Long-term associations between time-varying exposure to ambient PM2.5 and mortality: an analysis of the UK Biobank.

BACKGROUND: Evidence for long-term mortality risks of PM2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM2.5-mortality associations in the UK Biobank cohort using detailed information on confounders and exposure. METHODS: We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM2.5 concentrations from spatio-temporal machine learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions. RESULTS: In fully adjusted models, an increase of 10 µg/m³ in PM2.5 was associated with hazard ratios (HRs) of 1.27 (95%CI: 1.06-1.53) for all-cause, 1.24 (1.03-1.50) for non-accidental, 2.07 (1.04-4.10) for respiratory, and 1.66 (0.86-3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (HR=0.88, 95%CI: 0.59-1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates. CONCLUSIONS: We found associations of long-term PM2.5 exposure with all-cause, non-accidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM2.5 and mortality.

Characterizing the seasonal influenza disease burden attributable to climate variability: A nationwide time-series modelling study in Japan, 2000-2019.

BACKGROUND: Ambient temperature and humidity are established environmental stressors with regard to influenza infections; however, mapping disease burden is difficult owing to the complexities of the underlying associations and differences in vulnerable population distributions. In this study, we aimed to quantify the burden of influenza attributable to non-optimal ambient temperature and absolute humidity in Japan considering geographical differences in vulnerability. METHODS: The exposure-lag-response relationships between influenza incidence, ambient temperature, and absolute humidity in all 47 Japanese prefectures for 2000-2019 were quantified using a distributed lag non-linear model for each prefecture; the estimates from all the prefectures were then pooled using a multivariate mixed-effects meta-regression model to derive nationwide average associations. Association between prefecture-specific indicators and the risk were also examined. Attributable risks were estimated for non-optimal ambient temperature and absolute humidity according to the exposure-lag-response relationships obtained before. RESULTS: A total of 25,596,525 influenza cases were reported during the study period. Cold and dry conditions significantly increased influenza incidence risk. Compared with the minimum incidence weekly mean ambient temperature (29.8 °C) and the minimum incidence weekly mean absolute humidity (20.2 g/m3), the cumulative relative risks (RRs) of influenza in cold (2.5 °C) and dry (3.6 g/m3) conditions were 2.79 (95% confidence interval [CI]: 1.78-4.37) and 3.20 (95% CI: 2.37-4.31), respectively. The higher RRs for cold and dry conditions were associated with geographical and climatic indicators corresponding to the central and northern prefectures; demographic, socioeconomic, and health resources indicators showed no clear trends. Finally, 27.25% (95% empirical CI [eCI]: 5.54-36.35) and 32.35% (95% eCI: 22.39-37.87) of all cases were attributable to non-optimal ambient temperature and absolute humidity (6,976,300 [95% eCI: 1,420,068-9,306,128] and 8,280,981 [95% eCI: 8,280,981-9,693,532] cases), respectively. CONCLUSIONS: These findings could help identify the most vulnerable populations in Japan and design adaptation policies to reduce the attributable burden of influenza due to climate variability.

High Summer Temperatures and Heat Stroke Mortality in Spain.

BACKGROUND: Heat stroke is a significant cause of mortality in response to high summer temperatures. There is limited evidence on the pattern and magnitude of the association between temperature and heat stroke mortality. We examined this association in Spain, using data from a 27-year follow-up period. METHODS: We used a space-time-stratified case-crossover design. We analyzed data using conditional quasi-Poisson regression with distributed lag nonlinear models. RESULTS: Spain recorded a total of 285 heat stroke deaths between 1990 and 2016. Heat stroke deaths occurred in 6% of the days in the summer months. The mean temperature was, on average, 5 °C higher on days when a heat stroke was recorded than on days without heat stroke deaths. The overall relative risk was 1.74 (95% confidence interval = 1.54, 1.96) for a 1 °C rise in mean temperature above the threshold of 16 °C, at which a heat stroke death was first recorded. We observed lagged effects as long as 10 days. CONCLUSIONS: Although heat stroke represents a small fraction of total heat-attributable mortality during the summer, it is strongly associated with high temperatures, providing an immediately visible warning of heat-related risk.

Long-term trends in mortality risk associated with short-term exposure to air pollution in 10 Japanese cities between 1977 and 2015.

