  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.
