Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

Modelling Europe-wide fine resolution daily ambient temperature for 2003-2020 using machine learning

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003-2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

Projection of Mortality Burden Attributable to Nonoptimum Temperature with High Spatial Resolution in China

The updated climate models provide projections at a fine scale, allowing us to estimate health risks due to future warming after accounting for spatial heterogeneity. Here, we utilized an ensemble of high-resolution (25 km) climate simulations and nationwide mortality data from 306 Chinese cities to estimate death anomalies attributable to future warming. Historical estimation (1986-2014) reveals that about 15.5% [95% empirical confidence interval (eCI):13.1%, 17.6%] of deaths are attributable to nonoptimal temperature, of which heat and cold corresponded to attributable fractions of 4.1% (eCI:2.4%, 5.5%) and 11.4% (eCI:10.7%, 12.1%), respectively. Under three climate scenarios (SSP126, SSP245, and SSP585), the national average temperature was projected to increase by 1.45, 2.57, and 4.98 °C by the 2090s, respectively. The corresponding mortality fractions attributable to heat would be 6.5% (eCI:5.2%, 7.7%), 7.9% (eCI:6.3%, 9.4%), and 11.4% (eCI:9.2%, 13.3%). More than half of the attributable deaths due to future warming would occur in north China and cardiovascular mortality would increase more drastically than respiratory mortality. Our study shows that the increased heat-attributable mortality burden would outweigh the decreased cold-attributable burden even under a moderate climate change scenario across China. The results are helpful for national or local policymakers to better address the challenges of future warming.

Effects of temperature anomaly on sperm quality: A multi-center study of 33,234 men

Backgrounds

Global fertility rates continue to decline and sperm quality is a prime factor affecting male fertility. Both extreme cold and heat have been demonstrated to be associated with decreased sperm quality, but no epidemiological studies have considered human adaptation to long-term temperature. Our aim was to conduct a multi-center retrospective cohort study to investigate exposure-response relationship between temperature anomaly (TA) that deviate from long-term climate patterns and sperm quality.

Methods

A total of 78,952 semen samples measured in 33,234 donors from 6 provincial human sperm banks in China were collected. This study considered heat and cold acclimatization to prolonged exposure in humans and explored the exposure-response relationship between TAs and sperm quality parameters (sperm concentrations, sperm count, progressive motility, progressive sperm count, total motility and total motile sperm count) during the hot and cold seasons, respectively. Linear mixed models and generalized linear models were built separately for specific centers to pool in a meta-analysis to obtain the pooled effect of TA on sperm quality, considering repeated measurements data structure and spatial heterogeneity.

Results

We identified an inverted U-shaped exposure-response relationship between TA and sperm quality during the hot season. Significant negative effect of anomalous cold on sperm quality during the hot season was found after additional adjustment for Body mass index, marital status and childbearing history. The heat-related TA in hot season was significantly negatively associated with sperm concentration, progressive sperm count and total motile sperm count (all P-values<0.05). After adjusting the relative humidity, the cold-related TA in cold season was negatively associated with the sperm total motility (P-values<0.05).

Conclusions

Our results suggest both heat-related and cold-related TAs are associated with decreased sperm quality. The findings highlight the importance of reducing exposure to anomalous temperatures to protect male fertility.

Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece

Existing assessments of the thermal-related impact of the environment on humans are often limited by the use of data that are not representative of the population exposure and/or not consider a human centred approach. Here, we combine high resolution regional retrospective analysis (reanalysis), population data and human energy balance modelling, in order to produce a human thermal bioclimate dataset capable of addressing the above limitations. The dataset consists of hourly, population-weighted values of an advanced human-biometeorological index, namely the modified physiologically equivalent temperature (mPET), at fine-scale administrative level and for 10 different population groups. It also includes the main environmental drivers of mPET at the same spatiotemporal resolution, covering the period from 1991 to 2020. The study area is Greece, but the provided code allows for the ease replication of the dataset in countries included in the domains of the climate reanalysis and population data, which focus over Europe. Thus, the presented data and code can be exploited for human-biometeorological and environmental epidemiological studies in the European continent.

