Explore the vast range of titles available.

Reliable estimates of future health impacts due to climate change are needed to inform and contribute to the design of efficient adaptation and mitigation strategies. However, projecting health burdens associated to specific environmental stressors is a challenging task because of the complex risk patterns and inherent uncertainty of future climate scenarios. These assessments involve multidisciplinary knowledge, requiring expertise in epidemiology, statistics, and climate science, among other subjects. Here, we present a methodologic framework to estimate future health impacts under climate change scenarios based on a defined set of assumptions and advanced statistical techniques developed in time-series analysis in environmental epidemiology. The proposed methodology is illustrated through a step-by-step hands-on tutorial structured in well-defined sections that cover the main methodological steps and essential elements. Each section provides a thorough description of each step, along with a discussion on available analytical options and the rationale on the choices made in the proposed framework. The illustration is complemented with a practical example of study using real-world data and a series of R scripts included as Supplementary Digital Content; http://links.lww.com/EDE/B504, which facilitates its replication and extension on other environmental stressors, outcomes, study settings, and projection scenarios. Users should critically assess the potential modeling alternatives and modify the framework and R code to adapt them to their research on health impact projections.

Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5 °C and 2 °C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives. The risk of heat-mortality is increasing sharply. The authors report that heat-mortality levels of a 1-in-100-year summer in the climate of 2000 can be expected once every ten to twenty years in the current climate and at least once in five years with 2 °C of global warming.

New gridded climate datasets (GCDs) on spatially resolved modeled weather data have recently been released to explore the impacts of climate change. GCDs have been suggested as potential alternatives to weather station data in epidemiological assessments on health impacts of temperature and climate change. These can be particularly useful for assessment in regions that have remained understudied due to limited or low quality weather station data. However to date, no study has critically evaluated the application of GCDs of variable spatial resolution in temperature‐mortality assessments across regions of different orography, climate, and size. Here we explored the performance of population‐weighted daily mean temperature data from the global ERA5 reanalysis dataset in the 10 regions in the United Kingdom and the 26 cantons in Switzerland, combined with two local high‐resolution GCDs (HadUK‐grid UKPOC‐9 and MeteoSwiss‐grid‐product, respectively) and compared these to weather station data and unweighted homologous series. We applied quasi‐Poisson time series regression with distributed lag nonlinear models to obtain the GCD‐ and region‐specific temperature‐mortality associations and calculated the corresponding cold‐ and heat‐related excess mortality. Although the five exposure datasets yielded different average area‐level temperature estimates, these deviations did not result in substantial variations in the temperature‐mortality association or impacts. Moreover, local population‐weighted GCDs showed better overall performance, suggesting that they could be excellent alternatives to help advance knowledge on climate change impacts in remote regions with large climate and population distribution variability, which has remained largely unexplored in present literature due to the lack of reliable exposure data. Plain Language Summary Thus far, most studies attempting to study the impact of heat and cold on health have used data from weather stations around cities as a proxy for the temperature exposure of a population. Recently, new spatially resolved weather datasets have been released, which provide continuous temperature measurements at local or global scale, and can be particularly useful for supplying data in regions with limited or low quality weather station data. In this study, we aimed to explore the performance of these newly developed exposure datasets compared to weather stations in the United Kingdom and Switzerland, two regions which are heterogeneous in terms of topography and population distribution. We found that despite different temperature observations the datasets yield very similar results. In particular, high‐resolution population‐weighted temperature datasets showed better performance and thus it can be a good alternative to weather stations, especially in densely populated urban areas with large intracity temperature variability. Key Points New products on spatially resolved weather datasets have become available but little is known on their suitability in health studies Here, different exposure datasets yielded similar patterns in temperature‐mortality impacts across heterogeneous areas Globally available modeled weather data could help advance knowledge on health impacts in areas with limited weather station data