BACKGROUND AND AIM: Short-term associations between air pollution and mortality have been well reported in Japan, but the historical changes in mortality risk remain unknown. We examined temporal changes in the mortality risks associated with short-term exposure to four criteria air pollutants in selected Japanese cities. METHODS: We collected daily mortality data for non-accidental causes (n = 5,748,206), cardiovascular (n = 1,938,743) and respiratory diseases (n = 777,266), and air pollutants (sulfur dioxide [SO2], nitrogen dioxide [NO2], suspended particulate matter [SPM], and oxidants [Ox]) in 10 cities from 1977 to 2015. We performed two-stage analysis with 5-year stratification to estimate the relative risk (RR) of mortality per 10-unit increase in the 2-day moving average of air pollutant concentrations. In the first stage, city-specific associations were assessed using a quasi-Poisson generalized linear regression model. In the second stage, city-specific estimates were pooled using a random-effects meta-analysis. Linear trend and ratio of relative risks (RRR) were computed to examine temporal changes. RESULTS: When stratifying the analysis by every 5 years, average concentrations in each sub-period decreased for SO2, NO2, and SPM (14.2-2.3 ppb, 29.4-17.5 ppb, 52.1-20.6 µg/m3, respectively) but increased for Ox (29.1-39.1 ppb) over the study period. We found evidence of a negative linear trend in the risk of cardiovascular mortality associated with SPM across sub-periods. However, the risks of non-accidental and respiratory mortality per 10-unit increase in SPM concentration were significantly higher in the most recent period than in the earliest period. Other gaseous pollutants did not show such temporal risk change. The risks posed by these pollutants were slightly to moderately heterogeneous in the different cities. CONCLUSIONS: The mortality risks associated with short-term exposure to SPM changed, with different trends by cause of death, in 10 cities over 39 years whereas the risks for other gaseous pollutants were relatively stable.

COVID-19 pandemic modifies temperature and heat-related illness ambulance transport association in Japan: a nationwide observational study.

BACKGROUND: During the COVID-19 pandemic, several illnesses were reduced. In Japan, heat-related illnesses were reduced by 22% compared to pre-pandemic period. However, it is uncertain as to what has led to this reduction. Here, we model the association of maximum temperature and heat-related illnesses in the 47 Japanese prefectures. We specifically examined how the exposure and lag associations varied before and during the pandemic. METHODS: We obtained the summer-specific, daily heat-related illness ambulance transport (HIAT), exposure variable (maximum temperature) and covariate data from relevant data sources. We utilized a stratified (pre-pandemic and pandemic), two-stage approach. In each stratified group, we estimated the 1) prefecture-level association using a quasi-Poisson regression coupled with a distributed lag non-linear model, which was 2) pooled using a random-effects meta-analysis. The difference between pooled pre-pandemic and pandemic associations was examined across the exposure and the lag dimensions. RESULTS: A total of 321,655 HIAT cases was recorded in Japan from 2016 to 2020. We found an overall reduction of heat-related risks for HIAT during the pandemic, with a wide range of reduction (10.85 to 57.47%) in the HIAT risk, across exposure levels ranging from 21.69 °C to 36.31 °C. On the contrary, we found an increment in the delayed heat-related risks during the pandemic at Lag 2 (16.33%; 95% CI: 1.00, 33.98%). CONCLUSION: This study provides evidence of the impact of COVID-19, particularly on the possible roles of physical interventions and behavioral changes, in modifying the temperature-health association. These findings would have implications on subsequent policies or heat-related warning strategies in light of ongoing or future pandemics.

Seasonality of mortality under a changing climate: a time-series analysis of mortality in Japan between 1972 and 2015.

BACKGROUND: Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate. METHODS: Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics. RESULTS: The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27-1.30), 1.53 (95% CI: 1.50-1.55), and 1.46 (95% CI: 1.44-1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08-1.10), 1.10 (95% CI: 1.08-1.11), and 1.35 (95% CI: 1.32-1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54-1.42), 1.39% (95% CI: 0.82-1.97), and 0.13% (95% CI: - 1.24 to 1.48), respectively. CONCLUSION: Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.

Estimated postnatal p,p'-DDT and p,p'-DDE levels and body mass index at 42 months of age in a longitudinal study of Japanese children.

