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Rai, M. ; Breitner-Busch, S. ; Wolf, K. ; Peters, A. ; Schneider, A.E. ; Chen, K.*

Future temperature-related mortality considering physiological and socioeconomic adaptation: A modelling framework.

Lancet Planet Health 6, e784-e792 (2022)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
BACKGROUND: As the climate changes, it is crucial to focus not only on mitigation measures but also on building climate change resilience by developing efficient adaptation strategies. Although population adaptation is a major determinant of future climate-related health burden, it is not well accounted for in studies that project the health impact of climate change. We propose a methodological framework for temperature-related mortality that incorporates two simultaneous adaptation-sensitivity pathways: the physiological pathway, considering both heat adaptation and cold sensitivity, and the socioeconomic pathway, which is influenced by changes in future adaptive capacities. To demonstrate its utility we apply the framework to a case study mortality time-series dataset from Bavaria, Germany. METHODS: In this modelling framework, we used extrapolated location-specific and age-specific baseline exposure-response functions and propose different future scenarios of cold sensitivity and heat adaptation on the basis of varying slopes of these exposure-response functions. We also incorporated future socioeconomic adaptation in the exposure-response functions using projections of gross domestic product under the respective shared socioeconomic pathways. Future adaptable fractions, representing the deaths avoided under each of the future scenarios, are projected under combinations of two climate change scenarios (shared socioeconomic pathway [SSP]1-2.6 and SSP3-7.0) and the respective plausible population projection scenarios (SSP1 and SSP3), also incorporating the future changes in demographic age structure and mortality. The case study for this framework was done for five districts in Bavaria, for both total non-accidental mortality and cardiovascular disease mortality. The baseline data was obtained for the period 1990-2006, and the future period was defined as 2083-99. FINDINGS: In our Bavaria case study, average temperature was projected to increase by 2099 by an average of 1·1°C under SSP1-2.6 and by 4·1°C under SSP3-7.0. We observed the adaptable fraction to be largely influenced by socioeconomic adaptation for both total mortality and cardiovascular disease mortality, and for both climate change scenarios. For example, for total mortality, the highest adaptable fraction of 18·56% (95% empirical CI 10·77-23·67) was observed under the SSP1-2.6 future scenario, in the presence of socioeconomic adaptation and under the highest heat adaptation (10%) provided the cold sensitivity remains 0%. The cold adaptable fraction is lower than the heat adaptable fraction under all scenarios. In the absence of socioeconomic adaptation, population ageing will lead to higher temperature-related mortality. INTERPRETATION: Our developed framework helps to systematically understand the effectiveness of adaptation mechanisms. In the future, socioeconomic adaptation is estimated to play a major role in determining temperature-related excess mortality. Furthermore, cold sensitivity might outweigh heat adaptation in the majority of locations worldwide. Similarly, population ageing is projected to continue to determine future temperature-related mortality. FUNDING: EU Horizon 2020 (EXHAUSTION).
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter MOHIT, Wetter/ Klima und Gesundheit, Herz-Kreislauf
ISSN (print) / ISBN 2542-5196
e-ISSN 2542-5196
Zeitschrift The Lancet planetary health
Quellenangaben Band: 6, Heft: 10, Seiten: e784-e792 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology II (EPI2)
Förderungen Horizon 2020
Population Council
Samir KC
ISIMIP3b climate modelling team