Heatwaves pose a range of severe impacts on human health, including an increase in premature mortality. The summers of 2018 and 2022 are two examples with record‐breaking temperatures leading to thousands of heat‐related excess deaths in Europe. Some of the extreme temperatures experienced during these summers were predictable several weeks in advance by subseasonal forecasts. Subseasonal forecasts provide weather predictions from 2 weeks to 2 months ahead, offering advance planning capabilities. Nevertheless, there is only limited assessment of the potential for heat‐health warning systems at a regional level on subseasonal timescales. Here we combine methods of climate epidemiology and subseasonal forecasts to retrospectively predict the 2018 and 2022 heat‐related mortality for the cantons of Zurich and Geneva in Switzerland. The temperature‐mortality association for these cantons is estimated using observed daily temperature and mortality during summers between 1990 and 2017. The temperature‐mortality association is subsequently combined with bias‐corrected subseasonal forecasts at a spatial resolution of 2‐km to predict the daily heat‐related mortality counts of 2018 and 2022. The mortality predictions are compared against the daily heat‐related mortality estimated based on observed temperature during these two summers. Heat‐related mortality peaks occurring for a few days can be accurately predicted up to 2 weeks ahead, while longer periods of heat‐related mortality lasting a few weeks can be anticipated 3 to even 4 weeks ahead. Our findings demonstrate that subseasonal forecasts are a valuable—but yet untapped—tool for potentially issuing warnings for the excess health burden observed during central European summers. Plain Language Summary Heatwaves can have serious impacts on human health, often leading to a rise in premature deaths. The summers of 2018 and 2022 in Europe were particularly extreme, with record‐high temperatures causing thousands of heat‐related deaths. Some of these extreme heat events were predictable weeks in advance by subseasonal forecasts, which provide weather predictions from 2 weeks to 2 months ahead. However, there has been limited research on how these forecasts could be used to create early warning systems for heat‐related health risks at a regional level. We explore whether subseasonal forecasts could be used to predict heat‐related deaths in two regions of Switzerland. First, we establish the relationship between daily temperatures and deaths in these regions during the summers from 1990 to 2017. We use this information to predict heat‐related deaths during the summers of 2018 and 2022, based on subseasonal forecasts. We find that it is possible to predict spikes of a few days in heat‐related deaths up to 2 weeks ahead, and longer periods of a few weeks of high heat‐related deaths up to 3 or even 4 weeks ahead. Our results show that subseasonal forecasts could be a valuable tool for issuing warnings and reducing the health impacts of future heatwaves in Europe. Key Points Heat‐related mortality peaks can be successfully predicted up to 2 weeks ahead by subseasonal forecasts Multi‐week periods of heat‐related mortality can be anticipated 3–4 weeks ahead

The record-breaking temperatures in Europe during the 2022 summer were associated with over 60,000 heat-related deaths. By combining epidemiological models with detection and attribution techniques, we attribute half of this mortality burden (~56% [95% CI 39–77%]) to anthropogenic warming. Likewise, this applies to all sexes, ages, and heat-related mortality burdens during previous years (2015–2021). Our results urgently call for increasing ambition in adaptation and mitigation.

The impact of heatwaves (HWs) on human health is a topic of growing interest due to the global magnification of these phenomena and their substantial socio‐economic impacts. As for other countries of Southern Europe, Spain is a region highly affected by heat and its increase under climate change. This is observed in the mean values and the increasing incidence of extreme weather events and associated mortality. Despite the vast knowledge on this topic, it remains unclear whether specific types and characteristics of HW are particularly harmful to the population and whether this shows a regional interdependency. The present study provides a comprehensive analysis of the relationship between HW characteristics and mortality in 12 Spanish cities. We used separated time series analysis in each city applying a quasi‐Poisson regression model and distributed lag linear and non‐linear models. Results show an increase in the mortality risk under HW conditions in the cities with a lower HW frequency. However, this increase exhibits remarkable differences across the cities under study not showing any general pattern in the HW characteristics‐mortality association. This relationship is shown to be complex and strongly dependent on the local properties of each city pointing out the crucial need to examine and understand on a local scale the HW characteristics and the HW‐mortality relationship for an efficient design and implementation of prevention measures. Plain Language Summary Heatwaves (HWs) are episodes of extreme heat sustained in time with devastating socio‐economic impacts. Due to their global magnification, the interest in their impacts on human health has increased. Spain, in Southern Europe, is a climate change hot spot, particularly in relation to increasing temperature extremes. Despite the relevance of the topic, it is still unclear if there are particular characteristics of heatwaves with a larger impact on mortality. In the present study, we analyze the relationship between heatwaves’ characteristics and mortality risk in 12 Spanish cities. Results show no general pattern for the relationship between mortality and heatwave characteristics over the 12 cities under study, but local relations point out the need for local studies to accurately assess the relationship between heatwave characteristics and mortality for an efficient implementation of prevention measures. Key Points No general patterns found describing the heatwave characteristics‐mortality association The heatwave characteristics‐mortality coupling needs a local scale analysis due to its complex and strong dependence on local properties The health indices recovery factor and excess heat factor do not always represent the heatwave‐mortality association

