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首页> 外文期刊>The Annals of occupational hygiene. >Comparison of algorithm-based estimates of occupational diesel exhaust exposure to those of multiple independent raters in a population-based case-control study
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Comparison of algorithm-based estimates of occupational diesel exhaust exposure to those of multiple independent raters in a population-based case-control study

机译：在基于人群的病例对照研究中，基于算法的职业性柴油机废气暴露与多个独立评估人的估算值的比较

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Objectives: Algorithm-based exposure assessments based on patterns in questionnaire responses and professional judgment can readily apply transparent exposure decision rules to thousands of jobs quickly. However, we need to better understand how algorithms compare to a one-by-one job review by an exposure assessor. We compared algorithm-based estimates of diesel exhaust exposure to those of three independent raters within the New England Bladder Cancer Study, a population-based case-control study, and identified conditions under which disparities occurred in the assessments of the algorithm and the raters.Methods: Occupational diesel exhaust exposure was assessed previously using an algorithm and a single rater for all 14 983 jobs reported by 2631 study participants during personal interviews conducted from 2001 to 2004. Two additional raters independently assessed a random subset of 324 jobs that were selected based on strata defined by the cross-tabulations of the algorithm and the first rater's probability assessments for each job, oversampling their disagreements. The algorithm and each rater assessed the probability, intensity and frequency of occupational diesel exhaust exposure, as well as a confidence rating for each metric. Agreement among the raters, their aggregate rating (average of the three raters' ratings) and the algorithm were evaluated using proportion of agreement, kappa and weighted kappa (κw). Agreement analyses on the subset used inverse probability weighting to extrapolate the subset to estimate agreement for all jobs. Classification and Regression Tree (CART) models were used to identify patterns in questionnaire responses that predicted disparities in exposure status (i.e., unexposed versus exposed) between the first rater and the algorithm-based estimates.Results: For the probability, intensity and frequency exposure metrics, moderate to moderately high agreement was observed among raters (κw = 0.50-0.76) and between the algorithm and the individual raters (κw = 0.58-0.81). For these metrics, the algorithm estimates had consistently higher agreement with the aggregate rating (κw = 0.82) than with the individual raters. For all metrics, the agreement between the algorithm and the aggregate ratings was highest for the unexposed category (90-93%) and was poor to moderate for the exposed categories (9-64%). Lower agreement was observed for jobs with a start year 1965 versus ≥1965. For the confidence metrics, the agreement was poor to moderate among raters (κw = 0.17-0.45) and between the algorithm and the individual raters (κw = 0.24-0.61). CART models identified patterns in the questionnaire responses that predicted a fair-to-moderate (33-89%) proportion of the disagreements between the raters' and the algorithm estimates.Discussion: The agreement between any two raters was similar to the agreement between an algorithm-based approach and individual raters, providing additional support for using the more efficient and transparent algorithm-based approach. CART models identified some patterns in disagreements between the first rater and the algorithm. Given the absence of a gold standard for estimating exposure, these patterns can be reviewed by a team of exposure assessors to determine whether the algorithm should be revised for future studies.
机译：目标：基于调查问卷回复和专业判断模式的基于算法的暴露评估可以轻松地将透明的暴露决策规则快速应用于数千个工作。但是，我们需要更好地了解算法与暴露评估人员进行的一对一工作审查的比较。我们将基于算法的柴油机废气暴露估计值与新英格兰膀胱癌研究（基于人群的病例对照研究）中的三个独立评估者的评估结果进行了比较，并确定了在算法和评估者评估中出现差异的条件。方法：2001年至2004年进行的个人访谈中，先前使用算法和职业评估师对2631名研究参与者报告的全部14983个工作进行了评估。另外两个评估师独立评估了324个工作的随机子集在算法交叉表和第一评估者对每个工作的概率评估所定义的层次上，对他们的分歧进行了过度抽样。该算法和每个评估者评估了职业性柴油机废气暴露的概率，强度和频率，以及每个度量标准的置信度。评估者之间的一致性，总评分（三个评估者评分的平均值）和算法使用一致性，kappa和加权kappa（κw）的比例进行评估。对子集进行一致性分析，使用逆概率加权外推子集以估计所有工作的一致性。分类和回归树（CART）模型用于识别问卷调查问卷中的模式，这些模式可预测第一评级者与基于算法的估计之间的暴露状态（即未暴露与暴露）之间的差异。结果：用于概率，强度和频率暴露指标，在评估者之间（κw= 0.50-0.76）以及算法与单个评估者之间（κw= 0.58-0.81）观察到中度到中等高度的一致性。对于这些指标，算法估计与总评分（κw= 0.82）的一致性始终高于单个评分者。对于所有指标，对于未暴露类别，算法与总评分之间的一致性最高（90-93％），而对于暴露类别则差于中度（9-64％）。开始年份<1965年与≥1965年相比，观察到的一致性较低。对于置信度指标，评估者之间（κw = 0.17-0.45）以及算法与单个评估者之间（κw = 0.24-0.61）之间的一致性差到中等。 CART模型确定了调查问卷答复中的模式，这些模式预测了评估者与算法估计之间的意见争端的中等到中等（33-89％）。讨论：任何两个评估者之间的共识都类似于一个评估者之间的共识。基于算法的方法和各个评估者，为使用更高效，更透明的基于算法的方法提供了额外的支持。 CART模型确定了第一评级者与算法之间分歧的一些模式。鉴于缺乏估计接触量的黄金标准，接触量评估者小组可以审查这些模式，以确定是否应修改该算法以供将来研究。

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