A Transparent Method for Medicolegal Decision-Making: The FAIR Framework for Individualized Attribution in Occupational Disease.

IF 1.4
Eytan Ellenberg, Akshaya Bhagavathula, Julie Cwikel, Nadav Davidovitch, Jalees Razavi, Eldad Katorza, Tee Guidotti
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Abstract

Objective: To present and evaluate Factor Attribution for Integrated Risk (FAIR), a framework for proportional attribution of excess disease risk in occupational medicine. FAIR aligns epidemiologic evidence with medicolegal thresholds (>50%, ≥20%) and distinguishes population-level causal evidence from case-level apportionment.

Methods: FAIR integrates structured evidence appraisal, extraction of relative risks and baseline incidence, and Shapley value-based partitioning of excess risk across determinants. Attribution outputs are interpreted against medicolegal thresholds. The framework was applied to three exposure-disease pairs: mesothelioma, cutaneous squamous cell carcinoma, and bladder cancer.

Results: FAIR reproduced established attribution hierarchies: asbestos >50%, occupational ultraviolet radiation 30-40%, and solvents 20-25%. Sensitivity analyses varying baseline incidence and model specification showed stable attribution (<±7%).

Conclusions: FAIR provides a transparent approach for translating epidemiologic evidence into threshold-aligned individual attribution in occupational disease assessment.

透明的医疗法律决策方法:职业病个体化归因的公平框架。
目的:提出并评价职业医学过度疾病风险比例归因框架——综合风险因子归因(FAIR)。FAIR将流行病学证据与医学法律阈值(低于50%,≥20%)进行了比对,并将人群水平的因果证据与病例水平的分配区分开来。方法:FAIR整合了结构化证据评估,提取相对风险和基线发生率,以及基于Shapley值的跨决定因素的超额风险划分。归因输出根据医学法律阈值进行解释。该框架应用于三种暴露-疾病对:间皮瘤、皮肤鳞状细胞癌和膀胱癌。结果:FAIR重现了既定的归因层次:石棉>50%,职业紫外线辐射30-40%,溶剂20-25%。不同基线发病率和模型规格的敏感性分析显示归因稳定(结论:FAIR为将流行病学证据转化为职业病评估中符合阈值的个体归因提供了透明的方法)。
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