Psychiatric disorders constitute a major public health concern that are associated with substantial health and socioeconomic burden. Psychiatric patients may be more vulnerable to high temperatures, which under current climate change projections will most likely increase the burden of this public health concern. This study investigated the short-term association between ambient temperature and mental health hospitalizations in Bern, Switzerland. Daily hospitalizations for mental disorders between 1973 and 2017 were collected from the University Hospital of Psychiatry and Psychotherapy in Bern. Population-weighted daily mean ambient temperatures were derived for the catchment area of the hospital from 2.3-km gridded weather maps. Conditional quasi-Poisson regression with distributed lag linear models were applied to assess the association up to three days after the exposure. Stratified analyses were conducted by age, sex, and subdiagnosis, and by subperiods (1973-1989 and 1990-2017). Additional subanalyses were performed to assess whether larger risks were found during the warm season or were due to heatwaves. The study included a total number of 88,996 hospitalizations. Overall, the hospitalization risk increased linearly by 4.0% (95% CI 2.0%, 7.0%) for every 10°C increase in mean daily temperature. No evidence of a nonlinear association or larger risks during the warm season or heatwaves was found. Similar estimates were found across for all sex and age categories, and larger risks were found for hospitalizations related to developmental disorders (29.0%; 95% CI 9.0%, 54.0%), schizophrenia (10.0%; 95% CI 4.0%, 15.0%), and for the later rather than the earlier period (5.0%; 95% CI 2.0%, 8.0% vs. 2.0%; 95% CI -3.0%, 8.0%). Our findings suggest that increasing temperatures could negatively affect mental status in psychiatric patients. Specific public health policies are urgently needed to protect this vulnerable population from the effects of climate change.

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.

The risk of kidney stone presentations increases after hot days, likely due to greater insensible water losses resulting in more concentrated urine and altered urinary flow. It is thus expected that higher temperatures from climate change will increase the global prevalence of kidney stones if no adaptation measures are put in place. This study aims to quantify the impact of heat on kidney stone presentations through 2089, using South Carolina as a model state. We used a time series analysis of historical kidney stone presentations (1997–2014) and distributed lag non-linear models to estimate the temperature dependence of kidney stone presentations, and then quantified the projected impact of climate change on future heat-related kidney stone presentations using daily projections of wet-bulb temperatures to 2089, assuming no adaptation or demographic changes. Two climate change models were considered—one assuming aggressive reduction in greenhouse gas emissions (RCP 4.5) and one representing uninibited greenhouse gas emissions (RCP 8.5). The estimated total statewide kidney stone presentations attributable to heat are projected to increase by 2.2% in RCP 4.5 and 3.9% in RCP 8.5 by 2085–89 (vs. 2010–2014), with an associated total excess cost of ~ $57 million and ~ $99 million, respectively.

Heat is among the most hazardous environmental factors for human health, but humidity’s role in heat-related health effects remains unclear. This study will assess the effect of humid heat and other environmental conditions on health in a representative population in Basse Santa Su, The Gambia, a region at high risk of humid heat exposure. We will examine the association between humid heat exposure and physiological heat strain, identify vulnerable sub-groups, and evaluate adaptive behaviours. We will recruit 60–90 healthy adults from Basse Santa Su and surrounding areas. Participants will be monitored for four non-consecutive weeks across dry (November–May) and rainy (June–October) seasons. Daily questionnaires will assess activities, thermal comfort, adaptation behaviours, heat strain symptoms, mood, and sleep quality. Wearables will collect time-resolved personal and indoor exposure (temperature and humidity), heat strain, and further physiological covariates. A fixed monitoring network will measure outdoor air temperature, humidity, air quality, and environmental noise. Descriptive analyses will assess baseline characteristics, heat stress and heat strain. Case-time series analysis with distributed non-linear lagged models will estimate immediate and delayed associations between exposure to humid heat and physiological heat strain. Stratified analyses by individual characteristics will explore possible vulnerability groups. Multiple exposure models and interaction terms will explore cumulative effects of multiple environmental factors. Multilinear land use regression modelling will develop high-resolution maps of temperature, humidity, and heat stress. This study will provide new insights into humid heat’s effect on health, particularly in low-income, high-exposure settings. This study addresses limitations in prior epidemiological research on heat, humidity, and health, including lack of high-resolution and individual-level data, and limited focus on humidity as a heat-health driver, on non-mortality outcomes and on climate-vulnerable populations. This study combines high-resolution microclimate mapping and individual-level measurements which may inform future epidemiological studies and heat-health interventions.