Sensing the impact of extreme heat on physical activity and sleep

Introduction

This study assesses the person-specific impact of extreme heat on low-income households using wearable sensors. The focus is on the intensive and longitudinal assessment of physical activity and sleep with the rising person-specific ambient temperature.

Methods

This study recruited 30 participants in a low-income and predominantly Black community in Houston, Texas in August and September of 2022. Each participant wore on his/her wrist an accelerometer that recorded person-specific ambient temperature, sedentary behavior, physical activity intensity (low and moderate to vigorous), and sleep efficiency 24 h over 14 days. Mixed effects models were used to analyze associations among physical activity, sleep, and person-specific ambient temperature.

Results

The main findings include increased sedentary time, sleep impairment with the rise of person-level ambient temperature, and the mitigating role of AC.

Conclusions

Extreme heat negatively affects physical activity and sleep. The negative consequences are especially critical for those with limited use of AC in lower-income neighborhoods of color. Staying home with a high indoor temperature during hot days can lead to various adverse health outcomes including accelerated cognitive decline, higher cancer risk, and social isolation.

The influence of humid heat on morbidity of megacity Shanghai in China

BACKGROUND

Increased attention has been paid to humid-heat extremes as they are projected to increase in both frequency and intensity. However, it remains unclear how compound extremes of heat and humidity affects morbidity when the climate is projected to continue warming in the future, in particular for a megacity with a large population.

METHODS

We chose the Wet-Bulb Globe Temperature (WBGT) index as the metric to characterize the humid-heat exposure. The historical associations between daily outpatient visits and daily mean WBGT was established using a Distributed Lag Non-linear Model (DLNM) during the warm season (June to September) from 2013 to 2015 in Shanghai, a prominent megacity of China. Future morbidity burden related to the combined effect of high temperature and humidity were projected under four greenhouse gases (GHGs) emission scenarios (SSP126, SSP245, SSP370 and SSP585).

RESULTS

The humid-heat weather was significantly associated with a higher risk of outpatient visits in Shanghai than the high-temperature conditions. Relative to the baseline period (2010-2019), the morbidity burden due to humid-heat weather was projected to increase 4.4 % (95 % confidence interval (CI): 1.1 %-10.1 %) even under the strict emission control scenario (SSP126) by 2100. Under the high-GHGs emission scenario (SSP585), this burden was projected to be 25.4 % (95 % CI: 15.8 %-38.4 %), which is 10.1 % (95 % CI: 6.5 %-15.8 %) more than that due to high-temperature weather. Our results also indicate that humid-hot nights could cause large morbidity risks under high-GHGs emission scenarios particularly in heat-sensible diseases such as the respiratory and cardiovascular disease by the end of this century.

CONCLUSIONS

Humid heat exposures significantly increased the all-cause morbidity risk in the megacity Shanghai, especially in humid-hot nights. Our findings suggest that the combined effect of elevated temperature and humidity is projected to have more substantial impact on health compared to high temperature alone in a warming climate.

Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam

Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue's distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue's expansion throughout Vietnam.

Socio-economic factors and its influence on the association between temperature and dengue incidence in 61 Provinces of the Philippines, 2010-2019

BACKGROUND

Temperature has a significant impact on dengue incidence, however, changes on the temperature-dengue relationship across axes of socio-economic vulnerability is not well described. This study sought to determine the association between dengue and temperature in multiple locations in the Philippines and explore the effect modification by socio-economic factors.

METHOD

Nationwide dengue cases per province from 2010 to 2019 and data on temperature were obtained from the Philippines' Department of Health-Epidemiological Bureau and ERA5-land, respectively. A generalized additive mixed model (GAMM) with a distributed lag non-linear model was utilized to examine the association between temperature and dengue incidence. We further implemented an interaction analysis in determining how socio-economic factors modify the association. All analyses were implemented using R programming.

RESULTS

Nationwide temperature-dengue risk function was noted to depict an inverted U-shaped pattern. Dengue risk increased linearly alongside increasing mean temperature from 15.8 degrees Celsius and peaking at 27.5 degrees Celsius before declining. However, province-specific analyses revealed significant heterogeneity. Socio-economic factors had varying impact on the temperature-dengue association. Provinces with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes were noted to exhibit statistically higher dengue risk compared to their counterparts at the upper temperature range.

