Defining the effect of exposure of interest and selecting an appropriate estimation method are prerequisite for causal inference. Understanding the ways in which association between heatwaves (i.e., consecutive days of extreme high temperature) and an outcome depends on whether adjustment was made for temperature and how such adjustment was conducted, is limited. This paper aims to investigate this dependency, demonstrate that temperature is a confounder in heatwave-outcome associations, and introduce a new modeling approach to estimate a new heatwave-outcome relation: E[R(Y)|HW=1, Z]/E[R(Y)|T=OT, Z], where HW is a daily binary variable to indicate the presence of a heatwave; R(Y) is the risk of an outcome, Y; T is a temperature variable; OT is optimal temperature; and Z is a set of confounders including typical confounders but also some types of T as a confounder. We recommend characterization of heatwave-outcome relations and careful selection of modeling approaches to understand the impacts of heatwaves under climate change. We demonstrate our approach using real-world data for Seoul, which suggests that the effect of heatwaves may be larger than what may be inferred from the extant literature. An R package, HEAT (Heatwave effect Estimation via Adjustment for Temperature), was developed and made publicly available.

翻译：定义感兴趣的暴露效应并选择合适的估计方法是因果推断的前提。目前对于热浪（即连续极端高温日）与结局间的关联如何受温度调整及其调整方式的影响，认识尚有限。本文旨在探讨这种依赖性，证明温度是热浪-结局关联中的混杂因素，并引入一种新的建模方法来估计新的热浪-结局关系：E[R(Y)|HW=1, Z]/E[R(Y)|T=OT, Z]，其中HW是表示是否存在热浪的每日二值变量；R(Y)是结局Y的风险；T是温度变量；OT是最适温度；Z是一组混杂因素，既包含典型混杂变量，也包含某些温度类型的变量作为混杂因素。我们建议对热浪-结局关系进行特征刻画，并审慎选择建模方法，以理解气候变化下热浪的影响。我们利用首尔市的真实数据进行了实证分析，结果表明热浪效应可能比现有文献推断的更大。我们开发了R语言软件包HEAT（通过温度调整进行热浪效应估计），并已公开发布。