Objective To systematically assess the non-linear and lag effects of daily mean temperature on varicella incidence in Weifang using a distributed lag non-linear model(DLNM).Methods Surveillance data on varicella cases and concurrent meteorological data in the Weifang area from January 2014 to December 2023 were collected.A DLNM was applied to analyze the exposure-response relationship between daily mean temperature and varicella incidence in Weifang area.Results A total of 7,871 varicella cases were reported in Weifang area during the study period.Daily mean temperature correlated negatively with case counts(rs=-0.235,P<0.001).With the median of daily mean temperature(15.69℃)as the reference value,the relative risk(RR)value was higher under low temperature conditions,and the influence of low temperature appeared in the early lag period(0～9 d),while the effect of hightemperature showed a two-stage characteristic.Inthe earlylag(0～7days),it was a weak riskfactor,but inthe later lag(e.g.,14 days),it may turnintoa protective factor.Subgroup analysis revealed that varicella incidence had significant seasonal heterogeneity and age differences,and the incidence risk of the 3～6 years old group in winter was 1.9 times that in summer,showing an obvious seasonal fluctuation pattern.Sensitivity analysis confirmed model robustness.Conclusion Temperature nonlinearly influences varicella incidence in Weifang,with low temperatures posing sustained risks and high temperatures exhibiting a mild and biphasic lag pattern.Prevention and control measures should be strengthened in crowded places such as schools and childcare institutions.

Background: Varicella remains a major cause of outbreak-related morbidity worldwide, yet the extent to which socioeconomic structure and climate jointly shape its spatiotemporal dynamics is poorly understood, particularly at national scale. We aimed to identify high-risk spatiotemporal clusters of varicella outbreaks and to quantify the relative and context-dependent contributions of socioeconomic and meteorological factors across diverse climatic regions. Methods: We conducted a nationwide space–time analysis of varicella public health emergency events (PHEEs) reported in China from Jan 1, 2006, to Dec 31, 2022, at the county level. Spatiotemporal clustering was assessed using a discrete Poisson space–time scan statistic. To characterize structural drivers of spatial heterogeneity, we integrated high-resolution socioeconomic, demographic, and environmental data and applied Random Forest regression to rank variable importance. Short-term exposure–response relationships between meteorological variables and outbreak occurrence were estimated using a time-stratified case-crossover design with natural cubic splines, stratified by region. Findings: A total of 10 607 varicella PHEEs involving 279 688 cases were identified across 2 844 counties. Outbreaks exhibited a pronounced bimodal seasonal pattern, peaking in October–December and April–June. Approximately 80% of cases occurred in southern China. Fourteen statistically significant high-risk spatiotemporal clusters were detected, with the largest and most persistent clusters concentrated in south-western China. Elevation, youth population density in residential areas, relative humidity, school student density, and wind speed were the strongest contributors to spatial variation in incidence. Nationally, lower temperatures were associated with higher outbreak risk, whereas precipitation showed an inverted U-shaped association. Marked regional heterogeneity was observed: in northern China, temperature showed a U-shaped relationship with increased risk at both low (< 0°C) and high (> 25°C) extremes, and precipitation was positively associated with risk; in southern China, both temperature and precipitation displayed pronounced inverted U-shaped associations, with peak risks at approximately 10–15°C and 20–80 mm of monthly precipitation, respectively. Interpretation: Varicella outbreak risk is shaped by the combined and region-specific effects of socioeconomic organization and climate. Integrating climate-informed risk assessment with population structure, particularly school-based contact environments, could substantially improve early-warning systems and enable targeted, context-sensitive prevention strategies.

