Over 70,000 excess deaths occurred in Europe during the summer of 2003. The resulting societal awareness led to the design and implementation of adaptation strategies to protect at-risk populations. We aimed to quantify heat-related mortality burden during the summer of 2022, the hottest season on record in Europe. We analyzed the Eurostat mortality database, which includes 45,184,044 counts of death from 823 contiguous regions in 35 European countries, representing the whole population of over 543 million people. We estimated 61,672 (95% confidence interval (CI) = 37,643–86,807) heat-related deaths in Europe between 30 May and 4 September 2022. Italy (18,010 deaths; 95% CI = 13,793–22,225), Spain (11,324; 95% CI = 7,908–14,880) and Germany (8,173; 95% CI = 5,374–11,018) had the highest summer heat-related mortality numbers, while Italy (295 deaths per million, 95% CI = 226–364), Greece (280, 95% CI = 201–355), Spain (237, 95% CI = 166–312) and Portugal (211, 95% CI = 162–255) had the highest heat-related mortality rates. Relative to population, we estimated 56% more heat-related deaths in women than men, with higher rates in men aged 0–64 (+41%) and 65–79 (+14%) years, and in women aged 80+ years (+27%). Our results call for a reevaluation and strengthening of existing heat surveillance platforms, prevention plans and long-term adaptation strategies.

Europe has emerged as a major climate change hotspot, both in terms of an increase in seasonal averages and climate extremes. Projections of temperature-attributable mortality, however, have not been comprehensively reported for an extensive part of the continent. Therefore, we aim to estimate the future effect of climate change on temperature-attributable mortality across Europe. We did a time series analysis study. We derived temperature-mortality associations by collecting daily temperature and all-cause mortality records of both urban and rural areas for the observational period between 1998 and 2012 from 147 regions in 16 European countries. We estimated the location-specific temperature-mortality relationships by using standard time series quasi-Poisson regression in conjunction with a distributed lag non-linear model. These associations were used to transform the daily temperature simulations from the climate models in the historical period (1971-2005) and scenario period (2006-2099) into projections of temperature-attributable mortality. We combined the resulting risk functions with daily time series of future temperatures simulated by four climate models (ie, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) under three greenhouse gas emission scenarios (ie, Representative Concentration Pathway [RCP]2.6, RCP6.0, and RCP8.5), providing projections of future mortality attributable fraction due to moderate and extreme cold and heat temperatures. Overall, 7·17% (95% CI 5·81-8·50) of deaths registered in the observational period were attributed to non-optimal temperatures, cold being more harmful than heat by a factor of ten (6·51% [95% CI 5·14-7·80] vs 0·65% [0·40-0·89]), and with large regional differences across countries-eg, ranging from 4·85% (95% CI 3·75-6·00) in Germany to 9·87% (8·53-11·19) in Italy. The projection of temperature anomalies by RCP scenario depicts a progressive increase in temperatures, more exacerbated in the high-emission scenario RCP8.5 (4·54°C by 2070-2099) than in RCP6.0 (2·89°C) and RCP2.6 (1·67°C). This increase in temperatures was transformed into attributable fraction. Projections consistently indicated that the increase in heat attributable fraction will start to exceed the reduction of cold attributable fraction in the second half of the 21st century, especially in the Mediterranean and in the higher emission scenarios. The comparison between scenarios highlighted the important role of mitigation, given that the total attributable fraction will only remain stable in RCP2.6, whereas the total attributable fraction will rapidly start to increase in RCP6.0 by the end of the century and in RCP8.5 already by the middle of the century. The increase in heat attributable fraction will start to exceed the reduction of cold attributable fraction in the second half of the 21st century. This finding highlights the importance of implementing mitigation policies. These measures would be especially beneficial in the Mediterranean, where the high vulnerability to heat will lead to an imbalance between the decreasing cold and increasing heat-attributable mortality. None.

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.