De Pretis Francesco, Zhou Yun, Shao Kan
Department of Environmental and Occupational Health, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, USA.
Department of Communication and Economics, University of Modena and Reggio Emilia, Reggio Emilia, Modena, Emilia-Romagna, Italy.
Risk Anal. 2025 Jun;45(6):1386-1398. doi: 10.1111/risa.17671. Epub 2024 Nov 3.
Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.
继之前一篇专注于将前瞻性队列研究的流行病学数据整合到毒理学风险评估中的文章之后，本文将重点转向病例对照研究。具体而言，它采用比值比（OR）作为主要的流行病学指标，并将其与基准剂量（BMD）方法相结合，作为确定化学毒性值以进行监管风险评估的标准剂量反应建模方法。已经为毒理学数据建立了一个标准化的BMD分析框架，包括输入数据要求、剂量反应模型、基准反应的定义以及模型不确定性的考虑。最近能够使用针对混杂因素进行调整的汇总数据处理队列研究和病例对照研究的方法对该框架进行了改进。本研究旨在调查和比较基于“有效计数”的BMD建模方法，该方法与一种用于在具有部分数据信息的队列研究中将比值比转换为相对风险的算法（即Wang算法）相结合，与基于调整后的OR的BMD分析方法。目标是开发一个适当的BMD建模框架，该框架可以推广用于分析已发表的病例对照研究数据。与之前的研究一样，这些方法应用于一个检查膀胱癌和肺癌与无机砷暴露之间关联的数据库。结果表明，两种方法估计的BMD和BMDL相对一致。然而，将调整后的OR值建模为连续数据以进行BMD估计与毒理学BMD分析中的既定做法更一致，使其成为一种更具通用性的方法。
