The healthy worker effect in asthma: work may cause asthma, but asthma may also influence work

Abstract

Despite the increasing attention to the relationship between asthma and work exposures, occupational asthma remains under-recognized and its population burden underestimated. This may be due, in part, to the fact that traditional approaches to studying asthma in populations cannot adequately take into account the Healthy Worker Effect (HWE). The HWE is the potential bias caused by the phenomenon that sicker individuals may choose work environments in which exposures are low; may be excluded from being hired; or once hired, may seek transfer to less exposed jobs or leave work. This article demonstrates that population and workplace based asthma studies are particularly subject to HWE bias, which leads to underestimates of relative risks. Our objective is to describe the HWE as it relates to asthma research, and to discuss the significance of taking HWE bias into account in designing and interpreting asthma studies. We also discuss the importance of understanding HWE bias for public health practitioners and for clinicians. Finally, we emphasize the timeliness of this review in light of the many longitudinal ‘child to young adult’ asthma cohort studies currently underway. These prospective studies will soon provide an ideal opportunity to examine the impact of early workplace environments on asthma in young adults. We urge occupational and childhood asthma epidemiologists collaborate to ensure that this opportunity is not lost.

Introduction

Despite the increasing attention to the relationship between asthma and work exposures, in both clinical and public health practice^1,2, occupational asthma remains under-recognized by physicians, patients, and occupational health policy makers. This may be due, in part, to the fact that traditional approaches to studying asthma in populations cannot adequately take into account the possibility of “reverse causation”, i.e. the possibility that the presence of asthma symptoms may influence job choices or changes in exposure. Concurrently, numerous studies of asthma in children have focused on potential environmental risk factors for asthma in home, school and community exposures.^3,4 The phenomenon of “reverse causation” may also occur in these studies, as symptoms may lead to changes in exposure (eg. avoiding household pets), but further complexity arises as some early exposures may in fact be ‘protective’.

In the coming years, the children in several ongoing cohort studies will reach young adulthood and enter the workforce. These studies could provide an ideal setting in which to better evaluate the impact of work exposures on exacerbation of pre-existing asthma or on the development of new asthma in adults. However, as is the case for occupational asthma epidemiology in general, the follow-up into the workplace of these child cohorts will need to be carefully designed to ensure that the potential “reverse causation” phenomenon is taken into account.

In occupational epidemiology, this “reverse causation” phenomenon is called the “healthy worker effect” (HWE). The HWE is the potential bias caused by the phenomenon that sicker or more ‘sensitive’ individuals may choose work environments in which exposures are low; may be excluded from being hired; or once hired, may seek transfer to less exposed jobs or leave work. This bias has been well described in occupational mortality studies^5,6. However, although asthma morbidity studies are particularly subject to HWE bias (as we will demonstrate), the impact of this bias in asthma epidemiology has received little attention.

Therefore, our objective for this Pulmonary Perspectives article is to describe the HWE as it relates specifically to asthma research, and to discuss the significance of this bias for interpreting the results of population based asthma studies.

Overview of the HWE concept

The concept of the HWE dates to the 18th century when Ramazzini suggested the presence of selection effects in some jobs such as miners or cleaners⁷. According to Fox et al⁸, the HWE was formally described for the first time in 1885 by Ogle who explained « some occupations may repel, while others attract, the unfit at the age of starting work, and conversely some occupations may be of necessity recruited from men of supernormal physical condition ».

The HWE phenomenon often leads, paradoxically, to lower death rates observed in subjects exposed to workplace toxins compared to the general population⁵. Thus the bias generally leads to an underestimation of relative risk for occupational exposure and disease⁹.

