BACKGROUND:Ambient fine particulate matter (PM2.5) and its chemical components are closely linked to human health, but evidence on their association with violence-related injury remains limited. This study aimed to explore the risk and burden of violence-related injury from short-term exposure to PM2.5 and its chemical components in China. METHODS:Integrating violence-related injury data during 2006-2021 from the National Injury Surveillance System (NISS) of China with PM2.5 components (black carbon (BC), organic matter (OM), ammonium (NH+4), nitrate (NO-3), and sulfate (SO2-4)) data from the Tracking Air Pollution (TAP, https://tapdata.org.cn/), we employed conditional logistic regression to quantify the associations and attributable fractions (AFs) of PM2.5 and its components with violence-related injury. We further conducted stratified analyses to identify vulnerable populations. RESULTS:PM2.5 and its constituents were associated with elevated risk of violence-related injury. Specifically, the excess risks (ERs) for each interquartile range (IQR) rise were 1.97% (95%CI: 1.43-2.51%, IQR: 39.39 µg/m3) for PM2.5, 1.81% (95%CI: 1.30-2.32%, IQR: 7.92 µg/m3) for OM, 1.77% (95%CI: 1.23-2.32%, IQR: 8.84 µg/m3) for SO2-4, 1.73% (95%CI: 1.21-2.25%, IQR: 2.08 µg/m3) for BC, 1.63% (95%CI: 1.14-2.13%, IQR: 9.38 µg/m3) for NH+4 and 1.51% (95%CI: 1.03-1.99%, IQR: 5.92 µg/m3) for NO-3. The associations were stronger for females, students, people aged 45-59 years, those living in Northeast China, and the cold season. The AF of violent injury associated with PM2.5 exposure was 2.53% (95%CI: 1.85-3.20%), with higher AF for OM (AF=2.36%, 95%CI: 1.71-3.01%), followed by BC (AF=2.17%, 95%CI: 1.53-2.81%), SO2-4(AF=2.14%, 95%CI: 1.50-2.78%), NH+4 (AF=1.78%, 95%CI: 1.25-2.30%), and NO-3 (AF=1.55%, 95%CI: 1.07-2.04%). CONCLUSIONS:Short-term exposure to PM2.5 and its constituents elevates the risk and burden of violence-related injury, providing novel evidence for air pollution control and injury prevention policies.

Injury is a major cause of morbidity and mortality among children, yet evidence on the influence of ambient temperature on childhood injury remains limited. This study quantified the association between temperature and child injury across age groups in China from 2006 to 2021 and projected the future burden attributable to temperature under climate change scenarios. Injury data were derived from the National Injury Surveillance System, and meteorological data were obtained from the fifth generation of the European ReAnalysis-Land dataset. Conditional logistic regression combined with distributed lag non-linear models was used to estimate temperature-injury associations, with stratified analyses by sex, injury mechanism, and location. A total of 1,849,211 injury cases among children were included. Temperature showed an approximately linear relationship with injury risk, with a 1.14 % (95 %CI: 1.08 %, 1.20 %) increase in child injury risk for each 1 °C rise. Children aged 2-4 years were most vulnerable, exhibiting a 1.36 % (95 %CI: 1.24 %, 1.48 %) excess risk. Under the SSP5-8.5 scenario, the temperature-attributable fraction for this group is projected to increase from 0.58 % (95 %CI: 0.53 %, 0.62 %) in the 2020s to 6.87 % (95 %CI: 6.27 %, 7.54 %) in the 2090s. Traffic injuries accounted for the largest burden among children aged 0-9 years, while sharp instrument injuries predominated in those aged 10-14 years. These findings indicate that rising ambient temperatures substantially increase childhood injury risk, particularly among younger age groups, and that climate change may amplify the future burden of temperature-related injuries in China.