CONCLUSIONS

This observational study found that temperature was associated with dengue incidence, and that this association is more apparent in locations with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes. Differences with socio-economic conditions is linked with dengue risk. This highlights the need to develop interventions tailor-fit to local conditions.

Exposure to Fine Particulate Matter Constituents and Human Semen Quality Decline: A Multicenter Study

Exposure to fine particulate matter (PM < 2.5 μm in diameter [PM2.5]) may accelerate human sperm quality decline, although research on this association is limited. Our objective was to investigate the relationship between exposure to the chemical constituents of PM2.5 air pollution and decreased sperm quality and to further explore the exposure-response relationship. We conducted a multicenter population-based cohort study including 78,952 semen samples from 33,234 donors at 6 provincial human sperm banks (covering central, northern, southern, eastern, and southwestern parts of China) between 2014 and 2020. Daily exposure to PM2.5 chemical composition was estimated using a deep learning model integrating a density ground-based measure network at a 1 km resolution. Linear mixed models with subject- and center-specific intercepts were used to quantify the harmful impacts of PM2.5 constituents on semen quality and explore their exposure-response relationships. Per interquartile range (IQR) increase in PM2.5 exposure levels during spermatogenesis was significantly associated with decreased sperm concentration, progressive motility, and total motility. For PM2.5 constituents, per IQR increment in Cl- (β: -0.02, 95% CI: [-0.03, -0.00]) and NO3- (β: -0.05, 95% CI: [-0.08, -0.02]) exposure was negatively associated with sperm count, while NH4+ (β: -0.03, 95% CI: [-0.06, -0.00]) was significantly linked to decreased progressive motility. These results suggest that exposure to PM2.5 chemical constituents may adversely affect human sperm quality, highlighting the urgent need to reduce PM2.5 exposure.

Association of Ambient Temperature With the Prevalence of Intimate Partner Violence Among Partnered Women in Low- and Middle-Income South Asian Countries

Importance

Intimate partner violence (IPV), including physical, sexual, and emotional violence, constitutes a critical public health problem, particularly in low- and middle-income countries. While climate change could escalate violent events, data quantifying its possible association with IPV are scant.

Objective

To evaluate the association of ambient temperature with the prevalence of IPV among partnered women in low- and middle-income countries in South Asia, and to estimate the association of future climate warming with IPV.

Design, Setting, and Participants

This cross-sectional study used data from the Demographic and Health Survey and included 194 871 ever-partnered women aged 15 to 49 years from 3 South Asian countries (India, Nepal, and Pakistan). The study applied the mixed-effect multivariable logistic regression model to investigate the association of ambient temperature with IPV prevalence. The study further modeled the change in IPV prevalence under various future climate change scenarios. The data included in the analyses were collected from October 1, 2010, to April 30, 2018, and the current analyses were performed from January 2, 2022, to July 11, 2022.

Exposure

Annual ambient temperature exposure for each woman, estimated based on an atmospheric reanalysis model of the global climate.

Main Outcomes and Measures

The prevalence of IPV and its types (physical, sexual, and emotional violence) were assessed based on self-reported questionnaires from October 1, 2010, to April 30, 2018, and the changes in the prevalence with climate changes were estimated through the 2090s.

Results

The study included 194 871 ever-partnered women aged 15 to 49 years (mean [SD] age, 35.4 [7.6] years; overall IPV prevalence, 27.0%) from 3 South Asian countries. The prevalence of physical violence was highest (23.0%), followed by emotional (12.5%), and sexual violence (9.5%). The annual temperature ranges were mostly between 20 °C and 30 °C. A significant association was found between high ambient temperature and the prevalence of IPV against women, with each 1 °C increase in the annual mean temperature associated with a mean increase in IPV prevalence of 4.49% (95% CI, 4.20%-4.78%). According to the study's projections under the unlimited emissions scenarios (SSPs [shared socioeconomic pathways], as defined by the Intergovernmental Panel on Climate Change] 5-8.5), IPV prevalence would increase by 21.0% by the end of the 21st century, while it would only moderately increase under increasingly stricter scenarios (SSP2-4.5 [9.8%] and SSP1-2.6 [5.8%]). In addition, the projected increases in the prevalence of physical (28.3%) and sexual (26.1%) violence were greater than that of emotional violence (8.9%). In the 2090s, India was estimated to experience the highest IPV prevalence increase (23.5%) among the 3 countries, compared with Nepal (14.8%) and Pakistan (5.9%).

