Occupational asthma in Europe and other industrialised areas:
a population-based study
Lancet 1999; 353 (9166): 1750-1754 22may99
Kogevinas, Manolis; Maria Anto, Josep; Sunyer, Jordi; Tobias, Aurelio; Kromhout, Hans; Burney, Peter; Survey Study Group*, European Community Respiratory Health
Respiratory and Environmental Health Research Unit, Institut Municipal d'Investigacio Medica (IMIM), Barcelona, Spain; Environmental and Occupational Health Group, Wageningen Agricultural University, Netherlands; Department of Public Health Medicine, Guy's and St Thomas's Medical and Dental School, London, UK
Correspondence to: Dr Manolis Kogevinas, Respiratory and Environmental Health Research Unit, Institut Municipal d'Investigacio Medica (IMIM), 80 Doctor Aiguader Road, Barcelona 08003, Spain, e-mail: email@example.com
Background There are no large population-based studies on occupational asthma, and few estimates of the proportion of asthma attributed to occupation, even though asthma is the most common occupational respiratory disorder in industrialised countries.
We assessed data on 15 637 people aged 20-44, randomly selected from the general population of 26 areas in 12 industrialised countries. Asthma was assessed by methacholine challenge test and by questionnaire data on respiratory symptoms and use of medication. Occupation was defined by job-titles and a job exposure matrix was constructed.
Highest risk of asthma, defined as bronchial hyperresponsiveness and reported asthma symptoms or medication, was shown for farmers (odds ratio 2.62 95% CI 1.29-5.35 ), painters (2.34 1.04-5.28 ), plastic workers (2.20 0.59-8.29 ), cleaners (1.97 1.33-2.92 ), spray painters (1.96 0.72-5.34 ), and agricultural workers (1.79 1.02-3.16 ). Similar risks were shown for asthma defined as reported asthma symptoms or medication. The most consistent results across countries were shown for farmers and cleaners. Excess asthma risk was associated with high exposure to biological dusts, mineral dusts, and gases and fumes. The proportion of asthma among young adults attributed to occupation was 5%-10%.
The prevalence of occupational asthma in women and in specific occupations has been underestimated. Given a mean prevalence of asthma of about 5%, about 0.2%-0.5% of young adults become asthmatics or have their asthma exacerbated because of their occupations.
Asthma is the most common occupational respiratory disorder in industrialised countries. About 250 specific occupational exposures are associated with
asthma.1 Only some of these exposures have been assessed in epidemiological studies, and only a few studies have assessed occupational asthma in the general population. Studies that have used information from occupational registers in the USA and Japan suggest that about 15%-20% of all asthma may be related to
occupation. 1, 2, 3 Other studies, including two population-based studies in
Spain 4 and New Zealand, 5 gave lower percentages, and suggested a raised risk of asthma for occupations such as cleaners, which are not generally recognised as high-risk
We analysed data from the European Community Respiratory Health Survey (ECRHS). The survey was done in western European and other industrialised countries, and incorporated information from random samples of the general population of young adults in selected areas.7 Results from two national studies included in our analysis are available elsewhere.4, 5 We aimed to verify which occupations carry a high risk of asthma, and we estimated the proportion of asthma cases in the general population attributable to occupational exposures.
Information on participants' occupation was available from 26 centres in 12 countries. In the first phase of the ECRHS study, which took place in most of the countries in 1992, a random population sample aged 20-44 years from selected study areas was contacted and asked to complete a short screening questionnaire on respiratory symptoms. Since this sample was drawn from selected areas and towns, it may not have been representative of the general population. In the second phase, a 20% random sample of the study population contacted in the first phase was re-contacted, along with a "symptoms" subsample that included all those who reported asthma- type respiratory symptoms in the screening questionnaire but who had not been selected in the random sample. Participants in the second phase were asked to complete a second detailed questionnaire, which included information on smoking, occupation, housing, and use of medical care, to do a forced spirometric test, and to take a methacholine challenge test of bronchial reactivity. Cases were derived from the two second-phase samples, and controls were drawn exclusively from the 20% random sample. The institutional review board of the participating centres approved the study protocol, and patients gave written informed consent.