In mortality studies, the magnitude of downward bias due to the HWE is approximated by how much the expected number of deaths exceeds the observed number, as measured by the standardised mortality ratio (SMR) for all causes of death combined. It is common to observe SMRs of 0.8 to 0.9 in occupational cohort mortality studies, suggesting an underestimation of risk by 10% to 20%. Deficits in SMR for mortality are greater for chronic non-malignant respiratory disease and heart disease than for cancer, although HWE bias affects cancer as well.⁸

By contrast, there is no single measure of HWE bias in morbidity studies and evidence of the HWE usually involves documentation of less healthy workers changing or quitting jobs during follow-up,^10–12 or is inferred from an absence of an expected association between exposure and disease. In morbidity studies HWE bias has been shown to be more important for diseases that appear in childhood, present early symptoms, or have a shorter latency between exposure and symptoms^9,13. Stronger healthy worker selection bias was observed for asthma than for diabetes¹⁴, rhinitis¹⁵, and chronic bronchitis¹⁶.

Components of HWE Bias

HWE bias arises from two complementary mechanisms (see Figure 1) induced by initial and continuing selection process^5,9 : the selection of healthier workers at hire (« healthy worker hire effect ») and the interruption, change, or cessation of work by less healthy subjects (« healthy worker survivor effect »).

Figure 1.

Phenomenon of selection at work

The figure presents selection forces according to health status and periods in the working life (from the career choice till retirement). Arrows depict the evolution of exposure in relation to health status.

Healthy worker hire effect

Selection at hire may be due to¹³ selection by the subject (personal choice or in response to medical advice) or selection by the employer, in either case this may be related to health status or perceived risk factors. In general, healthier subjects at lower risk of disease (for example non smokers or physically strong people) tend to be employed preferentially.^6,17

Childhood asthmatics may be advised, reasonably, not to pursue job training in dusty occupations, and asthmatics in general, are less likely to be hired into exposed trades. Pre-employment screening or post-hire placement may be important interventions in preventing exacerbation of pre-existing asthma. Yet, selection out of exposed jobs may have negative economic impacts, which can also affect general health status indirectly through reduced socio-economic position¹³.

Healthy worker survivor effect

Once hired, less healthy workers are more likely than healthy co-workers to leave high exposure jobs, either by transfer or termination. For example, in a Swedish study, subjects who reported illness at hire had a 45% higher risk of being unemployed seven years later.¹⁸ Although this selection away from exposed jobs may protect individual health by reducing the impact of exposure in a given patient, it remains a source of bias in epidemiology, potentially leading to a false conclusion that the higher exposure jobs are ‘safe’.

A decline in health (such as asthma symptoms) could induce: behaviour modification, e.g, use of masks to reduce exposure; leaving work permanently; transfer to a less exposed job; or temporary removal from exposure as a result of physician intervention^9,13,17. If workers who lower their exposures are also more likely to develop clinical asthma, then healthy worker bias will result^9,19,20.

Cross-sectional workplace surveys usually include only active workers at the time of the survey, thus introducing both healthy worker hire and survivor bias^9,13,20. By contrast, population-based surveys including inactive as well as active subjects are less biased by healthy worker survivor bias provided the information relevant for examining the timing of exposures in relation to potential health impacts is recorded.

Estimating the impact of components of HWE bias in asthma epidemiology

Although many asthma epidemiology studies comment on the potential for HWE bias, few are able to estimate the magnitude of the impact of such biases. Table 1 summarizes data from all occupational studies we were able to locate up to the end of 2006 (by searching all available medical literature, starting with keywords and following up using reference lists of identified studies) in which data were provided from which one can infer the magnitude of these two components of HWE bias. As shown, the healthy worker hire effect has seldom been measured directly in asthma, with the exception of the study from Radon and colleagues²¹ in adolescents school-based vocational training (Table 1). More studies have estimated the magnitude of the healthy worker survivor effect with remarkably similar results, most indicating risk ratios of about 2, comparing asthmatics and non-asthmatics with respect to quitting or changing jobs in a wide variety of work environments.

Table 1.