Conclusions and Relevance

This cross-sectional, multicountry study provides ample epidemiological evidence to support that high ambient temperature may be associated with the risk of IPV against women. These findings highlight the vulnerabilities and inequalities of women experiencing IPV in low- and middle-income countries in the context of global climate warming.

Urban heat island impacts on heat-related cardiovascular morbidity: A time series analysis of older adults in US metropolitan areas

Many United States (US) cities are experiencing urban heat islands (UHIs) and climate change-driven temperature increases. Extreme heat increases cardiovascular disease (CVD) risk, yet little is known about how this association varies with UHI intensity (UHII) within and between cities. We aimed to identify the urban populations most at-risk of and burdened by heat-related CVD morbidity in UHI-affected areas compared to unaffected areas. ZIP code-level daily counts of CVD hospitalizations among Medicare enrollees, aged 65-114, were obtained for 120 US metropolitan statistical areas (MSAs) between 2000 and 2017. Mean ambient temperature exposure was estimated by interpolating daily weather station observations. ZIP codes were classified as low and high UHII using the first and fourth quartiles of an existing surface UHII metric, weighted to each have 25% of all CVD hospitalizations. MSA-specific associations between ambient temperature and CVD hospitalization were estimated using quasi-Poisson regression with distributed lag non-linear models and pooled via multivariate meta-analyses. Across the US, extreme heat (MSA-specific 99th percentile, on average 28.6 °C) increased the risk of CVD hospitalization by 1.5% (95% CI: 0.4%, 2.6%), with considerable variation among MSAs. Extreme heat-related CVD hospitalization risk in high UHII areas (2.4% [95% CI: 0.4%, 4.3%]) exceeded that in low UHII areas (1.0% [95% CI: -0.8%, 2.8%]), with upwards of a 10% difference in some MSAs. During the 18-year study period, there were an estimated 37,028 (95% CI: 35,741, 37,988) heat-attributable CVD admissions. High UHII areas accounted for 35% of the total heat-related CVD burden, while low UHII areas accounted for 4%. High UHII disproportionately impacted already heat-vulnerable populations; females, individuals aged 75-114, and those with chronic conditions living in high UHII areas experienced the largest heat-related CVD impacts. Overall, extreme heat increased cardiovascular morbidity risk and burden in older urban populations, with UHIs exacerbating these impacts among those with existing vulnerabilities.

Higher Temperatures in Socially Vulnerable US Communities Increasingly Limit Safe Use of Electric Fans for Cooling

As the globe warms, people will increasingly need affordable, safe methods to stay cool and minimize the worst health impacts of heat exposure. One of the cheapest cooling methods is electric fans. Recent research has recommended ambient air temperature thresholds for safe fan use in adults. Here we use hourly weather reanalysis data (1950-2021) to examine the temporal and spatial evolution of ambient climate conditions in the continental United States (CONUS) considered safe for fan use, focusing on high social vulnerability index (SVI) regions. We find that although most hours in the day are safe for fan use, there are regions that experience hundreds to thousands of hours per year that are too hot for safe fan use. Over the last several decades, the number of hours considered unsafe for fan use has increased across most of the CONUS (on average by ∼70%), with hotspots across the US West and South, suggesting that many individuals will increasingly need alternative cooling strategies. People living in high-SVI locations are 1.5-2 times more likely to experience hotter climate conditions than the overall US population. High-SVI locations also experience higher rates of warming that are approaching and exceeding important safety thresholds that relate to climate adaptation. These results highlight the need to direct additional resources to these communities for heat adaptive strategies.