We used two definitions of asthma. The first (asthma symptoms or medication) was based on the questionnaire alone. We defined asthma as an attack of asthma during the past 12 months, having been woken by an attack of shortness of breath during the past 12 months, or current use of asthma medication. The second definition (bronchial hyperresponsiveness and asthma symptoms or medication) was more specific and combined questionnaire information with data on bronchial responsiveness. We defined bronchial responsiveness as a 20% fall in forced expiratory volume in 1 s from the largest post- diluent volume during methacholine challenge with an estimated cumulative dose of 8 mumol methacholine. In our study, we gave a cumulative dose of 1 g (5.117 mumol methacholine). Bronchial response to 8 mumol methacholine was estimated by linear extrapolation. Neither definition of asthma allowed us to differentiate between new cases of asthma and asthma that was exacerbated by work.
Participants were classified by current occupation or, for those who had changed occupation for health reasons, their occupation at the time when respiratory problems occurred. This classification avoided selection
bias.8 The European Community socioeconomic status groups classification was used to code reported occupation: 350 occupational categories were aggregated into 30 sets, each of which covered all possible codes for similar occupations. Most of the sets are similar to the classification used by the SWORD project in England and
We assessed exposure to specific airborne pollutants directly from reported exposure to dusts, vapours, gases, or fumes, and also by use of a job-exposure matrix. The matrix was developed by two industrial hygienists (HK, RV) who assessed separately exposure to biological dusts, mineral dusts, fumes, and gases,10 and then reached consensus in cases of disagreement. Each of the 350 occupation types were classified as not exposed, or exposed to a low amount or a high amount of each of the three groups of pollutants.
We used unconditional logistic regression for the pooled analysis. All odds ratios and 95% CIs were adjusted for age, sex, and study centre. If the regression model did not converge, the data were adjusted for country and smoking status. Adjustment for smoking status and daily tobacco consumption made minimal difference to the risk estimates and, therefore, our results show models adjusted for smoking status only. We compared each occupational set with the group of professional, administrative, and clerical workers who comprised more than 40% of the total study population. We checked the heterogeneity of effects between study centres in the logistic regression models by testing the significance of the interaction between country and exposure, to compare the SD of the model with and without the interaction term. Odds ratios were used to calculate adjusted estimates of attributable risk.11 Our analyses used Stata software (version 5.0).
Of the 26 848 people contacted in the second phase, 15 637 (58%) completed the questionnaire. Of these, 12 967 were from the random sample and 2670 from the "symptoms" subsample (table 1). 9476 of these people completed the test for bronchial reactivity. There were minimal differences in age and smoking status between the 6161 people who completed only the second questionnaire and the 9476 people who completed both the second questionnaire and the methacholine-challenge test. There were, however, proportionally fewer women in the former group than the latter (42% vs 50%). There were significant differences between the two groups by country (p<0.001), which reflected the different participation rates in the methacholine-challenge test by country. For our occupational analysis, we further excluded 832 people classified as inactive, including students and unemployed people not seeking work, and 224 people with missing data.
Table 1: ECRHS study population
Number of Country participants (%) Australia 876 (5.6) Belgium 1285 (8.2) Germany 1983 (12.7) Iceland 646 (4.1) Ireland 581 (3.7) Italy 1023 (6.5) New Zealand 1607 (10.3) Norway 650 (4.2) Spain 2661 (17.0) Sweden 2397 (15.3) UK 1475 (9.4) USA 453 (3.0) Demographic Male 7375 (47.2) Female 8262 (52.8) Age (years) 20-29 5507 (35.3) 30-39 6359 (40.8) 40-44 3736 (23.9) Smoking status Non-smokers 6688 (43.1) Ex-smokers 3143 (20.3) Current smokers 5671 (36.1) Total 15637 (100.0)
*Data missing for 35 people. ata missing for 135 people.