Background: Despite the health impacts of socioeconomic status, physical inactivity, and air pollution being well-documented, fewer studies have quantified their combined effects on mortality, life expectancy, and economic burden. Methods: This prospective cohort study used data from the China Health and Retirement Longitudinal Study (CHARLS, 2011 - 2020). Cox proportional hazards regression estimated HRs and population attributable fractions (PAF) for eight modifiable risk factors. Flexible Cox models assessed life expectancy changes using a risk score. Economic burden was estimated using the value of a statistical life (VSL). Results: This study included 19,846 participants aged 45 and older from 28 provinces. The Combined PAF of mortality due to the eight modifiable risk factors was 71.64%, with major contributions by low socioeconomic status (37.01%), low physical activity (27.64%), and high PM2.5 exposure (13.87%). Life expectancy losses were 4.48 years for current smokers and 3.19 years for current drinkers; former smokers and drinkers lost 4.49 and 4.64 years, respectively. Each one-point increase in the risk score was associated with an average loss of approximately 2 years in life expectancy at age 45. Participants with a score of zero were expected to live 18.37 years longer than those scoring nine. The average economic loss attributable to these risk factors was 1833.95 million US dollars per 100,000 population. Subgroup analyses revealed greater impacts in rural and northern regions. Conclusions: Modifiable risk factors contribute substantially to mortality, reduced life expectancy, and economic losses among middle-aged and older adults, highlighting the need for targeted public health strategies. (c) 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).

Fall injury poses considerable disease burden. However, limited studies investigated the age-sex-specific risk and burden of fall injury associated with ambient temperature. A time-stratified case-crossover study with 4.19 million fall injury cases from the National Injury Surveillance System was conducted in China. We identified a U-shaped relationship between temperature and fall injury in the total population, with higher risk from heat than cold. There were significant age-sex-specific disparities in temperature-related fall injury risk, with diminished heat effects and increased cold effects as age rises, particularly among females. Ambient temperature exhibited a substantial burden on fall injuries, with much higher burden associated with heat than with cold across almost all age groups. Compared to the 2020s, our projections indicate a 1.22-fold increase in heat-related attributable fraction (AF) and a 0.38-fold decrease in cold-related AF under the SSP5-8.5 scenario. These findings can guide injury prevention and control strategies in the context of climate change.

Background: Although the health risks of increasing extreme weather events such as heatwaves and cold spells are well recognized, there remains a research gap on how these events specifically affect physical activity, particularly among individuals with different weight status. In addition, limited attention has been given to how sociodemographic factors might interact with extreme weather to influence physical activity. Objectives: This study aimed to examine the association between extreme weather events and daily step counts by body weight and to investigate whether these associations are modified by other sociodemographic factors. Methods: To address this, we conducted a longitudinal panel study in Henan Province, China, and applied distributed lag nonlinear models to examine how daily step counts were influenced by extreme weather events-including heatwaves and cold spells-and their intensity and duration. We also explored whether sociodemographic factors modified these effects. Results: Our findings showed that extreme weather events significantly reduced step counts. Specifically, heatwaves were associated with a substantial decline in individuals with overweight [-959.68 steps; 95% confidence interval (CI):-1198.13,-721.22], which was significantly larger than the reduction observed in participants with normal weight (-331.16 steps; 95% CI:-625.50,-36.83). Conversely, cold spells were associated with a disproportionately larger reduction in step counts among participants with normal weight (-1832.46 steps; 95% CI:-2136.15,-1528.76), compared with a milder reduction in the overweight group (-1067.66 steps; 95% CI:-1317.40,-817.91). Interaction analysis revealed that high income consistently attenuated these disparities (P-interaction < 0.05). Although age and sex also modified these associations, body weight emerged as a primary factor. Conclusions: These findings suggest that individuals with low income or high body mass index (BMI) are associated with heightened vulnerability to heatwaves, whereas individuals with low income or normal BMI are linked to greater sensitivity during cold spells.

