Ambient air temperature exposure and foetal size and growth in three European birth cohorts.

INTRODUCTION: Ambient air temperature may affect birth outcomes adversely, but little is known about their impact on foetal growth throughout pregnancy. We evaluated the association between temperature exposure during pregnancy and foetal size and growth in three European birth cohorts. METHODS: We studied 23,408 pregnant women from the English Born in Bradford cohort, Dutch Generation R Study, and Spanish INMA Project. Using the UrbClimTM model, weekly ambient air temperature exposure at 100x100m resolution at the mothers' residences during pregnancy was calculated. Estimated foetal weight, head circumference, and femur length at mid and late pregnancy and weight, head circumference, and length at birth were converted into standard deviation scores (SDS). Foetal growth from mid to late pregnancy was calculated (grams or centimetres/week). Cohort/region-specific distributed lag non-linear models were combined using a random-effects meta-analysis and results presented in reference to the median percentile of temperature (14 °C). RESULTS: Weekly temperatures ranged from -5.6 (Bradford) to 30.3 °C (INMA-Sabadell). Cold and heat exposure during weeks 1-28 were associated with a smaller and larger head circumference in late pregnancy, respectively (e.g., for 9.5 °C: -1.6 SDS [95 %CI -2.0; -0.4] and for 20.0 °C: 1.8 SDS [0.7; 2.9]). A susceptibility period from weeks 1-7 was identified for cold exposure and a smaller head circumference at late pregnancy. Cold exposure was associated with a slower head circumference growth from mid to late pregnancy (for 5.5 °C: -0.1 cm/week [-0.2; -0.04]), with a susceptibility period from weeks 4-12. No associations that survived multiple testing correction were found for other foetal or any birth outcomes. CONCLUSIONS: Cumulative exposure to cold and heat during pregnancy was associated with changes in foetal head circumference throughout gestation, with susceptibility periods for cold during the first pregnancy trimester. No associations were found at birth, suggesting potential recovery. Future research should replicate this study across different climatic regions including varying temperature profiles.

Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling.

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.

A systematic review of ambient heat and sleep in a warming climate.

Climate change is elevating nighttime and daytime temperatures worldwide, affecting a broad continuum of behavioral and health outcomes. Disturbed sleep is a plausible pathway linking rising ambient temperatures with several observed adverse human responses shown to increase during hot weather. This systematic review aims to provide a comprehensive overview of the literature investigating the relationship between ambient temperature and valid sleep outcomes measured in real-world settings, globally. We show that higher outdoor or indoor temperatures are generally associated with degraded sleep quality and quantity worldwide. The negative effect of heat persists across sleep measures, and is stronger during the hottest months and days, in vulnerable populations, and the warmest regions. Although we identify opportunities to strengthen the state of the science, limited evidence of fast sleep adaptation to heat suggests rising temperatures induced by climate change and urbanization pose a planetary threat to human sleep, and therefore health, performance, and wellbeing.

Population exposure to multiple air pollutants and its compound episodes in Europe.

Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data. Here we analyze daily ambient PM2.5, PM10, NO2 and O3 concentrations at a 0.1-degree resolution during 2003-2019 across 1426 contiguous regions in 35 European countries, representing 543 million people. We find that PM10 levels decline by 2.72% annually, followed by NO2 (2.45%) and PM2.5 (1.72%). In contrast, O3 increase by 0.58% in southern Europe, leading to a surge in unclean air days. Despite air quality advances, 86.3% of Europeans experience at least one compound event day per year, especially for PM2.5-NO2 and PM2.5-O3. We highlight the improvements in air quality control but emphasize the need for targeted measures addressing specific pollutants and their compound events, particularly amidst rising temperatures.

Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study.

BACKGROUND: A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. METHODS: The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days. RESULTS: Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. CONCLUSIONS: Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Ambient temperature and risk of cardiovascular and respiratory adverse health outcomes: a nationwide cross-sectional study from Spain.

