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结合贝叶斯框架的铅暴露测量和专家判断。

Combining Lead Exposure Measurements and Experts' Judgment Through a Bayesian Framework.

机构信息

Department of Occupational and Environmental Medicine, International St. Mary's Hospital, Catholic Kwandong University, Incheon, Korea.

Department of Statistics, Dongguk University, Seoul, Korea.

出版信息

Ann Work Expo Health. 2017 Nov 10;61(9):1054-1075. doi: 10.1093/annweh/wxx072.

DOI:10.1093/annweh/wxx072
PMID:29136412
Abstract

OBJECTIVES

CARcinogen EXposure (CAREX) is a carcinogen-surveillance system employed in many countries. To develop Korean CAREX, the intensity of exposure to lead, as an example, was estimated across industries.

METHODS

Airborne-lead measurement records were extracted from the work-environment measurement database (WEMD), which is a nationwide workplace-monitoring database. Lead measurements were log-transformed; then, the log-transformed geometric means (LGMs) and log-transformed geometric standard deviations (LGSDs) were calculated for each industry. However, the data of many industries was limited. To address this shortcoming, experts' judgments of the lead exposure levels across industries were elicited. Experts provided their estimates of lead exposure levels as the boundary of the 5th and 95th percentiles, and it is assumed that these estimates are based on the log-normal distributions of exposure levels. Estimates of LGM and LGSD were extracted from each expert's response and then combined to quantify the experts' prior distribution. Then, the experts' prior distributions for each industry were updated with the corresponding LGMs and LGSDs calculated from the WEMD data through a Bayesian framework, yielding posterior distributions of the LGM and LGSD.

RESULTS

The WEMD contains 83035 airborne-lead measurements that were collected between 2002 and 2007. A total of 17 occupational-hygiene professionals with >20 years of experience provided lead exposure estimates. In industries where measurement data were abundant, the measurement data dominated the posterior exposure estimates. For example, for one industry, 'Manufacture of Accumulator, Primary Cells, and Primary Batteries,' 1152 lead measurements [with a geometric mean (GM) of 14.42 µg m-3 and a geometric standard deviation (GSD) of 3.31] were available and 15 experts' responses (with a GM of 7.06 µg m-3 and a GSD of 4.15) were collected, resulting in a posterior exposure estimate of 14.41µg m-3 as the GM with a GSD of 3.31. For industries with a limited number of measurements available in the WEMD, experts' decisions played a significant role in determining the posterior exposure estimates. For example, for the 'Manufacture of Weapons and Ammunition' industry, 15 lead measurements (with a GM of 6.45 µg m-3 and a GSD of 3.37) were available and seven experts' responses (with a GM of 3.28 µg m-3 and a GSD of 4.54) were obtained, resulting in a posterior exposure estimate of 5.42 µg m-3 as the GM with a GSD of 3.73.

CONCLUSIONS

The proposed method for estimating the intensity of exposure to carcinogens may introduce an unbiased approach to the development process by simultaneously utilizing both prior knowledge of experts and measurement data. In addition, it supplies a framework for future updates.

摘要

目的

致癌物质暴露监测系统(CAREX)是一种在许多国家使用的致癌物监测系统。为了开发韩国 CAREX,以铅为例,对各行业的接触强度进行了估计。

方法

从工作环境监测数据库(WEMD)中提取了空气中铅测量记录,该数据库是一个全国性的工作场所监测数据库。对空气铅测量结果进行了对数转换;然后,为每个行业计算了对数转换几何平均值(LGM)和对数转换几何标准差(LGSD)。然而,许多行业的数据是有限的。为了解决这个问题,征求了专家对各行业铅暴露水平的判断。专家们提供了他们对铅暴露水平的估计,作为第 5 百分位和第 95 百分位的边界,假设这些估计是基于暴露水平的对数正态分布。从每位专家的答复中提取 LGM 和 LGSD 的估计值,并将其组合以量化专家的先验分布。然后,通过贝叶斯框架,用从 WEMD 数据计算得出的相应 LGM 和 LGSD 更新每个行业的专家先验分布,得到 LGM 和 LGSD 的后验分布。

结果

WEMD 包含了 2002 年至 2007 年间收集的 83035 份空气中铅测量数据。共有 17 名具有 20 年以上经验的职业卫生专业人员提供了铅暴露估计值。在测量数据丰富的行业,测量数据主导了后续的暴露估计。例如,在一个名为“蓄电池、原电池和原电池制造”的行业,有 1152 个铅测量值(GM 为 14.42µg m-3,GSD 为 3.31)可用,15 位专家的答复(GM 为 7.06µg m-3,GSD 为 4.15)收集,导致 GM 为 14.41µg m-3 的后验暴露估计值,GSD 为 3.31。在 WEMD 中可用的测量值数量有限的行业中,专家的决策在确定后验暴露估计值方面起着重要作用。例如,在“武器和弹药制造”行业,有 15 个铅测量值(GM 为 6.45µg m-3,GSD 为 3.37)可用,7 位专家的答复(GM 为 3.28µg m-3,GSD 为 4.54)获得,导致 GM 为 5.42µg m-3 的后验暴露估计值,GSD 为 3.73。

结论

所提出的估计致癌物接触强度的方法可以通过同时利用专家的先验知识和测量数据,为开发过程提供一种无偏的方法。此外,它还为未来的更新提供了一个框架。

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