposure variations within job titles are
not taken into account, and it does not identify asthma risks associated with unknown
agents.22;33 Another limitation of our study was that we applied the JEM without the
recommended expert reviewing step. This might have resulted in our insufficiently
incorporating country-specific exposure risks and in reduced specificity in estimates for jobs
with uncertain exposures. In our main results, we saw only minor changes after excluding
jobs with imprecise exposure estimates. In an ECRHS study, the associations did not change
considerably after the expert review step,7 so we believe that our use of the JEM without the
expert reviewing step was likely to have had minimal impact on our findings.
Discrepancies with previous findings could also have arisen from asthma
misclassification, as our diagnoses were based on self-reported respiratory symptoms and/or
medication. This study did not include validation of asthma diagnosis by bronchial hyper-
responsiveness or atopy status using the skin-prick test. This could lead to an under
estimation of the associations between differing occupational exposures and asthma.
Nevertheless, our results were fairly consistent using the three asthma definitions (ranging
from relatively sensitive to relatively specific). The ECRHS definition of asthma symptoms
or medication has been validated against bronchial responsiveness,8 indicating that such
underestimation, if present, would be minimal. We did not validate the reported association
of nasal allergy with atopy status using the skin-prick test, which could lead have led to the
underestimation of the associations of asthma with nasal allergy and occupation.
17
The response rate in this study was fair (73%), but it could have biased the results
away from the null hypothesis. Nevertheless, we believe that potential bias was non-
differential according to the primary aim of the survey, namely, identifying risk factors for
adult asthma in general, and not occupational risk factors in particular. Therefore, it is
unlikely that participants in certain jobs were more likely to participate in the study.
In 2007, 102 cases of occupational asthma were identified in the Danish National
Board of Industrial Injuries. A crude estimation with our data showed a mean asthma
incidence rate of 3.3 per 1000 person-years in 2003.28 Using a conservative population-
attributable risk of 15% of asthma due to occupation, we estimate 1,341 new cases per year
of occupational asthma among the employed population in Denmark, suggesting that
occupational asthma is considerably under-reported in Denmark.
Occupational asthma is the most frequently reported occupational respiratory disorder
in western industrialised populations.1As the true prevalence of occupational asthma is
underestimated, there remains a potential problem for many individuals who are exposed to
known asthmagenic agents in Denmark and other industrialised countries.
Main conclusion: Our data demonstrate a consistent increased prevalence of asthma in
female cleaners and caretakers and in men with workplace exposure to HMW agents.
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ACKNOWLEDGEMENTS
The authors thank Estel Plana (CREAL, IMIM, Barcelona, Spain) for her counselling on data
analysis. The authors appreciate the RAV cohort members’ contributions to this work.
The study has been founded by Hospital of Southern Jutland, Department of Research,
Sønderborg, Denmark, and Department of Occupational and Environmental Medicine,
Hospital of Southern Jutland, Haderslev, Denmark. Institute of Clinical Research, University
of Southern Denmark, Department of Occupational and Environmental Medicine, Odense
University Hospital, Odense, Denmark. Faculty of Health Sciences, University of Southern
Denmark, Odense, Denmark. Institute of Regional Health Services Research, University of
Southern Denmark, Odense, Denmark. Global Allergy and Asthma European Network (GA2
LEN), Work Package: WP 2.4 Occupation and Adolescents, EU Framework programme for
research contract n0 FOOD-CT-2004-506378, University of Ghent, Belgium
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25
FIGURE LEGENDS
Figure 1. Prevalence ratios of current asthma in participants exposed to JEM high-risk
asthma agents (n=4,981). Participants with high-risk asthma agent exposures (n=1,309)
compared to unexposed individuals (n=3,672), both overall and grouped by: gender; nasal
allergy (29 missing values); smoking status; parental asthma history; growing up in rural
area, on a farm or on a farm with animals; and the number of siblings. Prevalence ratios with
95% CIs were calculated for each group and were adjusted for age, county, gender (in
analyses other than those grouped by gender) and smoking status (in analyses other than
those grouped by smoking status). Each association was derived from a separate regression
model.
