Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Statistics, National Cheng Kung University, Tainan.
Ann Epidemiol. 2022 Nov;75:57-66. doi: 10.1016/j.annepidem.2022.08.050. Epub 2022 Sep 7.
Population attributable fraction (PAF), defined as the proportion of the occurrence of a disease which will be reduced by eliminating risk factors in a population, is one of the most common measurements for evaluating the benefit of a health-related policy in epidemiologic study. In this article, we propose an alternative PAF defined based on sufficient cause framework, which decompose the occurrence of a disease into several pathways including mediation and mechanistic interaction.
We propose a formal statistical definition and regression-based estimator for PAF based on sufficient cause framework within mediation settings. Under monotonicity assumption, the proposed method can decompose the occurrence of a disease into nine PAFs corresponding to all types of mechanisms attributing to exposure and the mediator, including the portion attributing to exposure directly, to mediator, to indirect effect through mediator, to the mechanistic interaction, to both of mediation and interaction, and to none of exposure or mediator.
We apply the proposed method to explore the mechanism of a hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) mediated by and/or interacted with alanine aminotransferase (ALT) and hepatitis B virus (HBV). When treating ALT as mediator, 56.77% of diseased subjects can be attributable to either HCV or abnormal ALT. When treating HBV as mediator, HCC is mainly induced by an exogenous high HBV viral load directly.
The proposed method can identify the impact of exposure and pathway effects, and benefit to allocate the resources on intervention strategies.
人群归因分数（PAF）定义为通过消除人群中的危险因素可以降低疾病发生的比例，是评估与健康相关政策在流行病学研究中效益的最常用方法之一。本文提出了一种基于充分原因框架的替代 PAF，它将疾病的发生分解为包括中介和机制相互作用的几种途径。
我们提出了一种基于中介环境下充分原因框架的正式统计定义和基于回归的 PAF 估计方法。在单调假设下，该方法可以将疾病的发生分解为九个归因于暴露和中介的 PAF，包括直接归因于暴露、归因于中介、通过中介的间接效应、机制相互作用、中介和相互作用的部分、以及暴露或中介都不归因的部分。
我们应用该方法探索丙型肝炎病毒（HCV）通过丙氨酸氨基转移酶（ALT）和乙型肝炎病毒（HBV）介导和/或相互作用引起肝细胞癌（HCC）的机制。当将 ALT 作为中介时，56.77%的患病个体可归因于 HCV 或异常 ALT。当将 HBV 作为中介时，HCC 主要是由外源性高 HBV 病毒载量直接引起的。
该方法可以识别暴露和途径效应的影响，有助于为干预策略分配资源。