AIMS: We assessed the association of temperature and temperature variability with cause-specific emergency hospitalizations and mortality from cardiovascular and respiratory diseases in Spain, as well as the effect modification of this association by individual and contextual factors. METHODS AND RESULTS: We collected data on health (hospital admissions and mortality), weather (temperature and relative humidity), and relevant contextual indicators for 48 Spanish provinces during 2004-2019. The statistical analysis was separately performed for the summer (June-September) and winter (December-March) seasons. We first applied a generalized linear regression model with quasi-Poisson distribution to estimate daily province-specific temperature-health associations, and then we fitted multilevel multivariate meta-regression models to the evaluate effect modification of the contextual characteristics on heat- and cold-related risks. High temperature increased the risk of mortality across all cardiovascular and respiratory diseases, with the strongest effect for hypertension (relative risk (RR) at 99th temperature percentile vs. optimum temperature: 1.510 [95% empirical confidence interval {eCI} 1.251 to 1.821]), heart failure (1.528 [1.353 to 1.725]), and pneumonia (2.224 [1.685 to 2.936]). Heat also had an impact on all respiratory hospitalization causes (except asthma), with similar risks between pneumonia (1.288 [1.240 to 1.339]), acute bronchitis and bronchiolitis (1.307 [1.219 to 1.402]), and chronic obstructive pulmonary disease (1.260 [1.158 to 1.372]). We generally found significant risks related to low temperature for all cardiovascular and respiratory causes, with heart failure (RR at 1st temperature percentile vs. optimum temperature: 1.537 [1.329 to 1.779]) and chronic obstructive pulmonary disease (1.885 [1.646 to 2.159]) exhibiting the greatest risk for hospitalization, and acute myocardial infarction (1.860 [1.546 to 2.238]) and pneumonia (1.734 [1.219 to 2.468]) for mortality. Women and the elderly were more vulnerable to heat, while people with secondary education were less susceptible to cold compared to those not achieving this educational stage. Results from meta-regression showed that increasing heating access to the highest current provincial value (i.e. 95.6%) could reduce deaths due to cold by 59.5% (57.2 to 63.5). CONCLUSION: Exposure to low and high temperatures was associated with a greater risk of morbidity and mortality from multiple cardiovascular and respiratory conditions, and heating was the most effective societal adaptive measure to reduce cold-related mortality.

Exposure to low and high temperatures increases the risk of morbidity and mortality from several cardiovascular and respiratory diseases, especially among the elderly. Increasing access to heating could substantially reduce cold-related mortality burden.

Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments.

Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92 %, 11-13 % and 115-132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.

Remeasuring the influence of ageing on heat-related mortality in Spain, 1980 to 2018.

Climate change and population ageing are converging challenges that are expected to significantly worsen the health impacts of high temperatures. We aimed to remeasure the implications of ageing for heat-related mortality by comparing time trends based on chronological age (number of years already lived) with those derived from the application of state-of-the-art demographic methodology which better captures the dynamics of evolving longevity: prospective age (number of years still to be lived). We conducted a nationwide time-series analysis of 13 regions in Spain over 1980-2018 using all-cause mortality microdata for people aged 65+ and annual life tables from the Spanish National Institute of Statistics, and daily mean temperatures from E-OBS. Based on confounder-adjusted quasi-Poisson regression with distributed lag non-linear models and multivariate meta-analysis in moving 15-year timeslices, we assessed sex-specific changes in absolute risk and impacts for heat-related mortality at extreme and moderate temperatures, for chronological and prospective age groups. In the conventional chronological age analysis, absolute risk fell over the study period (e.g. females, extreme heat: -54%; moderate heat: -23%); after accounting for rising longevity, the prospective age analysis, however, found a smaller decline in risk for extreme heat (-15%) and a rise for moderate heat (+46%). Additionally, while the chronological age analysis suggested a shift in mortality towards higher ages, the prospective age analysis showed that over the study period, people of largely the same (prospective) age were impacted. Further, the prospective age analysis revealed excess risk in females (compared to males) rose from 20% to 27% for extreme heat, and from 40% to 70% for moderate heat. Assessing the implications of ageing using a prospective age perspective showed the urgency of re-doubling risk reduction efforts, including accelerating healthy ageing programs that incorporate climate considerations. The age patterns of impacts suggested that such actions have the potential to mitigate ageing-related heat-health threats to generate climate change-ready, healthy societies.

