JMIR Publications

ABSTRACT

Background:

Due to climate change, the population and healthcare systems face an increasing burden of weather-related health risks. Emergency departments (ED) are one of the first points of contact for acute and emergency care and insights into population health. Previous research has demonstrated that climate change-based weather phenomena have an impact on ED utilization and morbidity. However, research shows inconsistent results for some weather phenomena and disease groups, and no corresponding evidence is yet available for Germany.

Objective:

This study aims to investigate the association between climate-related weather conditions and ED utilization and morbidity in Germany. It focuses on identifying particularly vulnerable patient groups, developing indicators for syndromic surveillance, and testing prediction models to support clinical and public health decision-making.

Methods:

KlimaNot is a consortium project using a mixed-methods approach. The study comprises several work packages, focusing on health care research, surveillance and prediction. The primary data source for all analyses are ED routine data collected from the AKTIN Emergency Department Data Registry. The dataset contains patient-related, administrative and treatment information on approx. 4,7 million ED visits from up to 43 EDs from the years 2019 to 2023; an extension to 2024 will be requested. The dataset will be linked to environmental data on the basis of the respective ED location. For the health care research work package, specific patient groups will be investigated, with a focus on children, old and multimorbid people. For the old age group, additional data on mobility, fall risk and health-related quality of life will be collected. As further part of the work package, special geographical characteristics will be assed using the hospital Stuttgart as case study. For the surveillance work package, climate change-related indicators, e.g. for heat-sensitive ED visits, will be developed and validated for syndromic public health surveillance. Regarding the prediction work package, machine learning methods are tested to predict expected ED utilization considering different weather constellations. Various strategies and tools for disseminating and visualizing the results will be developed to provide scientific evidence to the public and support clinicians and policy makers in decision making

Results:

Retrospective data analyses are ongoing and scheduled for completion by December 31, 2025. The prospective study phase will commence on September 1, 2025, with final results expected by September 1, 2026.

Conclusions:

This study will provide the first robust evidence on the impact of climate change–related weather conditions on ED utilization and morbidity in Germany. The findings aim to support early detection, preparedness, and targeted protection strategies for vulnerable populations and inform clinical and public health decision-making. Clinical Trial: The trial is registered in the German Clinical Trials Registry under the registration number DRKS00033214 as of 2024/05/24.