Melissa C. Friesen

Background: Job-exposure matrices (JEMs) provide a systematic method to assign exposures based on job titles, thereby enabling efficient occupational exposure assessment in observational studies. However, few JEMs have been specifically developed for chemical occupational exposures in China. Objectives: We aimed to construct semi-quantitative JEMs for trichloroethylene (TCE), tetrachloroethylene (PCE), and formaldehyde in the Chinese context. These JEMs were developed to support occupational exposure assessment needs in the AsiaLymph study, and to highlight key methodological considerations in JEM construction. Methods: Exposure information was synthesized from the published literature, routine industrial hygiene inspection data retrospectively collected as part of the AsiaLymph study (2012 to 2017), and established international JEMs. The JEMs were structured across 3 dimensions: chemical agents (TCE, PCE, formaldehyde), job titles (ISCO-88, 3- and 4-digit), and calendar period (1980 to 1990, 1991 to 2000, 2001 to 2010, ≥2011). For each chemical-job-period cell, 2 epidemiologists independently assigned exposure probability and level using a harmonized semi-quantitative scale. Consensus estimates across chemicals and inter-rater agreement were evaluated using Spearman's rank correlation coefficients and Cohen's κ statistics, respectively. Results: Among 478 job titles, non-zero exposure probability was assigned to 116 (24%) for TCE, 109 (23%) for PCE, and 118 (25%) for formaldehyde. When applying a more stringent, high-specificity exposure definition (probability = 1), the number of job titles classified as exposed decreased to 31 (7%), 25 (5%), and 30 (6%), respectively. Consensus exposure probabilities showed the strongest correlation between TCE and PCE (ρ = 0.95). Inter-rater agreement for exposure probability was 0.90 for TCE, 0.78 for PCE, and 0.44 for formaldehyde. Discussion: Our semi-quantitative JEMs combine empirical data with expert judgment, offering a practical tool to assess occupational exposures in observational studies. While developed to address the specific needs of the Asialymph study, the JEMs are adaptable and can be validated and extended to other chemical agents in similar settings.

Supplemental Table 5 shows the interaction between genetic variants and smoking status, common referent.

Supplemental Table 6 displays the associations between ever solvent exposure and risk of bladder cancer, stratified by the 24-marker bladder cancer polygenic risk score (PRS).

Supplemental Table 1 displays the odds ratios for the association between solvents and bladder cancer in the subset with genetic information.

Supplemental Table 2 shows the interaction between GWAS markers and ever or cumulative toluene and xylene exposure and risk of bladder cancer, common referent.

Supplemental Table 4 shows the interaction between rs72826305 (CASC15/LOC105374970), rs2896518 (FGFR3), GSTT1, and rs2070676 (CYP2E1) and ever or cumulative solvent exposure and risk of bladder cancer, stratified.

Supplemental Table 3 displays the interaction between candidate markers and ever or cumulative toluene and xylene exposure and risk of bladder cancer, common referent.

The New England Bladder Cancer Study has recently reported an increased bladder cancer risk with occupational exposure to mononuclear aromatic organic solvents, including exposure to benzene, toluene, and xylene and their combination BTX. However, the mechanisms by which BTX influence bladder cancer are unclear. In this study, we evaluated the interaction between BTX and genetic markers in known bladder cancer susceptibility loci and in variants shown to impact the metabolism of these solvents. We used multivariate logistic regression to calculate the ORs, 95% confidence intervals, and P values for multiplicative interaction in 1,182 cases and 1,408 controls from a population-based case–control study from New England. Lifetime occupational exposure to benzene, toluene, xylene, and BTX were assessed using occupational histories and exposure-oriented modules in conjunction with a job–exposure matrix. Buccal cells from mouthwash samples were used to conduct genotyping. Subjects with the highest cumulative exposure to benzene and who carried a risk allele in rs72826305 (CASC15) had an increased risk of bladder cancer (OR = 2.56, 95% confidence interval, 1.28–5.12) compared with those never exposed with no risk alleles (P interaction = 0.03). Additional suggestive joint effects with benzene were evident for those carrying genetic risk variants in FGFR3 (P value = 0.01) and GSTT1 (P interaction = 0.007). Bladder cancer risk is higher among those exposed to BTX-containing solvents who also harbor common variants in CASC15, FGFR3, and GSTT1, adding to the evidence of a plausible link between these exposures and bladder cancer risk.Prevention Relevance:Our findings suggest that bladder cancer risk is higher among those exposed to BTX-containing solvents who also harbor common genetic polymorphisms associated with bladder cancer. The joint contribution of genetics and occupational exposures may play an important role in the etiology of bladder cancer.