The relationship between airborne pollutants and subtypes of neurodegenerative disorders remains poorly understood. This study aims to assess the associations between major air pollutants and the risk of multiple neurodegenerative disease outcomes. We searched three databases (PubMed, Web of Science, and Scopus), with the most recent update on February 21st, 2026. We included studies involving adults (without the disease at baseline) with exposure to air pollutants such as PM2.5, PM10, NO2, NOx, and O3, and outcomes related to all-cause dementia, Alzheimer’s disease (AD), Parkinson’s disease (PD), and vascular dementia (VaD). Data extracted included study details, population characteristics, and risk of bias, assessed using a modified Newcastle-Ottawa Scale. Hazard ratios (HR) and 95

Background: Short-term exposure to ambient air pollutants is closely associated with elevated cardiocerebrovascular mortality. However, epidemiological evidence on the causal effects of short-term exposure to specific PM₂.₅ chemical constituents and their mixtures remains limited, particularly in arid and semi-arid plateau regions. Methods: We collected daily mortality data for cardiocerebrovascular diseases, including cardiovascular disease, ischemic heart disease, and stroke between January 1, 2016, and November 30, 2022, in Hohhot and Baotou, two cities situated in an arid plateau region of Asia. Concurrently, we obtained daily concentrations of PM₂.₅ and its chemical constituents, including secondary inorganic aerosols, metal elements, and polycyclic aromatic hydrocarbons (PAHs). We applied generalized additive models (GAMs), weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) to estimate independent and joint effects of air pollutants on cardiocerebrovascular mortality. Counterfactual analyses quantified mortality burden attributable to exposure to key pollutants, with stratified analyses by sex and age. Findings: During the study period, 9,770 cardiovascular deaths, 2,143 ischemic heart disease deaths, and 3,169 stroke deaths were recorded. Nitrate ions (NO₃⁻) were the strongest contributors to cardiocerebrovascular mortality, accounting for 34.6% and 47.1% of cardiovascular and stroke deaths, respectively. Each interquartile range increase in NO₃⁻ was associated with a relative risk (RR) of 1.02 (95% CI 1.00–1.03; P < 0.05) for cardiovascular disease and 1.04 (95% CI 1.01–1.06; P < 0.01) for stroke. Mercury (Hg) exposure also significantly increased the risk of cardiovascular disease (RR 1.01, 95% CI 1.001–1.02; P < 0.05) and stroke (RR 1.03, 95% CI 1.01–1.04; P < 0.001). PAH components were positively associated with cardiocerebrovascular mortality, exhibiting notable lag effects. Chrysene exposure showed progressively increasing risk with longer lag periods, whereas benzo[a]anthracene and benzo[b]fluoranthene were significantly associated with cardiovascular and ischemic heart disease mortality, with pronounced lag effects. Counterfactual analyses reducing concentrations of key toxic pollutants to the 5th percentile suggested a substantial decrease in deaths across all disease categories. The population-attributable fraction (PAF) for cardiovascular disease was 3.53% (95% CI 3.25–3.84%), corresponding to 343 avoidable deaths (95% CI 314–374). Interpretation: Short-term exposure to specific PM₂.₅ chemical constituents and pollutant mixtures is significantly linked to increased cardiocerebrovascular mortality. Key toxic contributors include NO₃⁻, Hg, chrysene, benzo[a]anthracene, and benzo[b]fluoranthene. These findings provide robust epidemiological evidence to identify high-risk pollutants, target vulnerable populations, and inform precise mitigation strategies in arid and semi-arid plateau regions experiencing complex air pollution.

Respiratory syncytial virus (RSV), a major cause of acute respiratory infections (ARIs) globally, poses a significant threat, especially to vulnerable populations. However, the spatial transmission dynamics of RSV strains, including the influence of environmental and socioeconomic factors, remain inadequately understood. This study applied genetic sequences and phylogenetic methods to quantify evolutionary and spatial dispersal dynamics of RSV subgroup A (RSVA) across China from 2011 to 2019. We assessed viral population trends, mapped interprovincial transmission patterns, and evaluated the influence of meteorological and socioeconomic factors on viral spread. Our results revealed cyclical fluctuations in effective population size every 3-5 years, and a predominant southward spread driven by interprovincial transmission networks. We found that higher winter relative humidity (RH), urbanization rate, and human mobility promoted viral spread, while higher winter temperature and elevated urban population density appeared to inhibit it. These findings provide crucial insights into RSVA dispersal in China, underscoring the importance of regional surveillance networks and targeted interventions to curb cross-regional spread, and offer a valuable framework to inform RSV vaccine rollout strategies and guide resource allocation in high-risk areas.

