Projections of excess mortality related to diurnal temperature range under climate change scenarios: a multi-country modelling study

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.

Observed and future shifts in climate zone of Borneo based on CMIP6 models

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950-2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172-2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8-94.6%), MSE (57.0-99.5%), NRMSE (42.7-92.1%), PBIAS (100-108%), MD (23.0-85.3%), KGE (21.1-78.1%), and VE (5.1-9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

Shared insights for heat health risk adaptation in metropolitan areas of developing countries

Global warming has led to a surge in heat health risks (HHRs), the impacts of which are particularly pronounced in metropolitan areas of developing countries. In the current study, six metropolitan areas - Beijing, China; Cairo, Egypt; Jakarta, Indonesia; Mumbai, India; Rio de Janeiro, Brazil; and Tehran, Iran - were selected as the study area to further differentiate the built-up landscapes by utilizing the concept of local climate zones. Moreover, we assessed the similarities and differences in HHR associated with the landscape. Results revealed a 30.67% higher HHR in compact built-up landscapes than in the open built-up type. Urban green spaces played an effective but differentiated role in mitigating HHR. That is, low vegetation in urbanized areas and trees in suburban areas significantly mitigated HHR. Collectively, our findings emphasize the role of effective planning and management in addressing HHR and provide empirical support for implementing HHR mitigation and adaptation strategies.

Racial and ethnic minorities disproportionately exposed to extreme daily temperature variation in the United States

In the history of Homo sapiens, well-populated habitats have featured relatively stable temperatures with generally small daily variations. As the global population is increasingly residing in highly disparate climates, a burgeoning literature has documented the adverse health effects of single-day and day-to-day variation in temperature, raising questions of inequality in exposure to this environmental health risk. Yet, we continue to lack understanding of inequality in exposure to daily temperature variation (DTV) in the highly unequal United States. Using nighttime and daytime land surface temperature data between 2000 and 2017, this study analyzes population exposure to long-term DTV by race and ethnicity, income, and age for the 50 states and the District of Columbia. The analysis is based on population-weighted exposure at the census-tract level. We find that, on average, non-White (especially Black and Hispanic) and low-income Americans are exposed disproportionately to larger DTV. Race-based inequalities in exposure to DTV are larger than income-based disparities, with inequalities heightened in the summer months. In May, for example, the DTV difference by race and ethnicity of 51 states is between 0.20 and 3.01 °C (up to 21.0%). We find that younger populations are, on average, exposed to larger DTV, though the difference is marginal.

Heat Exposure and Cause-Specific Hospital Admissions in Spain: A Nationwide Cross-Sectional Study

BACKGROUND

More frequent and intense exposure to extreme heat conditions poses a serious threat to public health. However, evidence on the association between heat and specific diagnoses of morbidity is still limited. We aimed to comprehensively assess the short-term association between cause-specific hospital admissions and high temperature, including the added effect of temperature variability and heat waves and the effect modification by humidity and air pollution.

METHODS

We used data on cause-specific hospital admissions, weather (i.e., temperature and relative humidity), and air pollution [i.e., fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ), fine particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), NO 2 , and ozone (O 3 )] for 48 provinces in mainland Spain and the Balearic Islands between 1 January 2006 and 31 December 2019. The statistical analysis was performed for the summer season (June-September) and consisted of two steps. We first applied quasi-Poisson generalized linear regression models in combination with distributed lag nonlinear models (DLNM) to estimate province-specific temperature-morbidity associations, which were then pooled through multilevel univariate/multivariate random-effect meta-analysis.

RESULTS

High temperature had a generalized impact on cause-specific hospitalizations, while the added effect of temperature variability [i.e., diurnal temperature range (DTR)] and heat waves was limited to a reduced number of diagnoses. The strongest impact of heat was observed for metabolic disorders and obesity [relative risk (RR) = 1.978; 95% empirical confidence interval (eCI): 1.772, 2.208], followed by renal failure (1.777; 95% eCI: 1.629, 1.939), urinary tract infection (1.746; 95% eCI: 1.578, 1.933), sepsis (1.543; 95% eCI: 1.387, 1.718), urolithiasis (1.490; 95% eCI: 1.338, 1.658), and poisoning by drugs and nonmedicinal substances (1.470; 95% eCI: 1.298, 1.665). We also found differences by sex (depending on the diagnosis of hospitalization) and age (very young children and the elderly were more at risk). Humidity played a role in the association of heat with hospitalizations from acute bronchitis and bronchiolitis and diseases of the muscular system and connective tissue, which were higher in dry days. Moreover, heat-related effects were exacerbated on high pollution days for metabolic disorders and obesity (PM 2.5 ) and diabetes (PM 10 , O 3 ).

DISCUSSION

Short-term exposure to heat was found to be associated with new diagnoses (e.g., metabolic diseases and obesity, blood diseases, acute bronchitis and bronchiolitis, muscular and connective tissue diseases, poisoning by drugs and nonmedicinal substances, complications of surgical and medical care, and symptoms, signs, and ill-defined conditions) and previously identified diagnoses of hospital admissions. The characterization of the vulnerability to heat can help improve clinical and public health practices to reduce the health risks posed by a warming planet. https://doi.org/10.1289/EHP13254.

Ambient temperature and preterm birth: A case-crossover study

OBJECTIVE

To investigate the association between ambient temperature and preterm birth (PTB) and to estimate the population attributable fraction (PAF) of PTBs due to low and high temperatures.

DESIGN

Time-stratified case-crossover design.

SETTING

Japan (46 prefectures, excluding Okinawa), 2011-2020.

SAMPLE

214 050 PTBs registered in the Japan Perinatal Registry Network database among 1 908 168 singleton live births.

METHODS

A quasi-Poisson regression model with a distributed lag nonlinear model was employed to assess the associations between daily mean temperature and PTBs for a lag of 0-27 days in each prefecture. A random effects meta-analysis was conducted by combining effect estimates from the 46 prefectures to estimate pooled relative risks (RRs). The PAFs of the PTBs due to below or above the mean of the 46 median temperatures (16.0°C) were calculated.

MAIN OUTCOME MEASURES

Preterm singleton live births.