The effect of temporal data aggregation to assess the impact of changing temperatures in Europe: an epidemiological modelling study.

Background: Daily time-series regression models are commonly used to estimate the lagged nonlinear relation between temperature and mortality. A major impediment to this type of analysis is the restricted access to daily health records. The use of weekly and monthly data represents a possible solution unexplored to date. Methods: We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation. Findings: We observed progressively larger differences in the epidemiological estimates with the degree of temporal data aggregation. The daily data model estimated an annual cold and heat-related mortality of 290,104 (213,745-359,636) and 39,434 (30,782-47,084) deaths, respectively, and the weekly model underestimated these numbers by 8.56% and 21.56%. Importantly, differences were systematically smaller during extreme cold and heat periods, such as the summer of 2003, with an underestimation of only 4.62% in the weekly data model. We applied this framework to infer that the heat-related mortality burden during the year 2022 in Europe may have exceeded the 70,000 deaths. Interpretation: The present work represents a first reference study validating the use of weekly time series as an approximation to the short-term effects of cold and heat on human mortality. This approach can be adopted to complement access-restricted data networks, and facilitate data access for research, translation and policy-making. Funding: The study was supported by the ERC Consolidator Grant EARLY-ADAPT (https://www.early-adapt.eu/), and the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Countrywide analysis of heat- and cold-related mortality trends in the Czech Republic: growing inequalities under recent climate warming.

BACKGROUND: Only little is known about trends in temperature-mortality associations among the most vulnerable subgroups, especially in the areas of central and eastern Europe, which are considered major climatic hotspots in terms of heatwave exposure. Thus, we aimed to assess trends in temperature-related mortality in the Czech Republic by sex, age and cause of death, and to quantify the temporal evolution of possible inequalities. METHODS: We collected daily time series of all-cause (1987-2019) and cause-specific (1994-2019) mortality by sex and age category, and population-weighted daily mean 2-metre temperatures for each region of the Czech Republic. We applied a quasi-Poisson regression model to estimate the trends in region-specific temperature-mortality associations, with distributed lag non-linear models and multivariate random-effects meta-analysis to derive average associations across the country. We then calculated mortality attributable to non-optimal temperatures and implemented the indicator of sex- and age-dependent inequalities. RESULTS: We observed a similar risk of mortality due to cold temperatures for men and women. Conversely, for warm temperatures, a higher risk was observed for women. Results by age showed a clear pattern of increasing risk due to non-optimum temperatures with increasing age category. The relative risk (RR) related to cold was considerably attenuated in most of the studied subgroups during the study period, whereas an increase in the RR associated with heat was seen in the overall population, in women, in the age category 90+ years and with respect to respiratory causes. Moreover, underlying sex- and age-dependent inequalities experienced substantial growth. CONCLUSIONS: Our findings suggest ongoing adaptation to cold temperatures. Mal/adaptation to hot temperatures occurred unequally among population subgroups and resulted in growing inequalities between the sexes and among age categories.

Ambient temperature and seasonal variation in inpatient mortality from respiratory diseases: a retrospective observational study.

