Estimation of Source-Specific Occupational Benzene Exposure in a Population-Based Case-Control Study of Non-Hodgkin Lymphoma

An algorithm for quantitatively estimating occupational endotoxin exposure in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study: I. Development of task-specific exposure levels from published data

BACKGROUND/OBJECTIVE

Farmers conduct numerous tasks with potential for endotoxin exposure. As a first step to characterize endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study, we used published data to estimate task-specific endotoxin concentrations.

METHODS

We extracted published data on task-specific, personal, inhalable endotoxin concentrations for agricultural tasks queried in the study questionnaire. The data, usually abstracted as summary measures, were evaluated using meta-regression models that weighted each geometric mean (GM, natural-log transformed) by the inverse of its within-study variance to obtain task-specific predicted GMs.

RESULTS

We extracted 90 endotoxin summary statistics from 26 studies for 9 animal-related tasks, 30 summary statistics from 6 studies for 3 crop-related tasks, and 10 summary statistics from 5 studies for 4 stored grain-related tasks. Work in poultry and swine confinement facilities, grinding feed, veterinarian services, and cleaning grain bins had predicted GMs > 1000 EU/m3 . In contrast, harvesting or hauling grain and other crop-related tasks had predicted GMs below 100 EU/m3 .

SIGNIFICANCE

These task-specific endotoxin GMs demonstrated exposure variability across common agricultural tasks. These estimates will be used in conjunction with questionnaire responses on task duration to quantitatively estimate endotoxin exposure for study participants, described in a companion paper.

An algorithm for quantitatively estimating occupational endotoxin exposure in the biomarkers of exposure and effect in agriculture study: II. Application to the study population

BACKGROUND

We developed an algorithm to quantitatively estimate endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study.

METHODS

The algorithm combined task intensity estimates derived from published data with questionnaire responses on activity duration to estimate task-specific cumulative endotoxin exposures for 13 tasks during four time windows, ranging from "past 12 months" to "yesterday/today." We applied the algorithm to 1681 participants in Iowa and North Carolina. We examined correlations in endotoxin metrics within- and between-task. We also compared these metrics to prior day full-shift inhalable endotoxin concentrations from 32 farmers.

RESULTS

The highest median task-specific cumulative exposures were observed for swine confinement, poultry confinement, and grind feed. Inter-quartile ranges showed substantial between-subject variability for most tasks. Time window-specific metrics of the same task were moderately-highly correlated. Between-task correlation was variable, with moderately-high correlations observed for similar tasks (e.g., between animal-related tasks). Prior day endotoxin concentration increased with the total metric and with task metrics for swine confinement, clean other animal facilities, and clean grain bins.

SIGNIFICANCE

This study provides insight into the variability and sources of endotoxin exposure among farmers in the BEEA study and summarizes exposure estimates for future investigations in this population.

Adapting Decision Rules to Estimate Occupational Metalworking Fluid Exposure in a Case-Control Study of Bladder Cancer in Spain

OBJECTIVES

We adapted previously developed decision rules from the New England Bladder Cancer Study (NEBCS) to assign occupational exposure to straight, soluble, and synthetic metalworking fluids (MWFs) to participants of the Spanish Bladder Cancer Study (SBCS).

METHODS

The SBCS and NEBCS are case-control studies that used the same lifetime occupational history and job module questionnaires. We adapted published decision rules from the NEBCS that linked questionnaire responses to estimates of the probability (<5, ≥5 to <50, ≥50 to <100, and 100%), frequency (in h week-1), and intensity (in mg m-3) of exposure to each of the three broad classes of MWFs to assign exposure to 10 182 reported jobs in the SBCS. The decision rules used the participant's module responses to MWF questions wherever possible. We then used these SBCS module responses to calculate job-, industry-, and time-specific patterns in the prevalence and frequency of MWF exposure. These estimates replaced the NEBCS-specific estimates in decision rules applied to jobs without MWF module responses. Intensity estimates were predicted using a previously developed statistical model that used the decade, industry (three categories), operation (grinding versus machining), and MWF type extracted from the SBCS questionnaire responses. We also developed new decision rules to assess mineral oil exposure from non-machining sources (possibly exposed versus not exposed). The decision rules for MWF and mineral oil identified questionnaire response patterns that required job-by-job expert review.

RESULTS

To assign MWF exposure, we applied decision rules that incorporated participant's responses and job group patterns for 99% of the jobs and conducted expert review of the remaining 1% (145) jobs. Overall, 14% of the jobs were assessed as having ≥5% probability of exposure to at least one of the three MWFs. Probability of exposure of ≥50% to soluble, straight, and synthetic MWFs was identified in 2.5, 1.7, and 0.5% of the jobs, respectively. To assign mineral oil from non-machining sources, we used module responses for 49% of jobs, a job-exposure matrix for 41% of jobs, and expert review for the remaining 10%. We identified 24% of jobs as possibly exposed to mineral oil from non-machining sources.

CONCLUSIONS

We demonstrated that we could adapt existing decision rules to assess exposure in a new population by deriving population-specific job group patterns.

Influence of Rurality, Race, and Ethnicity on Non-Hodgkin Lymphoma Incidence

INTRODUCTION

Exposure to lymphomagens vary by geography. The extent to which these contribute to racial and ethnic disparities in non-Hodgkin lymphoma (NHL) incidence is not well understood. We sought to evaluate the association between urban-rural status and racial and ethnic disparities in the 3 major NHL subtypes: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL).

PATIENTS AND METHODS

We used data on NHL incidence from 21 Surveillance, Epidemiology, and End Results (SEER) population-based registries for the period 2000 to 2016. Population characteristics were compared by NHL subtype and urban-rural status, using rural-urban continuum codes from the US Department of Agriculture. Incidence rate ratios were calculated, and Poisson regression was used to assess the association between incidence and rurality.

RESULTS

A total of 136,197 DLBCL, 70,882 FL, and 120,319 CLL incident cases aged ≥ 20 years were reported. The majority of DLBCL patients were non-Hispanic white (73.5%), with 11.9% Hispanic and 7.3% non-Hispanic black, with a similar distribution observed in FL and CLL. Adjusting for age, sex, and family poverty, we found increased DLBCL incidence among Hispanics in increasingly urban areas compared to rural areas (rural incidence rate ratio [IRR] = 1.00; nonmetropolitan urban IRR = 1.32, 95% CI 1.16, 1.51; metropolitan urban IRR = 1.55, 95% CI 1.36, 1.76). Among non-Hispanic blacks, urban areas, relative to rural areas, were associated with increased CLL incidence (IRR = 1.48; 95% CI 1.27, 1.72).

CONCLUSION

Urban-rural incidence patterns suggest that environmental exposures in urban areas associated with DLBCL and CLL pathogenesis may disproportionately affect Hispanics and non-Hispanic blacks.