Studies estimating the magnitude of HWE bias in asthma epidemiology

Young adults
Study	Study Population	Study Design	Exposure metric	HWE bias, evidence
Kivity et al 199535	107,636 Israel soldiers, 18–21 yrs	7-year follow-up	Soldiers (administrative, technical, combat)	Survivor Effect More job transfer among moderate(71%) vs mild(52%) asthmatics
Kennedy et al 199911	Canadian apprentices : 82 machinists 152 others, mean age - 24	2-yr follow-up study	Machinists (exposed to MWF) compared to unexposed apprentices in other occupations	Survivor Effect 15% of machinists quit trade within 2 years vs 3% of unexposed (p<0.001)
Monso et al 200012	769 Canadian apprentices in animal health, pastry and dental hygiene; age - 20yrs	4-year repeated measures study	Exposure to high molecular weight agents	Survivor Effect OR for quitting work was 1.6 [0.9–2.7] for shortness of breath, 1.4 [0.9–2.4] for symptoms of asthma, and 0.9 [0.4 – 1.9] for MD diagnosed asthma.
Iwatsubo et al 200510	French women apprentices 280 hairdressers 250 others mean age - 17	2-yr follow-up study	Hairdressers potentially exposed to persulphates vs unexposed office workers	Healthy Hire Effect Hairdresser apprentices had at both initial and final phase of the study significantly fewer symptoms (cough, wheezing, dyspnea; all OR < 0.5), but more BHR (OR= 1.6 [1.0 – 2.6]) than office apprentices at the final phase of the study
Radon et al 200621	1416 German teenagers in vocational and high school (ISAAC II) 16–18yrs	7-year follow-up study	Jobs classified as high exposed (asthmagens), low exposed (irritants) or unexposed by a job exposure matrix	Healthy Hire Effect Subjects with past asthma symptoms perhaps less likely to choose high exposed jobs, OR = 0.7 [0.3–1.6]
Adults, population-based surveys
Blanc et al 200115	125 asthmatics, 175 rhinitics in general California population survey USA; 18–50 yrs	Cross-sectional	Employment	Survivor Effect Asthmatics less active in the workforce after diagnosis, OR= 3.0 [1.1 – 7.7] and likely to be unemployment, OR= 1.6 [1.0 – 2.6] than those with rhinitis
Henneberger et al 200337	474 subjects with current physician diagnosed asthma or asthma like symptoms USA; 18–65 yrs	Cross-sectional	Jobs classified as high risk (exposed to asthmagens)/low risk by experts	Survivor Effect Higher prevalence of asthma in high risk jobs compared to low risks job (38% versus 19%, p=0.17) Lower prevalence of exacerbation of previously identified asthma in high risk (14.3%) vs low risk (23.8%) jobs OR = 0.6 [0.1 – 4.3]; but which is not the case for asthma-like symptoms OR=3.7 [1.1–11.9]
Turner et al 200514	165 asthmatics 283 diabetics in outpatient clinic UK; 16–60 yrs	Cross-sectional study	Exposure to irritants, sensitizers and physical activity estimated by experts	Survivor Effect Asthmatics compared to diabetics: less likely to be employed a year after diagnosis OR=2.1[1.3–3.5] more likely to stop working for illness reason (35% vs 18%).
Adults, occupational cohorts
Dosman et al 1991 52	207 Canadian male grain workers (first work) 120 agricultural students	Follow-up: one year later	Grain work	Survivor Effect Prevalence of positive skin tests at baseline: 33% (unexposed) vs 21 % (exposed), p< 0.01 1 yr later : 28% (unexposed) vs 8 % (exposed), p< 0.001. high turnover, stayers: 33%(unexposed) vs 19%(exposed), p< 0.01
Eisen et al 199731	1705 USA male auto workers mean age 40	Cross-sectional study with analysis based on pseudo-incidence study.	Exposure to straight, soluble, or synthetic metalworking fluid (versus unexposed assembly) in 2 years prior to diagnosis and at time of survey	Survivor Effect Asthmatics more likely exposed to synthetics than unexposed prior to diagnosis, OR = 4.0, and more likely to have moved to unexposed jobs by time of survey than controls (p<0.10)
Zock et al 199833	135 Netherlands potato processing workers mean age 40	Cross-sectional study	Duration of employment in potato processing with potential exposure to endotoxin and potato antigens	Survivor Effect Prevalence of asthma symptoms was higher among subjects who worked less than 5 years, compared to those who worked longer; 22% versus 9%, and so was prevalence of IgE, 56% versus 24%.
Drexler et al 199934	110 German workers in electrical equipment plant	5-yr follow-up study	Potentially exposed to epoxy resins	Survivor Effect Sensitised subjects 2.6 times more likely to leave work than subjects without sensitisation.
Redlich et al 200240	75 USA auto body shop workers, mean age 35	1-yr follow-up study	Auto body shop workers with potential exposure to HDI	Survivor Effect Workers who left trade had more baseline symptoms then those who remained at work

