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 total 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：界定暴露因素的效应及选择合适的估计方法是因果推断的前提。目前对于热浪（即连续极端高温天气）与结局之间的关联如何受温度调整及其调整方式的影响，尚缺乏深入理解。本文旨在探究这种依赖性，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型热浪-结局关系：E[R(Y)|HW=1, Z]/E[R(Y)|T=OT, Z]，其中HW为每日二元变量，指示是否存在热浪；R(Y)为结局Y的风险；T为温度变量；OT为最适温度；Z为一组混杂因素，包括典型混杂因素及作为混杂变量的部分温度指标。我们建议对热浪-结局关系进行特征描述，并谨慎选择建模方法以理解气候变化下热浪的影响。基于首尔市的真实数据演示我们的方法，结果表明热浪的总效应可能大于现有文献推断的结果。本研究开发并公开发布了R语言包HEAT（通过温度调整估计热浪效应）。