BACKGROUND:Although handgrip strength (HS) is a useful indicator of various diseases, no study has yet investigated the effects of various influencing factors on HS over time. This study aims to analyze the spatiotemporal variation of relative HS, and its influencing factors, across diverse population groups in China. METHODS:Five national physical fitness surveys encompassing 831,878 adults aged 20-69 years were conducted in China from 2000 to 2020. Trained personnel recorded relative HS. A Bayesian spatiotemporal hierarchical model was employed to illustrate the spatiotemporal characteristics of HS, while a geodetector model assessed the impact and interaction of socioeconomic levels, living environments, and geographical factors on relative HS over time. RESULTS:From 2000 to 2020, the average relative HS in China declined, with a more pronounced decrease in men than women and among younger individuals relative to middle-aged and older counterparts. Spatial analysis revealed significant heterogeneity, with HS hot spots primarily in southern regions (excluding Tibet) and cold spots in the north. The 20-year decline in relative HS was primarily characterized by a more substantial decrease in individuals with initially higher HS. Before 2010, HS differences were primarily attributed to geographic factors, whereas post-2010, the influence of socioeconomic factors increased. CONCLUSIONS:There is a need to address the greater decline of HS in men and especially in young individuals. China should implement targeted interventions in specific regions to mitigate the rapid decline in HS, taking into consideration diverse regional socioeconomic factors.

>Clostridium difficile infection （CDI） is a major global public health concern, accounting for 15%–25% of antibiotic-associated diarrhea, 50%–75% of antibiotic-associated colitis, and nearly all cases of pseudomembranous colitis. Over the past decade, CDI outbreaks have become increasingly prevalent in North America and Europe, with rising incidence and mortality rates. In 2019, the Centers for Disease Control and Prevention （CDC） in the United States classified CDI as a “critical” public health threat in their report on antibiotic resistance threats [1] .

Spatial count data are a prevalent data type in natural and social sciences. As the data present complicated spatial autocorrelation and heterogeneity inherent in geographical analysis, the current methods lack a theoretical approach to model and predict counts, especially with limited spatial samples. To address the gap, this study develops a new method named Poisson Means of Stratified Nonhomogeneity (PoiMSN). The method considers both autocorrelation and heterogeneity but not covariates. Moreover, it incorporates local samples and out-stratum neighbors that traditional methods neglect to model and predict the latent process for data in a Poisson distribution. This study compares PoiMSN with Poisson geostatistics and traditional MSN and designs simulations to validate the model. PoiMSN outperforms the other models as it has the lowest mean absolute error and root-mean-squared error, and furthermore, at least 5% improvement in accuracy for autocorrelated and stratified Poisson data. The case study with hand, foot, and mouth disease data shows PoiMSN can precisely map the disease risks with lower uncertainty. PoiMSN has the ability to accommodate spatially non-stationary count data from autocorrelated and heterogeneous populations and leverage extensive sample information.

