Sarah J. Locke

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.

Occupational Lead Exposure and Associations with Selected Cancers: The Shanghai Men’s and Women’s Health Study Cohorts

Background Epidemiologic studies of occupational lead exposure have suggested increased risks of cancers of the stomach, lung, kidney, brain, and meninges; however, the totality of the evidence is inconsistent. Objective We investigated the relationship between occupational lead exposure and cancer incidence at the five abovementioned sites in two prospective cohorts in Shanghai, China. Methods Annual job/industry-specific estimates of lead fume and lead dust exposure, derived from a statistical model combining expert lead intensity ratings with inspection measurements, were applied to the lifetime work histories of participants from the Shanghai Women’s Health Study (SWHS; n = 73,363) and the Shanghai Men’s Health Study (SMHS; n = 61,379) to estimate cumulative exposure to lead fume and lead dust. These metrics were then combined into an overall occupational lead exposure variable. Cohort-specific relative hazard rate ratios (RRs) and 95% confidence intervals (CIs) comparing exposed and unexposed participants were estimated using Cox proportional hazards regression and combined by meta-analysis. Results The proportions of SWHS and SMHS participants with estimated occupational lead exposure were 8.9% and 6.9%, respectively. Lead exposure was positively associated with meningioma risk in women only (n = 38 unexposed and 9 exposed cases; RR = 2.4; 95% CI: 1.1, 5.0), particularly with above-median cumulative exposure (RR = 3.1; 95% CI: 1.3, 7.4). However, all 12 meningioma cases among men were classified as unexposed to lead. We also observed non-significant associations with lead exposure for cancers of the kidney (n = 157 unexposed and 17 ever exposed cases; RR = 1.4; 95% CI: 0.9, 2.3) and brain (n = 67 unexposed and 10 ever exposed cases; RR = 1.8; 95% CI: 0.7, 4.8) overall. Conclusions Our findings, though limited by small numbers of cases, suggest that lead is associated with the risk of several cancers in women and men. Citation Liao LM, Friesen MC, Xiang YB, Cai H, Koh DH, Ji BT, Yang G, Li HL, Locke SJ, Rothman N, Zheng W, Gao YT, Shu XO, Purdue MP. 2016. Occupational lead exposure and associations with selected cancers: the Shanghai Men’s and Women’s Health Study cohorts. Environ Health Perspect 124:97–103; http://dx.doi.org/10.1289/ehp.1408171

Identifying gender differences in reported occupational information from three US population-based case-control studies

Objectives Growing evidence suggests that gender-blind assessment of exposure may introduce exposure misclassification, but few studies have characterised gender differences across occupations and industries. We pooled control responses to job-specific, industry-specific and exposure-specific questionnaires (modules) that asked detailed questions about work activities from three US population-based case–control studies to examine gender differences in work tasks and their frequencies. Methods We calculated the ratio of female-to-male controls that completed each module. For four job modules (assembly worker, machinist, health professional, janitor/cleaner) and for subgroups of jobs that completed those modules, we evaluated gender differences in task prevalence and frequency using χ2 and Mann–Whitney U tests, respectively. Results The 1360 female and 2245 male controls reported 6033 and 12 083 jobs, respectively. Gender differences in female:male module completion ratios were observed for 39 of 45 modules completed by ≥20 controls. Gender differences in task prevalence varied in direction and magnitude. For example, female janitors were significantly more likely to polish furniture (79% vs 44%), while male janitors were more likely to strip floors (73% vs 50%). Women usually reported more time spent on tasks than men. For example, the median hours per week spent degreasing for production workers in product manufacturing industries was 6.3 for women and 3.0 for men. Conclusions Observed gender differences may reflect actual differences in tasks performed or differences in recall, reporting or perception, all of which contribute to exposure misclassification and impact relative risk estimates. Our findings reinforce the need to capture subject-specific information on work tasks.