Risk of asthma varied between occupations (table 2). For asthma defined as bronchial hyperresponsiveness and asthma symptoms or medication, the occupations with the highest risk of asthma were farmers (farmers, farm workers, farm managers), agricultural workers (horticultural workers, gardeners, agricultural machinery drivers, forestry workers), painters (spray painters, other), plastic and rubber workers, and cleaners (cleaners, caretakers, window-cleaners, chimney and road sweepers). High risk of asthma was also shown for the same occupations for asthma defined by questionnaire data alone (asthma symptoms or medication). A significant excess of risk of asthma was shown for metalworkers (furnace operators, smiths, moulders, die casters, electroplaters, sheet-metal workers, galvanisers, others) and for housewives (table 2).
Table 2: Odds ratios for asthma in occupations with excess asthma risk >30%
Occupation Odds ratios (95% CI) Bronchial hyper- responsiveness and asthma symptoms Asthma symptoms Occupation n or medication or medication Farmers 71 2.62 (1.29-5.35) 1.73 (1.00-3.01) Other painters 65 2.34 (1.04-5.28) 1.44 (0.80-2.59) Plastics 34 2.20 (0.59-8.29) 1.96 (0.87-4.40) Cleaners 443 1.97 (1.33-2.92) 1.82 (1.44-2.30) Spray painters 56 1.96 (0.72-5.34) 2.83 (1.53-5.24) Agricultural 181 1.79 (1.02-3.16) 1.41 (0.98-2.02) Other non-metal, non-electrical 189 1.65 (0.82-3.31) 1.30 (0.88-1.92) Textiles 212 1.59 (0.94-2.68) 1.13 (0.79-1.61) Glass, ceramics 36 1.38 (0.31-6.13) 1.02 (0.38-2.71) Chemicals 75 1.33 (0.46-3.83) 0.98 (0.51-1.89) Construction and minmig 285 1.31 (0.78-2.20) 1.28 (0.94-1.76) Welders 100 1.26 (0.56-2.83) 1.30 (0.80-2.11) Housewives 845 1.23 (0.89-1.70) 1.34 (1.11-1.62) Bakers 65 1.12 (0.43-2.90) 1.32 (0.71-2.43) Metal-making 139 1.10 (0.49-2.45) 1.65 (1.10-2.47) Other food 143 1.06 (0.47-2.36) 1.47 (0.99-2.20) Professional, clerical, 8878 1.00 1.00 administrative *Adjusted for age, sex, smoking status, and study centre. reference group.
Cleaners had an excess risk of asthma in 11 of 12 countries (table 3). Results were less consistent if asthma was defined as bronchial hyperresponsiveness and asthma symptoms or medication, probably because of smaller total numbers of participants whose asthma was defined that way. However, six of seven countries with data on more than 10 cleaners showed an excess risk of that type of asthma (range 1.7-4.2). Results for farmers were also consistent between counties, with the highest risk shown for Italy (odds ratio=10.7), Australia, and New Zealand. High risk of asthma, usually calculated from data on small total numbers of participants, was shown in specific countries for agricultural workers, food processors, plastic and rubber workers, welders, metalworkers, spray painters, other painters, bakers, and other non-metal industrial workers.
Table 3: Asthma risk in cleaners
Country Cleaners Professionals, clerical, (cases/controls) administrative (cases/controls) Belgium 10/45 106/685 Germany 5/37 70/1175 Spain 33/60 229/896 Ireland 7/10 49/191 Italy 2/9 74/399 UK 12/20 196/584 Iceland 3/10 24/306 Norway 2/18 25/390 Sweden 27/52 228/887 New Zealand 9/17 257/597 Australia 1/3 176/458 USA 2/7 40/231
Country Odds ratio (95%CI) Belgium 1.14 (0.55-2.37) Germany 2.02 (0.76-5.40) Spain 2.07 (1.29-3.32) Ireland 2.75 (0.94-8.05) Italy 1.63 (0.32-8.29) UK 1.63 (0.77-3.45) Iceland 4.50 (1.10-18.45) Norway 1.43 (0.30-6.94) Sweden 1.96 (1.20-3.20) New Zealand 1.18 (0.51-2.71) Australia 0.90 (0.09-8.81) USA 1.66 (0.32-8.63)
Asthma defined as asthma symptoms and medication. Adjusted for age, sex, smoking status, and country.