First author (Yr); Study pop (industry, N, age, gender, employment status); Study Design; Exposure metric, Results (measure of association).

Determinants of HWE bias

In general population epidemiology

Factors that determine the magnitude of HWE bias have been identified for mortality studies and are likely to impact this bias in morbidity studies as well^9,13,22 (table 2). As discussed above, because asthmatics may have already made job change decisions prior to the start of a cross-sectional epidemiologic study, stronger HWE bias is seen in studies of only active workforces than in studies that include former and active workers⁹. For most disease outcomes, HWE bias is also stronger among populations with a shorter time since first hire^6,23, and in younger cohorts^13,24,25 and will decline with population age²⁵. As discussed below, this may not hold true for asthma.

Table 2.

Determinants of healthy worker effect (HWE) bias: characteristics of the study population, outcome, and social environment that influence healthy worker bias

Determinants	Impact on HWE
Employment factors
Time since hire	Mortality rate increases (healthy hire (HH) wears off) as workers are followed longer5,23,26. Opposite trend for asthma, with incidence of new cases highest soon after hire and decreasing with time31.
Active versus inactive	Active workers have lower mortality than retirees or inactive workers followed over time38. Incidence of asthma has not been compared between active and inactive person-time or workers.
Time since termination	Mortality rate peaks in the few years after leaving work and then plateaus at a level higher than active workers 53. This trend may also be true for asthma incidence, but has not been studied.
Socio-demographic factors
Gender	Stronger selection of healthy males into the workforce (HH), but stronger selection out of the workforce for less healthy women (Healthy worker survivor effect)26 in mortality studies. Expect similar trends for asthma..
Age	HH (mortality) is strongest among youngest workers and declines with age25. By contrast, asthma incidence may be highest among younger new hires31.
Ethnic groups	HWE (mortality) stronger in non-whites13,22,25 probably due to higher turnover and higher loss of follow-up in non-whites compared to white. Impact in asthma may be similar (for same reasons) or opposite if ethnicity is linked to employment options.
Community Unemployment Rate	Unemployment rate likely to impact HWE. Willingness of asthmatic to leave job depends on employment options.
Outcome
Asthma	Stronger HWE in studies of symptomatic chronic conditions, such as chronic respiratory or heart disease, than of outcomes with longer latency and shorter symptomatic periods31.

Gender, social class, and ethnicity have also been shown to play a role in HWE bias in other disease outcomes^13,22, although few asthma studies taking these determinants into account are available. A stronger healthy hire effect for men and a stronger healthy worker survivor effect for women has been reported.²⁶ Lower HWE bias is predicted in times of high unemployment and among lower social classes, where job choices are more constrained²². However, the effect of these factors on HWE bias varies by gender and socio-economic factors^18,27. Evidence indicates that populations with few employment choices (low social class, women, older) will be less affected by HWE bias, suggesting these groups may be less protected (by job change) from adverse health effects of workplace exposures.