BACKGROUND: Children are exposed to p,p'-dichlorodiphenyltrichloroethane (p,p'-DDT) and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) through placental and lactational transfer. Some studies have suggested that early-life exposure to these compounds could lead to increased body mass index (BMI) during childhood. Our aim was to assess whether children's exposure during the first 2 years of life is associated with BMI z-score in Japanese children at 42 months of age. METHODS: We used data from a birth cohort (n = 290) of the Tohoku Study of Child Development. p,p'-DDT and p,p'-DDE levels were measured in breast milk samples collected 1 month after birth, and levels in children were estimated using a toxicokinetic model for three exposure periods (0-6 months, 6-12 months, 12-24 months). Associations between exposure estimates and BMI z-score at 42 months of age were assessed using multivariate linear regression models. RESULTS: We found no significant association between levels of p,p'-DDT measured in breast milk or estimated in children and BMI z-score. However, we observed associations between estimated p,p'-DDE levels in girls during all postnatal exposure periods and BMI z-score; for each log increase in the estimated p,p'-DDE levels, BMI z-score increased by 0.23 (C.I. 95%: 0.01, 0.45) for the 0-6 months exposure period, 0.26 (C.I. 95%: 0.06, 0.47) for the 6-12 months exposure period, and 0.24 (C.I. 95%: 0.05, 0.43) for the 12-24 months exposure period. CONCLUSION: In this study of Japanese children, estimated postnatal p,p'-DDE levels were associated with increased BMI z-score at 42 months of age, mostly in girls. These results are in line with previous studies supporting that early-life exposure to p,p'-DDE may be associated with higher BMI during childhood.

Correction to: Estimated postnatal p,p'-DDT and p,p'-DDE levels and body mass index at 42 months of age in a longitudinal study of Japanese children.

An amendment to this paper has been published and can be accessed via the original article.

The nonlinear association between outdoor temperature and cholesterol levels, with modifying effect of individual characteristics and behaviors.

Few studies have been conducted to investigate the underlying mechanisms of the effect of temperature on cardiovascular disease at population level, especially among Chinese population. A total of 56,039 participants were recruited from Kailuan cohort study, China. The lipoprotein profile indicators, including triglycerides (TG), low-density lipoprotein (LDL), and high-density lipoprotein, were collected. Non-linear associations between temperature and the lipoprotein profile indicators were examined using a nonlinear function for temperature. Stratified analyses were performed in groups by individual characteristics (age, gender, and body mass index) and individual behaviors (physical activities and smoking habits). Generally, a non-linear relationship was found between cholesterol levels and temperature. A 1 °C decrease in temperature below the threshold was related with 0.004 mmol/L (95% CI 0.0004, 0.008), 0.022 mmol/L (95% CI 0.020, 0.025), and 0.009 mmol/L (95% CI 0.008, 0.011) increase in TG, LDL, and HDL, respectively; a 1 °C increase in temperature above the threshold was associated with 0.005 mmol/L (95% CI 0.003, 0.007), 0.012 mmol/L (95% CI 0.009, 0.015), and 0.002 mmol/L (95% CI 0.001, 0.004) increase in TG, LDL, and HDL, respectively. Stratified analyses showed that effect estimates on TG and LDL were larger among females, subjects with higher BMI, and those with smoking habits, while effect estimates on HDL were smaller among these subjects (expect for female). Our results suggest both cold and hot effect of temperature on cholesterol. Furthermore, females, and people with higher BMI or smoking habit may be more susceptible to outdoor temperature.

Projecting Fine Particulate Matter-Related Mortality in East China.

China is suffering from severe air pollution from fine particulate matter [≤ 2.5 µm in aerodynamic diameter (PM2.5)], especially East China. But its future trends and potential health impacts remain unclear. The study objectives were to project future trends of PM2.5 and its short-term effect on mortality in East China by 2030. First, daily changes in PM2.5 concentrations between 2005 and 2030 were projected under the "current legislation" scenario (CLE) and the "maximum technically feasible reduction" scenario (MFR). Then, they were linked to six population projections, two mortality rate projections, and PM2.5-mortality associations to estimate the changes in PM2.5-related mortality in East China between 2005 and 2030. Under the CLE scenario, the annual mean PM2.5 concentration was projected to decrease by 0.62 µg/m(3) in East China, which could cause up to 124,000 additional deaths, when considering the population growth. Under the MFR scenario, the annual mean PM2.5 concentration was projected to decrease by 20.41 µg/m(3) in East China. At least 230,000 deaths could be avoided by such a large reduction in PM2.5 concentration under MFR scenario, even after accounting for the population growth. Therefore, our results suggest that reducing PM2.5 concentration substantially in East China would benefit the public health. Otherwise, it may still remain as a great health risk in the future, especially when the population keeps growing.