Objectives: Coding of job descriptions to occupation codes is a key step in exposure assessment in occupational epidemiology studies. We assessed agreement between occupation codes assigned by coder pairs in a hospital-based case-control study in East Asia and evaluated agreement between job-exposure matrix (JEM) estimates assigned to these codes. Methods: Each participant-reported job was classified following the 1988 version of the International Standard Classification of Occupations (ISCO-88) independently by 2 research centers. We calculated percent agreement between the ISCO-88 codes from different research center pairings (center 1-Chengdu versus 2-Tianjin; 3-Hong Kong versus 4-Taiwan). Stratified analyses for job coding agreement were conducted for different occupations, industries, and study phases. The different sets of job codes were linked to a population-based JEM and the exposure estimates were compared using weighted Cohen's kappa (κ). Results: Our study included 34,353 reported jobs from 12,590 subjects. Centers 1 and 2 coded 21,774 jobs from Mainland China; centers 3 and 4 coded 12,579 jobs from Hong Kong and Taiwan. Job coding percentage agreement ranged from 51.0% to 77.1% depending on job code granularity and different center pairing. JEM-based exposure estimates comparisons resulted in κs ranging from 0.72 to 0.95 for job codes from centers 1 and 2 (median: 0.86) and 0.62 to 0.95 for job codes from centers 3 and 4 (median: 0.75). Agreement in both measures varied depending on occupation and industry. Conclusion: Job coding agreement was similar with agreement reported in other occupational studies. Agreement between JEM estimates was higher than between assigned job codes, as not all job coding disagreements led to different exposure assignment.

Background/objective: Systems to automatically code job descriptions to standardized occupations, such as SOCcer, reduce the burden of expert coding in occupational studies, but expert coding remains needed. We evaluated how frequently experts select a SOCcer-suggested code when using the SOCAssign platform, which shows SOCcer's top 10 scoring codes (Si, with i denoting results ranking, compared to independent coding). Methods: We evaluated 2 protocols for SOCcer-assisted coding using the SOCAssign platform that we undertook when we had insufficient resources to employ the best practice of independent double-coding, with discordances resolved by consensus. Protocol 1 used a single coder, with a supervisor providing additional review for a subset based on features we identified that were more likely to indicate potential discordance between coders. Protocol 2 allocated jobs to be coded by 1 or 2 coders based on the SOCcer score (the probability an expert would have selected that code) for the highest scoring code (S1 score) and the score difference between the top 2 scoring codes. For comparison purposes, we included a third set of jobs that had previously undergone independent expert coding. For each job, we identified if the expert selected the top-scoring (S1) code, or one of the top 3 (S1–3) or top 10 (S1–10) scoring codes from SOCcer and compared findings across coding protocols, overall and by S1 score. Results: In Protocol 1, 25% of 6,824 jobs underwent supervisory review. In Protocol 2, jobs with an S1 score <0.2 or S1 − S2 score difference of <0.2 underwent coding by 2 experts (45% of 7,381 jobs); the remainder were coded by one expert. In both protocols and in independent coding, experts selected S1, one of S1–3 or one of S1–10 with similar prevalences (48% to 50%, 62% to 64%, and 73% to 74%, respectively). Expert coders were more likely to deviate from the SOCcer-suggested list for jobs with low score/score differences (39% to 44%) than high score/score differences (11% to 13%). In all evaluations, the frequency of selecting a SOCcer-suggested code increased with score, with some attenuation observed when the S1 − S2 score difference was <0.2. Conclusions: Our evaluations provide support for SOCcer-assisted expert coding. We provide recommendations for identifying jobs where multiple reviewers may be most needed.