Decision rule approach applied to estimate occupational lead exposure in a case-control study of kidney cancer

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.

Inorganic elements in occupational settings: A review on the effects on telomere length and biology

The past decades have shown that telomere crisis is highly affected by external factors. Effects of human exposure to xenobiotics on telomere length (TL), particularly in their workplace, have been largely studied. TL has been shown to be an efficient biomarker in occupational risk assessment. This is the first review focusing on studies about the effects on TL from occupational exposures to metals (lead [Pb] and mixtures), and particulate matter (PM) related to inorganic elements. Data from 15 studies were evaluated regarding occupational exposure to metals and PM-associated inorganic elements and impact on TL. Potential complementary analyses and subjects' background (age, length of employment and gender) were also assessed. There was limited information on the correlations between work length and TL dynamics, and that was also true for the correlation between age and TL. Results indicated that TL is affected differently across the types of occupational exposure investigated in this review, and even within the same exposure, a variety of effects can be observed. Fifty-three percent of the studies observed decreased TL in occupational exposure among welding fumes, open-cast coal mine, Pb and PM industries workers. Two studies focused particularly on the levels of metals and association with TL, and both linear and non-linear associations were found. Interestingly, TL modifications were accompanied by increase in DNA damage in 7 out of 8 studies that investigated it, measured either by Cytokinesis-block Micronucleus Assay or Comet assay. Five studies also investigated oxidative stress parameters, and 4 of them found increased levels of oxidative damage along with TL impairment. Oxidative stress is one of the main mechanisms by which telomeres are affected due to their high guanine content. Our review highlights the need of further studies accessing TL in simultaneous occupational exposure to mixtures of xenobiotics.

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.

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

OBJECTIVES

Occupational exposures in population-based case-control studies are increasingly being assessed using decision rules that link participants' responses to occupational questionnaires to exposure estimates. We used a hierarchical process that incorporated decision rules and job-by-job expert review to assign occupational benzene exposure estimates in a US population-based case-control study of non-Hodgkin lymphoma.

METHODS

We conducted a literature review to identify scenarios in which occupational benzene exposure has occurred, which we grouped into 12 categories of benzene exposure sources. For each source category, we then developed decision rules for assessing probability (ordinal scale based on the likelihood of exposure > 0.02 ppm), frequency (proportion of work time exposed), and intensity of exposure (in ppm). The rules used the participants' occupational history responses and, for a subset of jobs, responses to job- and industry-specific modules. For probability and frequency, we used a hierarchical assignment procedure that prioritized subject-specific module information when available. Next, we derived job-group medians from the module responses to assign estimates to jobs with only occupational history responses. Last, we used job-by-job expert review to assign estimates when job-group medians were not available or when the decision rules identified possible heterogeneous or rare exposure scenarios. For intensity, we developed separate estimates for each benzene source category that were based on published measurement data whenever possible. Frequency and intensity annual source-specific estimates were assigned only for those jobs assigned ≥75% probability of exposure. Annual source-specific concentrations (intensity × frequency) were summed to obtain a total annual benzene concentration for each job.

RESULTS

Of the 8827 jobs reported by participants, 8% required expert review for one or more source categories. Overall, 287 (3.3%) jobs were assigned ≥75% probability of exposure from any benzene source category. The source categories most commonly assigned ≥75% probability of exposure were gasoline and degreasing. The median total annual benzene concentration among jobs assigned ≥75% probability was 0.11 ppm (interquartile range: 0.06-0.55). The highest source-specific median annual concentrations were observed for ink and printing (2.3 and 1.2 ppm, respectively).

CONCLUSIONS

The applied framework captures some subject-specific variability in work tasks, provides transparency to the exposure decision process, and facilitates future sensitivity analyses. The developed decision rules can be used as a starting point by other researchers to assess occupational benzene exposure in future population-based studies.

Using Decision Rules to Assess Occupational Exposure in Population-Based Studies

PURPOSE OF REVIEW

Population-based studies increasingly link task-based occupational questionnaire responses collected from subjects to exposure estimates via transparent, programmable decision rules. We reviewed recent applications and methodological developments of rule-based approaches.

RECENT FINDINGS

Agent-specific decision rules require interviews incorporating work-task-based questions. Some studies have developed rules before the interviews took place, while others developed rules after the interviews were completed. Agreement between rule-based estimates and exposures assigned using job-by-job expert review were generally moderate to good (Kappa = 0.4-0.8). Rules providing quantitative intensity levels using measurement data or that integrate multiple independent exposure sources for the same job represent further advances to improve the characterization of occupational exposures in population studies. Decision rules have provided transparent and reproducible assessments, reduce job-by-job review, and facilitate sensitivity analyses in epidemiologic studies. Future studies should consider the development of decision rules concurrent with the questionnaire design to facilitate occupational exposure assessment efforts.

Case-control investigation of occupational lead exposure and kidney cancer

Objectives

Lead is a suspected carcinogen that has been inconsistently associated with kidney cancer. To clarify this relationship, we conducted an analysis of occupational lead exposure within a population-based study of kidney cancer using detailed exposure assessment methods.

Methods

Study participants (1217 cases and 1235 controls), enrolled between 2002 and 2007, provided information on their occupational histories and, for selected lead-related occupations, answered questions regarding workplace tasks, and use of protective equipment. Industrial hygienists used this information to develop several estimates of occupational lead exposure, including probability, duration and cumulative exposure. Unconditional logistic regression was used to compute ORs and 95% CIs for different exposure metrics, with unexposed subjects serving as the reference group. Analyses were also conducted stratifying on several factors, including for subjects of European ancestry only, single nucleotide polymorphisms in ALAD (rs1805313, rs1800435, rs8177796, rs2761016), a gene involved in lead toxicokinetics.

Results

In our study, cumulative occupational lead exposure was not associated with kidney cancer (OR 0.9, 95% CI 0.7 to 1.3 for highest quartile vs unexposed; ptrend=0.80). Other lead exposure metrics were similarly null. We observed no evidence of effect modification for the evaluated ALAD variants (subjects of European ancestry only, 662 cases and 561 controls) and most stratifying factors, although lead exposure was associated with increased risk among never smokers.

Conclusions

The findings of this study do not offer clear support for an association between occupational lead exposure and kidney cancer.