The association between ambient temperature and children's lung function in Baotou, China.

The objective of this study is to examine the association between ambient temperature and children's lung function in Baotou, China. We recruited 315 children (8-12 years) from Baotou, China in the spring of 2004, 2005, and 2006. They performed three successive forced expiratory measurements three times daily (morning, noon, and evening) for about 5 weeks. The highest peak expiratory flow (PEF) was recorded for each session. Daily data on ambient temperature, relative humidity, and air pollution were monitored during the same period. Mixed models with a distributed lag structure were used to examine the effects of temperature on lung function while adjusting for individual characteristics and environmental factors. Low temperatures were significantly associated with decreases in PEF. The effects lasted for lag 0-2 days. For all participants, the cumulative effect estimates (lag 0-2 days) were -1.44 (-1.93, -0.94) L/min, -1.39 (-1.92, -0.86) L/min, -1.40 (-1.97, -0.82) L/min, and -1.28 (-1.69, -0.88) L/min for morning, noon, evening, and daily mean PEF, respectively, associated with 1 °C decrease in daily mean temperature. Generally, the effects of temperature were slightly stronger in boys than in girls for noon, evening, and daily mean PEF, while the effects were stronger in girls for morning PEF. PM2.5 had joint effects with temperature on children's PEF. Higher PM2.5 increased the impacts of low temperature. Low ambient temperatures are associated with lower lung function in children in Baotou, China. Preventive health policies will be required for protecting children from the cold weather.

Time-stratified case-crossover studies for aggregated data in environmental epidemiology: a tutorial.

The case-crossover design is widely used in environmental epidemiology as an effective alternative to the conventional time-series regression design to estimate short-term associations of environmental exposures with a range of acute events. This tutorial illustrates the implementation of the time-stratified case-crossover design to study aggregated health outcomes and environmental exposures, such as particulate matter air pollution, focusing on adjusting covariates and investigating effect modification using conditional Poisson regression. Time-varying confounders can be adjusted directly in the conditional regression model accounting for the adequate lagged exposure-response function. Time-invariant covariates at the subpopulation level require reshaping the typical time-series data set into a long format and conditioning out the covariate in the expanded stratum set. When environmental exposure data are available at geographical units, the stratum set should combine time and spatial dimensions. Moreover, it is possible to examine effect modification using interaction models. The time-stratified case-crossover design offers a flexible framework to properly account for a wide range of covariates in environmental epidemiology studies.

Compilation of open access time-series datasets for studying temperature-mortality association.

In this article, we present a comprehensive compilation of open access daily time-series datasets tailored to assess the temperature-mortality association. The data consists of daily mortality counts and average ambient temperature at various levels of geographic aggregation, including data from four cities, ten regions, and two counties, which have been utilised in previously published studies. These datasets are applicable for time-series regression analysis to estimate location-specific temperature-mortality associations. Additionally, the availability of data from multiple geographical locations enabled the exploration of geographical differences by pooling associations using meta-analysis. This compilation aims to serve as a valuable resource for researchers, educators, and students, facilitating their application of time-series regression modelling for research endeavours and training activities.

Incidence and seasonality of Kawasaki disease in children in the Philippines, and its association with ambient air temperature.

Background: Despite an unknown cause, Kawasaki disease (KD) is currently the primary leading cause of acquired heart disease in developed countries in children and has been increasing in recent years. Research efforts have explored environmental factors related to KD, but they are still unclear especially in the tropics. We aimed to describe the incidence of KD in children, assess its seasonality, and determine its association with ambient air temperature in the National Capital Region (NCR), Philippines from January 2009 to December 2019. Methods: Monthly number of KD cases from the Philippine Pediatric Society (PPS) disease registry was collected to determine the incidence of KD. A generalized linear model (GLM) with quasi-Poisson regression was utilized to assess the seasonality of KD and determine its association with ambient air temperature after adjusting for the relevant confounders. Results: The majority of KD cases (68.52%) occurred in children less than five years old, with incidence rates ranging from 14.98 to 23.20 cases per 100,000 population, and a male-to-female ratio of 1.43:1. Seasonal variation followed a unimodal shape with a rate ratio of 1.13 from the average, peaking in March and reaching the lowest in September. After adjusting for seasonality and long-term trend, every one-degree Celsius increase in the monthly mean temperature significantly increased the risk of developing KD by 8.28% (95% CI: 2.12%, 14.80%). Season-specific analysis revealed a positive association during the dry season (RR: 1.06, 95% CI: 1.01, 1.11), whereas no evidence of association was found during the wet season (RR: 1.10, 95% CI: 0.95, 1.27). Conclusion: We have presented the incidence of KD in the Philippines which is relatively varied from its neighboring countries. The unimodal seasonality of KD and its linear association with temperature, independent of season and secular trend, especially during dry season, may provide insights into its etiology and may support enhanced KD detection efforts in the country.