RESULTS

The association between daily mean temperature and PTB risk exhibited a U-shaped curve. The adjusted RRs were 1.15 (95% confidence interval [CI] 1.05-1.25) at the mean of the 1st percentiles (0.8°C) and 1.08 (95% CI 1.00-1.17) at the mean of the 99th percentiles (30.2°C) of 46 prefectures, with 16.0°C as the reference temperature. Approximately 2.3% (95% CI 0.6-4.0) of PTBs were attributable to low temperatures.

CONCLUSIONS

Both low and possibly high temperatures were associated with an increased risk of PTBs. These findings may help to inform preventive measures for pregnant women.

Association between diurnal temperature range and sperm quality: Evidence from a prospective cohort in Central China

Inter-day temperature variability has been reported to be associated with sperm quality in a city-level exposure assessment study. However, studies exploring the impact of temperature variability within a single day on sperm quality at individual level are still lacking. The present study aims to bridge this research gap by analyzing the linear and non-linear associations between diurnal temperature range (DTR) exposure and sperm quality, utilizing data from the Anhui Prospective Assisted Reproduction Cohort. The study included 15,112 males (totaling 28,267 tests) and assessed individual exposure to various environmental factors (residential greenness, ambient particulate matter, sulfur dioxide, relative humidity, ambient temperature, and DTR) during the 0-90 day period before semen analysis. A combination of a linear mixed model, natural cubic splines, and subgroup analysis was employed. Significant "U"-shaped non-linear associations were observed between DTR exposure and total motility, sperm concentration, sperm count, total motile sperm count, and progressive motile sperm count. Lower DTR levels negatively impacted these parameters, whereas higher DTR levels showed a positive effect. Notably, these associations were more pronounced at ambient temperatures below 16.5 °C, while absent in warmer conditions. Sperm quality demonstrates increased sensitivity to DTR exposure in cooler environments. Therefore, implementing effective individual temperature management strategies is crucial for mitigating decreased sperm quality associated with DTR exposure, highlighting the potential benefits of government policies aimed at achieving carbon neutrality to enhance overall sperm quality in the general population.

Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.

Association between short-term daily temperature variability and blood pressure in the Chinese population: From the China hypertension survey

BACKGROUND

We aimed to evaluate the impacts of short-term daily temperature variability (DTV) on blood pressure (BP) among participants with normotension, prehypertension, and hypertension, respectively, and explore the effects in different climate zones and seasons.

METHODS

A representative population sample (n = 397,173) covering the subtropical, temperate continental, and temperate monsoon zones was obtained from the China Hypertension Survey. DTV was calculated as the standard deviation of daily minimum and maximum temperatures during the exposure days. The linear mixed effect regression model was used to estimate the associations between DTV exposure and BP among normotension, prehypertension, and hypertension, respectively, and further stratified analysis was performed by climate zones and seasons.

RESULTS

After adjustment for confounders, per interquartile range (IQR) increase in DTV (2.28 °C) at 0-6 days of exposure was associated with an increase of 0.41 mmHg (95 % confidence interval [CI]: 0.07, 0.75) in systolic BP (SBP) and 0.41 mmHg (95 % CI: 0.09, 0.72) in pulse pressure (PP) among hypertensive participants in the subtropical zone. Similarly, DTV exposure was associated with an increase of 0.31 mmHg (95 % CI: 0.06, 0.55) in SBP and 0.59 mmHg (95 % CI: 0.24, 0.94) in PP among prehypertensive participants in the temperate continental zone. Additionally, during the warm season, DTV was positively associated with SBP among populations with prehypertension and hypertension, and with PP among all three populations.

CONCLUSION

Short-term DTV exposure was associated with an increase in SBP and PP among hypertensive and prehypertensive participants in the subtropical zone and the temperate continental zone. In addition, positive associations of DTV with SBP and PP were observed among participants with prehypertension and hypertension in the warm season. Comprehensive health education and effective intervention strategies should be implemented to mitigate the effects of temperature variations on BP, particularly among prehypertensive and hypertensive populations.

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.

The effects of diurnal temperature range on mortality and emergency department presentations in Victoria state of Australia: A time-series analysis

State of Victoria, Australia (SVA) has a wide variation of diurnal temperatures (DTR). DTR has been reported to be associated with risk of mortality and morbidity. We examined the association between exposure to DTR and risk of all-cause mortality and emergency department (ED) presentations in the SVA. We obtained data on daily counts of deaths and ED presentations, and weather data from 1 st January 2000─2019. We applied a quasi-Poisson time-series regression analysis to examine the association between daily DTR exposures and risk of mortality and ED presentations. The analyses were queried by age, sex, seasons, ED presentations triages, and departure status. Risk of mortality and ED presentation increased by 0.33% (95% CI: 0.24%-0.43%), and 0.094% (95% CI: 0.077%-0.11%) in relation to one degree increase in the daily DTR. The association between DTR and ED presentations was stronger in children (0-15 years) (0.38% [95% CI: 0.34%-0.42%]) and the elderly (75+ years) (0.34% [95% CI: 0.29%-0.39%]). Resuscitation, which was consistently accounted for the highest vulnerability to DTR variation, increased by 0.79% (95% CI: 0.60%-0.99%). This study suggests that the risk of mortality and ED presentations associates with the increase of DTR. Children, the elderly, and their caregivers need to be made aware of the health risk posed by DTR.

Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations?

The methodological development of this article is motivated by the need to address the following scientific question: does the issuance of heat alerts prevent adverse health effects? Our goal is to address this question within a causal inference framework in the context of time series data. A key challenge is that causal inference methods require the overlap assumption to hold: each unit (i.e., a day) must have a positive probability of receiving the treatment (i.e., issuing a heat alert on that day). In our motivating example, the overlap assumption is often violated: the probability of issuing a heat alert on a cooler day is near zero. To overcome this challenge, we propose a stochastic intervention for time series data which is implemented via an incremental time-varying propensity score (ItvPS). The ItvPS intervention is executed by multiplying the probability of issuing a heat alert on day $t$-conditional on past information up to day $t$-by an odds ratio $\delta_t$. First, we introduce a new class of causal estimands, which relies on the ItvPS intervention. We provide theoretical results to show that these causal estimands can be identified and estimated under a weaker version of the overlap assumption. Second, we propose nonparametric estimators based on the ItvPS and derive an upper bound for the variances of these estimators. Third, we extend this framework to multisite time series using a spatial meta-analysis approach. Fourth, we show that the proposed estimators perform well in terms of bias and root mean squared error via simulations. Finally, we apply our proposed approach to estimate the causal effects of increasing the probability of issuing heat alerts on each warm-season day in reducing deaths and hospitalizations among Medicare enrollees in 2837 US counties.