Optimal heat stress metric for modelling heat-related mortality varies from country to country

Combined heat and humidity is frequently described as the main driver of human heat-related mortality, more so than dry-bulb temperature alone. While based on physiological thinking, this assumption has not been robustly supported by epidemiological evidence. By performing the first systematic comparison of eight heat stress metrics (i.e., temperature combined with humidity and other climate variables) with warm-season mortality, in 604 locations over 39 countries, we find that the optimal metric for modelling mortality varies from country to country. Temperature metrics with no or little humidity modification associates best with mortality in ~40% of the studied countries. Apparent temperature (combined temperature, humidity and wind speed) dominates in another 40% of countries. There is no obvious climate grouping in these results. We recommend, where possible, that researchers use the optimal metric for each country. However, dry-bulb temperature performs similarly to humidity-based heat stress metrics in estimating heat-related mortality in present-day climate.

Heat-mortality relationship in North Carolina: Comparison using different exposure methods

BACKGROUND

Many studies have explored the heat-mortality relationship; however, comparability of results is hindered by the studies' use of different exposure methods.

OBJECTIVE

This study evaluated different methods for estimating exposure to temperature using individual-level data and examined the impacts on the heat-mortality relationship.

METHODS

We calculated different temperature exposures for each individual death by using a modeled, gridded temperature dataset and a monitoring station dataset in North Carolina for 2000-2016. We considered individual-level vs. county-level averages and measured vs. modeled temperature data. A case-crossover analysis was conducted to examine the heat-mortality risk under different exposure methods.

RESULTS

The minimum mortality temperature (MMT) (i.e., the temperature with the lowest mortality rate) for the monitoring station dataset was 23.87 °C and 22.67 °C (individual monitor and county average, respectively), whereas for the modeled temperature dataset the MMT was 19.46 °C and 19.61 °C (individual and county, respectively). We found higher heat-mortality risk while using temperature exposure estimated from monitoring stations compared to risk based on exposure using the modeled temperature dataset. Individual-aggregated monitoring station temperature exposure resulted in higher heat mortality risk (odds ratio (95% CI): 2.24 (95% CI: 2.21, 2.27)) for a relative temperature change comparing the 99th and 90th temperature percentiles, while modeled temperature exposure resulted in lower odds ratio of 1.27 (95% CI: 1.25, 1.29).

SIGNIFICANCE

Our findings indicate that using different temperature exposure methods can result in different temperature-mortality risk. The impact of using various exposure methods should be considered in planning health policies related to high temperatures, including under climate change. IMPACT STATEMENT: (1) We estimated the heat-mortality association using different methods to estimate exposure to temperature. (2) The mean temperature value among different exposure methods were similar although lower for the modeled data, however, use of the monitoring station temperature dataset resulted in higher heat-mortality risk than the modeled temperature dataset. (3) Differences in mortality risk from heat by urbanicity varies depending on the method used to estimate temperature exposure.

Exploring vulnerability to heat and cold across urban and rural populations in Switzerland

Heat- and cold-related mortality risks are highly variable across different geographies, suggesting a differential distribution of vulnerability factors between and within countries, which could partly be driven by urban-to-rural disparities. Identifying these drivers of risk is crucial to characterize local vulnerability and design tailored public health interventions to improve adaptation of populations to climate change. We aimed to assess how heat- and cold-mortality risks change across urban, peri-urban and rural areas in Switzerland and to identify and compare the factors associated with increased vulnerability within and between different area typologies. We estimated the heat- and cold-related mortality association using the case time-series design and distributed lag non-linear models over daily mean temperature and all-cause mortality series between 1990-2017 in each municipality in Switzerland. Then, through multivariate meta-regression, we derived pooled heat and cold-mortality associations by typology (i.e. urban/rural/peri-urban) and assessed potential vulnerability factors among a wealth of demographic, socioeconomic, topographic, climatic, land use and other environmental data. Urban clusters reported larger pooled heat-related mortality risk (at 99th percentile, vs. temperature of minimum mortality (MMT)) (relative risk=1.17(95%CI:1.10;1.24, vs peri-urban 1.03(1.00;1.06), and rural 1.03 (0.99;1.08)), but similar cold-mortality risk (at 1st percentile, vs. MMT) (1.35(1.28;1.43), vs rural 1.28(1.14;1.44) and peri-urban 1.39 (1.27-1.53)) clusters. We found different sets of vulnerability factors explaining the differential risk patterns across typologies. In urban clusters, mainly environmental factors (i.e. PM2.5) drove differences in heat-mortality association, while for peri-urban/rural clusters socio-economic variables were also important. For cold, socio-economic variables drove changes in vulnerability across all typologies, while environmental factors and ageing were other important drivers of larger vulnerability in peri-urban/rural clusters, with heterogeneity in the direction of the association. Our findings suggest that urban populations in Switzerland may be more vulnerable to heat, compared to rural locations, and different sets of vulnerability factors may drive these associations in each typology. Thus, future public health adaptation strategies should consider local and more tailored interventions rather than a one-size fits all approach. size fits all approach.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Heat waves and mortality in the Brazilian Amazon: Effect modification by heat wave characteristics, population subgroup, and cause of death