Short-term associations of diarrhoeal diseases in children with temperature and precipitation in seven low- and middle-income countries from Sub-Saharan Africa and South Asia in the Global Enteric Multicenter Study.

BACKGROUND: Diarrhoeal diseases cause a heavy burden in developing countries. Although studies have described the seasonality of diarrhoeal diseases, the association of weather variables with diarrhoeal diseases has not been well characterized in resource-limited settings where the burden remains high. We examined short-term associations between ambient temperature, precipitation and hospital visits due to diarrhoea among children in seven low- and middle-income countries. METHODOLOGY: Hospital visits due to diarrhoeal diseases under 5 years old were collected from seven sites in The Gambia, Mali, Mozambique, Kenya, India, Bangladesh, and Pakistan via the Global Enteric Multicenter Study from December 2007 to March 2011. Daily weather data during the same period were downloaded from the ERA5-Land. We fitted time-series regression models to examine the relationships of daily diarrhoea cases with daily ambient temperature and precipitation. Then, we used meta-analytic tools to examine the heterogeneity between the site-specific estimates. PRINCIPAL FINDINGS: The cumulative relative risk (RR) of diarrhoea for temperature exposure (95th percentile vs. 1st percentile) ranged from 0.24 to 8.07, with Mozambique and Bangladesh showing positive associations, while Mali and Pakistan showed negative associations. The RR for precipitation (95th percentile vs. 1st percentile) ranged from 0.77 to 1.55, with Mali and India showing positive associations, while the only negative association was observed in Pakistan. Meta-analysis showed substantial heterogeneity in the association between temperature-diarrhoea and precipitation-diarrhoea across sites, with I2 of 84.2% and 67.5%, respectively. CONCLUSIONS: Child diarrhoea and weather factors have diverse and complex associations across South Asia and Sub-Saharan Africa. Diarrhoeal surveillance system settings should be conceptualized based on the observed pattern of climate change in these locations.

Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank.

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM2.5) exposures to the UK Biobank cohort.

Non-optimal temperature-attributable mortality and morbidity burden by cause, age and sex under climate and population change scenarios: a nationwide modelling study in Japan.

Background: Future temperature effects on mortality and morbidity may differ. However, studies comparing projected future temperature-attributable mortality and morbidity in the same setting are limited. Moreover, these studies did not consider future population change, human adaptation, and the variations in subpopulation susceptibility. Thus, we simultaneously projected the temperature-related mortality and morbidity by cause, age, and sex under population change, and human adaptation scenarios in Japan, a super-ageing society. Methods: We used daily mean temperatures, mortality, and emergency ambulance dispatch (a sensitive indicator for morbidity) in 47 prefectures of Japan from 2015 to 2019 as the reference for future projections. Future mortality and morbidity were generated at prefecture level using four shared socioeconomic pathway (SSP) scenarios considering population changes. We calculated future temperature-related mortality and morbidity by combining baseline values with future temperatures and existing temperature risk functions by cause (all-cause, circulatory, respiratory), age (<65 years, ≥65 years), and sex under various climate change and SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Full human adaptation was simulated based on empirical evidence using a fixed percentile of minimum mortality or morbidity temperature (MMT), while no adaptation was simulated with a fixed absolute MMT. Findings: A future temporal decline in mortality burden attributable to non-optimal temperatures was observed, driven by greater cold-related deaths than heat-related deaths. In contrast, temperature-related morbidity increased over time, which was primarily driven by heat. In the 2050s and 2090s, under a moderate scenario, there are 83.69 (95% empirical confidence interval [eCI] 38.32-124.97) and 77.31 (95% eCI 36.84-114.47) all-cause deaths per 100,000 population, while there are 345.07 (95% eCI 258.31-438.66) and 379.62 (95% eCI 271.45-509.05) all-cause morbidity associated with non-optimal temperatures. These trends were largely consistent across causes, age, and sex groups. Future heat-attributable health burden is projected to increase substantially, with spatiotemporal variations and is particularly pronounced among individuals ≥65 y and males. Full human adaptation could yield a decreasing temperature-attributable mortality and morbidity in line with a decreasing population. Interpretation: Our findings could support the development of targeted mitigation and adaptation strategies to address future heat-related impacts effectively. This includes improved healthcare allocations for ambulance dispatch and hospital preventive measures during heat periods, particularly custom-tailored to address specific health outcomes and vulnerable subpopulations. Funding: Japan Science and Technology Agency and Environmental Restoration and Conservation Agency and Ministry of the Environment of Japan.