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.

Global warming may significantly increase childhood anemia burden in sub-Saharan Africa

Childhood anemia constitutes a global public health problem, especially in low- and middle-income countries (LMICs). However, it remains unknown whether global warming has an impact on childhood anemia. Here, we examined the association between annual temperatures and childhood anemia prevalence in sub-Saharan Africa and then projected childhood anemia burden attributable to climate change. Each 1°C increment in annual temperature was associated with increased odds of childhood anemia (odd ratio = 1.138, 95% confidence interval: 1.134-1.142). Compared with the baseline period (1985-2014), the attributable childhood anemia cases would increase by 7,597 per 100,000 person-years under a high-emission scenario in the 2090s, which would be almost 2-fold and over 3-fold more than those projected in moderate- and low-emission scenarios. Our results reveal the vulnerabilities and inequalities of children for the excess burden of anemia due to climate warming and highlight the importance of climate mitigation and adaptation strategies in LMICs.

Cause-specific mortality and burden attributable to temperature variability in China

BACKGROUND

Few large-scale, nationwide studies have assessed cause-specific mortality risks and burdens associated with temperature variability (TV).

OBJECTIVE

To estimate associations between TV and cause-specific mortality and quantify the mortality burden in China.

METHODS

Data on daily total and cause-specific mortality in 272 Chinese cities between 2013 and 2015 were recorded. TVs were computed as the standard deviations of daily minimum and maximum temperatures over a duration of 2 to 7 days. The time-series quasi-Poisson regression model with adjustment of the cumulative effects of daily mean temperature over the same duration was applied to evaluate the city-specific associations of TV and mortality. Then, we pooled the effect estimates using a random-effects meta-analysis and calculated the mortality burdens.

RESULTS

Overall, TV showed significant and positive associations with total and cause-specific mortality. The TV-mortality associations were generally stronger when using longer durations. A 1 °C increase in TV at 0-7 days (TV0-7) was associated with a 0.79 % [95 % confidence interval (CI): 0.55 %, 0.96 %] increase in total mortality. Mortality fractions attributable to TV0-7 were 4.37 % for total causes, 4.75 % for overall cardiovascular disease, 4.37 % for coronary heart disease, 5.05 % for stroke, 8.28 % for ischaemic stroke, 1.08 % for haemorrhagic stroke, 6.93 % for respiratory disease, and 6.81 % for COPD, respectively. The mortality risk and burden were generally higher in the temperate monsoon zone, females, and elders.

CONCLUSION

This nationwide study indicated that TV was an independent risk factor of mortality, and could result in significant burden for main cardiorespiratory diseases.

Nationwide projections of heat- and cold-related mortality impacts under various climate change and population development scenarios in Switzerland

Climate change and progressive population development (i.e., ageing and changes in population size) are altering the temporal patterns of temperature-related mortality in Switzerland. However, limited evidence exists on how current trends in heat- and cold-related mortality would evolve in future decades under composite scenarios of global warming and population development. Moreover, the contribution of these drivers to future mortality impacts is not well-understood. Therefore, we aimed to project heat- and cold-related mortality in Switzerland under various combinations of emission and population development scenarios and to disentangle the contribution of each of these two drivers using high-resolution mortality and temperature data. We combined age-specific (<75 and ⩾75 years) temperature-mortality associations in each district in Switzerland (1990-2010), estimated through a two-stage time series analysis, with 2 km downscaled CMIP5 temperature data and population and mortality rate projections under two scenarios: RCP4.5/SSP2 and RCP8.5/SSP5. We derived heat and cold-related mortality for different warming targets (1.5 °C, 2.0 °C and 3.0 °C) using different emission and population development scenarios and compared this to the baseline period (1990-2010). Heat-related mortality is projected to increase from 312 (116; 510) in the 1990-2010 period to 1274 (537; 2284) annual deaths under 2.0 °C of warming (RCP4.5/SSP2) and to 1871 (791; 3284) under 3.0 °C of warming (RCP8.5/SSP5). Cold-related mortality will substantially increase from 4069 (1898; 6016) to 6558 (3223; 9589) annual deaths under 2.0 °C (RCP4.5/SSP2) and to 5997 (2951; 8759) under 3.0 °C (RCP8.5/SSP5). Moreover, while the increase in cold-related mortality is solely driven by population development, for heat, both components (i.e., changes in climate and population) have a similar contribution of around 50% to the projected heat-related mortality trends. In conclusion, our findings suggest that both heat- and cold-related mortality will substantially increase under all scenarios of climate change and population development in Switzerland. Population development will lead to an increase in cold-related mortality despite the decrease in cold temperature under warmer scenarios. Whereas the combination of the progressive warming of the climate and population development will substantially increase and exacerbate the total temperature-related mortality burden in Switzerland.

Seasonal Temperature Variability and Mortality in the Medicare Population

BACKGROUND

Seasonal temperature variability remains understudied and may be modified by climate change. Most temperature-mortality studies examine short-term exposures using time-series data. These studies are limited by regional adaptation, short-term mortality displacement, and an inability to observe longer-term relationships in temperature and mortality. Seasonal temperature and cohort analyses allow the long-term effects of regional climatic change on mortality to be analyzed.

OBJECTIVES

We aimed to carry out one of the first investigations of seasonal temperature variability and mortality across the contiguous United States. We also investigated factors that modify this association. Using adapted quasi-experimental methods, we hoped to account for unobserved confounding and to investigate regional adaptation and acclimatization at the ZIP code level.