Environmental determinants of polychlorinated biphenyl concentrations in residential carpet dust

Polychlorinated biphenyls (PCBs), banned in the United Sates in the late 1970s, are still found in indoor and outdoor environments. Little is known about the determinants of PCB levels in homes. We measured concentrations of five PCB congeners (105, 138, 153, 170, and 180) in carpet dust collected between 1998 and 2000 from 1187 homes in four sites: Detroit, Iowa, Los Angeles, and Seattle. Home characteristics, occupational history, and demographic information were obtained by interview. We used a geographic information system to geocode addresses and determine distances to the nearest major road, freight route, and railroad; percentage of developed land; number of industrial facilities within 2 km of residences; and population density. Ordinal logistic regression was used to estimate the associations between the covariates of interest and the odds of PCB detection in each site separately. Total PCB levels [all congeners < maximum practical quantitation limit (MPQL) vs at least one congener ≥ MPQL to < median concentration vs at least one congener > median concentration] were positively associated with either percentage of developed land [odds ratio (OR) range 1.01-1.04 for each percentage increase] or population density (OR 1.08 for every 1000/mi(2)) in each site. The number of industrial facilities within 2 km of a home was associated with PCB concentrations; however, facility type and direction of the association varied by site. Our findings suggest that outdoor sources of PCBs may be significant determinants of indoor concentrations.

Lead exposure in US worksites: A literature review and development of an occupational lead exposure database from the published literature

BACKGROUND Retrospective exposure assessment of occupational lead exposure in population-based studies requires historical exposure information from many occupations and industries. METHODS We reviewed published US exposure monitoring studies to identify lead measurement data. We developed an occupational lead exposure database from the 175 identified papers containing 1,111 sets of lead concentration summary statistics (21% area air, 47% personal air, 32% blood). We also extracted ancillary exposure-related information, including job, industry, task/location, year collected, sampling strategy, control measures in place, and sampling and analytical methods. RESULTS The measurements were published between 1940 and 2010 and represented 27 2-digit standardized industry classification codes. The majority of the measurements were related to lead-based paint work, joining or cutting metal using heat, primary and secondary metal manufacturing, and lead acid battery manufacturing. CONCLUSIONS This database can be used in future statistical analyses to characterize differences in lead exposure across time, jobs, and industries.

Evaluating Temporal Trends from Occupational Lead Exposure Data Reported in the Published Literature Using Meta-Regression

OBJECTIVES The published literature provides useful exposure measurements that can aid retrospective exposure assessment efforts, but the analysis of this data is challenging as it is usually reported as means, ranges, and measures of variability. We used mixed-effects meta-analysis regression models, which are commonly used to summarize health risks from multiple studies, to predict temporal trends of blood and air lead concentrations in multiple US industries from the published data while accounting for within- and between-study variability in exposure. METHODS We extracted the geometric mean (GM), geometric standard deviation (GSD), and number of measurements from journal articles reporting blood and personal air measurements from US worksites. When not reported, we derived the GM and GSD from other summary measures. Only industries with measurements in ≥2 time points and spanning ≥10 years were included in our analyses. Meta-regression models were developed separately for each industry and sample type. Each model used the log-transformed GM as the dependent variable and calendar year as the independent variable. It also incorporated a random intercept that weighted each study by a combination of the between- and within-study variances. The within-study variances were calculated as the squared log-transformed GSD divided by the number of measurements. Maximum likelihood estimation was used to obtain the regression parameters and between-study variances. RESULTS The blood measurement models predicted statistically significant declining trends of 2-11% per year in 8 of the 13 industries. The air measurement models predicted a statistically significant declining trend (3% per year) in only one of the seven industries; an increasing trend (7% per year) was also observed for one industry. Of the five industries that met our inclusion criteria for both air and blood, the exposure declines per year tended to be slightly greater based on blood measurements than on air measurements. CONCLUSIONS Meta-analysis provides a useful tool for synthesizing occupational exposure data to examine exposure trends that can aid future retrospective exposure assessment. Data remained too sparse to account for other exposure predictors, such as job category or sampling strategy, but this limitation may be overcome by using additional data sources.

