Sivaraj Sidharth, Zscheischler Jakob, Buzan Jonathan R, Martius Olivia, Brönnimann Stefan, Vicedo-Cabrera Ana M
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland.
Environ Res Lett. 2024 Jul 5;19(7):074069. doi: 10.1088/1748-9326/ad5a25.
The global health burden associated with exposure to heat is a grave concern and is projected to further increase under climate change. While physiological studies have demonstrated the role of humidity alongside temperature in exacerbating heat stress for humans, epidemiological findings remain conflicted. Understanding the intricate relationships between heat, humidity, and health outcomes is crucial to inform adaptation and drive increased global climate change mitigation efforts. This article introduces 'directed acyclic graphs' (DAGs) as causal models to elucidate the analytical complexity in observational epidemiological studies that focus on humid-heat-related health impacts. DAGs are employed to delineate implicit assumptions often overlooked in such studies, depicting humidity as a confounder, mediator, or an effect modifier. We also discuss complexities arising from using composite indices, such as wet-bulb temperature. DAGs representing the health impacts associated with wet-bulb temperature help to understand the limitations in separating the individual effect of humidity from the perceived effect of wet-bulb temperature on health. General examples for regression models corresponding to each of the causal assumptions are also discussed. Our goal is not to prioritize one causal model but to discuss the causal models suitable for representing humid-heat health impacts and highlight the implications of selecting one model over another. We anticipate that the article will pave the way for future quantitative studies on the topic and motivate researchers to explicitly characterize the assumptions underlying their models with DAGs, facilitating accurate interpretations of the findings. This methodology is applicable to similarly complex compound events.
与热暴露相关的全球健康负担令人严重关切，并且预计在气候变化的情况下还会进一步增加。虽然生理学研究已经证明湿度与温度共同作用会加剧人类的热应激，但流行病学研究结果仍存在矛盾。了解热、湿度与健康结果之间的复杂关系对于指导适应措施和推动全球加强气候变化缓解工作至关重要。本文引入“有向无环图”（DAGs）作为因果模型，以阐明关注湿热相关健康影响的观察性流行病学研究中的分析复杂性。有向无环图用于描绘此类研究中常常被忽视的隐含假设，将湿度描述为混杂因素、中介因素或效应修饰因素。我们还讨论了使用复合指数（如湿球温度）所产生的复杂性。表示与湿球温度相关的健康影响的有向无环图有助于理解在将湿度的个体效应与湿球温度对健康的感知效应区分开来时的局限性。还讨论了与每个因果假设相对应的回归模型的一般示例。我们的目标不是优先考虑一种因果模型，而是讨论适合表示湿热健康影响的因果模型，并强调选择一种模型而非另一种模型的影响。我们预计本文将为该主题的未来定量研究铺平道路，并促使研究人员用有向无环图明确描述其模型背后的假设，从而便于对研究结果进行准确解释。这种方法适用于类似的复杂复合事件。
