• Energy Research

Record-breaking temperatures were recorded across the globe in 2023. Without climate action, adverse climate-related health impacts are expected to worsen worldwide, affecting billions of people. Temperatures in Europe are warming at twice the rate of the global average, threatening the health of populations across the continent and leading to unnecessary loss of life. The Lancet Countdown in Europe was established in 2021, to assess the health profile of climate change aiming to stimulate European social and political will to implement rapid health-responsive climate mitigation and adaptation actions. In 2022, the collaboration published its indicator report, tracking progress on health and climate change via 33 indicators and across five domains.

The COVID-19 pandemic drastically disrupted usual seasonal mortality patterns, creating challenges in assessing temperature-related mortality. While previous studies explored the effect of temperature on SARS-CoV-2 transmission, few examined its relationship with mortality during the pandemic, often excluding COVID-19 deaths or relying on pre-pandemic models. In this study, we developed an innovative methodological framework that accounts for COVID-19 waves, allowing us to estimate changes in the short-term effects of temperature on mortality and assess the role of adaptation and maladaptation before and after the onset of the pandemic.We analyzed pre- (2015-2019) and pandemic (2020-2023) mortality data from Eurostat, covering 805 contiguous regions across 32 European countries. To adjust for COVID-19 deaths, we selected specific time windows during COVID-19 waves, and increased the degrees of freedom (d.f.) for these windows as necessary until achieving well-behaved residuals.Adjusting for COVID-19 deaths reduced uncertainty in the pandemic association, providing more precise estimates. When adjusting for COVID-19 deaths, we observed a significant reduction in cold and heat-related mortality risks in all sub-regions except in the southern regions for heat, which experienced a significant increase. When assessing the role of adaptation between pre- and pandemic periods, we observed significant changes for heat risks in southern and western regions with higher risks in the pandemic period than in the pre-pandemic one. For cold, all sub-regions except the southern ones had higher risks in the pre-pandemic period.Our work defines a new innovative methodological framework for future epidemiological studies using data from the pandemic period. The proposed methodology demonstrates the importance of using pandemic data and adjusting for COVID-19 deaths to accurately capture current vulnerabilities. The findings highlight different regional adaptation processes and underscore the need for enhanced heat adaptation measures, particularly in vulnerable regions.

More than 110,000 Europeans died as a result of the record-breaking temperatures of 2022 and 2023. A new generation of impact-based early warning systems, using epidemiological models to transform weather forecasts into health forecasts for targeted population subgroups, is an essential adaptation strategy to increase resilience against climate change. Here, we assessed the skill of an operational continental heat-cold-health forecasting system. We used state-of-the-art temperature-lag-mortality epidemiological models to transform bias-corrected ensemble weather forecasts into daily temperature-related mortality forecasts. We found that temperature forecasts can be used to issue skillful forecasts of temperature-related mortality. However, the forecast skill varied by season and location, and it was different for temperature and temperature-related mortality due to the use of epidemiological models. Overall, our study demonstrates and quantifies the forecast skill horizon of heat-cold-health forecasting systems, which is a necessary step toward generating trust among public health authorities and end users.

Abstract Background Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power. Methods Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks. Results The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies. Conclusions SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.

The work of MSo and RH has been performed within the scope of H2020 project EXHAUSTION (grant number 820655) and Academy of Finland HEATCOST (grant number 334798). JMA acknowledges support from the Spanish Ministry of Science and Innovation and State Research Agency through the Centro de Excelencia Severo Ochoa 2019–23 programme (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA programme. JB acknowledges funding from the Ministry of Science and Innovation (MCIU) under grant agreement number RYC2018-025446-I (programme Ramón y Cajal). The computations of the mechanistic dengue-models (MOS and HS) were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N. JR has been awarded a Chair in Artificial Intelligence in the Research of Infectious Diseases Impacted by Climate Change provided by the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research. HA, MQ-Z, and SJL were supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 865564 (European Research Council Consolidator Grant EARLY-ADAPT). JP was supported by Academy of Finland projects PS4A and ALL-Impress. The Lancet Countdown in Europe received invaluable support from Shuzhou Yuan, Ran Zhang, Krishnamoorthy Manohara, and Reed Garvin (Data Science Lab, Hertie School, Germany), Tom de Groeve and Peter Salamon (European Commission), and Raúl Fernando Méndez Turrubiates (ISGlobal, Barcelona, Spain). We also thank Wenjia Cai, Shihui Zhang, and Jiyao Zhao (Department of Earth System Science, Tsinghua University, Beijing, China) for their technical advice.