Background Incident HIV infection is a critical indicator of an ongoing epidemic, particularly in high-burden regions such as Liangshan Yi Autonomous Prefecture in China, where HIV prevalence exceeds 1% in 4 key counties (Butuo, Zhaojue, Meigu, and Yuexi). Identifying spatial clusters and drivers of recent infections is essential for implementing targeted interventions. Despite advancements in geospatial analyses of HIV prevalence, studies identifying drivers of incident HIV clustering remain limited, especially in low-resource settings. Objective This study aims to identify spatial clusters of recent HIV infections and investigate potential driving factors in 4 key counties of the Liangshan Yi Autonomous Prefecture to inform targeted intervention strategies. Methods From November 2017 to June 2018, we identified 246 (4.42%) recent HIV infection cases from 5555 newly diagnosed cases through expanded testing of the whole population in 4 key counties of Liangshan Yi Autonomous Prefecture. Recent infection cases were confirmed using limiting antigen avidity enzyme immunoassays or documented seroconversion within 6 months. The spatial distribution of incident HIV infection cases was analyzed using kernel density. Potential drivers, including population density, HIV prevalence, elevation, nighttime light index, urban proximity, and antiretroviral therapy (ART) coverage, were analyzed. The spatial lag regression model was used to identify factors associated with clustering of recent infection cases. The Geodetector q-statistic was used to quantify nonlinear interactive effects among these factors. Results Significant spatial autocorrelation was observed in the distribution of recent HIV cases (Moran I=0.11; P<.01). Six spatial clusters were identified, and all were located near urban centers or major roads. Furthermore, 5 factors were identified by the spatial lag regression model as being significantly correlated with the clustering of recent HIV infection cases, including population density (β=0.59; P<.001), HIV prevalence (β=0.02; P<.001), distance to local urban area (β=–3.10; P=.01), SD of elevation (β=–0.15; P=.02), and ART coverage rate (β=183.80; P<.01). Geodetector analysis revealed strong interactive effects among these 5 factors, with population density and HIV prevalence exhibiting the largest interactive effect (q=0.69). Conclusions This study reveals that besides HIV prevalence, urbanization-related factors (population density and proximity to urban area) and transportation accessibility drive incident HIV clustering in Liangshan Yi Autonomous Prefecture. Paradoxically, higher ART coverage was associated with increased transmission, suggesting the need for integrated prevention strategies beyond ART expansion. Furthermore, the township-level geospatial approach provides a valuable model for pinpointing transmission hot spots and tailoring interventions in high-burden regions globally.

What is already known about this topic?:Depression among older adults represents a critical public health challenge in China. While previous research has documented spatial heterogeneity in the prevalence of depressive symptoms among elderly populations across China, the comprehensive spatiotemporal patterns remain inadequately characterized. What is added by this report?:This study provides the first comprehensive city-level analysis of elderly depressive symptom prevalence in China from 2013 to 2020. The findings reveal a substantial increase in average prevalence from 30.27% [95% confidence interval (CI): 24.53%, 36.02%] in 2013 to 37.79% (95% CI: 31.01%, 44.56%) in 2020. Higher prevalence rates were consistently observed in cities across Southwest, Northwest, and Central China, though without significant spatial clustering patterns. What are the implications for public health practice?:Enhanced mental health interventions and preventive strategies targeting elderly populations are particularly warranted in China's western and central regions, where the burden of depressive symptoms is highest.

Cognitive impairment, hypertension and diabetes are prevalent chronic conditions in populations of older ages. Previous studies have shown that hypertension and diabetes are risk factors for the development of cognitive impairment. However, the impact of hypertension combined with diabetes (HD) and their cumulative effects on cognitive impairment remain unclear. We aimed to investigate whether HD influences development of cognitive impairment and whether the effect is cumulative. A case–control study was conducted. From 40,103 subjects aged 60 years or older, enrolled from 28 representative communities of 9 provinces of China between January 2015 and December 2021 into the Prevention and Intervention on Neurodegenerative Disease for Elderly in China program using multi-stage stratified random sampling, individuals not meeting our propensity score matching criteria were excluded, and 13,252 individuals were finally selected for the study. Exposure factors included hypertension, diabetes and their comorbidity. Odds ratios (ORs) of exposure factors on cognitive impairment were measured using multiple logistic regression. We found significant impacts of hypertension, diabetes and their comorbidity on cognitive impairment occurrence. The OR values for dementia were 1.18 for individuals with hypertension only, 1.26 for those with diabetes only, and 1.53 for those with HD. Compared to participants without hypertension and diabetes, the OR values for mild cognitive impairment (MCI) were 1.11 for individuals with hypertension only, 1.32 for those with diabetes only, and 1.27 for those with HD. For subjects with HD longer than 5 years, the comorbidity significantly impacted on MCI and dementia, and the degree of impact increased with the duration of comorbidity. For hypertension, the influence of hypertension on dementia were most influential in middle-aged (45–64 years old) people. By contrast, the influence of diabetes on people younger than 45-year-old was most significant, with the middle-age group being the second most impacted subjects. The elderly with HD have a heightened risk of developing cognitive impairment, particularly dementia, compared to those with either hypertension or diabetes alone. The study revealed a significant cumulative impact of HD on cognitive impairment.