Figure 2. Prevalence ratios of current asthma in participants exposed to JEM low-risk asthma
agents (n=5,018). Participants with low-risk asthma agent exposures (n=1,346) compared to
unexposed individuals (n=3,672), both overall and grouped by: gender; nasal allergy (32
missing values); smoking status; parental asthma history; growing up in rural area, on a farm
or on a farm with animals; and the number of siblings. Prevalence ratios with 95% CIs were
calculated for each group and were adjusted for age, county, gender (in analyses other than
those grouped by gender) and smoking status (in analyses other than those grouped by
smoking status). Each association was derived from a separate regression model.
26
AUTHORS CONTRIBUTIONS
All authors have contributed to: formulation in the concept phase of the basic scientific
problem; planning of analyses and formulation of methodology; analysis of the concrete
investigation; presentation, interpretation and discussion of the results. All authors approved
the final version of the submitted report
Conflict of Interest Statement: All authors state no conflict of interest
27
TABLE 1. CHARACTERISTICS OF THE STUDY POPULATION BY GENDER
Men (n= 2,937) Women (n= 3,390) Age, years (mean (SD*)) 33.1 (6.9) 32.8 (7.0)
Age group, years (%) 20-24 25-29 30-34 35-39 40-44
14.9 18.4 21.2 22.4 23.1
16.2 19.3 21.0 21.8 21.7
County (%)
Funen Vejle Ribe Southern Jutland Northern Jutland
20.7 20.6 18.7 19.8 20.2
20.7 20.1 18.7 20.5 20.0
Smoking (%)
Non-smokers Ex-smokers
50.9 20.1 29.0
49.8 22.1 28.1
Current smokers Definitions of asthma (%)
Current wheeze Current asthma Doctor-diagnosed adult-onset asthma
14.4 8.0 2.9
12.9 9.4 4.8
Nasal allergy** (%) 22.5 23.2 Parental asthma (%) 13.1 17.9
Number of siblings (%)
0 1 2 3 4 or more Don’t know/missing
4.5 35.4 28.7 15.2 9.9 6.3
4.8 36.6 29.9 15.2 10.3 3.2
38.5 19.3
Growing up environment In a rural area (%) In a rural area on farm (%)
On farm with animals (%) 18.3
36.7 18.4 17.7
*SD, standard deviation **Data missing for 39 participants
28
Table 2. Prevalence ratios (PR) with 95% Confidence Interval (95% CI) for associations between occupational groups and current asthma by gender adjusted for county, age, and smoking status
Men Women Occupational group Number
Number current asthma
Number no current asthma
PR (95% CI) Number Number current asthma
Number no current asthma
PR (95% CI)
Legislators, managers, administrators (reference) 1,204 103 1,101 1.00 1,924 166 1,758 1.00 Cleaners and caretakers 45 5 40 1.20 (0.51-2.82) 124 24 100 2.17 (1.47-3.21) Hairdressers, barbers, beauticians 2 0 2 NE 39 0 39 NE Nurses 4 0 4 NE 158 13 145 0.97 (0.56-1.66) Other medical and pharmacy 56 4 52 NE 577 63 514 1.25 (0.95-1.65) Agriculture and forestry 166 12 154 0.80 (0.45-1.43) 63 7 56 1.24 (0.61-2.52) Wood workers 142 13 129 1.07 (0.61-1.86) 6 1 5 NE Bakery workers 16 3 13 NE 4 0 4 NE Food and tobacco processing 70 7 63 1.22 (0.59-2.53) 83 10 73 1.41 (0.78-2.56) Chemical and physical science technicians 3 0 3 NE 15 1 14 NE Plastic and rubber workers 13 0 13 NE 6 0 6 NE Chemical processors 0 0 0 NE 0 0 0 NE Welders and flame cutters 11 0 11 NE 0 0 0 NE Metal making and treating 99 11 88 1.26 (0.70-2.28) 6 0 6 NE Other metal workers 221 16 205 0.85 (0.51-1.42) 16 2 14 NE Electrical processors 106 7 99 0.74 (0.35 -1.57) 6 1 5 NE Painters 22 3 19 NE 28 1 27 NE Spray painters 4 1 3 NE 0 0 0 NE Textile, leather and fur workers 4 0 4 NE 13 1 12 NE Paper workers 5 0 5 NE 3 0 3 NE Printing workers 16 4 12 NE 3 0 3 NE Glass and ceramics workers 7 0 7 NE 5 1 4 NE Construction and mining 192 13 179 0.79 (0.45-1.39) 24 1 23 NE Drivers 128 8 120 0.74 (0.37-1.48) 12 2 10 NE Remainder transport and storage 70 4 66 NE 27 2 25 NE Remainder blue-collar 203 11 192 0.63 (0.34-1.16) 132 11 121 0.99 (0.55-1.78) Not classifiable 128 9 119 0.79 (0.41-1.52) 116 12 104 1.17 (0.67-2.05)
NE, not estimated due to less than 5 asthma cases.
