Development of an asthma specific job exposure matrix and its application in the epidemiological study of genetics and environment in asthma (EGEA).

OBJECTIVES

To develop a method suitable for estimating exposure risks in population studies of asthma from job titles and international codes, by combining a new job exposure matrix (JEM) with the expert judgement approach. The method was applied in the French epidemiological study of the genetics and environment in asthma (EGEA).

METHODS

The JEM contains 22 exposure groups including 18 high risk groups based on known risk factors for occupational asthma, divided into high molecular weight agents, low molecular weight agents, and mixed environments. After applying the JEM to job codes, exposure estimates for each subject were re-evaluated by examining job title texts. Three high risk exposure estimates for asthma were compared: firstly, applying the JEM to original codes (from different coders in each study centre); secondly, applying the JEM to revised codes (from one experienced coder); and thirdly, after reviewing JEM exposure estimates in the light of job title texts.

RESULTS

The study comprised 173 cases with asthma and 285 controls (age 18-65). Odds ratios (ORs) for asthma for high risk jobs were 1.0 (95% confidence interval (95% CI) 0.6 to 1.7), applying the JEM to original codes; 1.4 (95% CI 0.8 to 2.3), applying the JEM to revised codes; and 1.7 (95% CI 1.1 to 2.7), applying the JEM and subsequently re-evaluating exposure estimates from job title texts. Asthma ORs were 1.4 (95% CI 0.6 to 2.9) for high molecular weight agents, 2.3 (95% CI 1.2 to 4.4) for low molecular weight agents, and 2.1 (95% CI 0.9 to 5.2) for mixed environments.

CONCLUSIONS

This asthma JEM, when enhanced by expert re-evaluation of exposure estimates from job title texts, may be a useful tool in general population studies of asthma. In this study, a 1.7-fold increase in prevalence odds of high risk exposures was found among asthmatic workers compared with controls, with risk magnitude varying for different classes of exposure.