Background: The seasonal fluctuation in mortality and hospital admissions from respiratory diseases, with a winter peak and a summer trough, is widely recognized in extratropical countries. However, little is known about the seasonality of inpatient mortality and the role of ambient temperature remains uncertain. We aimed to analyse the association between ambient temperature and in-hospital mortality from respiratory diseases in the provinces of Madrid and Barcelona, Spain. Methods: We used data on daily hospitalisations, weather (ie, temperature and relative humidity) and air pollutants (ie, PM2.5, PM10, NO2 and O3) for the Spanish provinces of Madrid and Barcelona during 2006-2019. We applied a daily time-series quasi-Poisson regression in combination with distributed lag non-linear models (DLNM) to assess, on the one hand, the seasonal variation in fatal hospitalisations and the contribution of ambient temperature, and on the other hand, the day-to-day association between temperature and fatal hospital admissions. The analyses were stratified by sex, age and primary diagnostic of hospitalisation. Findings: The study analysed 1 710 012 emergency hospital admissions for respiratory diseases (mean [SD] age, 60.4 [31.0] years; 44.2% women), from which 103 845 resulted in in-hospital death (81.4 [12.3] years; 45.1%). We found a strong seasonal fluctuation in in-hospital mortality from respiratory diseases. While hospital admissions were higher during the cold season, the maximum incidence of inpatient mortality was during the summer and was strongly related to high temperatures. When analysing the day-to-day association between temperature and in-hospital mortality, we only found an effect for high temperatures. The relative risk (RR) of fatal hospitalisation at the 99th percentile of the distribution of daily temperatures vs the minimum mortality temperature (MMT) was 1.395 (95% eCI: 1.211-1.606) in Madrid and 1.612 (1.379-1.885) in Barcelona. In terms of attributable burden, summer temperatures (June-September) were responsible for 16.2% (8.8-23.3) and 22.3% (15.4-29.2) of overall fatal hospitalisations from respiratory diseases in Madrid and Barcelona, respectively. Women were more vulnerable to heat than men, whereas the results by diagnostic of admission showed heat effects for acute bronchitis and bronchiolitis, pneumonia and respiratory failure. Interpretation: Unless effective adaptation measures are taken in hospital facilities, climate warming could exacerbate the burden of inpatient mortality from respiratory diseases during the warm season. Funding: European Research Council Consolidator Grant EARLY-ADAPT, European Research Council Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Drivers of the time-varying heat-cold-mortality association in Spain: A longitudinal observational study.

BACKGROUND: A number of studies have reported reductions in mortality risk due to heat and cold over time. However, questions remain about the drivers of these adaptation processes to ambient temperatures. We aimed to analyse the demographic and socioeconomic drivers of the downward trends in vulnerability to heat- and cold-related mortality observed in Spain during recent decades (1980-2018). METHODS: We collected data on all-cause mortality, temperature and relevant contextual indicators for 48 provinces in mainland Spain and the Balearic Islands between Jan 1, 1980, and Dec 31, 2018. Fourteen contextual indicators were analysed representing ageing, isolation, urbanicity, heating, air conditioning (AC), house antiquity and ownership, education, life expectancy, macroeconomics, socioeconomics, and health investment. The statistical analysis was separately performed for the range of months mostly causing heat- (June-September) and cold- (October-May) related mortality. We first applied a quasi-Poisson generalised linear regression in combination with distributed lag non-linear models (DLNM) to estimate province-specific temperature-mortality associations for different periods, and then we fitted univariable and multivariable multilevel spatiotemporal meta-regression models to evaluate the effect modification of the contextual characteristics on heat- and cold-related mortality risks over time. FINDINGS: The average annual mean temperature has risen at an average rate of 0·36 °C per decade in Spain over 1980-2012, although the increase in temperature has been more pronounced in summer (0·40 °C per decade in June-September) than during the rest of the year (0·33 °C per decade). This warming has been observed, however, in parallel with a progressive reduction in the mortality risk associated to both hot and cold temperatures. We found independent associations for AC with heat-related mortality, and heating with cold-related mortality. AC was responsible for about 28·6% (31·5%) of the decrease in deaths due to heat (extreme heat) between 1989 and 1993 and 2009-2013, and heating for about 38·3% (50·8%) of the reductions in deaths due to cold (extreme cold) temperatures. Ageing (ie, proportion of population over 64 years) attenuated the decrease in cold-related mortality. INTERPRETATION: AC and heating are effective societal adaptive measures to heat and cold temperatures. This evidence holds important implications for climate change health adaptation policies, and for the projections of climate change impacts on human health.

Estimating daily air temperature and pollution in Catalonia: A comprehensive spatiotemporal modelling of multiple exposures.