Mosquitoes pose a significant threat to global health, impacting over 40% of the world’s population. Currently, a dearth of large-scale and long-term data on mosquito populations in China hinders related research and public health endeavors. In response, we meticulously compiled and analyzed existing studies to construct a comprehensive dataset illustrating the spatial variation of mosquito populations, density, and composition across mainland China. This dataset furnishes invaluable information to support further research on mosquitoes and mosquito-borne diseases on broader spatial and temporal scales. The primary aim of this research is to contribute to efforts aimed at controlling mosquito transmission and mosquito-borne diseases, ultimately enhancing human well-being.

Additive manufacturing (AM) of multiple metallic materials suffers from microcracks at the dissimilar material interface due to brittle intermetallic compounds (IMCs). While avoiding IMCs through specialized composition design is a conventional approach, the interdependence between molten pool material mixing, IMC characteristics, and microcracks are not well understood. In this work, we compared typical process conditions for laser powder bed fusion of aluminum alloy substrate and Inconel particles. We revealed that the insufficient dissimilar material mixing under the lower energy density condition can exacerbate element clustering, IMC concentration, and cracking. High-speed synchrotron X-ray imaging shows that Ni-rich clusters can abruptly plunge into the molten pool to cause localization of IMCs and microcracks. In the high energy density case, the keyhole oscillation can disperse the Ni-rich clusters and suppress cracks but lead to keyhole porosities. Microstructural characterization and multiphysics simulations support the X-ray imaging observations. We propose that control of molten pool flow to enhance mixing while preventing porosities is the key to crack-free AM of metallurgically incompatible dual alloys.

Soil pollution threatens human health and food security, particularly in industrial legacy sites. Accurate three-dimensional distribution modeling of soil contamination is crucial for understanding pollutant migration and guiding targeted remediation. Yet, the strong heterogeneity of contaminants limits the performance of traditional methods. We proposed a GeoAI-based approach, the three-dimensional deep kriging neural network (3D-DKNN), which combines deep learning with geostatistical principles to enhance interpolation accuracy in heterogeneous environments. Applied to polycyclic aromatic hydrocarbons at a typical industrial site, 3D-DKNN was benchmarked against traditional three-dimensional ordinary kriging (3D-OK) and inverse distance weighting (IDW). Cross-validation shows that 3D-DKNN achieved the lowest RMSE and MAE, and the highest correlation coefficient. Relative to IDW, it reduced RMSE by 36 %-80 %, MAE by 40 %-58 %, and increased correlation by over 19 %. Based on risk thresholds, contamination hotspots were identified in the northern and northwestern areas, particularly manufacturing and warehouse areas, which were recognized as key risk and remediation areas. This study demonstrates the potential of GeoAI for modeling complex pollutants and improving soil risk assessment.

A new global land–ocean merged surface temperature dataset, China Meteorological Administration global merged surface temperature (CMA-GMST), has been developed. It is constructed from the monthly China Meteorological Administration global reconstructed land surface temperature (CMA-GLST) and sea surface temperature (CMA-SST) analyses, which benefit from the improved in-situ observation coverage. Additionally, the Arctic area is also reconstructed based on air temperatures and merged into CMA-GMST. This dataset provides a spatially homogeneous surface temperature anomaly field in 2° × 2° resolution for each month since 1850, and covers the majority of the earth’s surface: reaches 90

What is already known about this topic?:Disability-free life expectancy (DFLE) is a vital measure of older adults' quality of life. Although its temporal trends and determinants have been examined in previous studies, spatial heterogeneity has often been underestimated, because most analyses were conducted at aggregated national or provincial levels, masking local variations due to limited data availability. What is added by this report?:Compared with 2018, DFLE increased in 2023, and the urban-rural gap narrowed overall, with slower improvement in the western region. The determinants of DFLE varied by region, age, and urban-rural context. What are the implications for public health practice?:Public health strategies should be tailored to urban and rural contexts. Priority should be given to strengthening healthcare access, social security systems, and climate-adaptive infrastructure, particularly in rural areas of western China.