Significant excess risks of asthma of 30%-50% were shown for a high degree of exposure to biological dusts, mineral dusts, gases and fumes, or a combination of exposures assessed by use of the job- exposure matrix (table 4). A significant excess risk of about 20% was shown by assessment of self-reported exposure to this combination of risk factors. Self-reported exposure and exposure assessed through the job-exposure matrix were significantly correlated (p<0.0001).
Table 4: Risk of asthma and occupational exposure to specific agents assessed by job-exposure matrix or self-reported by questionnaire
Exposure n Odds ratio (95%CI) Job-exposure matrix Biological dusts None 6650 1.0 Low 914 1.15 (0.92-1.44) High 357 1.40 (1.01-1.93) Mineral dusts None 5894 1.0 Low 1521 0.99 (0.81-1.23) High 506 1.58 (1.21-2.06) Gases or fumes None 5357 1.0 Low 1897 1.01 (0.84-1.21) High 667 1.37 (1.07-1.76) All None 4149 1.0 Low 2520 1.02 (0.85-1.22) High 1252 1.37 (1.08-1.66) Self-reported exposure Vapours, gases, dusts, fumes No 4239 1.0 Yes 4075 1.19 (1.02-1.40)
Asthma defined as bronchial responsiveness and asthma symptoms or medication. Adjusted for age, sex, smoking status, and study centre.
For calculation of the attributable risk of asthma by occupational sets, we grouped the nine occupational sets with significant excess risk or risk of more than 50% by either asthma definition (farmers, agricultural workers, spray painters, other painters, plastic and rubber workers, cleaners, metalworkers, textile and clothes, other non-metal workers). Attributable risk estimated by occupation was 6.9% if asthma was defined by questionnaire data, and 9.9% if combined questionnaire and methacholine-challenge data were used (table 5). Attributable risk was slightly higher in women than men. About 5% of asthma risk among women could be attributed to household exposure. Attributable risk varied by country, with estimates ranging from no excess risk in Australia and low excess risk in New Zealand, to a risk of more than 10% in Germany, the USA, and Iceland. Estimates of the attributable risk of asthma for each occupational exposure assessed by use of the job-exposure matrix ranged from 3%-4% (around 5% in combination), and an attributable risk of 9% was estimated for self-reported exposure to any risk factor (table 5). Comparison of risk estimates within the subgroup of 9476 people showed that their occupational risk of asthma was slightly higher if asthma was defined as bronchial reactivity and asthma symptoms or medication (attributable risk for both sexes for high-risk occupations=9.9%) than if asthma was defined by questionnaire data on asthma symptoms or medication only (8.8%). The attributable risks were 1% to 2% higher when data from Spain and New Zealand were excluded. sup 4,5
Table 5: Attributable risk of asthma and occupational exposures
Exposure n Attributable risk (95%CI) High-risk occupations$ Men 384 9.1% (0-20.8) Women 318 11.5% (1.2-20.8) Total 702 9.9% (2.8-16.5) Job-exposure matrix Biological dusts 1271 3.4% (0-9.1) Mineral dusts 2027 4.0% (0-10.1) Gases and fumes 2564 3.5% (0-10.0) All 3772 4.9% (0-12.5) Self reported Vapours, gases, dusts, fumes 4075 9.1% (0.1-17.3)
Asthma defined as bronchial hyperresponsiveness and asthma symptoms or medication. Adjusted for age, sex, smoking status, and study centre. $8 occupational sets with significant excess risks (excluding housewives) or risks >50%.
Our data show that occupational asthma accounts for 5%-10% of asthma in young adults. These estimates are consistent with a study from Spain included in the ECRHS, and with estimates by Xu and Christiani,12 who showed that the excess fraction of physician- diagnosed asthma in China was about 12% for people exposed to dust and 5% for those exposed to gases or fumes. Our study was done in several countries and used the same methods, which suggests that occupational asthma should be an important public-health issue in industrialised areas.