BACKGROUND

The Brazilian Amazon faces overlapping socio-environmental, sanitary, and climate challenges, and is a hotspot of concern due to projected increases in temperature and in the frequency of heat waves. Understanding the effects of extreme events on health is a central issue for developing climate policies focused on the population's health.

OBJECTIVES

We investigated the effects of heat waves on mortality in the Brazilian Amazon, examining effect modification according to various heat wave definitions, population subgroups, and causes of death.

METHODS

We included all 32 Amazonian municipalities with more than 100,000 inhabitants. The study period was from 2000 to 2018. We obtained mortality data from the Information Technology Department of the Brazilian Public Healthcare System, and meteorological data were derived from the ERA5-Land reanalysis dataset. Heat waves were defined according to their intensity (90th; 92.5th; 95th; 97.5th and 99th temperature percentiles) and duration (≥2, ≥3, and ≥4 days). In each city, we used a time-stratified case-crossover study to estimate the effects of each heat wave definition on mortality, according to population subgroup and cause of death. The lagged effects of heat waves were estimated using conditional Poisson regression combined with distributed lag non-linear models. Models were adjusted for specific humidity and public holidays. Risk ratios were pooled for the Brazilian Amazon using a univariate random-effects meta-analysis.

RESULTS

The pooled relative risks (RR) for mortality from total non-external causes varied between 1.03 (95% CI: 1.01-1.06), for the less stringent heat wave definition, and 1.18 (95% CI: 1.04-1.33) for the more stringent definition. The mortality risk rose as the heat wave intensity increased, although the increase from 2 to 3, and 3-4 days was small. Although not statistically different, our results suggest a higher mortality risk for the elderly, this was also higher for women than men, and for cardiovascular causes than for non-external or respiratory ones.

CONCLUSIONS

Heat waves were associated with a higher risk of mortality from non-external causes and cardiovascular diseases. Heat wave intensity played a more important role than duration in determining this risk. Suggestive evidence indicated that the elderly and women were more vulnerable to the effects of heat waves on mortality.

Associations Between Extreme Temperatures and Cardiovascular Cause-Specific Mortality: Results From 27 Countries

BACKGROUND

Cardiovascular disease is the leading cause of death worldwide. Existing studies on the association between temperatures and cardiovascular deaths have been limited in geographic zones and have generally considered associations with total cardiovascular deaths rather than cause-specific cardiovascular deaths.

METHODS

We used unified data collection protocols within the Multi-Country Multi-City Collaborative Network to assemble a database of daily counts of specific cardiovascular causes of death from 567 cities in 27 countries across 5 continents in overlapping periods ranging from 1979 to 2019. City-specific daily ambient temperatures were obtained from weather stations and climate reanalysis models. To investigate cardiovascular mortality associations with extreme hot and cold temperatures, we fit case-crossover models in each city and then used a mixed-effects meta-analytic framework to pool individual city estimates. Extreme temperature percentiles were compared with the minimum mortality temperature in each location. Excess deaths were calculated for a range of extreme temperature days.