METHODS

We examined the mean and standard deviation (SD) of daily temperature in the warm (April-September) and cold (October-March) season in the Medicare cohort from 2000 to 2016. This cohort comprised 622,427,230 y of person-time in all adults over the age of 65 y from 2000 to 2016. We used daily mean temperature obtained from gridMET to develop yearly seasonal temperature variables for each ZIP code. We used an adapted difference-in-difference approach model with a three-tiered clustering approach and meta-analysis to observe the relationship between temperature variability and mortality within ZIP codes. Effect modification was assessed with stratified analyses by race and population density.

RESULTS

For every 1°C increase in the SD of warm and cold season temperature, the mortality rate increased by 1.54% [95% confidence interval (CI): 0.73%, 2.15%] and 0.69% (95% CI: 0.22%, 1.15%) respectively. We did not see significant effects for seasonal mean temperatures. Participants who were classified by Medicare into an "other" race group had smaller effects than those classified as White for Cold and Cold SD and areas with lower population density had larger effects for Warm SD.

DISCUSSION

Warm and cold season temperature variability were significantly associated with increased mortality rates in U.S. individuals over the age of 65 y, even after controlling for seasonal temperature averages. Warm and cold season mean temperatures showed null effects on mortality. Cold SD had a larger effect size for those who were in the racial subgroup other, whereas Warm SD was more harmful for those living in lower population density areas. This study adds to the growing calls for urgent climate mitigation and environmental health adaptation and resiliency. https://doi.org/10.1289/EHP11588.

A new method to separate the impacts of interday and intraday temperature variability on mortality

BACKGROUND

Temperature variability (TV) is associated with increased mortality risks. However, the independent impacts of interday and intraday are still unknown.

METHODS

We proposed a new method to decompose TV into interday TV and intraday TV through algebra derivation. Intraday TV was defined as the weighted average standard deviation (SD) of minimum temperature and maximum temperature on each day. Interday TV was defined as the weighted SD of daily mean temperatures during the exposure period. We then performed an illustrative analysis using data on daily mortality and temperature in France in 2019-2021.

RESULTS

The novel interday and intraday TV indices were good proxies for existing indicators, inlcluding diurnal temperature range (DTR) and temperature change between neighbouring days (TCN). In the illustrative analyses, interday and intraday TVs showed differentiated mortality risks. Mortality burden related to TV was mainly explained by the intraday component, accounting for an attributable fraction (AF) of 1.81% (95% CI: 0.64%, 2.97%) of total mortality, more than twice the AF of interday TV (0.86%, 95% CI: 0.47%, 1.24%).

CONCLUSIONS

This study proposed a novel method for identifying and isolating the different components of temperature variability and offered a comprehensive way to investigate their health impacts.

Hospital admission tendencies caused by day-to-day temperature changes during summer: a case study for the city of Novi Sad (Serbia)

Increased temperature risk in cities threatens the health and well-being of urban population and is fueled by climate change and intensive urbanization. Consequently, further steps must be taken for assessing temperature conditions in cities and their association with public health, in order to improve public health prevention at local or regional level. This study contributes to solving the problems by analyzing the connection between extreme temperatures and the tendencies of all-cause hospital admissions. The analyses used (a) 1-h air temperature data, and (b) daily data of all-cause hospital admissions. The datasets include the summer period (June, July, August) for the years 2016 and 2017. We tested the effects of two temperature indices, day-to-day change in maximum temperature - T_max,c and daily temperature range - T_r, with all-cause hospital admission subgroups, such as all-cause cases - H_a, hospital admissions in the population below 65 - H_a<65, and hospital admissions in the population aged 65 and over - H_a≥65. The results show the highest values of H_a when T_max,c is between 6 and 10 °C. Therefore, more intensive hospital admissions can be expected when T_max increases from day-to-day (positive values of T_max,c), and it is more visible for H_a and H_a<65 (1 °C = 1% increase in hospital admissions). Also, T_r values between 10 °C and 14 °C cause an increase in the number of hospital admissions, and it is more noticeable for H_a≥65.

Projecting the excess mortality related to diurnal temperature range: A nationwide analysis in China

The projection of excess mortality due to diurnal temperature range (DTR) in future has not been evaluated yet in China. Based on daily cause-specific mortality data from 266 cities in China, this study aimed to examine the association between DTR and mortality, which help project the future mortality burden attributable to DTR by considering the modification effects of altitude and population migration. We first found that every 10 °C increase in the DTR would result in a 3.3 % (95 % confidence interval: 2.6 %-4.1 %) excess risk of non-accidental mortality. The unit risk of DTR-associated cause-specific mortality at moderate or high altitudes was significantly lower than at lower altitudes, especially for cardiovascular disease. Subsequently, DTR-associated excess mortality in 2017 in China was 233,154 deaths (with a population-weighted attributable fraction of 2.9 %). Furthermore, we projected DTR-attributable additional mortality in the future, with the associated mortalities to be 221,860 deaths in 2050-2059 (2050s) and 132,305 deaths in 2090-2099 (2090s), under the SSP1-2.6 scenario. Meanwhile, the regional inequalities were exacerbated by 18 % in 2050s and 13 % in 2090s when considering the modification effects of city altitude. Future population migration would increase excess mortality in most areas in central and southern China, and reduce the disease burden in most areas in eastern, western, and northern China. Our findings underpinned that regional strategies should be adopted to mitigate excess mortality attributable to global climate change.

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities

In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69 years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

The role of meteorological factors in suicide mortality in Wuhu, a humid city along the Yangtze River in Eastern China

As the climate continues to change, suicide is becoming more frequent. In this study, absolute humidity (AH) was included for the first time and Wuhu, a typical subtropical city along the Yangtze River, was taken as the research object to explore the impact of suicide death risk on meteorological factors. The daily meteorological factors and suicide mortality data of Wuhu city from 2014 to 2020 were collected. Guided by structural equation model (SEM), a time series analysis method combining distributed lag nonlinear model (DLNM) and generalized additive model (GAM) was adopted. To investigate the correlation among different populations, we stratified age and gender at different meteorological levels. A total of 1259 suicide deaths were collected in Wuhu. The results indicated that exceedingly low and low levels of AH short-term exposure increased suicide mortality, with the maximum effect occurring at lag 14 for both levels of exposure, when the relative risk (RR) was 1.131 (95% CI: 1.030, 1.242) and 1.065 (95% CI: 1.006, 1.127), respectively. Exposure to exceedingly high and exceedingly low levels of temperature mean (T mean) also increased suicide mortality, with maximum RR values of 1.132 (lag 14, 95% CI: 1.015, 1.263) and 1.203 (lag 0, 95% CI: 1.079, 1.340), sequentially. As for diurnal temperature range (DTR), low-level exposure decreased the risk of suicide, while high-level exposure increased this risk, with RR values of 0.955 (lag 0, 95% CI: 0.920, 0.991, minimum) and 1.060 (lag 0, 95% CI: 1.018, 1.104, maximum), sequentially. Stratified analysis showed that AH and DTR increased the suicide death risk in male and elderly people, while the risk effect of T mean have no effect on young people only. In summary, male and elderly people appear to be more vulnerable to adverse weather effects.

