A graphical tool to evaluate temporal coverage of occupational history by exposure measurements

Developing a company-specific job exposure matrix for the Asbest Chrysotile Cohort Study

OBJECTIVES

Exposure assessment for retrospective industrial cohorts are often hampered by limited availability of historical measurements. This study describes the development of company-specific job-exposure matrices (JEMs) based on measurements collected over five decades for a cohort study of 35 837 workers (Asbest Chrysotile Cohort Study) in the Russian Federation to estimate their cumulative exposure to chrysotile containing dust and fibres.

METHODS

Almost 100 000 recorded stationary dust measurements were available from 1951-2001 (factories) and 1964-2001 (mine). Linear mixed models were used to extrapolate for years where measurements were not available or missing. Fibre concentrations were estimated using conversion factors based on side-by-side comparisons. Dust and fibre JEMs were developed and exposures were allocated by linking them to individual workers' detailed occupational histories.

RESULTS

The cohort covered a total of 515 355 employment-years from 1930 to 2010. Of these individuals, 15% worked in jobs not considered professionally exposed to chrysotile. The median cumulative dust exposure was 26 mg/m3 years for the entire cohort and 37.2 mg/m3 years for those professionally exposed. Median cumulative fibre exposure was 16.4 fibre/cm3 years for the entire cohort and 23.4 fibre/cm3 years for those professionally exposed. Cumulative exposure was highly dependent on birth cohort and gender. Of those professionally exposed, women had higher cumulative exposures than men as they were more often employed in factories with higher exposure concentrations rather than in the mine.

CONCLUSIONS

Unique company-specific JEMs were derived using a rich measurement database that overlapped with most employment-years of cohort members and will enable estimation of quantitative exposure-response.

Influence of exposure assessment and parameterization on exposure response. Aspects of epidemiologic cohort analysis using the Libby Amphibole asbestos worker cohort

Recent meta-analyses of occupational epidemiology studies identified two important exposure data quality factors in predicting summary effect measures for asbestos-associated lung cancer mortality risk: sufficiency of job history data and percent coverage of work history by measured exposures. The objective was to evaluate different exposure parameterizations suggested in the asbestos literature using the Libby, MT asbestos worker cohort and to evaluate influences of exposure measurement error caused by historically estimated exposure data on lung cancer risks. Focusing on workers hired after 1959, when job histories were well-known and occupational exposures were predominantly based on measured exposures (85% coverage), we found that cumulative exposure alone, and with allowance of exponential decay, fit lung cancer mortality data similarly. Residence-time-weighted metrics did not fit well. Compared with previous analyses based on the whole cohort of Libby workers hired after 1935, when job histories were less well-known and exposures less frequently measured (47% coverage), our analyses based on higher quality exposure data yielded an effect size as much as 3.6 times higher. Future occupational cohort studies should continue to refine retrospective exposure assessment methods, consider multiple exposure metrics, and explore new methods of maintaining statistical power while minimizing exposure measurement error.

Calibrating a population-based job-exposure matrix using inspection measurements to estimate historical occupational exposure to lead for a population-based cohort in Shanghai, China

The epidemiologic evidence for the carcinogenicity of lead is inconsistent and requires improved exposure assessment to estimate risk. We evaluated historical occupational lead exposure for a population-based cohort of women (n=74,942) by calibrating a job-exposure matrix (JEM) with lead fume (n=20,084) and lead dust (n=5383) measurements collected over four decades in Shanghai, China. Using mixed-effect models, we calibrated intensity JEM ratings to the measurements using fixed-effects terms for year and JEM rating. We developed job/industry-specific estimates from the random-effects terms for job and industry. The model estimates were applied to subjects' jobs when the JEM probability rating was high for either job or industry; remaining jobs were considered unexposed. The models predicted that exposure increased monotonically with JEM intensity rating and decreased 20-50-fold over time. The cumulative calibrated JEM estimates and job/industry-specific estimates were highly correlated (Pearson correlation=0.79-0.84). Overall, 5% of the person-years and 8% of the women were exposed to lead fume; 2% of the person-years and 4% of the women were exposed to lead dust. The most common lead-exposed jobs were manufacturing electronic equipment. These historical lead estimates should enhance our ability to detect associations between lead exposure and cancer risk in the future epidemiologic analyses.

A meta-analysis of asbestos and lung cancer: is better quality exposure assessment associated with steeper slopes of the exposure-response relationships?

BACKGROUND

Asbestos is a well-recognized cause of lung cancer, but there is considerable between-study heterogeneity in the slope of the exposure-response relationship.

OBJECTIVE

We considered the role of quality of the exposure assessment to potentially explain heterogeneity in exposure-response slope estimates.

DATA SOURCES

We searched PubMed MEDLINE (1950-2009) for studies with quantitative estimates of cumulative asbestos exposure and lung cancer mortality and identified 19 original epidemiological studies. One was a population-based case-control study, and the others were industry-based cohort studies.