RESULTS

The analyses included deaths from any cardiovascular cause (32 154  935), ischemic heart disease (11 745 880), stroke (9 351 312), heart failure (3 673 723), and arrhythmia (670 859). At extreme temperature percentiles, heat (99th percentile) and cold (1st percentile) were associated with higher risk of dying from any cardiovascular cause, ischemic heart disease, stroke, and heart failure as compared to the minimum mortality temperature, which is the temperature associated with least mortality. Across a range of extreme temperatures, hot days (above 97.5th percentile) and cold days (below 2.5th percentile) accounted for 2.2 (95% empirical CI [eCI], 2.1-2.3) and 9.1 (95% eCI, 8.9-9.2) excess deaths for every 1000 cardiovascular deaths, respectively. Heart failure was associated with the highest excess deaths proportion from extreme hot and cold days with 2.6 (95% eCI, 2.4-2.8) and 12.8 (95% eCI, 12.2-13.1) for every 1000 heart failure deaths, respectively.

CONCLUSIONS

Across a large, multinational sample, exposure to extreme hot and cold temperatures was associated with a greater risk of mortality from multiple common cardiovascular conditions. The intersections between extreme temperatures and cardiovascular health need to be thoroughly characterized in the present day-and especially under a changing climate.

Baseline scenarios of heat-related ambulance transportations under climate change in Tokyo, Japan

Background

Predictive scenarios of heatstroke over the long-term future have yet to be formulated. The purpose of the present study was to generate baseline scenarios of heat-related ambulance transportations using climate change scenario datasets in Tokyo, Japan.

Methods

Data on the number of heat-related ambulance transportations in Tokyo from 2015 to 2019 were examined, and the relationship between the risk of heat-related ambulance transportations and the daily maximum wet-bulb globe temperature (WBGT) was modeled using three simple dose-response models. To quantify the risk of heatstroke, future climatological variables were then retrieved to compute the WBGT up to the year 2100 from climate change scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) using two scenario models. The predicted risk of heat-related ambulance transportations was embedded onto the future age-specific projected population.

Results

The proportion of the number of days with a WBGT above 28°C is predicted to increase every five years by 0.16% for RCP2.6, 0.31% for RCP4.5, and 0.68% for RCP8.5. In 2100, compared with 2000, the number of heat-related ambulance transportations is predicted to be more than three times greater among people aged 0-64 years and six times greater among people aged 65 years or older. The variance of the heatstroke risk becomes greater as the WBGT increases.

Conclusions

The increased risk of heatstroke for the long-term future was demonstrated using a simple statistical approach. Even with the RCP2.6 scenario, with the mildest impact of global warming, the risk of heatstroke is expected to increase. The future course of heatstroke predicted by our approach acts as a baseline for future studies.

Effects of rainfall on human leptospirosis in Thailand: evidence of multi-province study using distributed lag non-linear model

Leptospirosis is a zoonotic bacterial disease that remains an important public health problem, especially in tropical developing countries. Many previous studies in Thailand have revealed the outbreak of human leptospirosis after heavy rainfall, but research determining its quantitative risks associated with rainfall, especially at the national level, remains limited. This study aims to examine the association between rainfall and human leptospirosis across 60 provinces of Thailand. A quasi-Poisson regression framework combined with the distributed lag non-linear model was used to estimate province-specific association between rainfall and human leptospirosis, adjusting for potential confounders. Province-specific estimates were then pooled to derive regional and national estimates using random-effect meta-analysis. The highest risk of leptospirosis associated with rainfall at national level was observed at the same month (lag 0). Using 0 cm/month of rainfall as a reference, the relative risks of leptospirosis associated with heavy (90th percentile), very heavy (95th percentile), and extremely heavy (99th percentile) rainfall at the national level were 1.0994 (95% CI 0.9747, 1.2401), 1.1428 (95% CI 1.0154, 1.2862), and 1.1848 (95% CI 1.0494, 1.3378), respectively. The highest risk of human leptospirosis associated with rainfall was observed in the northern and north-eastern regions. Specifically, the relative risks of leptospirosis associated with extremely heavy rainfall in northern and north-eastern regions were 1.2362 (95% CI 0.9110, 1.6775) and 1.2046 (95% CI 0.9728, 1.4918), respectively. Increasing rainfall was associated with increased risks of leptospirosis, especially in the northern and northeastern regions of Thailand. This finding could be used for precautionary warnings against heavy rainfall.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02250-x.