Temperature-related chest pain presentations and future projections with climate change

BACKGROUND

Climate change has led to increased interest in studying adverse health effects relating to ambient temperatures. It is unclear whether incident chest pain is associated with non-optimal temperatures and how chest pain presentation rates might be affected by climate change.

METHODS

The study included ambulance data of chest pain presentations in Melbourne, Australia from 1/1/2015 to 30/6/2019 with linkage to hospital and emergency discharge diagnosis data. A time series quasi-Poisson regression with a distributed lag nonlinear model was fitted to assess the temperature-chest pain presentation associations overall and according to age, sex, socioeconomic status, and event location subgroups, with adjustment for season, day of the week and long-term trend. Future excess chest pain presentations associated with cold and heat were projected under six general circulation models under medium and high emission scenarios.

RESULTS

In 206,789 chest pain presentations, mean (SD) age was 61.2 (18.9) years and 50.3 % were female. Significant heat- and cold-related increased risk of chest pain presentations were observed for mean air temperatures above and below 20.8 °C, respectively. Excess chest pain presentations related to heat were observed in all subgroups, but appeared to be attenuated for older patients (≥70 years), patients of higher socioeconomic status (SES), and patients developing chest pain at home. We projected increases in heat-related chest pain presentations with climate change under both medium- and high-emission scenarios, which are offset by decreases in chest pain presentations related to cold temperatures.

CONCLUSIONS

Heat- and cold- exposure appear to increase the risk of chest pain presentations, especially among younger patients and patients of lower SES. This will have important implications with climate change modelling of chest pain, in particular highlighting the importance of risk mitigation strategies to minimise adverse health impacts on hotter days.

An analysis of past and future heatwaves based on a heat-associated mortality threshold: towards a heat health warning system

Heatwaves can have severe impacts on human health extending from illness to mortality. These health effects are related to not only the physical phenomenon of heat itself but other characteristics such as frequency, intensity, and duration of heatwaves. Therefore, understanding heatwave characteristics is a crucial step in the development of heat-health warning systems (HHWS) that could prevent or reduce negative heat-related health outcomes. However, there are no South African studies that have quantified heatwaves with a threshold that incorporated a temperature metric based on a health outcome. To fill this gap, this study aimed to assess the spatial and temporal distribution and frequency of past (2014 - 2019) and future (period 2020 - 2039) heatwaves across South Africa. Heatwaves were defined using a threshold for diurnal temperature range (DTR) that was found to have measurable impacts on mortality. In the current climate, inland provinces experienced fewer heatwaves of longer duration and greater intensity compared to coastal provinces that experienced heatwaves of lower intensity. The highest frequency of heatwaves occurred during the austral summer accounting for a total of 150 events out of 270 from 2014 to 2019. The heatwave definition applied in this study also identified severe heatwaves across the country during late 2015 to early 2016 which was during the strongest El Niño event ever recorded to date. Record-breaking global temperatures were reported during this period; the North West province in South Africa was the worst affected experiencing heatwaves ranging from 12 to 77 days. Future climate analysis showed increasing trends in heatwave events with the greatest increases (80%-87%) expected to occur during summer months. The number of heatwaves occurring in cooler seasons is expected to increase with more events projected from the winter months of July and August, onwards. The findings of this study show that the identification of provinces and towns that experience intense, long-lasting heatwaves is crucial to inform development and implementation of targeted heat-health adaptation strategies. These findings could also guide authorities to prioritise vulnerable population groups such as the elderly and children living in high-risk areas likely to be affected by heatwaves.

Seasonal variation in association between temperature change and emergency department visits: A multi-site study in China

BACKGROUND

There is limited evidence of effects and seasonal variation of temperature change on emergency department visits (EDVs).

OBJECTIVE

To investigate the association between diurnal temperature range (DTR), temperature change between neighboring days (TCN) and a comprehensive collection of cause-specific EDVs in China.

METHODS

We collected EDVs, weather, and air pollution data in 20 sites in China from 2014 to 2018. We applied a quasi-Poisson regression with distributed lag nonlinear model to evaluate DTR- and TCN-EDVs association. We used meta-analysis to pool site-specific estimates. We also conducted seasonal analysis and assess effects of modifiers.

RESULTS

A 1 °C increase of DTR and TCN was associated with 0.29% [95% confidence interval (CI): 0.07%, 0.51%)] and 1.44% (95% CI: 0.93%, 1.96%) increase of total EDVs, respectively. People aged 18-44 were sensitive to DTR and TCN, while the elderly population was sensitive to TCN only in spring and autumn. In seasonal analysis, effects of temperature change on total EDVs were lower in summer. TCN increased risks of genitourinary diseases in summer, respiratory diseases in winter, injury in autumn, and mental diseases in spring. DTR increased the risk of respiratory diseases in autumn.

CONCLUSION

Exposure to DTR and TCN was associated with elevated risk of EDVs but with great seasonal variations. Our results provided potential time and target populations for adaptive strategies and preventive measures.

Daily diurnal temperature range associated with outpatient visits of acute lower respiratory infection in children: A time-series study in Guangzhou, China

Background

Diurnal temperature range (DTR) has been increasingly recognized as a risk factor for mortality and morbidity, but the association between DTR and acute lower respiratory infection (ALRI) outpatient visits has not been examined among children in China.