DATA EXTRACTION

Cumulative exposure categories and corresponding risks were abstracted. Exposure-response slopes [KL (lung cancer potency factor of asbestos)] were calculated using linear relative risk regression models.

DATA SYNTHESIS

We assessed the quality of five exposure assessment aspects of each study and conducted random effects univariate and multivariate meta-regressions. Heterogeneity in exposure-response relationships was greater than expected by chance (I2 = 64%). Stratification by exposure assessment characteristics revealed that studies with well-documented exposure assessment, larger contrast in exposure, greater coverage of the exposure history by exposure measurement data, and more complete job histories had higher meta-KL values than did studies without these characteristics. The latter two covariates were most strongly associated with the KL value. Meta-KL values increased when we incrementally restricted analyses to higher-quality studies.

CONCLUSIONS

This meta-analysis indicates that studies with higher-quality asbestos exposure assessment yield higher meta-estimates of the lung cancer risk per unit of exposure. Potency differences for predominantly chrysotile versus amphibole asbestos-exposed cohorts become difficult to ascertain when meta-analyses are restricted to studies with fewer exposure assessment limitations.

Combining a job-exposure matrix with exposure measurements to assess occupational exposure to benzene in a population cohort in shanghai, china

BACKGROUND

Generic job-exposure matrices (JEMs) are often used in population-based epidemiologic studies to assess occupational risk factors when only the job and industry information of each subject is available. JEM ratings are often based on professional judgment, are usually ordinal or semi-quantitative, and often do not account for changes in exposure over time. We present an empirical Bayesian framework that combines ordinal subjective JEM ratings with benzene measurements. Our aim was to better discriminate between job, industry, and time differences in exposure levels compared to using a JEM alone.

METHODS

We combined 63 221 short-term area air measurements of benzene exposure (1954-2000) collected during routine health and safety inspections in Shanghai, China, with independently developed JEM intensity ratings for each job and industry using a mixed-effects model. The fixed-effects terms included the JEM intensity ratings for job and industry (both ordinal, 0-3) and a time trend that we incorporated as a b-spline. The random-effects terms included job (n = 33) and industry nested within job (n = 399). We predicted the benzene concentration in two ways: (i) a calibrated JEM estimate was calculated using the fixed-effects model parameters for calendar year and JEM intensity ratings; (ii) a job-/industry-specific estimate was calculated using the fixed-effects model parameters and the best linear unbiased predictors from the random effects for job and industry using an empirical Bayes estimation procedure. Finally, we applied the predicted benzene exposures to a prospective population-based cohort of women in Shanghai, China (n = 74 942).

RESULTS

Exposure levels were 13 times higher in 1965 than in 2000 and declined at a rate that varied from 4 to 15% per year from 1965 to 1985, followed by a small peak in the mid-1990s. The job-/industry-specific estimates had greater differences between exposure levels than the calibrated JEM estimates (97.5th percentile/2.5th percentile exposure level, (B)(G)R(95)(B): 20.4 versus 3.0, respectively). The calibrated JEM and job-/industry-specific estimates were moderately correlated in any given year (Pearson correlation, r(p) = 0.58). We classified only those jobs and industries with a job or industry JEM exposure probability rating of 3 (>50% of workers exposed) as exposed. As a result, 14.8% of the subjects and 8.7% of the employed person-years in the study population were classified as benzene exposed. The cumulative exposure metrics based on the calibrated JEM and job-/industry-specific estimates were highly correlated (r(p) = 0.88).

CONCLUSIONS

We provide a useful framework for combining quantitative exposure data with expert-based exposure ratings in population-based studies that maximized the information from both sources. Our framework calibrated the ratings to a concentration scale between ratings and across time and provided a mechanism to estimate exposure when a job/industry group reported by a subject was not represented in the exposure database. It also allowed the job/industry groups' exposure levels to deviate from the pooled average for their respective JEM intensity ratings.

The impact of saturable metabolism on exposure-response relations in 2 studies of benzene-induced leukemia

Enzymatic saturation of metabolic pathways is one factor that potentially contributes to the nonlinear exposure-response relations that are frequently reported in occupational epidemiologic studies. The authors propose an approach to explore the contribution of saturable metabolism to previously reported exposure-response relations by integrating predictive models of relevant biomarkers of exposure into the epidemiologic analysis. The approach is demonstrated with 2 studies of leukemia in benzene-exposed workers, one conducted in the Australian petroleum industry (1981-1999) and one conducted in a US rubber hydrochloride production factory in Ohio (1940-1996). The studies differed greatly in their magnitudes and durations of exposure. Substitution of biomarker levels for external estimates of benzene exposure reduced the fold difference of the log relative risk of leukemia per unit of cumulative exposure between the 2 studies by 11%-44%. Nevertheless, a considerable difference in the log relative risk per unit of cumulative exposure remained between the 2 studies, suggesting that exposure misclassification, differences in study design, and potential confounding factors also contributed to the heterogeneity in risk estimates.