Increased risk of asthma is associated with agents such as isocyanates, reactive dyes, grain dusts, mites, enzymes, and animal- derived allergens.1 We showed a consistent excess risk for farmers and cleaners in nearly all countries in our study. The group of cleaners was the largest of the 16 occupational groups to show an increase in asthma risk of more than 30%. A similar excess risk was shown in a case-control study in Singapore,6 but exposure of cleaners are not among the major recognised causes of occupational asthma.1, 9
Cleaners are commonly exposed to substances known to cause asthma, including irritants such as chlorine and acids, detergents, indoor allergens such as dusts, and outdoor pollutants such as nitrogen dioxide. Cleaners are known to have a high risk of dermatitis.13, 14 Case reports have shown that asthma can be caused by floor- cleaning products,15, 16 and in a study of 329 hospital admissions for respiratory disorders after exposure to chemical fumes and vapours, the most common occupational exposures were to chlorine, sulphur dioxide, and industrial cleaning agents.17 Acute exposure to irritants is associated with reactive airways dysfunction syndrome, and exposure to high concentrations of chlorine gas is associated with a deterioration of airways function and with bronchial responsiveness.18, 19, 20 Chronic exposure to low concentrations of irritants has been associated with asthma in pulp-mill workers,21 but there is little epidemiological evidence for the importance of such exposure.22 Underestimation of the excess asthma risk among cleaners suggests that the occupational-health risks for self-employed people, or those who work in small, transitory companies, may go unrecognised and unprevented. Moreover, exposure to non-allergens in the workplace, frequently of an irritant nature, may be a risk factor for asthma morbidity.
Housewives had a small but significant excess risk of asthma, and are likely to share some of the exposures of cleaners, particularly exposure to irritant gases from cleaning materials, detergents, and other indoor allergens or air pollutants. Reilly and Rosenmann 17 showed that exposure to household cleaning agents was among the most frequent causes of non-work-related hospital admission. Interpretation of an excess risk in housewives may be confounded by presence at home because of previous asthma symptoms.
The risk of asthma attributable to occupational exposures among women, excluding housewives, was higher than expected. Studies of respiratory symptoms in the UK,9 France,23 and Italyn 24 also identified a high risk of occupational asthma among women. Many occupational groups at high risk for asthma, such as textile workers, cleaners, and farmers, include a substantial proportion of female workers. Under-reporting of occupational asthma 25 may be more common among women, particularly if the potential adverse effects of household exposures on health are included.
The attributable risks that we estimated are a mean estimate for our study population of young adults in industrialised countries. Country-specific analysis shows that other occupations are also at high risk of asthma, presumably because of specific exposures in individual centres,5 but the wide CIs in the country-specific estimates do not allow precise analysis of observed geographical variation.
Exposure to biological dusts, mineral dusts, and gases and fumes was reported more frequently through self-report than through the job- exposure matrix, possibly indicating that positive recall of exposure is influenced by previous symptoms.
The questionnaire was not completed by about 40% of the people contacted, and the possibility of differential non-response cannot be excluded. Sensitivity analyses show that the effect of non- response on estimates of prevalence of bronchial reactivity in the ECRHS is likely to be minimal.26 In another analysis of the same study,27 adjustment for non-response had no real effect on the distribution of IgE between study centres. Non-response has to be very biased and very high to affect odds ratios markedly.28
Our risk estimates were slightly, but not consistently higher for asthma defined as bronchial hyperresponsiveness and asthma symptoms or medication then for asthma defined by questionnaire data alone. These definitions may measure different facets of asthma. Moreover, there may have been differences in the study populations, since only 9476 of 15 637 people who responded to the questionnaire also did the methacholine-challenge test. Data on bronchial hyperresponsiveness and symptoms provide a more specific definition of asthma than questionnaire data alone, but little empirical evidence on the validity of asthma definitions in epidemiological studies compared with those used in clinical practice.29
Occupational exposures cause between 5%-10% of cases of asthma among young men and women in European and other industrial countries. With a mean prevalence of asthma of about 5% in most areas,30 our study shows that 0.2%-0.5% of young adults become asthmatics or have their asthma exacerbated by their occupation.