Methods

A total of 79,416 ALRI outpatient visits among children were obtained from the Guangdong Second Provincial General Hospital between 2013 and 2019. DTR was calculated by taking the difference between the maximum and the minimum temperatures. Generalized additive models using a quasi-Poisson distribution were used to model the relationship between DTR and ALRI outpatient visits.

Results

Diurnal temperature range was significantly associated with elevated risks of ALRI outpatient visits: the excess risks (ERs) and 95% confidence intervals (CIs) were 2.31% (1.26, 3.36%) for ALRI, 3.19% (1.86, 4.54%) for pneumonia, and 1.79% (0.59, 3.01%) for bronchiolitis, respectively. Subgroup analyses suggested that the associations were significantly stronger during rainy seasons (ER for ALRI: 3.02%, 95% CI: 1.43, 4.64%) than those in dry seasons (ER for ALRI: 2.21%, 95% CI: 0.65, 3.81%), while no significant effect modifications were found in sex and age groups.

Conclusion

Diurnal temperature range may elevate the risk of ALRI outpatient visits among children in China, especially during rainy seasons. Public health policies are needed to mitigate the adverse health impacts of DTR on children.

Excess out-of-hospital cardiac arrests due to ambient temperatures in South Korea from 2008 to 2018

Out-of-hospital cardiac arrest (OHCA) is a notable public health issue with negative outcomes, such as high mortality and aftereffects. Additionally, the adverse effects of extreme temperatures on health have become more important under climate change; however, few studies have investigated the relationship between temperature and OHCA. In this study, we examined the association between temperature and OHCA and its underlying risk factors. We conducted a two-stage time-series analysis using a Poisson regression model with a distributed lag non-linear model (DLNM) and meta-analysis, based on a nationwide dataset from South Korea (2008-2018). We found that 17.4% of excess OHCA was attributed to cold, while 0.9% was attributed to heat. Based on central estimates, excess OHCA attributed to cold were more prominent in the population with hypertension comorbidity (31.0%) than the populations with diabetes (24.3%) and heart disease (17.4%). Excess OHCA attributed to heat were larger in the populations with diabetes (2.7%) and heart disease comorbidity (2.7%) than the population with hypertension (1.2%) based on central estimates. Furthermore, the time-varying excess OHCA attributed to cold have decreased over time, and although those of heat did not show a certain pattern during the study period, there was a weak increasing tendency since 2011. In conclusion, we found that OHCAs were associated with temperature, and cold temperatures showed a greater impact than that of hot temperatures. The effects of cold and hot temperatures on OHCA were more evident in the populations with hypertension, diabetes, and heart diseases, compared to the general population. In addition, the impacts of heat on OHCA increased in recent years, while those of cold temperatures decreased. Our results provide scientific evidence for policymakers to mitigate the OHCA burden attributed to temperature.

The effects of night-time warming on mortality burden under future climate change scenarios: a modelling study

BACKGROUND

The health impacts of climate warming are usually quantified based on daily average temperatures. However, extra health risks might result from hot nights. We project the future mortality burden due to hot nights.

METHODS

We selected the hot night excess (HNE) to represent the intensity of night-time heat, which was calculated as the excess sum of high temperature during night time. We collected historical mortality data in 28 cities from three east Asian countries, from 1981 to 2010. The associations between HNE and mortality in each city were firstly examined using a generalised additive model in combination with a distributed lag non-linear model over lag 0-10 days. We then pooled the cumulative associations using a univariate meta-regression model at the national or regional levels. Historical and future hourly temperature series were projected under two scenarios of greenhouse-gas emissions from 1980-2099, with ten general circulation models. We then projected the attributable fraction of mortality due to HNE under each scenario.

FINDINGS

Our dataset comprised 28 cities across three countries (Japan, South Korea, and China), including 9 185 598 deaths. The time-series analyses showed the HNE was significantly associated with increased mortality risks, the relative mortality risk on days with hot nights could be 50% higher than on days with non-hot nights. Compared with the rise in daily mean temperature (lower than 20%), the frequency of hot nights would increase more than 30% and the intensity of hot night would increase by 50% by 2100s. The attributable fraction of mortality due to hot nights was projected to be 3·68% (95% CI 1·20 to 6·17) under a strict emission control scenario (SSP126). Under a medium emission control scenario (SSP245), the attributable fraction of mortality was projected to increase up to 5·79% (2·07 to 9·52), which is 0·95% (-0·39 to 2·29) more than the attributable fraction of mortality due to daily mean temperature.

INTERPRETATION

Our study provides evidence for significant mortality risks and burden in association with night-time warming across Japan, South Korea, and China. Our findings suggest a growing role of night-time warming in heat-related health effects in a changing climate.

FUNDING

The National Natural Science Foundation of China, Shanghai International Science and Technology Partnership Project.

Short-term changes in meteorological conditions and suicide: A systematic review and meta-analysis

BACKGROUND

Suicide is a leading cause of death, particularly for young adults. Suicidal behaviours are influenced by a wide-range of personal, social, and cultural factors. Emerging evidence suggests that daily changes in meteorological conditions, including temperature, increases the risk of suicide.

METHODS

We conducted a systematic review and meta-analysis of studies that examined associations between either daily, or weekly, variations for eight meteorological variables and suicide outcomes (attempts, or deaths). Meta-analytic methods were applied to derive summary measures of association using random effect models. We assessed the heterogeneity in these associations by region and biological sex.

RESULTS

We identified 29 studies of suicide. Of these, 26 reported associations between temperature, while fewer studies reported on rain (n = 4), solar radiation (n = 4), humidity (n = 3), sunshine (n = 3), atmospheric pressure (n = 2), wind (n = 2) and cloud cover (n = 2). The overall relative risk for suicide deaths/attempts per 1 °C increase in ambient temperature was 1.016 (95% CI: 1.013-1.019). Subgroup analysis of temperature found stronger associations with suicide when using the maximum rather than the mean daily temperature, among men, and for completed suicides relative to attempts. Regionally, the strongest associations were found in the East Asia and Pacific region. While associations were found for solar radiation and cloud coverage and suicide, we did not undertake a meta-analysis for these exposures as it was not possible to standardize measures of association across studies. Statistically significant associations were not observed for other identified meteorological variables.