BACKGROUND AND OBJECTIVES:Ambient particulate matter <2.5 μm (PM2.5) has been extensively associated with morbidity and mortality of stroke. However, most previous findings were derived from separate clinical phase analyses and have not focused on the specific components of PM2.5. Using a national cohort study based on multistate analysis, we aimed to evaluate the associations of PM2.5 and its specific components with several different clinical progression phases of stroke. METHODS:We analyzed data from first-ever stroke patients in the Third China National Stroke Registry, and follow-up was conducted through March 2023. Ambient PM2.5 and its components were assessed using one-year average concentrations before the end point or the latest follow-up, obtained from Tracking Air Pollution in China. Outcomes were verified through hospital records and official registries. We used multistate models to estimate the associations and applied P-splines to assess potential nonlinear associations of PM2.5 and its components. We also conducted subgroup analyses to explore potential effect modification. RESULTS:We included a total of 11,491 participants with a mean age of 61.5 ± 11.5 years, of whom 7,821 (68.1%) were male. We observed positive associations of PM2.5 mass concentrations and all 5 chemical components with each stage of ischemic stroke progression. For PM2.5 mass concentration, the adjusted hazard ratios per 12.5 μg/m3 increase were as follows: 2.82 (95% CI 2.69-2.94) for the transition from first stroke to recurrence, 2.85 (95% CI 2.40-3.37) from first stroke to poststroke cardiovascular diseases (PCVDs), 2.50 (95% CI 2.28-2.73) from first stroke to death, 2.23 (95% CI 1.97-2.53) from stroke recurrence to death, and 1.93 (95% CI 1.33-2.80) from PCVDs to death. All 5 chemical components also showed strong associations across each transition stage. The exposure-response relationships followed J-shaped curves (p for nonlinearity <0.001). Of interest, patients with good functional outcomes (modified Rankin Scale score ≤3) or mild strokes (NIH Stroke Scale score <5) had a higher risk of recurrence or death, possibly because of increased exposure to outdoor air pollution. DISCUSSION:Long-term exposure to PM2.5 and its specific components was positively associated with the risk of recurrent stroke, and secondary morbidity and mortality. Exposure assessment relied on residential addresses rather than personal measurements, which should be noted as a limitation.

Many studies assessing urban air-pollution health impacts assign outdoor PM₂.₅ at a single residential address, ignoring children’s daily movements between home and school. This simplification may misclassify exposure and bias short-term risk estimates, potentially misguiding school-area prevention priorities. Using 2,543 laboratory-confirmed influenza cases among school-aged children in Guangzhou (2014–2019) and hourly monitoring–derived PM₂.₅ concentration fields, we tested whether mobility-aware exposure assignment changes short-term PM₂.₅–influenza risk estimates. We constructed a residence-based model (RBM) and a multi-context activity-weighted model (MCAWM) that reallocates hourly PM₂.₅ across home, school, and street-network commute corridors using stylized school-day schedules and open-source street networks. Although RBM and MCAWM lag-0 daily PM₂.₅ estimates were highly correlated (r = 0.93), 15.8

The relationship between urban vegetation and dengue risk remains unclear, partly because most studies rely on static residential exposure and coarse greenness measures. This study examined how street-view vegetation and human mobility jointly influence neighborhood-level dengue risk in Guangzhou, China. We analyzed 1,054 communities in Guangzhou using 3,972 locally acquired dengue cases from 2015 to 2019. Street-view images were used to derive grass, plant, and tree cover, while Landsat imagery provided NDVI. Row-standardized origin-destination mobility networks were used to construct mobility-weighted vegetation metrics. Negative binomial regression models with a population offset were fitted to estimate associations of local and mobility-weighted vegetation with dengue risk. Sub-group analyses were conducted by sex and age, and sensitivity analyses tested the robustness of the findings. In the local-only model, plant cover was significantly associated with lower dengue risk (IRR = 0.883, 95

