累积效应
背景(考古学)
统计
持续时间(音乐)
累积分布函数
公制(单位)
暴露评估
计量经济学
流行病学
概念化
强度(物理)
暴露持续时间
环境卫生
医学
数学
计算机科学
地理
工程类
人工智能
内科学
艺术
生态学
概率密度函数
运营管理
物理
文学类
考古
量子力学
生物
作者
Frank de Vocht,Igor Burstyn,Nuthchyawach Sanguanchaiyakrit
摘要
The use of cumulative exposure, the product of intensity and duration, has enjoyed great popularity in epidemiology of chronic diseases despite numerous known caveats in its interpretation. We briefly review the history of use of cumulative exposure in epidemiology and propose an alternative method for relating time-integrated exposures to health risks. We argue, as others before us have, that cumulative exposure metrics obscures the interplay of exposure intensity and duration. We propose to use a computationally simple alternative in which duration and intensity of exposure are modelled as a main effect and their interaction, cumulative exposure, only be added if there is evidence of deviation from this additive model. We also consider the Lubin–Caporaso model of interplay of exposure intensity and duration. The impact of measurement error in intensity on model selection was also examined. The value of this conceptualization is demonstrated using a simulation study and further illustrated in the context of respiratory health and occupational exposure to latex dust. We demonstrate why cumulative exposure has been so popular because the cumulative exposure metric per se gives a robust answer to the existence of an association, regardless of the underlying true mechanism of disease. Treating cumulative exposure as the interaction of main effects of exposure duration and intensity enables epidemiologists to derive more information about mechanism of disease then fitting cumulative exposure metric by itself, and without the need to collect additional data. We propose that the practice of fitting duration, intensity and cumulative exposure separately to epidemiologic data should lead to conceptualization of cumulative exposure as interaction of main effects of duration and intensity of exposure.
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