In asthma epidemiology

Age

Childhood onset asthma is more likely to contribute to the healthy hire effect, whereas adult onset asthma influences the healthy survivor effect. Among adults who develop asthma related to work exposures, age at onset (time since hire) varies considerably^28–30. One can theorize that new occupational asthma in young adults may be more likely to lead to HWE bias as voluntary job change is easier for young workers than for older workers. However, this has not been well documented, and in fact, in one study, cedar asthmatics who left employment were older than those who stayed on the job²⁷.

Atopy

Asthmatics may select jobs to avoid exposure to allergens³¹ or irritants³² and if hired, may leave this kind of work²⁰. Whether this effect depends on atopy is less clear. In a study of reported career preference among adolescents, Radon observed that vocational trainees with allergic rhinitis were more likely to prefer less dusty jobs than other teenagers, suggesting a potential healthy hire bias, but this association did not reach significance.²¹ Among apprentices, Monso observed that hay fever was a significant risk factor for quitting a job with exposure to high molecular weight allergens¹². Workers employed for less than five years in the potato industry were more likely to be atopic (and to have asthma symptoms) than those employed for a longer duration³³. In a prospective study, workers who were sensitized to acid anhydride were three times more likely to have quit work four years later than workers without sensitisation³⁴.

Asthma severity

Among young military recruits, increased job transfer was seen among asthmatics compared to non-asthmatics, and this effect was even greater in those with moderate asthma (71% changed jobs) compared to mild asthma (52%)³⁵. Severity has also been shown to be an important predictor for unemployment, change in jobs, and disability among asthmatics.³⁶

Exacerbation of symptoms at work

In a population-based study, an association between high occupational exposure (compared to low exposure) and work exacerbation of symptoms was observed among subjects with asthma symptoms; this effect was not seen for subjects with physician diagnosed asthma³⁷. This lack of association might be due to a HWE bias, if those with a diagnosis of asthma had already taken steps to avoid irritating exposure jobs or otherwise reduce their exposures at work.

Relevance for researchers and practitioners

Given the potential sources and determinants for HWE bias in asthma epidemiology described above, it is not surprising that researchers and occupational health practitioners often find low rates of asthma among active employees, in epidemiologic studies or in surveillance programs. Thus when planning asthma research, and in interpreting results of asthma studies, one should always anticipate healthy worker bias downwards. In the case of a true positive relationship, this downward bias results in bias towards the null hypothesis of no exposure-response effect. Of course, other explanations for null or inverse associations should also be considered. For example, the lack of association with farming exposure may relate to a protective effect of an associated factor (farming associated with exposure to endotoxin in childhood).

Strategies for reducing HWE bias in asthma epidemiology

Epidemiologists treat HWE as either a form of confounding or selection bias. Confounding can be reduced in data analysis whereas selection bias can only be avoided in the design phase of study.

To reduce HWE bias, employment status (currently working v. not working) could be treated as a simple confounder by adjusting the exposure-response analysis for employment status^23,38. This adjustment will pose a problem, however, if leaving work is an intermediate factor on the pathway from exposure to disease⁵, for example, if exposure leads to asthma symptoms which precipitate leaving work before the diagnosis of asthma is made. This is the case for healthy worker survivor bias and the potential for this bias is greater in a cross-sectional study than in a prospective study of workers observed over time^9,17.

To avoid this bias, prospective studies can include a dynamic cohort where subjects enter the study population when they are hired (or even before first employment, as is the case with childhood cohort studies) and are followed even after they leave employment. This study design allows for both adjustment by employment status as a time varying factor, as well as for the consideration of time varying exposure windows.

To illustrate the impact of the study design on HWE, consider studies of two agents recognized to cause occupational asthma in some workers; diisocyanates and synthetic water-based metalworking fluids.