Background:The ability to predict cardiometabolic multimorbidity (CMM) could significantly facilitate the identification of and intervention for middle-aged and older Chinese adults at risk. This study aimed to develop a prediction model for CMM progression trajectories based on multidimensional risk factors using an ensemble machine learning approach. Methods:Data from 4,518 participants were obtained from the World Health Organization's Study on Global AGEing and Adult Health (SAGE) in China, covering the period from 2007 to 2019. Information on the incidence of cardiometabolic diseases (CMDs) was collected via self-reported surveys. CMM was defined as the presence of at least two CMDs, including hypertension, diabetes, angina, stroke, and obesity. A multi-state model was used to examine the influence of multidimensional factors on the transition from health to a single CMD and subsequently to CMM. Predictive models for these transitions were then developed. Results:During follow-up, 52.19% of initially healthy individuals developed one cardiometabolic disease (CMD), among whom 15.61% progressed to CMM. Female, low GDP per capita, unhealthy behaviors, elevated PM2.5 concentrations, low humidity, and low temperatures were identified as shared risk factors across the progression from health to CMD and from CMD to CMM. Moreover, in the health-to-CMD transition, older age, low educational level, and physical fitness impairment emerged as independent risk factors. Distinctively, in the CMD-to-CMM transition, IC impairment was identified as an independent influencing factor. Using these key predictors, a stacking ensemble model incorporating five machine learning algorithms was developed, and the model demonstrated area under the curve (AUC) values of 0.89 (95% CI: 0.88-0.91) for predicting the transition from health to CMD, and 0.76 (95% CI: 0.72-0.82) for predicting progression from CMD to CMM. Conclusion:In conclusion, this study demonstrates that a stacking ensemble model based on multidimensional factors can effectively predict the progression of CMM. Our study not only identified distinct risk factors for different transitional stages but also highlighted the potential of machine learning to improve early risk stratification and inform targeted interventions for preventing CMM in the aging.

Background:Cardiometabolic multimorbidity (CMM) prevalence has risen, but longitudinal evidence on trajectories and mortality links remains limited. Objective:This study aimed to examine CMM trends and the impact of its statuses/trajectories on mortality in Guangdong, China. Design:This is a cohort study. Setting:Data were collected from the Guangdong Provincial adult chronic diseases and risk factors cohort (GDACRC). The follow-up data were collected from the hospitalization record system and the vital register system in Guangdong Province. Participants:Baseline surveys were separately conducted in 2007, 2010, 2013, 2015, and 2018, which recruited 34,244 participants aged 18 years or older. Measurements:Calculated age-sex standardized CMM prevalence. Cox proportional hazards models were used to assess the associations between CMM statuses/trajectories and mortality risk. Results:From 2007 to 2018, the prevalence of CMM increased significantly from 4.31 to 8.75% in Guangdong Province, China (p < 0.05). Compared with participants without any cardiometabolic diseases (CMD), the mortality risk of those with single CMD and CMM, respectively, increased by 30 and 85%, with a greater risk of patients aged < 65 years. The most common trajectory was from metabolic disease (M) to cardiovascular diseases (C), that 9.31% healthy participants at baseline developed CMM on this trajectory, which accounted for 53.5% of all CMM patients. Compared to patients with single M, patients with the trajectory from C to M had greater mortality risk (HR = 3.32, 95% CI: 2.13-5.16). Limitation:Single-province sample; self-report bias. Conclusion:CMM represents a major public health challenge in Guangdong Province, China. Our findings provide new insights on the CMM progression, which may be helpful for prevention and clinical management of CMM.

While extensive research has examined acute mortality risks associated with heat exposure, emerging evidence indicates a paradoxical decline in heat-attributable mortality across developed nations. Yet critical knowledge gaps persist regarding this epidemiological transition in China. Our study collected daily respiratory mortality data from 2,219 districts/counties in 31 provinces, China during 2005-2019. We investigated the minimum mortality temperature (MMT) experienced an increment of 0.124 degrees C per year from 2005 to 2019 (P = 0.038), while the excess risk (ER) associated with extreme heat declined from 9.46% (95% Confidence Interval (CI): 4.81-14.32%) in 2005-2007 to 3.51% (95% CI: 2.12-4.92%) in 2017-2019, representing a 62.88% (95% CI: 55.31-69.99%) reduction. Similarly, the attributable fraction (AF) also decreased from 1.26% (95% CI: 0.57-1.92%) to 0.38% (95% CI: 0.21-0.55%), marking a 69.84% (95% CI: 48.91-89.24%) decrease. Stratified analyses revealed the mortality burdens decreases were more pronounced among males, individuals 0-64 years, southern China, and patients with chronic obstructive pulmonary disease (COPD). Urbanization rate, the prevalence of air conditioning, and green space were top three socioeconomic factors driving this temporal shift. Our analysis reveals an attenuation of heat-associated respiratory mortality between 2005-2019, concurrent with rising MMT exhibiting pronounced population and spatial disparities. It underscores the critical role of adaptive capacity in mitigating climate change-related health burdens, informing targeted public health strategies.