Manolis Kogevinas, Josep Maria Anto, and Jordi Sunyer designed the occupational asthma analysis and wrote the paper. Aurelio Tobias did statistical analysis. Hans Kromhout did industrial hygiene assessment and developed the job-exposure matrix. Peter Burney, principal investigator of the ECRHS, took part in all phases of study design and analysis. All authors participated in preparation and subsequent revision of the paper.
European Community Respiratory Health Survey (ECRHS)
Coordinating centre-P Burney, S Chinn, C Luczynska, D Jarvis, E Lai (London, UK).
Australia-M Abramson, J Kutin (Melbourne).
Belgium-P Vermeire, F van Bastelaer (Antwerp South, Antwerp Central).
Germany-H Magnussen, D Nowak (Hamburg); H E Wichmann,J Heinrich, M Wjst (Erfurt).
Iceland-T Gislason, D Gislason (Reykjavik).
Ireland-J Prichard, S Allwright, D MacLeod (Dublin).
Italy-M Bugiani, C Bucca, C Romano (Turin); R de Marco lo Cascio, C Campello (Verona); A Marinoni, I Cerveri, L Casali (Pavia).
New Zealand-J Crane, W D'Souza, N Pearce, D Barry, I Town (Wellington, Christchurch, Hawkes Bay).
Norway-A Gulsvik, E Omenaas, P Bakke (Bergen).
Spain-J M Anto, J Sunyer, J Soriano, M Kogevinas, A Tobias, J Roca (Barcelona); N Muniozguren, J Ramos Gonzalez, A Capelastegui (Galdakao); J Martinez-Moratalla, E Almar (Albacete); J Maldonade Perez, A Oereira, J Sanchez (Huelva); J Quiros, I Huerta (Oviedo).
Sweden-G Boman, C Janson, E Bjornsson (Uppsala); L Rosenhall, E Norrman, B Lundback (Umea); N Lindholm, P Plaschke (Goteborg).
UK-M Burr, J Layzqll (Caerphilly); R Hall (Ipswich); B Harrison (Norwich); J Stark (Cambridge).
USA-S Buist, W Vollmer, M Osborne (Portland).
Job-exposure matrix development, occupational history assessment
Hans Kromhout, Roel Vermeulen, Helianthe Dubbeld (Wageningen, Germany).
This work was coordinated by the European Commission. We thank the late Colette Baya, and Manuel Hallen, for their assistance, and K Vuylsteek and the members of COMAC for their support. The following grants helped to fund the local studies: Allen and Hanbury's (Australia); Belgian Science Policy Office, National Fund for Scientific Research (Belgium); GSF, and Bundesminister fur Forschung und Technologie, Bonn; and GSF-National Centre for Environment and Health, Neuherberg (Germany); Ministero dell' Universita e della Ricerca Scientifica e Tecnologica, CNR, Regione Veneto grant RSF n. 381/05.93 (Italy); Asthma Foundation of New Zealand, Lotteries Grant Board, Health Research Council of New Zealand (New Zealand); Norwegian Research Council project no. 101422/310 (Norway); Ministerior Sanidad y Consumo FIS grants #91/0016060/00E-05E, #92/0319, #93/0393, #970035, Generalitat de Catalunya-CIRIT 19975 GR 00079, Hospital General de Albacete, Hospital General Juan Ramon Jimenez, Consejeria de Sanidad Principado de Asturias (Spain); The Swedish Medical Research Council, the Swedish Heart Lung Foundation, and the Swedish Association against Asthma and Allergy (Sweden); Swiss National Science Foundation grant 4026-28099 (Switzerland); National Asthma Campaign, British Lung Foundation, Department of Health, and South Thames Regional Health Authority (UK); United States Department of Health, Education and Welfare Public Health Service Grant #2 S07 RR05521-28 (USA).
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