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一种混合专家方法用于回顾性评估基于人群的癌症病例对照研究中的职业暴露。

A hybrid expert approach for retrospective assessment of occupational exposures in a population-based case-control study of cancer.

机构信息

Department of Environmental and Occupational Health, Université de Montréal, School of Public Health, Montréal, Québec, Canada.

Centre de recherche du CHUM, Montréal, Québec, Canada.

出版信息

Environ Health. 2019 Feb 15;18(1):14. doi: 10.1186/s12940-019-0451-0.

DOI:10.1186/s12940-019-0451-0
PMID:30770757
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6377721/
Abstract

BACKGROUND

While the expert-based occupational exposure assessment approach has been considered the reference method for retrospective population-based studies, its implementation in large study samples has become prohibitive. To facilitate its application and improve upon it we developed, in the context of a Montreal population-based study of prostate cancer (PROtEuS), a hybrid approach combining job-exposure profiles (JEPs) summarizing expert evaluations from previous studies and expert review. We aim to describe the hybrid expert method and its impacts on the exposures assigned in PROtEuS compared to those from a previous study coded using the traditional expert method.

METHODS

Applying the hybrid approach, experts evaluated semi-quantitative levels of confidence, concentration and frequency of exposure to 313 agents for 16,065 jobs held by 4005 subjects in PROtEuS. These assessments were compared to those from a different set of jobs coded in an earlier study of lung cancer, conducted on the same study base, for 90 blue-collar occupations and 203 agents. Endpoints evaluated included differences in the number of exposures and in the distribution of ratings across jobs, and the within-occupation variability in exposure.

RESULTS

Compared to jobs from the lung cancer study, jobs in PROtEuS had on average 0.3 more exposures. PROtEuS exposures were more often assigned definite confidence ratings, but concentration and frequency levels tended to be lower. The within-occupation variability in ratings assigned to jobs were lower in PROtEuS jobs for all metrics. This was particularly evident for concentration, although considerable variability remained with over 40% of occupation/agent cells in PROtEuS exposed at different levels. The hybrid approach reduced coding time by half, compared to the traditional expert assessment.

CONCLUSIONS

The new hybrid expert approach improved on efficiency and transparency, and resulted in greater confidence in assessments, compared to the traditional expert method applied in an earlier study involving a similar set of jobs. Assigned ratings were more homogeneous with the hybrid approach, possibly reflecting clearer guidelines for coding, greater coherence between experts and/or reliance on summaries of past assessments. Nevertheless, significant within-occupation variability remained with the hybrid approach, suggesting that experts took into account job-specific factors in their assessments.

摘要

背景

虽然基于专家的职业暴露评估方法已被认为是回顾性基于人群研究的参考方法,但在大型研究样本中实施该方法已变得不可行。为了促进其应用并加以改进,我们在一项基于蒙特利尔人群的前列腺癌研究(PROtEuS)中开发了一种混合方法,该方法结合了职业暴露概况(JEPs),总结了来自先前研究的专家评估和专家审查。我们旨在描述混合专家方法及其对 PROtEuS 中分配的暴露的影响,与使用传统专家方法编码的先前研究相比。

方法

应用混合方法,专家评估了 313 种物质在 4005 名受试者的 16065 种职业中半定量的置信度、浓度和频率暴露水平。这些评估与另一组在同一研究基地进行的肺癌研究中编码的不同职业进行了比较,涉及 90 种蓝领职业和 203 种物质。评估的终点包括暴露数量的差异、职业之间的评分分布差异以及职业内暴露的变异性。

结果

与肺癌研究中的职业相比,PROtEuS 中的职业平均多 0.3 种暴露。PROtEuS 的暴露更常被分配明确的置信度评级,但浓度和频率水平往往较低。在所有指标中,PROtEuS 职业的职业内评分变异性较低。对于浓度尤其如此,尽管仍有超过 40%的职业/物质细胞暴露在不同水平,因此仍存在相当大的变异性。与传统的专家评估相比,新的混合专家方法将编码时间减少了一半。

结论

与应用于先前涉及类似职业的研究的传统专家方法相比,新的混合专家方法提高了效率和透明度,并提高了评估的信心。与混合方法相比,分配的评级更加一致,这可能反映了编码更清晰的指南、专家之间的更大一致性和/或对过去评估的总结的依赖。然而,混合方法仍存在显著的职业内变异性,表明专家在评估中考虑了特定职业的因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/4cd3bc2dede8/12940_2019_451_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/4bcd69a7ff20/12940_2019_451_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/4036365bb2f8/12940_2019_451_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/101a4c789329/12940_2019_451_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/4cd3bc2dede8/12940_2019_451_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/4bcd69a7ff20/12940_2019_451_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/4036365bb2f8/12940_2019_451_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/101a4c789329/12940_2019_451_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23c6/6377721/4cd3bc2dede8/12940_2019_451_Fig4_HTML.jpg

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