CONCLUSIONS

Our findings suggest that daily increases in temperature increase the risk of suicide, particularly, among men and in the East Asia and Pacific region.

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study

BACKGROUND

Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5° × 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19.

METHODS

In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5° × 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades.

FINDINGS

An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6).

INTERPRETATION

Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability.

FUNDING

Australian Research Council, Australian National Health & Medical Research Council.

Association of ambient temperature and sun exposure with hip fractures in Japan: A time-series analysis using nationwide inpatient database

BACKGROUND

Evidence on whether meteorological conditions affect hip fractures (HFs) is limited. This study aimed to clarify the associations between ambient temperature and sun exposure and HFs in Japan.

METHODS

Record of daily hospital admissions for HFs between 2015 and 2018 were extracted from a Japanese nationwide inpatient database. We conducted a time-series quasi-Poisson regression analysis using a distributed lag non-linear model with lag 0-39 days to estimate prefecture-specific relative risks (RRs) of HFs. We also estimated pooled RRs using random-effects meta-analysis.

RESULTS

We identified 355,563 HFs. For mean temperature, immediate RRs (lag 0-2 days) were 1.349 (95% confidence interval (CI): 1.305, 1.395) and 0.754 (95% CI: 0.727, 0.782) for low (mean of the 2.5th percentile) and high (mean of the 97.5th percentile) mean temperature, respectively, relative to the reference (mean of medians). For sunshine duration, immediate RRs were 0.929 (95% CI: 0.913, 0.946) and 1.056 (95% CI: 1.029, 1.085) for short (mean of the 2.5th percentile) and long (mean of the 97.5th percentile) sunshine duration, respectively, and delayed RRs (lag 3-39 days) was 0.770 (95% CI: 0.696, 0.851) for long sunshine duration relative to the reference (mean of medians). Immediate RRs were larger for both exposures in patients admitted from home than in those from care facilities.

CONCLUSIONS

Lower mean temperature and longer sunshine duration were associated with immediate higher HF risks. Higher mean temperature and shorter sunshine duration were associated with immediate lower HF risks. These associations were modified by admission routes. Longer sunshine duration was also associated with delayed lower HF risks.

Comparative analysis of daily and hourly temperature variability in association with all-cause and cardiorespiratory mortality in 45 US cities

Temperature variability (TV) has been widely associated with increased mortality risk and burden. Extensive researches have used the standard deviations of several days' daily maximum and minimum temperatures or hourly mean temperatures as daily and hourly TV measures (TV_daily and TV_hourly). However, comparative analysis of daily and hourly TV related to cardiorespiratory mortality is still limited. We collected daily mortality and meteorological data in 45 US metropolises, 1987-2000. A three-stage analysis was adopted to investigate TV-mortality associations using TV_daily and TV_hourly as exposure metrics. We first applied a time-series quasi-Poisson regression to estimate location-specific TV-mortality relationships, which were then pooled using random-effects meta-analysis with maximum likelihood estimation. We additionally calculated attributable fraction (AF) as a reflection of mortality burden associated with TV. Stratified analyses by age were also performed to identify the susceptible group to TV-related risks. There were a total of 15.4 million all-cause deaths, of which 6.1 million were from cardiovascular causes and 1.2 million were from respiratory causes. Per 1 °C increase in TV_daily and TV_hourly was associated with an increase of 0.53% (95% confidence interval: 0.31-0.76%) and 0.52% (0.26-0.79%) in cardiovascular mortality risks, 0.62% (0.26-0.98%) and 0.53% (0.13-0.94%) in respiratory mortality risks. Estimates of cardiovascular AF for TV_daily and TV_hourly were 2.43% (1.42-3.43%) vs. 1.63% (0.82-2.43%), whereas estimates of respiratory AF were 3.07% (1.11-4.99%) vs. 1.89% (0.43-3.34%). Both daily and hourly TV indexes showed approximately linear relationships with different mortality categories and similar lag patterns, but greater fractions were estimated using TV_daily than those using TV_hourly. People over 75 years old were relatively more vulnerable to TV-induced risks of mortality. In conclusion, both TV_daily and TV_hourly significantly increased all-cause and cardiorespiratory mortality risks and burden. Daily and hourly TV metrics exhibited comparable effects of mortality risk, while greater mortality burden was estimated using TV_daily than TV_hourly. Our findings may add significance to TV-mortality research and help to promote optimal health management strategies to better mitigate TV-related health effects.

Short-term Exposure to Air Pollution and Attributable Risk of Kidney Diseases: A Nationwide Time-series Study

BACKGROUND

Several studies have shown that long-term exposure to air pollution is associated with reduced kidney function. However, less is known about effects of short-term exposure to air pollution on kidney disease aggravation and resultant emergency room (ER) burden. This study aimed to estimate excess ER visits attributable to short-term air pollution and to provide evidence relevant to air pollution standards to protect kidney patients.

METHODS

We conducted time-series analysis using National Health Insurance data covering all persons in South Korea (2003-2013). We collected daily data for air pollutants (particulate matter ≤10 µm [PM10], ozone [O3], carbon monoxide [CO], and sulfur dioxide [SO2]) and ER visits for total kidney and urinary system disease, acute kidney injury (AKI), and chronic kidney disease (CKD). We performed a two-stage time-series analysis to estimate excess ER visits attributable to air pollution by first calculating estimates for each of 16 regions, and then generating an overall estimate.

RESULTS

For all kidney and urinary disease (902,043 cases), excess ER visits attributable to air pollution existed for all pollutants studied. For AKI (76,330 cases), we estimated the highest impact on excess ER visits from O3, while for CKD (210,929 cases), the impacts of CO and SO2 were the highest. The associations between air pollution and kidney ER visits existed for days with air pollution concentrations below current World Health Organization guidelines.

CONCLUSION

This study provides quantitative estimates of ER burdens attributable to air pollution. Results are consistent with the hypothesis that stricter air quality standards benefit kidney patients.

