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Comparison of occupational exposure assessment methods in a case-control study of lead, genetic susceptibility and risk of adult brain tumours.

ABSTRACT: OBJECTIVES:There is great interest in evaluating gene-environment interactions with chemical exposures, but exposure assessment poses a unique challenge in case-control studies. Expert assessment of detailed work history data is usually considered the best approach, but it is a laborious and time-consuming process. We set out to determine if a less intensive method of exposure assessment (a job exposure matrix (JEM)) would produce similar results to a previous analysis that found evidence of effect modification of the association between expert-assessed lead exposure and risk of brain tumours by a single nucleotide polymorphism in the ALAD gene (rs1800435). METHODS:We used data from a study of 355 patients with glioma, 151 patients with meningioma and 505 controls. Logistic regression models were used to examine associations between brain tumour risk and lead exposure and effect modification by genotype. We evaluated Cohen's ?, sensitivity and specificity for the JEM compared to the expert-assessed exposure metrics. RESULTS:Although effect estimates were imprecise and driven by a small number of cases, we found evidence of effect modification between lead exposure and ALAD genotype when using expert- but not JEM-derived lead exposure estimates. ? Values indicated only modest agreement (<0.5) for the exposure metrics, with the JEM indicating high specificity (?0.9) but poor sensitivity (?0.5). Disagreement between the two methods was generally due to having additional information in the detailed work history. CONCLUSION:These results provide preliminary evidence suggesting that high quality exposure data are likely to improve the ability to detect genetic effect modification.

SUBMITTER: Bhatti P

PROVIDER: S-EPMC3828743 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Comparison of occupational exposure assessment methods in a case-control study of lead, genetic susceptibility and risk of adult brain tumours.

Bhatti Parveen P Stewart Patricia A PA Linet Martha S MS Blair Aaron A Inskip Peter D PD Rajaraman Preetha P

Occupational and environmental medicine 20100825 1

<h4>Objectives</h4>There is great interest in evaluating gene-environment interactions with chemical exposures, but exposure assessment poses a unique challenge in case-control studies. Expert assessment of detailed work history data is usually considered the best approach, but it is a laborious and time-consuming process. We set out to determine if a less intensive method of exposure assessment (a job exposure matrix (JEM)) would produce similar results to a previous analysis that found evidenc ...[more]

PMID: 20798009

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Project description:The National Institute for Environmental Health Sciences (NIEHS) is conducting an epidemiologic study (GuLF STUDY) to investigate the health of the workers and volunteers who participated from April to December of 2010 in the response and cleanup of the oil release after the Deepwater Horizon explosion in the Gulf of Mexico. The exposure assessment component of the study involves analyzing thousands of personal monitoring measurements that were collected during this effort. A substantial portion of these data has values reported by the analytic laboratories to be below the limits of detection (LOD). A simulation study was conducted to evaluate three established methods for analyzing data with censored observations to estimate the arithmetic mean (AM), geometric mean (GM), geometric standard deviation (GSD), and the 95th percentile (X0.95) of the exposure distribution: the maximum likelihood (ML) estimation, the β-substitution, and the Kaplan-Meier (K-M) methods. Each method was challenged with computer-generated exposure datasets drawn from lognormal and mixed lognormal distributions with sample sizes (N) varying from 5 to 100, GSDs ranging from 2 to 5, and censoring levels ranging from 10 to 90%, with single and multiple LODs. Using relative bias and relative root mean squared error (rMSE) as the evaluation metrics, the β-substitution method generally performed as well or better than the ML and K-M methods in most simulated lognormal and mixed lognormal distribution conditions. The ML method was suitable for large sample sizes (N ≥ 30) up to 80% censoring for lognormal distributions with small variability (GSD = 2-3). The K-M method generally provided accurate estimates of the AM when the censoring was <50% for lognormal and mixed distributions. The accuracy and precision of all methods decreased under high variability (GSD = 4 and 5) and small to moderate sample sizes (N < 20) but the β-substitution was still the best of the three methods. When using the ML method, practitioners are cautioned to be aware of different ways of estimating the AM as they could lead to biased interpretation. A limitation of the β-substitution method is the absence of a confidence interval for the estimate. More research is needed to develop methods that could improve the estimation accuracy for small sample sizes and high percent censored data and also provide uncertainty intervals.