Background:Cardiovascular disease (CVD) remains the leading cause of death in China and worldwide. However, a large proportion of CVD can be prevented by regulating the levels of cardiovascular risk predictors. Despite the contribution of well-established factors to changes in cardiovascular risk predictors, the role of the regional environment and its combined effects with individual factors, which could affect health outcomes, remain unclear. Methods:We included 10 308 middle-aged and older Chinese adults from the 2015 China Health and Retirement Longitudinal Study. High-sensitivity C-reactive protein (hs-CRP) is a cardiovascular risk predictor. Related potential factors including individual characteristics, regional air pollution, and regional socioeconomic status characteristics were also collected. The geographical detector method was used to quantify the explanatory power of individual and regional factors separately and in pairs in the hs-CRP levels according to regions (southern vs. northern China). Results:Blood triglyceride had the highest explanatory power for hs-CRP levels. Regional environment factors, including air pollution and socioeconomic status, significantly affected hs-CRP levels, and the results differed by region. Indoor air pollution and regional industrial structure had a stronger effect on hs-CRP levels in the south, whereas outdoor air pollution and economic level had a greater effect in the north. The interactions between any two of the paired factors enhanced the effects. Conclusions:Spatial stratified heterogeneity of the leading risk factors for hs-CRP, a powerful cardiovascular risk predictor, was found. The combined effect of individual factors and regional environment enhanced the explanatory power of each risk factor. The results suggest that policymakers should choose different optimal approaches to regulate the cardiovascular risk predictor levels of middle-aged and older Chinese adults in different regions and the interaction effects between individual factors and the regional environment should be considered.

BACKGROUND: Since the UNAIDS introduced HIV self-testing in 2014, its application has been widely adopted globally. Developed in China approximately 2015, HIV self-testing (HIVST), which involves the online purchase of testing kits via e-commerce platforms, has emerged as the most significant distribution method. This approach offers numerous benefits, including user-friendliness, speed, and enhanced privacy protection. OBJECTIVE: To understand the spatiotemporal heterogeneity of online HIVST purchasing behavior and its potential driving factors in China. METHODS: The online retail sales data of HIVST kits from the two largest e-commerce platforms in China from 2015 to 2020 were collected for this study. The Bayesian spatiotemporal hierarchical model (BSTHM) was used to investigate the spatiotemporal characteristics and identify the rapidly growing high-demand regions of online HIVST kit sales. Furthermore, the BSTHM was used to identify potential driving factors associated with online sales, including college students per 10,000 persons, GDP per capita, net per person, phone per person, population density, road density and HIV screening cases per 10,000 persons. RESULTS: From 2015 to 2020, the sales of online HIVST kits experienced a dramatic surge, increasing from 837,061 kits to reaching 7,827,411 kits annually, with a total of 23,987,155 kits sold, with annual peak sales in December. Four economically superior regions in China, the Pearl River Delta, Yangtze River Delta, Beijing-Tianjin areas, and Shandong Peninsula, presented relatively high spatial preferences for online purchased HIVST kits. Thirty-eight rapidly growing high-demand regions were identified, which were located mainly in northeastern China (28.9%, 11/38), the Pearl River Delta (28.9%, 11/38) and the Beijing-Tianjin areas (15.8%, 6/38). The number of college students per 10,000 persons, GDP per capita, net per person, population density and number of HIV screening cases per 10,000 persons were identified as the driving factors that were positively associated with the online purchase rate of HIVST kits. CONCLUSIONS: Our study illuminates the spatiotemporal dynamics and driving factors behind online HIVST kit sales across mainland China, offering pivotal insights for crafting detailed HIVST guidelines. We pinpointed regions with significant sales demand, guiding policymakers in resource reallocation and HIV care continuum optimization, particularly in identified critical ' rapidly growing high-demand’ regions.