Table 3. Prevalence ratios (PR) with 95% Confidence Interval (95% CI) for associations between occupational exposure groups and current asthma by gender adjusted for county, age, and smoking status Men Women Exposures grouped according to Asthma-specific Job Exposure Matrix*
Number total
Number current asthma
Number no current asthma
PR (95% CI) Number total
Number current asthma
Number no current asthma
PR (95% CI)
Not exposed (reference) 1,498 118 1,380 1.00 2,174 188 1,986 1.00 High risk asthma agents 482 44 438 1.14 (0.82-1.59) 827 87 740 1.21 (0.95-1.54) HMW agents 153 19 134 1.59 (1.01-2.51) 709 77 632 1.25 (0.97-1.61) Animals 33 5 28 1.91 (0.85-4.32) 28 1 27 NE Fish 11 1 10 NE 5 0 5 NE Flour 17 4 13 NE 4 0 4 NE Plants 1 1 0 NE 1 0 1 NE Mites 1 1 0 NE 20 6 14 3.55 (1.76-7.14) Enzymes 16 3 12 NE 4 0 4 NE Latex 42 2 40 NE 630 67 563 1.23 (0.94-1.60) Bioaerosols 71 8 63 1.44 (0.73-2.86) 23 3 20 NE Drugs 3 0 3 NE 161 14 148 0.95 (0.56-1.64) LMW agents 272 24 248 1.08 (0.71-1.65) 553 59 494 1.22 (0.92-1.61) Reactive chemicals 82 5 77 0.76 (0.32-1.81) 525 57 468 1.25 (0.94-1.65) Isocyanate 25 1 24 NE 8 0 8 NE Cleaning agents 40 2 38 NE 438 53 385 1.40 (1.04-1.87) Wood dust 0 0 0 NE 0 0 0 NE Metals 194 19 175 1.21 (0.76-1.91) 60 5 55 0.97 (0.42-2.27) Mixed environments 235 20 215 1.09 (0.69-1.72) 77 8 69 1.18 (0.60-2.31) Metal working fluids 81 8 73 1.24 (0.62-2.47) 8 0 8 NE Textile 4 0 4 NE 11 1 10 NE Agricultural antigens 150 12 138 1.05 (0.60-1.86) 58 7 51 1.36 (0.67-2.77) High irritant peaks 45 5 40 1.37 (0.59-3.18) 5 0 5 NE Low risk asthma agents 957 72 885 0.94 (0.71-1.25) 389 44 345 1.30 (0.95-1.77) Exhaust 212 11 201 0.66 (0.36-1.20) 16 2 14 NE Environmental tobacco smoke 11 2 9 NE 35 1 34 NE Possible irritants 418 31 387 0.91 (0.62-1.33) 146 21 125 1.68 (1.10-2.55) Low risk antigens 450 37 413 1.04 (0.73-1.48) 347 40 307 1.31 (0.95-1.81)
NE, not estimated due to less than 5 asthma cases. *According to published matrix33. Participants can be categorised in more than one exposure category in high risk asthma agents and low risk asthma agents, therefore, number exceed 100% in these groups.