Occupational exposure to chlorinated solvents and kidney cancer: a case–control study

Objectives Trichloroethylene, a chlorinated solvent widely used for metal degreasing, is classified by the International Agency for Research on Cancer as a kidney carcinogen. Other chlorinated solvents are suspected carcinogens, most notably the cleaning solvent perchloroethylene, although it is unclear whether they are associated with kidney cancer. We investigated kidney cancer associations with occupational exposure to 6 chlorinated solvents (trichloroethylene, perchloroethylene, 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride) within a case–control study using detailed exposure assessment methods. Methods Cases (n=1217) and controls (n=1235) provided information on their occupational histories and, for selected occupations, on tasks involving potential exposure to chlorinated solvents through job-specific interview modules. Using this information, an industrial hygienist assessed potential exposure to each solvent. We computed ORs and 95% CIs for different exposure metrics, with unexposed participants as the referent group. Results 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride were not associated with kidney cancer. Among jobs with high exposure intensity, high cumulative hours exposed to perchloroethylene was associated with increased risk, both overall (third tertile vs unexposed: OR 3.1, 95% CI 1.3 to 7.4) and after excluding participants with ≥50% exposure probability for trichloroethylene (OR 3.0, 95% CI 0.99 to 9.0). A non-significant association with high cumulative hours exposed to trichloroethylene was observed (OR 1.7, 95% CI 0.8 to 3.8). Conclusions In this study, high exposure to perchloroethylene was associated with kidney cancer, independent of trichloroethylene. Additional studies are needed to further investigate this finding.

Evaluating predictors of lead exposure for activities disturbing materials painted with or containing lead using historic published data from U.S. workplaces

OBJECTIVES We evaluated predictors of differences in published occupational lead concentrations for activities disturbing material painted with or containing lead in U.S. workplaces to aid historical exposure reconstruction. METHODS For the aforementioned tasks, 221 air and 113 blood lead summary results (1960-2010) were extracted from a previously developed database. Differences in the natural log-transformed geometric mean (GM) for year, industry, job, and other ancillary variables were evaluated in meta-regression models that weighted each summary result by its inverse variance and sample size. RESULTS Air and blood lead GMs declined 5%/year and 6%/year, respectively, in most industries. Exposure contrast in the GMs across the nine jobs and five industries was higher based on air versus blood concentrations. For welding activities, blood lead GMs were 1.7 times higher in worst-case versus non-worst case scenarios. CONCLUSIONS Job, industry, and time-specific exposure differences were identified; other determinants were too sparse or collinear to characterize. Am. J. Ind. Med. 60:189-197, 2017.

0199 Using machine learning to efficiently use multiple experts to assign occupational lead exposure estimates in a case-control study

Objectives We applied machine learning approaches to efficiently assist multiple experts to transparently estimate occupational lead exposure in a case-control study of renal cell carcinoma. Method We used hierarchical cluster models to classify the 7154 study jobs with occupational history and job/industry questionnaires into 360 groups with similar responses. Each group was reviewed independently by two or three experts and was assigned probabilities of lead exposure (<5%, ≥5– <50%, ≥50%) for three time periods (<1980, 1980–1994, ≥1995). When the group’s mean response pattern suggested within-group exposure variability, experts identified programmable conditions that defined the rating differences where possible or flagged the group for further review. After splitting jobs that overlapped time periods at the calendar cut point, the 9992 job/time periods were assigned their relevant expert/group/time period estimate. Classification and regression tree (CART) models were developed to predict each expert’s expected assignment, based on previous decisions, to assign estimates for jobs in groups that expert had not assessed and for jobs requiring further review. Results In preliminary analyses, CART models predicted 91–96% of the experts’ pre-1995 estimates and 77–96% of ≥1995 estimates. CART estimates were assigned to 3–48% of the job/time periods, varying by expert. Overall, 92% of the job/time periods were assigned the same estimate by at least two experts. Conclusions Our framework reduced the number of exposure decisions needed from each expert compared to job-by-job assessment. Future work will use CART models to identify differences between experts to be resolved and incorporate frequency and intensity of lead exposure estimates.

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

OBJECTIVES To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study. METHODS First, previously extracted CT decision rules were used to obtain initial ordinal (0-3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rules agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions. RESULTS Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81-0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42-0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09-0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available. CONCLUSIONS Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study.