Suicide is an important public health challenge globally, with ambient fine particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) identified as a risk factor for suicide mortality. However, the spatiotemporal changes of the suicide mortality burden associated with short-term exposures to PM2.5 chemical constituents in China remain unknown. In this study, we investigated the spatiotemporal trends of the mortality burden from suicide associated with short-term exposures to PM2.5 chemical constituents at both national and provincial levels across China. A nationwide, time-stratified case-crossover design was conducted using the individual suicide mortality data from the Chinese National Mortality Surveillance System from January 1, 2014, to December 31, 2019. Conditional logistic regression models were applied to evaluate the nonlinear excess risks (ERs) of suicide mortality for short-term exposures (lag0-1 day) to PM2.5 mass and chemical constituents, and the corresponding attributable fractions (AFs) associated with PM2.5 and its constituents were also calculated. Temporal changes in AFs from 2014 to 2019 were analyzed using linear regression models at the national and provincial levels. This study included 372,164 suicide deaths from 2,758 counties/districts across China from 2014 to 2019. The annual average changes in the concentrations of PM2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH4 +), nitrate (NO3 -), and sulfate (SO4 2-) during 2014-2019 were -3.47, -0.17, -0.72, -0.55, -0.68, and -0.78 μg/m3/year, respectively. Correspondingly, the annual average change in AFs of suicide mortality in PM2.5 mass was -0.38% (95% CI: -0.98%, 0.22%). Among the PM2.5 chemical constituents, SO4 2- (-0.56, 95% CI: -1.21, 0.09%) and NO3 - (-0.45, 95% CI: -0.89, -0.02%) showed greater decreasing trends in annual average changes of AFs, followed by NH4 + (-0.40, 95% CI: -0.75, -0.05%), BC (-0.40, 95% CI: -1.01, 0.22%), and OM (-0.23, 95% CI: -0.79, 0.34%). The temporal changes of AFs exhibited greater decreasing trends in violent suicides in the warm season, and people resided in Eastern and Western China. The top three provinces exhibiting the highest annual average reduction in PM2.5 mass-related AFs were Qinghai, Xizang, and Fujian. The primary associated socio-economic indicators influencing the spatiotemporal trends in PM2.5-related suicide mortality burden were identified as green space area, Gross Domestic Product per capita, and unemployment rate. Our findings robustly highlight the substantial health benefits of ambient PM2.5 pollution control, especially SO4 2- and NO3 -, offering a valuable reference for other regions grappling with severe air pollution issues.

Objective:Although many studies have examined temperature-related non-accidental mortality, the impact of heat waves on the mortality burden of chronic kidney disease (CKD) remains poorly understood. This study aimed to assess the CKD mortality burden associated with heat waves in China under global warming. Methods:Mortality data on CKD from 2,790 counties/districts in China from 2004 to 2022 were collected from the Chinese Center for Disease Control and Prevention; meteorological data for the same period were obtained from the fifth-generation European Reanalysis Land dataset. A time-stratified case-crossover design combined with a distributed lag nonlinear model was used to examine the association between heat waves and CKD mortality. Future CKD mortality burdens attributable to heat waves under climate change and future population scenarios were projected. Results:In total, 236,260 CKD deaths were included in this study. Compared to that during non-heat wave days, CKD mortality increased by 3.48% (95% confidence interval [ CI]: 1.67% to 5.33%) during heat waves, and the mortality risk escalated by 2.48% (95% confidence interval [ CI]: 0.12% to 4.91%) for each 1 °C increment during heat wave days. Stratified analyses revealed that CKD mortality risks were greater for women (Excess Risk [ER] = 5.52%, 95% CI: 2.71% to 8.40%), individuals aged 65 years and older (ER = 4.60%, 95% CI: 2.30% to 6.96%), and people in mesic/cold regions (ER = 6.20%, 95% CI: 1.13% to 11.53%). The projections showed that the attributable fraction(AF) of CKD mortality due to heat waves would rise from 0.64% (95% CI: 0.52% to 0.78%) in the 2020s to 2.44% (95% CI: 1.97% to 2.95%) in the 2090s under the SSP5-8.5 scenario, with the highest burden in southeastern China, including Hainan (3.31%, 95% CI: 1.66% to 5.02%), Yunnan (3.05%, 95% CI: 1.46% to 4.75%), and Guangdong Province (2.84%, 95% CI: 1.24% to 4.41%). Conclusion:This nationwide study demonstrated that exposure to heat waves significantly increased the mortality risk of CKD, and that women, older individuals, and people in mesic/cold regions are more susceptible to heat waves. Global warming will significantly increase the future CKD mortality burden attributed to heat waves, particularly in southeastern China. Our findings emphasize the need to address CKD in the context of ongoing climate change.