Environmental epidemiology studies require models of multiple exposures to adjust for co-exposure and explore interactions. We estimated spatiotemporal exposure to surface air temperature and pollution (PM2.5, PM10, NO2, O3) at high spatiotemporal resolution (daily, 250 m) for 2018-2020 in Catalonia. Innovations include the use of TROPOMI products, a data split for remote sensing gap-filling evaluation, estimation of prediction uncertainty, and use of explainable machine learning. We compiled meteorological and air quality station measurements, climate and atmospheric composition reanalyses, remote sensing products, and other spatiotemporal data. We performed gap-filling of remotely-sensed products using Random Forest (RF) models and validated them using Out-Of-Bag (OOB) samples and a structured data split. The exposure modelling workflow consisted of: 1) PM2.5 station imputation with PM10 data; 2) quantile RF (QRF) model fitting; and 3) geostatistical residual spatial interpolation. Prediction uncertainty was estimated using QRF. SHAP values were used to examine variable importance and the fitted relationships. Model performance was assessed via nested CV at the station level. Evaluation of the gap-filling models using the structured split showed error underestimation when using OOB. Temperature models had the best performance (R2 =0.98) followed by the gaseous air pollutants (R2 =0.81 for NO2 and 0.86 for O3), while the performance of the PM2.5 and PM10 models was lower (R2 =0.57 and 0.63 respectively). Predicted exposure patterns captured urban heat island effects, dust advection events, and NO2 hotspots. SHAP values estimated a high importance of TROPOMI tropospheric NO2 columns in PM and NO2 models, and confirmed that the fitted associations conformed to prior knowledge. Our work highlights the importance of correctly validating gap-filling models and the potential of TROPOMI measurements. Moderate performance in PM models can be partly explained by the poor station coverage. Our exposure estimates can be used in epidemiological studies potentially accounting for exposure uncertainty.

The Direct and Indirect Influences of Interrelated Regional-Level Sociodemographic Factors on Heat-Attributable Mortality in Europe: Insights for Adaptation Strategies.

BACKGROUND: Heat is a significant cause of mortality, but impact patterns are heterogenous. Previous studies assessing such heterogeneity focused exclusively on risk rather than heat-attributable mortality burdens and assume predictors are independent. OBJECTIVES: We assessed how four interrelated regional-level sociodemographic predictors-education, life expectancy, the ratio of older to younger people (aging index), and relative income-influence heterogeneity in heat-attributable mortality burdens in Europe and then derived insights into adaptation strategies. METHODS: We extracted four outcomes from a temperature-mortality study covering 16 European countries: the rate of increase in mortality risk at moderate and extreme temperatures (moderate and extreme slope, respectively), the minimum mortality temperature percentile (MMTP), and the underlying mortality rate. We used structural equation modeling with country-level random effects to quantify the direct and indirect influences of the predictors on the outcomes. RESULTS: Higher levels of education were directly associated with lower heat-related mortality at moderate and extreme temperatures via lower slopes and higher MMTPs. A one standard deviation increase in education was associated with a -0.46±0.14, -0.41±0.12, and 0.41±0.12 standard deviation (±standard error) change in the moderate slope, extreme slope, and MMTP, respectively. However, education had mixed indirect influences via associations with life expectancy, the aging index, and relative income. Higher life expectancy had mixed relations with heat-related mortality, being associated with higher risk at moderate temperatures (0.33±0.11 for the moderate slope; -0.19±0.097 for the MMTP) but lower underlying mortality rates (-0.72±0.097). A higher aging index was associated with higher burdens through higher risk at extreme temperatures (0.13±0.072 for the extreme slope) and higher underlying mortality rates (0.93±0.055). Relative income had relatively small, mixed influences. DISCUSSION: Our novel approach provided insights into actions for reducing the health impacts of heat. First, the results show the interrelations between possible vulnerability-generating mechanisms and suggest future research directions. Second, the findings point to the need for a dual approach to adaptation, with actions that explicitly target heat exposure reduction and actions focused explicitly on the root causes of vulnerability. For the latter, the climate crisis may be leveraged to accelerate ongoing general public health programs. https://doi.org/10.1289/EHP11766.

Heat-related mortality in Europe during the summer of 2022.

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.

Long-term exposure to air pollution and severe COVID-19 in Catalonia: a population-based cohort study.