Project description:IntroductionRetrospective occupational exposure assessment has been challenging in case-control studies in the general population. We aimed to review (i) trends of different assessment methods used in the last 40 years and (ii) evidence of reliability for various assessment methods.MethodsTwo separate literature reviews were conducted. We first reviewed all general population cancer case-control studies published from 1975 to 2016 to summarize the exposure assessment approach used. For the second review, we systematically reviewed evidence of reliability for all methods observed in the first review.ResultsAmong the 299 studies included in the first review, the most frequently used assessment methods were self-report/assessment (n = 143 studies), case-by-case expert assessment (n = 139), and job-exposure matrices (JEMs; n = 82). Usage trends for these methods remained relatively stable throughout the last four decades. Other approaches, such as the application of algorithms linking questionnaire responses to expert-assigned exposure estimates and modelling of exposure with historical measurement data, appeared in 21 studies that were published after 2000. The second review retrieved 34 comparison studies examining methodological reliability. Overall, we observed slightly higher median kappa agreement between exposure estimates from different expert assessors (~0.6) than between expert estimates and exposure estimates from self-reports (~0.5) or JEMs (~0.4). However, reported reliability measures were highly variable for different methods and agents. Limited evidence also indicates newer methods, such as assessment using algorithms and measurement-calibrated quantitative JEMs, may be as reliable as traditional methods.ConclusionThe majority of current research assesses exposures in the population with similar methods as studies did decades ago. Though there is evidence for the development of newer approaches, more concerted effort is needed to better adopt exposure assessment methods with more transparency, reliability, and efficiency.

Project description:To estimate the current occupational cancer burden due to past exposures in Britain, estimates of the number of exposed workers at different levels are required, as well as risk estimates of cancer due to the exposures. This paper describes the methods and results for estimating the historical exposures. All occupational carcinogens or exposure circumstances classified by the International Agency for Research on Cancer as definite or probable human carcinogens and potentially to be found in British workplaces over the past 20-40 years were included in this study. Estimates of the number of people exposed by industrial sector were based predominantly on two sources of data, the CARcinogen EXposure (CAREX) database and the UK Labour Force Survey. Where possible, multiple and overlapping exposures were taken into account. Dose-response risk estimates were generally not available in the epidemiological literature for the cancer-exposure pairs in this study, and none of the sources available for obtaining the numbers exposed provided data by different levels of exposure. Industrial sectors were therefore assigned using expert judgement to 'higher'- and 'lower'-exposure groups based on the similarity of exposure to the population in the key epidemiological studies from which risk estimates had been selected. Estimates of historical exposure prevalence were obtained for 41 carcinogens or occupational circumstances. These include exposures to chemicals and metals, combustion products, other mixtures or groups of chemicals, mineral and biological dusts, physical agents and work patterns, as well as occupations and industries that have been associated with increased risk of cancer, but for which the causative agents are unknown. There were more than half a million workers exposed to each of six carcinogens (radon, solar radiation, crystalline silica, mineral oils, non-arsenical insecticides and 2,3,7,8-tetrachlorodibenzo-p-dioxin); other agents to which a large number of workers are exposed included benzene, diesel engine exhaust and environmental tobacco smoke. The study has highlighted several industrial sectors with large proportions of workers potentially exposed to multiple carcinogens. The relevant available data have been used to generate estimates of the prevalence of past exposure to occupational carcinogens to enable the occupational cancer burden in Britain to be estimated. These data are considered adequate for the present purpose, but new data on the prevalence and intensity of current occupational exposure to carcinogens should be collected to ensure that future policy decisions be based on reliable evidence.

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Comparison of occupational exposure assessment methods in a case-control study of lead, genetic susceptibility and risk of adult brain tumours.

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Comparison of occupational exposure assessment methods in a case-control study of lead, genetic susceptibility and risk of adult brain tumours.

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