Temporal trends of the association between temperature variation and hospitalizations for schizophrenia in Hefei, China from 2005 to 2019: a time-varying distribution lag nonlinear model

Along with climate change, unstable weather patterns are becoming more frequent. However, the temporal trend associated with the effect of temperature variation on schizophrenia (SCZ) is not clear. Daily time-series data on SCZ and meteorological factors for 15-year between January 1, 2005 and December 31, 2019 were collected. And we used the Poisson regression model combined with the time-varying distribution lag nonlinear model (DLNM) to explore the temporal trend of the association between three temperature variation indicators (diurnal temperature range, DTR; temperature variability, TV; temperature change between neighboring days, TCN) and SCZ hospitalizations, respectively. Meanwhile, we also explore the temporal trend of the interaction between temperature and temperature variation. Stratified analyses were performed in different gender, age, and season. Across the whole population, we found a decreasing trend in the risk of SCZ hospitalizations associated with high DTR (from 1.721 to 1.029), TCN (from 1.642 to 1.066), and TV (TV0-1, from 1.034 to 0.994; TV0-2, from 1.041 to 0.994, TV0-3, from 1.044 to 0.992, TV0-4, from 1.049 to 0.992, TV0-5, from 1.055 to 0.993, TV0-6, from 1.059 to 0.991, TV0-7, from 1.059 to 0.990), but an increasing trend in low DTR (from 0.589 to 0.752). Subgroup analysis results further revealed different susceptible groups. Besides, the interactive effect suggests that temperature variation may cause greater harm under low-temperature conditions. There was a synergy between TCN and temperature on the addition and multiplication scales, which were 1.068 (1.007, 1.133) and 0.067 (0.009, 0.122), respectively. Our findings highlight public health interventions to mitigate temperature variation effects needed to focus not only on high temperature variations but also moderately low temperature variations. Future hospitalizations for SCZ associated with temperature variation may be more severely affected by temperature variability from low temperature environments. The temporal trend is associated with the effect of temperature variation on schizophrenia (SCZ).

The association between diurnal temperature range and clinic visits for upper respiratory tract infection among college students in Wuhan, China

The effects of daily mean temperature on health outcomes have been discussed in many previous studies, but few have considered the adverse impacts on upper respiratory tract infection (URTI) due to variance of temperature in one day. Diurnal temperature range (DTR) was a novel indicator calculated as maximum temperature minus minimum temperature on the same day. In this study, generalized additive model (GAM) with quasi-Poisson distribution was used to investigate the association between DTR and the number of daily outpatient visits for URTI among college students. Data about meteorological factors and air pollutants were provided by Hubei Meteorological Bureau and Wuhan Environmental Protection Bureau, respectively. Outpatient visits data were collected from the Hospital of Wuhan University from January 1, 2016, to December 31, 2018. Short-term exposure to DTR was associated with the increased risk of outpatient for URTI among all college students. Per 1 °C increased in DTR was associated with 0.73% (95%CI: 0.24, 1.21) increased in outpatient visits of all college students for URTI at lag 0 day. The greatest effect values were observed in males [1.35% (95%CI: 0.33,2.39)] at lag 0-6 days, and in females [0.86% (95%CI: 0.24, 1.49)] at lag 0-1 days. DTR had more adverse health impact in autumn and winter. Public health departments should consider the negative effect of DTR to formulate more effective prevention and control measures for protecting vulnerable people.

Impact of Heat Waves on Hospitalisation and Mortality in Nursing Homes: A Case-Crossover Study

Climate change leads to more days with extremely hot temperatures. Previous analyses of heat waves have documented a short-term rise in mortality. The results on the relationship between high temperatures and hospitalisations, especially in vulnerable patients admitted to nursing homes, are inconsistent. The objective of this research was to examine the discrepancy between heat-related mortality and morbidity in nursing homes. A time-stratified case-crossover study about the impact of heat waves on mortality and hospitalisations between 1 January 2013 and 31 December 2017 was conducted in 10 nursing homes over 5 years in Flanders, Belgium. In this study, the events were deaths and hospitalisations. We selected our control days during the same month as the events and matched them by day of the week. Heat waves were the exposure. Conditional logistic regression models were applied. The associations were reported as odds ratios at lag 0, 1, 2, and 3 and their 95% confidence intervals. In the investigated time period, 3048 hospitalisations took place and 1888 residents died. The conditional logistic regression showed that odds ratios of mortality and hospitalisations during heat waves were 1.61 (95% confidence interval 1.10–2.37) and 0.96 (95% confidence interval 0.67–1.36), respectively, at lag 0. Therefore, the increase in mortality during heat waves was statistically significant, but no significant changes in hospitalisations were obtained. Our result suggests that heat waves have an adverse effect on mortality in Flemish nursing homes but have no significant effect on the number of hospitalisations.

Temporal trends of the association between extreme temperatures and hospitalisations for schizophrenia in Hefei, China from 2005 to 2014

OBJECTIVE

We aimed to examine the temporal trends of the association between extreme temperature and schizophrenia (SCZ) hospitalisations in Hefei, China.

METHODS

We collected time-series data on SCZ hospitalisations for 10 years (2005-2014), with a total of 36 607 cases registered. We used quasi-Poisson regression and distributed lag non-linear model (DLNM) to assess the association between extreme temperature (cold and heat) and SCZ hospitalisations. A time-varying DLNM was then used to explore the temporal trends of the association between extreme temperature and SCZ hospitalisations in different periods. Subgroup analyses were conducted by age (0-39 and 40+ years) and gender, respectively.

RESULTS

We found that extreme cold and heat significantly increased the risk of SCZ hospitalisations (cold: 1st percentile of temperature 1.19 (95% CI 1.04 to 1.37) and 2.5th percentile of temperature 1.16 (95% CI 1.03 to 1.31); heat: 97.5th percentile of temperature 1.37 (95% CI 1.13 to 1.66) and 99th percentile of temperature 1.38 (95% CI 1.13 to 1.69)). We found a slightly decreasing trend in heat-related SCZ hospitalisations and a sharp increasing trend in cold effects from 2005 to 2014. However, the risk of heat-related hospitalisation has been rising since 2008. Stratified analyses showed that age and gender had different modification effects on temporal trends.

CONCLUSIONS

The findings highlight that as temperatures rise the body's adaptability to high temperatures may be accompanied by more threats from extreme cold. The burden of cold-related SCZ hospitalisations may increase in the future.