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Global warming is leading to increased heat stress in many regions around the world. An extensive number of heat stress indicators (HSIs) has been developed to measure the associated impacts on human health. Here we calculate eight HSIs for global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We compare their future trends as function of global mean temperature, with particular focus on highly populated regions. All analyzed HSIs increase significantly (p < 0.01) in all considered regions. Moreover, the different HSIs reveal a substantial spread ranging from trends close to the rate of global mean temperature up to an amplification of more than a factor of two. Trends change considerably when normalizing the HSIs by accounting for the different scales on which they are defined, but the large spread and strong trends remain. Consistently, exceedances of impact-relevant thresholds are strongly increasing globally, including in several densely populated regions, but also show substantial spread across the selected HSIs. The indicators with the highest exceedance rates vary for different threshold levels, suggesting that the large indicator spread is associated both to differences in trend magnitude and the definition of threshold levels. These results highlight the importance of choosing indicators and thresholds that are appropriate for the respective impact under consideration. Additionally, further validation of HSIs regarding their capability to quantify heat impacts on human health on regional-to-global scales would be of great value for assessing global impacts of future heat stress more reliably.

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.

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.

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.

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.

Heatwaves are a critical public health problem. There will be an increase in the frequency and severity of heatwaves under changing climate. However, evidence about the impacts of climate change on heatwave-related mortality at a global scale is limited. We collected historical daily time series of mean temperature and mortality for all causes or nonexternal causes, in periods ranging from January 1, 1984, to December 31, 2015, in 412 communities within 20 countries/regions. We estimated heatwave-mortality associations through a two-stage time series design. Current and future daily mean temperature series were projected under four scenarios of greenhouse gas emissions from 1971-2099, with five general circulation models. We projected excess mortality in relation to heatwaves in the future under each scenario of greenhouse gas emissions, with two assumptions for adaptation (no adaptation and hypothetical adaptation) and three scenarios of population change (high variant, median variant, and low variant). Results show that, if there is no adaptation, heatwave-related excess mortality is expected to increase the most in tropical and subtropical countries/regions (close to the equator), while European countries and the United States will have smaller percent increases in heatwave-related excess mortality. The higher the population variant and the greenhouse gas emissions, the higher the increase of heatwave-related excess mortality in the future. The changes in 2031-2080 compared with 1971-2020 range from approximately 2,000% in Colombia to 150% in Moldova under the highest emission scenario and high-variant population scenario, without any adaptation. If we considered hypothetical adaptation to future climate, under high-variant population scenario and all scenarios of greenhouse gas emissions, the heatwave-related excess mortality is expected to still increase across all the countries/regions except Moldova and Japan. However, the increase would be much smaller than the no adaptation scenario. The simple assumptions with respect to adaptation as follows: no adaptation and hypothetical adaptation results in some uncertainties of projections. This study provides a comprehensive characterisation of future heatwave-related excess mortality across various regions and under alternative scenarios of greenhouse gas emissions, different assumptions of adaptation, and different scenarios of population change. The projections can help decision makers in planning adaptation and mitigation strategies for climate change.

The regional disparity of heatwave-related mortality over a long period has not been sufficiently assessed across the globe, impeding the localisation of adaptation planning and risk management towards climate change. We quantified the global mortality burden associated with heatwaves at a spatial resolution of 0.5°×0.5° and the temporal change from 1990 to 2019. We collected data on daily deaths and temperature from 750 locations of 43 countries or regions, and 5 meta-predictors in 0.5°×0.5° resolution across the world. Heatwaves were defined as location-specific daily mean temperature ≥95th percentiles of year-round temperature range with duration ≥2 days. We first estimated the location-specific heatwave-mortality association. Secondly, a multivariate meta-regression was fitted between location-specific associations and 5 meta-predictors, which was in the third stage used with grid cell-specific meta-predictors to predict grid cell-specific association. Heatwave-related excess deaths were calculated for each grid and aggregated. During 1990 to 2019, 0.94% (95% CI: 0.68-1.19) of deaths [i.e., 153,078 cases (95% eCI: 109,950-194,227)] per warm season were estimated to be from heatwaves, accounting for 236 (95% eCI: 170-300) deaths per 10 million residents. The ratio between heatwave-related excess deaths and all premature deaths per warm season remained relatively unchanged over the 30 years, while the number of heatwave-related excess deaths per 10 million residents per warm season declined by 7.2% per decade in comparison to the 30-year average. Locations with the highest heatwave-related death ratio and rate were in Southern and Eastern Europe or areas had polar and alpine climates, and/or their residents had high incomes. The temporal change of heatwave-related mortality burden showed geographic disparities, such that locations with tropical climate or low incomes were observed with the greatest decline. The main limitation of this study was the lack of data from certain regions, e.g., Arabian Peninsula and South Asia. Heatwaves were associated with substantial mortality burden that varied spatiotemporally over the globe in the past 30 years. The findings indicate the potential benefit of governmental actions to enhance health sector adaptation and resilience, accounting for inequalities across communities.

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.

While several studies proved the relationship between increasing temperatures and poor mental health, limited evidence exists on the effect of other weather factors, such as precipitation. This study assessed the impact of precipitation on hospital admissions for mental disorders in Switzerland between 2009–2019. We defined different precipitation events based on the duration (daily precipitation ≥1mm for 2, 3, or 4 days; PP.2/PP.3/PP.4) and intensity (≥90th percentile for 2 consecutive days; PEP90.2). First, we conducted aggregated time-stratified case-crossover analysis in eight main Swiss cities with distributed lag models to assess the association up to 3 days after the exposure. Then, we pooled the estimates in each city using a multivariate random effects meta-analysis for all hospital admissions and by subgroups (sex, age, diagnosis). Evidence of an association between precipitation and hospital admission for mental disorders was not found in Switzerland (PP.2: 1.003[0.978–1.029]; PP.3: 1.005[0.985–1.026]; PP.4: 0.994[0.960–1.030]; PEP90.2: 1.000[0.953–1.050]). Although the results were highly uncertain, we found an indication of increasing risks of hospital admission with increasing intensity of precipitation in warmer seasons (PP.2: 1.001[0.971–1.032] vs PEP90.2: 1.014[0.955–1.078]), while the risks of hospital admission slightly increased by the duration in colder season (PP.2: 1.009[0.981–1.039]; PP.3: 1.008[0.980–1.036]; PP.4: 1.017[0.956–1.081]). Overall, risks tend to be higher in people aged < 65 years. Duration of the events may influence more than intensity in females, while opposite patterns were observed in males. Risks tended to be larger but still uncertain for schizophrenia, mood disorders, and adult personality disorders. An indication of a negative association was found in neurotic disorders and null risks in the remaining groups. Although our findings did not show a clear association between precipitation and mental disorders, further research is required to clarify the role of precipitation and the potential implications of climate change and extreme precipitation events on mental health.