Models and input parameters for the estimation of heat-related excess mortality: a scoping review

Objective: This scoping review aims to identify existing methodological approaches and input parameters to estimate heat-related excess mortality based on real-case human populations from studies conducted worldwide. Introduction: Human-induced climate change is increasing the frequency and severity of heatwaves, leading to a rise in heat-related deaths globally. Since direct measurement of heat-related mortality is challenging, many countries have implemented a statistical model to estimate excess deaths, with ongoing efforts to refine it. An overview of methodological approaches and input parameters used to date should help to improve existing models. Eligibility criteria: The included studies feature excess mortality due to high temperature as an outcome and specifically name and define parameters used in their model(s). The eligibility criteria targeted observational studies in English, German, Spanish, or French. Studies on non-humans, cold or indoor temperatures, using prognostic-only models, and all types of reviews were excluded. Methods: The main information sources searched were PubMed, GreenFILE, and Web of Science Core Collection, with the final search conducted in August 2023 and limited to studies in English, German, Spanish, or French. Study selection involved dual independent screening, with data extracted via a standardized form and partly supported by artificial intelligence (AI). Data were analyzed descriptively and qualitatively, with results presented in narrative format and visualized through graphs. Results: This review analyzed 197 studies on heat-related excess mortality, highlighting that most used daily death (97%) and daily temperature data (77%), with relative risk being the most common outcome measure. Various temperature indicators and environmental parameters were included. Most studies used more than 1 temperature indicator: mean temperature was used in 123 studies, daily maximum in 104, daily minimum 79, diurnal temperature change in 8, and other temperature indicators in 20. Environmental parameters were used in 58% of the studies, among them relative humidity, ozone, fine dust, and wind. Statistical models such as distributed lag nonlinear models were used in 68 studies, generalized linear models in 52, and generalized additive models in 28, were some studies used more than 1, with distributed lag models gaining popularity since 2010. The review also identified a wide range of alternative modeling approaches, emphasizing the diversity in methods and inputs used across studies. Conclusions: Heat-related excess mortality is a growing public health concern worldwide due to climate change. A wide range of studies highlights the diversity in data sources, temperature indicators, environmental parameters, and modeling approaches, underscoring the need for standardized, adaptable methods to inform effective heat-health interventions. Review registration: OSF https://osf.io/v356m/overview Supplemental digital content: A German-language version of the abstract of this review is available: http://links.lww.com/SRX/A175.