Existing studies have not characterized the temporal patterns and underlying drivers of temperature-related mortality across Chinese provinces. We utilized data from the Global Burden of Diseases Study (GBD) 2021 to analyze the tempo-spatial changes in mortality burden attributed to low and high temperatures from 1990 to 2021 and employed a random forest regression model to investigate the impact of socio-economic and demographic factors on these changes. We found that total deaths attributable to non-optimal temperatures rose from 0.376 million in 1990 to 0.589 million in 2021, of which 0.542 million were cold-related and 0.049 million heat-related, while males and those aged 70+ years bore disproportionately higher burdens. Nevertheless, age-standardized mortality due to non-optimal temperatures declined by 50.8% (95% uncertainty interval (UI): 40.8, 58.5), with cold-and heat-related mortality decreasing by 51.8% (95% UI: 42.5, 59.8) and 35.5% (95% UI: 0.7, 56.0), respectively. Eastern China, notably Shanghai, exhibited the most pronounced decline (-61.0%, 95% UI: -70.4, -49.5). In addition, we identified GDP, air-conditioner ownership per 100 households, sex ratio, and health facilities as primary drivers of the cold-related mortality, whereas air-conditioner ownership per 100 households and medical-bed availability per 10,000 persons chiefly influenced heat-related trends. Our findings underscore the importance of promoting health equity and reducing regional disparities in temperature-related mortality burdens. Future strategies should focus on improving heat-health surveillance, expanding early warning and response systems, and implementing targeted adaptation measures for heat-vulnerable communities.

Capturing spatial interaction remains a challenge in GIScience and spatial epidemiology, yet many studies rely on a single neighborhood definition. We proposed a multi-network spatial error workflow that treats spatial weight matrices as theory-guided synthetic connectivity channels and estimates channel-specific dependence parameters within one likelihood. Using county-level U.S. gonorrhea, chlamydia, and HIV outcomes (2018-2019), we controlled for established sexually transmitted infection (STI) covariates and evaluated candidate spatial error models based on queen contiguity (WQ), Twitter-derived mobility connectivity (WP), and Facebook-derived socio-spatial connectivity (WS). Model choice used a two-stage procedure that screened candidates in-sample and then evaluated shortlisted models out-of-sample via leave-one-state-out prediction and held-out residual autocorrelation diagnostics. The selected dependence structures were pathogen-specific: HIV supported a joint adjacency-plus-mobility specification (WQ+WP), chlamydia was dominated by mobility (WP), and gonorrhea was dominated by socio-spatial connectivity (WS). These channel choices were associated with systematic re-ordering of fitted risk rankings, but the resulting shifts remained model-derived rather than externally validated indicators of real-world transmission importance. This study supports a reproducible workflow for comparing and selecting adjacency-, mobility-, and socio-spatially defined neighborhood structures in large-scale spatial modeling, with applications beyond STIs.