FIGURE 1
FIGURE 2
32
Supplementary table 1. Prevalence ratios (PR) with 95% Confidence Interval (95% CI) for associations between occupational groups and current wheeze by gender adjusted for county, age, and smoking status
Men Women Occupational group Number
Number current wheeze
Number no current wheeze
PR (95% CI) Number Number current wheeze
Number no current wheeze
PR (95%CI)
Legislators, managers, administrators (reference) 1,204 184 1020 1.00 1,924 237 1,687 1.00 Cleaners and caretakers 45 10 35 1.42 (0.83-2.42) 124 24 100 1.50 (1.02-2.18) Hairdressers, barbers, beauticians 2 0 2 NE 39 3 36 0.60 (0.20-1.80) Nurses 4 1 3 1.99 (0.34-11.60) 158 15 143 0.80 (0.49-1.32) Other medical and pharmacy 56 9 47 1.13 (0.62-2.05) 577 87 490 1.19 (0.95-1.50 Agriculture and forestry 166 19 147 0.81 (0.52-1.26) 63 8 55 0.97 (0.50-1.88) Wood workers 142 16 126 0.69 (0.42-1.11) 6 1 5 1.12 (0.19-6.70) Bakery workers 16 1 15 0.49 (0.07-3.33) 4 0 4 NE Food and tobacco processing 70 12 58 1.01 (0.59-1.73) 83 13 70 1.31(0.79-2.18) Chemical and physical science technicians 3 1 2 2.87 (0.84-9.81) 15 0 15 NE Plastic and rubber workers 13 1 12 0.52 (0.09-3.16) 6 2 4 2.83 (0.95-8.43) Chemical processors 0 0 0 NE 0 0 0 NE Welders and flame cutters 11 2 9 1.19 (0.37-3.80) 0 0 0 0.83 (0.58-1.17) Metal making and treating 99 16 83 0.99 (0.62-1.57) 6 0 6 NE Other metal workers 221 32 189 0.91 (0.65-1.29) 16 4 12 1.98 (0.83-4.71) Electrical processors 106 14 92 0.89 (0.54-1.48) 6 2 4 2.66 (0.93-7.67) Painters 22 3 19 0.84 (0.28-2.47) 28 1 27 0.28 (0.04-1.92) Spray painters 4 1 3 1.35 (0.22-8.35) 0 0 0 0.83 (0.59-1.17) Textile, leather and fur workers 4 0 4 NE 13 1 12 0.64 (0.10-4.11) Paper workers 5 1 4 1.05 (0.19-5.98) 3 0 3 NE Printing workers 16 4 12 1.63 (0.71-3.78) 3 0 3 NE Glass and ceramics workers 7 1 6 1.09 (0.19-6.31) 5 4 1 4.87 (2.64-9.01) Construction and mining 192 24 168 0.76 (0.51-1.13) 24 4 20 1.43 (0.59-3.47) Drivers 128 15 113 0.74 (0.45-1.20) 12 2 10 1.39 (0.38-5.09) Remainder transport and storage 70 8 62 0.71 (0.37-1.36) 27 1 26 0.29 (0.04-1.99) Remainder blue-collar 203 28 175 0.88 (0.61-1.27) 132 14 118 0.86 (0.52-1.44) Not classifiable 128 19 109 0.99 (0.65-1.50) 116 15 101 1.02 (0.63-1.66)
NE, not estimable due to small numbers of cases
SUPPLEMENTARY TABLE 2. PREVALENCE RATIOS (PR) WITH 95% CONFIDENCE INTERVAL (95% CI) FOR ASSOCIATIONS BETWEEN OCCUPATIONAL GROUPS AND DOCTOR DIAGNOSED ADULT-ONSET ASTHMA BY GENDER ADJUSTED FOR COUNTY, AGE, AND SMOKING STATUS
Men Women Occupational group Number
Number doctor diagnosed adult-onset asthma
Number no doctor diagnosed adult-onset asthma
PR (95% CI) Number Number doctor diagnosed adult-onset asthma
Number no doctor diagnosed adult-onset asthma
PR (95% CI)