In a cross-sectional study of auto body shop workers, spray painters with the highest exposure to hexamethylene di-isocyanate (HDI) were compared to indirectly exposed technicians and office workers in the same workplaces. Painters had more HDI-specific lymphocyte proliferation, but no overt cases of clinically apparent diisocyanate asthma were identified³⁹. One year later, the 15% who had left were found to be younger, more likely to have a history of asthma and HDI-specific IgG than those who remained at work⁴⁰. Thus a high turnover rate, with susceptible young workers leaving, contributed to the underestimate of asthma prevalence (bias) among HDI-exposed workers. To estimate the correct (unbiased) exposure-response parameter, rather than merely document the presence of the bias, active and inactive workers should be followed longer and re-examined regularly. Since employment status may change over the study period if subjects leave work, it is a “time-varying” confounder and thus requires a larger sample size and more complex “structural” equations to model correctly^41,42.

Synthetic metalworking fluids are also known to cause asthma in exposed populations¹¹. Yet in a large cross-sectional study of autoworkers, asthma prevalence was lower among exposed than unexposed workers⁴³. A reanalysis was designed to address the hypothesis that the absence of a positive association was caused by the self-selection of asthmatics out of exposed jobs³¹. Employment records were used to define exposure in the two years prior to asthma diagnosis, and allowed the data to be reanalysed as “pseudo-incidence” study, treating exposure and outcome as time-varying covariates in a Cox model. Using this analytic approach reduced the bias and an elevated relative risk of asthma diagnosis was found among subjects exposed prior to diagnosis. It is important to note that, although accounting for job transfer reduced HWE bias, it could not be eliminated in this cross-sectional study because inactive workers were excluded at the time of the survey.

Therefore, even in a carefully designed cohort study that includes shorter-term workers and an internal reference group of low exposure workers, potential for residual HWE bias remains. As described above, when ‘affected’ workers migrate to jobs with lower exposure, they leave behind a more resistant population in the high exposure jobs, introducing the potential for job transfer bias⁹. Truncated exposures of symptomatic subjects can potentially distort even comparisons between high and low exposed workers in a longitudinal study. This bias can be minimized with appropriate attention to quantifying exposures in relevant time windows, but such detailed exposure quantification is not always feasible. Thus, awareness of the potential for HWE bias in interpreting results remains important despite attempts to reduce the bias in the study design and analysis ^6,9,13.

Considerations for public health surveillance

The HWE can also bias estimates of the population burden of asthma attributable to work exposures. Reviewing the 21 asthma studies considered by the American Thoracic Society working group² illustrates the difficulty in estimating the occupational contribution to the burden of asthma. First, is the frequent lack of time varying exposure information presented in biologically relevant time windows of exposure. A third of these papers only focussed on adult-onset asthma or used job or work-related exposures at time of asthma onset or in the few years before diagnosis^44,45. Several studies reported that lifetime work histories had been collected, but the data were not systematically used in analysis. Second, most studies were cross-sectional in design or used only cross-sectional information for the analyses. Other aspects, beyond the HWE, can modify the estimate of the attributable risk which depends of the outcome considered. Asthma has been defined by questionnaire, bronchial hyperresponsiveness² or recently reimbursement of costs⁴⁶. Even more importantly, the specificity of the exposure is often poor. The attributable risk is lower when exposure is based on known asthmagen exposures rather than less specific exposure estimates⁴⁷. Evaluating the specific impact of healthy worker effect, i.e., the magnitude of bias, on the population attributable risk has not been attempted.

Surveillance strategies that take into consideration both healthy worker hire and healthy worker survivor bias would provide more accurate estimates of the true population burden relevant for public and occupational health agencies. One approach to occupational asthma surveillance implemented in several US states involved sentinel event notification (ie. suspected occupational asthma cases) followed by additional active case finding among coworkers at the workplace suspected as ‘causal’⁴⁸. To improve the accuracy of the burden of occupational exposure, this co-worker follow-up would need to include not only active coworkers, but also former employees.