The association between long-term exposure to ambient air pollutants and severe COVID-19 is uncertain. We followed 4,660,502 adults from the general population in 2020 in Catalonia, Spain. Cox proportional models were fit to evaluate the association between annual averages of PM2.5, NO2, BC, and O3 at each participant's residential address and severe COVID-19. Higher exposure to PM2.5, NO2, and BC was associated with an increased risk of COVID-19 hospitalization, ICU admission, death, and hospital length of stay. An increase of 3.2 µg/m3 of PM2.5 was associated with a 19% (95% CI, 16-21) increase in hospitalizations. An increase of 16.1 µg/m3 of NO2 was associated with a 42% (95% CI, 30-55) increase in ICU admissions. An increase of 0.7 µg/m3 of BC was associated with a 6% (95% CI, 0-13) increase in deaths. O3 was positively associated with severe outcomes when adjusted by NO2. Our study contributes robust evidence that long-term exposure to air pollutants is associated with severe COVID-19.

Asthma hospitalisations and heat exposure in England: a case-crossover study during 2002-2019.

BACKGROUND: Previous studies have reported an association between warm temperature and asthma hospitalisation. They have reported different sex-related and age-related vulnerabilities; nevertheless, little is known about how this effect has changed over time and how it varies in space. This study aims to evaluate the association between asthma hospitalisation and warm temperature and investigate vulnerabilities by age, sex, time and space. METHODS: We retrieved individual-level data on summer asthma hospitalisation at high temporal (daily) and spatial (postcodes) resolutions during 2002-2019 in England from the NHS Digital. Daily mean temperature at 1 km×1 km resolution was retrieved from the UK Met Office. We focused on lag 0-3 days. We employed a case-crossover study design and fitted Bayesian hierarchical Poisson models accounting for possible confounders (rainfall, relative humidity, wind speed and national holidays). RESULTS: After accounting for confounding, we found an increase of 1.11% (95% credible interval: 0.88% to 1.34%) in the asthma hospitalisation risk for every 1°C increase in the ambient summer temperature. The effect was highest for males aged 16-64 (2.10%, 1.59% to 2.61%) and during the early years of our analysis. We also found evidence of a decreasing linear trend of the effect over time. Populations in Yorkshire and the Humber and East and West Midlands were the most vulnerable. CONCLUSION: This study provides evidence of an association between warm temperature and hospital admission for asthma. The effect has decreased over time with potential explanations including temporal differences in patterns of heat exposure, adaptive mechanisms, asthma management, lifestyle, comorbidities and occupation.

Thinking Health-related Behaviors in a Climate Change Context: A Narrative Review.

BACKGROUND: Human activities have changed the environment so profoundly over the past two centuries that human-induced climate change is now posing serious health-related threats to current and future generations. Rapid action from all scientific fields, including behavioral medicine, is needed to contribute to both mitigation of, and adaption to, climate change. PURPOSE: This article aims to identify potential bi-directional associations between climate change impacts and health-related behaviors, as well as a set of key actions for the behavioral medicine community. METHODS: We synthesized the existing literature about (i) the impacts of rising temperatures, extreme weather events, air pollution, and rising sea level on individual behaviors (e.g., eating behaviors, physical activity, sleep, substance use, and preventive care) as well as the structural factors related to these behaviors (e.g., the food system); and (ii) the concurrent positive and negative roles that health-related behaviors can play in mitigation and adaptation to climate change. RESULTS: Based on this literature review, we propose a first conceptual model of climate change and health-related behavior feedback loops. Key actions are proposed, with particular consideration for health equity implications of future behavioral interventions. Actions to bridge the fields of behavioral medicine and climate sciences are also discussed. CONCLUSIONS: We contend that climate change is among the most urgent issues facing all scientists and should become a central priority for the behavioral medicine community.

Daylight saving time affects European mortality patterns.

Daylight saving time (DST) consists in a one-hour advancement of legal time in spring offset by a backward transition of the same magnitude in fall. It creates a minimal circadian misalignment that could disrupt sleep and homoeostasis in susceptible individuals and lead to an increased incidence of pathologies and accidents during the weeks immediately following both transitions. How this shift affects mortality dynamics on a large population scale remains, however, unknown. This study examines the impact of DST on all-cause mortality in 16 European countries for the period 1998-2012. It shows that mortality decreases in spring and increases in fall during the first two weeks following each DST transition. Moreover, the alignment of time data around DST transition dates revealed a septadian mortality pattern (lowest on Sundays, highest on Mondays) that persists all-year round, irrespective of seasonal variations, in men and women aged above 40.