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A study by the Environment and Health Modelling (EHM) Lab at the London School of Hygiene & Tropical Medicine (LSHTM) outlines a new framework to address limitations of current real-time temperature-health alerts (THAs), and provide a solution.

Extreme weather events such as heatwaves and cold spells are linked with significant health burdens and excess deaths, and are expected to become more frequent and intense as a result of climate change. These temperature-related risks to public health may vary due to many factors, including age, climate, socioeconomic status, local infrastructure and geography. Regional health systems can face critical burdens on their resources during adverse weather events, such as additional ambulance callouts and an influx of patients in hospitals.

In an attempt to reduce health burdens from extreme temperatures, meteorological and health authorities such as the UK Met Office and UK Health Security Agency have developed real-time THAs that allow advanced warning and planning of national and local interventions. These tools are invaluable to the public, however there are some important limitations: they do not anticipate the expected burden on the public health system, and are generally designed to provide advice to populations at wider regional scales (e.g. states, counties, or geographic sub-regions). As a result, these THA systems have limited capacity to inform local public health authorities and emergency services of the varying degree of temperature-related vulnerability in a population during extreme weather events.

LSHTM researchers have developed an innovative design for forecasting temperature-related health risks by age categories, and importantly, at smaller geographical scales. This new framework, outlined in the , provides an additional layer of understanding to the existing THA frameworks, and has several benefits which could improve planning and allocation of public health services in real time.

Dr Malcolm Mistry, Assistant Professor in Climate and Geo-spatial Modelling in the EHM-Lab at LSHTM and lead author on the study, said: “The key feature of the framework is the lightweight (computationally inexpensive) design allowing it to be deployed even on a standard computer. The output, by way of maps and tables of expected excess deaths from heat or cold, can  facilitate an improved public response, decision making and allocation of emergency health services.”

The study demonstrates an application of the framework using the record-breaking heatwave of July 2022 in the UK.

Dr Antonio Gasparrini, Professor of Biostatistics and Epidemiology at LSHTM and co-author, said “By combining the forecast temperature for July 17-22, 2022 with age- and small geographic area- specific risk (ERFs), we mimic a real-time prediction of heat-related (expected) excess-deaths at finer geographic regions across England & Wales, as if made prior to the heatwave.”

This animation depicts the forecast of expected daily heat-related excess deaths in July 2022 using ECMWF forecast and the ERFs derived using the epidemiological model outlined in the study. Note: best viewed using speed x0.5. Credit: Arturo de la Cruz Libardi, EHM-Lab and animation by Daniel Boaden, LSHTM.

Another key feature of this framework is its flexibility and scalability, allowing it to be used for other thermal exposures, such as cold or humid-heat, and applied to different epidemiological models or temperature forecasts made available by other sources.

Professor Gasparrini said, “Importantly, the framework can be deployed in any inhabited region of the world, subject to availability of the underlying data described in the study”.

Dr Mistry said, “We believe this comprehensive framework has the potential to become a gold standard method for forecasting temperature-related excess mortality at a small-area level, enabling local authorities to implement more targeted preventive measures and potentially reduce the health burden during extreme weather events.”

Publication

Mistry, M. N. & Gasparrini, A. . Environmental Research: Health.