Legislators, managers, administrators (reference) 1,204 38 1,166 1.00 1,924 93 1,831 1.00 Cleaners and caretakers 45 3 42 1.96 (0.62-6.17) 124 9 115 1.59 (0.82-3.11) Hairdressers, barbers, beauticians 2 0 2 NE 39 2 37 1.17 (0.30-4.58) Nurses 4 0 4 NE 158 3 155 0.40 (0.13-1.23) Other medical and pharmacy 56 0 56 NE 577 31 546 1.14 (0.76-1.69) Agriculture and forestry 166 6 160 1.27 (0.54-3.00) 63 6 57 1.94 (0.88-4.29) Wood workers 142 2 140 0.49 (0.12-2.01) 6 0 6 NE Bakery workers 16 1 15 2.36 (0.33-16.74) 4 0 4 NE Food and tobacco processing 70 2 68 0.93 (0.23-3.80) 83 3 80 0.75 (0.24-2.32) Chemical and physical science technicians 3 0 3 NE 15 0 15 NE Plastic and rubber workers 13 0 13 NE 6 0 6 NE Chemical processors 0 0 0 NE 0 0 0 NE Welders and flame cutters 11 0 11 NE 0 0 0 NE Metal making and treating 99 2 97 0.61 (0.15-2.50) 6 0 6 NE Other metal workers 221 4 217 0.58 (0.21-1.61) 16 3 13 4.29 (1.51-12.21) Electrical processors 106 3 103 0.93 (0.29-2.99) 6 1 5 3.77 (0.63-22.51) Painters 22 1 21 1.61 (0.23-11.46) 28 2 26 1.39 (0.36-5.32) Spray painters 4 0 4 NE 0 0 0 NE Textile, leather and fur workers 4 0 4 NE 13 0 13 NE Paper workers 5 0 5 NE 3 0 3 NE Printing workers 16 2 14 5.24 (1.33-20.62) 3 0 3 NE Glass and ceramics workers 7 0 7 NE 5 0 5 NE Construction and mining 192 3 189 0.56 (0.17-1.80) 24 2 22 2.11 (0.54-8.21) Drivers 128 7 121 1.78 (0.82-3.89) 12 0 12 NE Remainder transport and storage 70 0 70 NE 27 0 27 NE Remainder blue-collar 203 8 195 1.27 (0.60-2.70) 132 3 129 0.48 (0.15-1.50) Not classifiable 128 3 125 0.71 (0.22-2.28) 116 4 112 0.75 (0.28-2.00)
NE, not estimable due to small numbers of cases
34
Supplementary table 3. Prevalence ratios (PR) with 95% Confidence Interval (95% CI) for associations between occupational exposure groups and current wheeze by gender adjusted for county, age, and smoking status Men Women Exposures grouped according to Asthma-specific Job Exposure Matrix*
Number total
Number current wheeze
Number no current wheeze
PR (95% CI) Number Total
Number current wheeze
Number no current wheeze
PR (95% CI)
Not exposed (reference) 1,498 219 1,279 1.00 2,174 261 1913 1.00 High risk asthma agents 482 71 411 0.98 (0.77-1.25) 827 113 714 1.12 (0.91-1.38) HMW agents 153 30 123 1.29 (0.93-1.80) 709 97 612 1.13 (0.91-1.40) Animals 33 5 28 1.01 (0.46-2.23) 28 4 24 1.19 (0.48-2.95) Fish 11 2 9 1.04 (0.29-3.73) 5 1 4 1.98 (0.36-10.79) Flour 17 1 16 0.49 (0.07-3.38) 4 0 4 NE Plants 1 1 0 5.87 (4.03-8.56) 1 0 1 NE Mites 1 0 1 NE 20 3 17 1.36 (0.48-3.85) Enzymes 16 1 15 0.58 (0.08-3.99) 4 0 4 NE Latex 42 9 33 1.47 (0.85-2.55) 630 86 544 1.13 (0.90-1.41) Bioaerosols 71 15 56 1.34 (0.86-2.10) 23 2 21 0.74 (0.19-2.76) Drugs 3 0 3 NE 161 15 146 0.82 (0.50-1.34) LMW agents 272 43 229 0.98 (0.73-1.31) 553 84 469 1.22 (0.97-1.53) Reactive chemicals 82 14 68 1.17 (0.74-1.87) 525 81 444 1.23 (0.98-1.56) Isocyanate 25 5 20 1.36 (0.65-2.85) 8 2 6 1.88 (0.57-6.29) Cleaning agents 40 7 33 1.14 (0.60-2.17) 438 68 370 1.24 (0.97-1.59) Wood dust 0 0 0 NE 0 0 0 NE Metals 194 30 164 0.93 (0.66-1.32) 60 9 51 1.26 (0.68-2.34) Mixed environments 235 29 206 0.84 (0.59-1.20) 77 8 69 0.85 (0.44-1.66) Metal working fluids 81 11 70 0.80 (0.46-1.40) 8 0 8 NE Textile 4 0 4 NE 11 0 11 NE Agricultural