Similarly, population health surveys increasingly used in many countries as one way of measuring chronic disease prevalence will also underestimate work-related asthma if strategies for taking HWE bias are omitted. For example, both the US National Health and Nutrition Examination Survey (NHANES) and the Canadian National Population Health Survey (CCHS) include questions about asthma and about occupation. However, neither survey is able to generate unbiased estimates of work-relatedness of asthma because they do not query age or job held at the time of onset of asthma symptoms.

Population surveillance protocols that collect data which allow stratification of asthma by age of onset (before or after the start of work), or even better, by job categories (where the job used is the one held at the time of asthma onset), thereby adjusting for HWE bias, will provide more accurate estimates of the population burden of work-related asthma. Relative risk estimates for the association between exposure and asthma clearly increased when analysis was restricted to adulthood asthma⁴⁹ especially in relation to severe asthma⁵⁰.

Considerations for clinical practice

Clinicians may wonder about the relevance of this discussion to clinical work. In fact, one might argue that at the level of the individual patient, implications of healthy worker selection, as a population level phenomenon, is positive for patients, i.e. that an asthmatic teenager or young adult is wise to avoid work that may exacerbate asthma, and that an adult who develops asthma related to (or exacerbated by) work is wise to change jobs.

In this light, the impact of the HWE among asthmatics is somewhat similar to a phenomenon that may be more familiar to chest physicians, namely the so-called “healthy smoker effect”⁵¹. This refers to the phenomenon that adolescents who continue to smoke into adulthood may have better lung function (at least in young adulthood) than those who try smoking but never take it up seriously or who quit at a young age, because they are the ones least susceptible to the early effects of smoking.

An important distinction between these two related phenomena is that the choice to smoke is voluntary whereas the choice of whether or not to work in an ‘exposed’ job is often much less so. Indeed, the outcome of job change for an asthmatic affected by exposures at work is not always positive if work change results in unemployment or significant loss of earnings, as has been shown to be the case frequently for occupational asthma patients^14,15,36. However, clinicians do need to be alert to the healthy worker survivor effect in diagnosing occupational asthma and in considering the best management of asthma patients. Occupational asthma is difficult to diagnose in the absence of a clearly identified sensitizer in the patient’s workplace. Surrogate indicators are often sought, such as evidence that coworkers may be experiencing similar symptoms, but such questioning needs to consider former as well as current co-workers.

The clinician’s role in identifying and counseling asthmatics with respect to exposure control in workplaces cannot be minimized, even if the only exposure control option is job change. As described above,³⁷ an asthma diagnosis (with, presumably, associated counseling and case management) can be ‘protective’ for work-related asthma, even if the occupational link is not recognized. Furthermore, because of the potential for HWE bias to obscure exposure-response associations in epidemiology studies, clinicians should not rule out occupational asthma in a patient with a clear clinical presentation because of a lack of supporting epidemiologic evidence.

Conclusions and recommendations

In summary, HWE bias is particularly strong in studies of asthma and its inevitable presence makes it difficult to develop unbiased risk estimates of the magnitude of exposure-response relationships, in working populations or population-based studies. However, it is not a completely intractable problem. As we have argued above, to generate less biased risk estimates, and to better quantify the burden of work-related asthma, the studies should be designed prospectively with follow-up starting before hire and including lifetime information regarding health events (eg. age of asthma onset), occupational history (initial job training choices, job transfers and exposure estimates by monitoring or other methods) and taking into account exposure windows in relation to the onset of asthma symptoms. Population surveillance programs should include similarly detailed information about the timing of disease onset in relation to jobs held. While some might argue that such studies and surveillance systems are not feasible, we suggest that the refinements needed to upgrade existing methods for data collection and analysis for asthma epidemiology are modest and often within reach.

Importantly, the many population-based birth or childhood cohort studies currently underway to examine risk factors for asthma represent a great opportunity as they have already been designed as prospective studies that will permit the ongoing collection of time varying exposure and health information as these young people enter the workforce. We recommend that occupational and childhood asthma epidemiologists collaborate to ensure that this opportunity is not lost.