Although short-term exposure to heatwaves has been linked to an elevated risk of stroke onset, evidence regarding the long-term impact of heatwave exposure on poststroke disability remains limited, particularly for nighttime heatwaves. We conducted a cross-sectional study that included 29,447 participants from the Guangdong Province High-Risk Population Screening and Intervention Project for Stroke (2016-2021). Using a generalized additive model, we compared the effects of long-term exposure to heatwaves between daytime and nighttime on poststroke disability, as measured by modified Rankin Scale (mRS) scores. Furthermore, we projected the future burden of poststroke disability attributable to heatwave changes under different climate change scenarios. Both long-term exposure to daytime and nighttime heatwaves increased poststroke disability risk. Specifically, each additional day of nighttime heatwave was associated with an excess risk (ER) of 4.62% (95% CI: 4.36%-4.89%) in poststroke disability, which is higher than daytime heatwave (ER = 3.71%, 95% CI: 3.46%-3.97%). Similarly, for each 1 degrees C increase in heatwave intensity, the risk was also greater for nighttime heatwaves (ER = 4.36%, 95% CI: 4.01%-4.72%) than for daytime events (ER = 1.60%, 95% CI: 1.45%-1.76%). Subgroup analyses revealed similar patterns. Projections indicate that global warming will escalate heatwave-related poststroke disability burden, with greater burden for nighttime heatwaves than daytime events. For instance, under the SSP5-8.5 scenario by the 2080s, the projected increases in the attributable fraction (AF) for both nighttime heatwave duration (15.63%, 95% eCI: 13.39%-17.99%) and intensity (14.86%, 95% eCI: 12.73%-17.11%) were higher than those for daytime heatwaves (5.22%, 95% eCI: 4.47%-6.01% for heatwave duration; 12.68%, 95% eCI: 10.86%-14.60% for heatwave intensity). These findings highlight nighttime heatwave as a critical and underaddressed risk factor for poststroke outcomes in the context of climate change.

Electrically assisted two-wheelers (e-bikes) have become an integral component of urban mobility in China. However, the sensitivity of e-bike accident risk to short-term meteorological variations at fine temporal scales remains underexplored. This study investigated the association between short-term hourly ambient temperature exposure and e-bike accident risk in Guangzhou, China, with particular attention to lag structure and effect heterogeneity. Using 4,041 accident records collected between 2022 and 2024, we applied a time-stratified case-crossover design combined with distributed lag non-linear models (DLNMs) to estimate temperature-associated risk while adjusting for atmospheric confounders. We found a positive association between ambient temperature and e-bike accident risk. The association was strongest within the first 1−2 hours after exposure (RR ≈ 1.023 per 1 °C increase) and gradually attenuated over the subsequent 12−14 hours. Stratified analyses showed that the association was more evident during the daytime period and in winter. Interaction analyses further suggested that the temperature-associated risk increase was more pronounced under low-to-moderate humidity and non-rainy conditions, whereas it appeared weaker under high-humidity or rainy conditions. These findings provide new evidence on the acute, hourly-scale effects of temperature on e-bike traffic safety and support the development of meteorology-informed early warning strategies for high-exposure rider groups in warming urban environments.

This study aimed to explore individual and combined impacts of ambient PM2.5, PM10, NO2, SO2 and O3 on eczema outpatient visits and identify key pollutants, as the joint effects of multi-pollutant exposure on eczema remain unclear. We retrieved 879,356 daily eczema outpatient records from two Guangdong dermatology hospitals and matched daily air pollutant concentrations. Distributed lag nonlinear models assessed single pollutant effects, while quantile g-computation evaluated two four-pollutant mixtures. Every 10 µg/m³ rise in each pollutant significantly boosted eczema visits, with SO2 exerting the strongest individual effect (22.63%). Each quartile increase of the PM10-based mixture lifted visits by 4.14%, dominated by PM10 (49.46%), while the PM2.5-based mixture caused a 4.21% rise, mainly driven by SO2(38.12%) and NO2 (37.52%). Both mixtures induced 18 extra daily eczema visits. Air pollutant mixtures markedly aggravate eczema burdens, requiring targeted air quality governance to cut related medical demands.