antigens 150 18 132 0.92 (0.60-1.42) 58 8 50 1.11 (0.58-2.14) High irritant peaks 45 8 37 1.18 (0.64-2.18) 5 2 3 3.85 (1.32-11.21) Low risk asthma agents 957 132 825 0.88 (0.72-1.07) 389 64 325 1.31 (1.02-1.68) Exhaust 212 26 186 0.83 (0.57-1.20) 16 4 12 1.92 (0.82-4.48) Environmental tobacco smoke 11 2 9 0.85 (0.24-3.04) 35 5 30 0.99 (0.43-2.25) Possible irritants 418 55 363 0.82 (0.62-1.07) 146 27 119 1.49 (1.04-2.14) Low risk antigens 450 75 375 1.07 (0.84-1.35) 347 56 291 1.27 (0.98-1.66)
NE, not estimable due to small numbers of cases. *According to published matrix33. Participants can be categorised in more than one exposure category, therefore, numbers exceed 100%
35
Supplementary table 4. Prevalence ratios (PR) with 95% Confidence Interval (95% CI) for associations between occupational exposure groups and doctor-diagnosed adult-onset asthma by gender adjusted for county, age, and smoking status Men Women Exposures grouped according to Asthma-specific Job Exposure Matrix*
Number total
Number doctor diagnosed adult-onset asthma
Number no doctor diagnosed adult-onset asthma
PR (95% CI) Numbertotal
Number doctor diagnosed adult-onset asthma
Number no doctor diagnosed adult-onset asthma
PR (95% CI)
Not exposed (reference) 1,498 43 1,455 1.00 2,174 99 2,075 1.00 High risk asthma agents 482 14 468 1.05 (0.58-1.90) 827 42 785 1.14 (0.80-1.63) HMW agents 153 3 150 0.72 (0.23-2.31) 709 36 673 1.14 (0.78-1.67) Animals 33 1 32 0.99 (0.14-6.90) 28 2 26 1.58 (0.40-6.15) Fish 11 1 10 2.78 (0.43-18.11) 5 0 5 NE Flour 17 1 16 2.24 (0.32-15.87) 4 0 4 NE Plants 1 0 1 NE 1 0 1 NE Mites 1 0 1 NE 20 2 18 2.57 (0.67-9.90) Enzymes 16 1 15 2.69 (0.37-19.24) 4 0 4 NE Latex 42 0 42 NE 630 31 599 1.12 (0.76-1.67) Bioaerosols 71 1 70 0.52 (0.07-3.76) 23 3 20 3.08 (1.04-9.10) Drugs 3 0 3 NE 161 3 158 0.42 (0.14-1.32) LMW agents 272 5 267 0.65 (0.26-1.62) 553 31 522 1.28 (0.86-1.90) Reactive chemicals 82 0 82 NE 525 30 495 1.31 (0.88-1.96) Isocyanate 25 0 25 NE 8 0 8 NE Cleaning agents 40 0 40 NE 438 26 412 1.37 (0.90-2.10) Wood dust 0 0 0 NE 0 0 0 NE Metals 194 5 189 0.90 (0.36-2.24) 60 3 57 1.07 (0.35-3.29) Mixed environments 235 9 226 1.47 (0.72-2.99) 77 7 70 2.08 (1,00-4.36) Metal working fluids 81 3 78 1.41 (0.44-4.52) 8 1 7 3.04 (0.48-19.23) Textile 4 0 4 NE 11 0 11 NE Agricultural antigens 150 6 144 1.62 (0.70-3.77) 58 6 52 2.32 (1.06-5.10) High irritant peaks 45 3 42 2.13 (0.68-6.62) 5 0 5 NE Low risk asthma agents 957 28 929 1.04 (0.65-1.66) 389 21 368 1.26 (0.80-2.00) Exhaust 212 8 204 1.41 (0.67-2.95) 16 1 15 1.51 (0.22-10.40) Environmental tobacco smoke 11 0 11 NE 35 0 35 NE Possible irritants 418 8 410 0.68 (0.32-1.43) 146 9 137 1.44 (0.74-2.80) Low risk antigens 450 15 435 1.17 (0.66-2.08) 347 18 329 1.21 (0.74-1.98)
NE, not estimable due to small numbers of cases. *According to published matrix 33. Participants can be categorised in more than one exposure category, therefore, numbers exceed 100%
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