Association between long-term exposure to low ambient PM2.5 and cardiovascular hospital admissions: A UK Biobank study.

INTRODUCTION: A causal link between air pollution exposure and cardiovascular events has been suggested. However fewer studies have investigated the shape of the associations at low levels of air pollution and identified the most important temporal window of exposure. Here we assessed long-term associations between particulate matter < 2.5 µm (PM2.5) at low concentrations and multiple cardiovascular endpoints using the UK Biobank cohort. METHODS: Using data on adults (aged > 40) from the UK Biobank cohort, we investigated the associations between 1-year, 3-year and 5-year time-varying averages of PM2.5 and incidence of major adverse cardiovascular events (MACE), myocardial infarction (MI), heart failure, atrial fibrillation and flutter and cardiac arrest. We also investigated outcome subtypes for MI and stroke. Events were defined as hospital inpatient admissions. We fitted Cox proportional hazard regression models applying extensive control for confounding at both individual and area level. Finally, we assessed the shape of the exposure-response functions to assess effects at low levels of exposure. RESULTS: We analysed data from 377,736 study participants after exclusion of prevalent subjects. The average follow-up (2006-2021) was 12.9 years. We detected 19,353 cases of MACE, 6,562 of acute MI, 6,278 of heart failure, 1,258 for atrial fibrillation and flutter, and 16,327 for cardiac arrest. Using a 5-year exposure window, we detected positive associations (for 5 µg/m3 increase in PM2.5) for 5-point MACE of [1.12 (95 %CI: 1.00-1.26)], heart failure [1.22 (1.00-1.50)] and cardiac arrest [1.16 (1.03-1.31)]. We did not find any association with acute MI, while non-ST-elevation MI was associated with the 1-year exposure window [1.52 (1.12-2.07)]. The assessment of the shape of the exposure-response relationships suggested that risk is approximately linear for most of the outcomes. CONCLUSIONS: We found positive associations between long-term exposure to PM2.5 and multiple cardiovascular outcomes for different exposure windows. The cardiovascular risk tends to rise even at exposure concentrations below 12-15 µg/m3, indicating high risk below UK national and international thresholds.

Short-term associations of PM10 attributed to biomass burning with respiratory and cardiovascular hospital admissions in Peninsular Malaysia.

BACKGROUND: Biomass burning (BB) is a major source of air pollution and particulate matter (PM) in Southeast Asia. However, the health effects of PM smaller than 10 µm (PM10) originating from BB may differ from those of other sources. This study aimed to estimate the short-term association of PM10 from BB with respiratory and cardiovascular hospital admissions in Peninsular Malaysia, a region often exposed to BB events. METHODS: We obtained and analyzed daily data on hospital admissions, PM10 levels and BB days from five districts from 2005 to 2015. We identified BB days by evaluating the BB hotspots and backward wind trajectories. We estimated PM10 attributable to BB from the excess of the moving average of PM10 during days without BB hotspots. We fitted time-series quasi-Poisson regression models for each district and pooled them using meta-analyses. We adjusted for potential confounders and examined the lagged effects up to 3 days, and potential effect modification by age and sex. RESULTS: We analyzed 210 960 respiratory and 178 952 cardiovascular admissions. Almost 50% of days were identified as BB days, with a mean PM10 level of 53.1 µg/m3 during BB days and 40.1 µg/m3 during normal days. A 10 µg/m3 increment in PM10 from BB was associated with a 0.44% (95% CI: 0.06, 0.82%) increase in respiratory admissions at lag 0-1, with a stronger association in adults aged 15-64 years and females. We did not see any significant associations for cardiovascular admissions. CONCLUSIONS: Our findings suggest that short-term exposure to PM10 from BB increased the risk of respiratory hospitalizations in Peninsular Malaysia.