Fast and accurate monitoring of economic dynamics is essential for evaluating economic policies and informing effective strategies, especially when timely official gross domestic product (GDP) statistics are unavailable. In this study, the capability of time-series nightlight satellite data to model urban economic activity was assessed by integrating it with observed GDP statistics. Experimental results showed that, when nightlight information was elaborately fused with core urban land-use data, urban GDP dynamics could be modeled with satisfactory accuracy. Model-based estimates indicated that China’s average GDP growth rate was approximately 4% across cities, with pronounced fluctuations during the pandemic, especially in 2020 and 2022. The proposed approach further quantified that the actual GDP losses in China due to the pandemic were about 1.5% of the total GDP, and the simulated results suggested that many coastal and industrial cities faced even more severe impacts than major provincial cities. Overall, the findings demonstrate the value of nighttime remote sensing for monitoring urban economic dynamics and support the transferability of the proposed approach to other countries with limited or delayed GDP statistics.

Growing evidence suggests that the health impacts of a given increment of fine particulate matter (PM 2.5 ) are changing over time. While these trends could influence environmental health disparities, how temporal trends vary across locations and subpopulations remains unknown. Using case-crossover analyses, we analyzed how PM 2.5 -mortality associations varied over time (2001-2016) in two U.S. states (North Carolina and Michigan) among persons 65+ years of age. We further characterized temporal variation in the PM 2.5 -mortality association by sociodemographic variables including age, sex, race/ethnicity, educational attainment, urbanicity, and substate region. From the 2001-2008 study period to the 2009-2016 study period, the odds of mortality per 10 μg/m 3 PM 2.5 decreased by 0.77% in North Carolina but increased by 0.28% in Michigan. Using a nonlinear temporal model, the associations between PM 2.5 concentration and mortality were variable over time in North Carolina and steadily increased over time in Michigan. We also found suggestive evidence of widening disparities in PM 2.5 -mortality odds ratios over time. For example, PM 2.5 -mortality odds in Michigan increased more dramatically for non-Hispanic black (1.71% [-1.17, 4.67%]) than for non-Hispanic white (0.14% [95% confidence interval (CI) = -0.93, 1.22%]) subpopulations. In North Carolina, these groups experienced a 0.40% (-3.56, 2.85%) and 0.96% (-2.52, 0.62%) reduction in PM 2.5 -mortality, respectively. Our results suggest that PM 2.5 -related mortality impacts may be changing over time, with different trends by location and subpopulation, potentially exacerbating environmental injustice.

Background: Associations between long-term fine particulate matter (PM2.5) exposure and mortality are well established. However, whether these associations differ between urban and rural populations remains understudied. We investigated urban-rural disparities in the mortality effects of long-term PM2.5 exposure among U.S. older adults. Methods: We conducted a cohort study of 73 million Medicare beneficiaries ≥ 65 years of age from 2000-2016. Annual ZIP-code level estimates of PM2.5 and its 15 components were obtained. Urban-rural areas were classified under five schemes, reflecting different aspects of urban-rural characteristics. Poisson regressions were used to estimate associations between PM2.5 and its components and all-cause mortality, stratified by urban–rural status and sociodemographic characteristics. Findings: Across all five urban-rural classification schemes, PM2.5-mortality risks were higher in rural than in urban areas. In the population density–based classification, the hazard ratio per 4.3 µg/m³ (interquartile range) increase in PM2.5 was 1.047 (95% CI, 1.043-1.051) in rural areas and 1.019 (95% CI, 1.015-1.023) in urban areas. Elevated risks in rural areas were observed across most PM2.5 components and sociodemographic subgroups. Medicaid-eligible individuals had higher risks than noneligible individuals in rural areas, whereas risks were similar by Medicaid eligibility in urban settings. Interpretation: PM2.5-mortality risk estimates were greater in rural than in urban areas, a pattern that not fully captured by differences in PM2.5 composition or population characteristics alone. Socioeconomic disadvantage and rural residence may interact to heighten vulnerability to PM2.5. Rural populations warrant greater attention in research and policy to ensure equitable protection.

BACKGROUND:Ambient PM10 is associated with mortality; however, potential changes in this association over time and the factors explaining such changes are unclear. Therefore, we aimed to examine whether mortality risk associated with PM10 has changed from 1979 to 2019 and whether changes in socioeconomic or environmental conditions can explain any temporal variation in the association between PM10 and mortality. METHODS:We applied an extended two-stage time-series design to assess temporal change in the association between PM10 and all-cause mortality across 143 cities in 26 countries from 1979 to 2019. In the first stage, city-specific and time-specific associations between PM10 and mortality were estimated using quasi-Poisson regression after each city time series was divided into non-overlapping 3-year segments. In the second stage, these estimates were pooled by use of longitudinal random-effects meta-regression with calendar year as a predictor. We further investigated whether selected socioeconomic and environmental factors explained observed temporal trends by including these variables in the second-stage model. FINDINGS:Totally, 23·2 million deaths were analysed. The overall association between PM10 and mortality had increased from 1979 to 2019, indicating a stronger association at a given PM10 concentration over time. A 10 μg/m3 increase in daily PM10 was associated with a 0·23% increase in all-cause mortality in 1979 (95% CI 0·05-0·41), and this association increased to 0·51% in 2019 (0·36-0·65). Temporal patterns in the PM10-mortality association varied across cities and were positively associated with population ageing over time and negatively associated with annual mean PM10 concentrations. INTERPRETATION:The findings of this study suggest that the effect of a given increment of PM10 on mortality has increased over time. Applying historical risk estimates could underestimate the current health burden. Continuous updating of evidence on the health impacts of air pollution is essential to ensure accurate and valid estimates. FUNDING:Wellcome Trust.

The importance of urban shade in city planning has been underscored by its various associations with thermal comfort, living environment quality, human health and well-being. However, existing methods for urban shade mapping are often limited to street-level scopes or singular sampling observational times, leaving citywide shade mapping, its spatiotemporal patterns, and controlling factors largely unexplored. To fill these knowledge gaps, this study proposed a novel protocol that enables seamless mapping of spatiotemporal urban shade patterns over the entire city by fusing look-up-table (LUT)-based ray-tracing approach with Google Earth Engine cloud computing technology, as well as high-resolution digital surface model (DSM) derived from LiDAR data. This protocol was applied to Hong Kong, a city characterized by complex 3-D built environments, and then delved into the spatiotemporal patterns of urban shade and the associated controlling factors. Validation results indicate that the proposed method can accurately quantify urban shade compared to ground-based photo references and high-resolution satellite imagery. The analysis uncovers that: (1) Shade exhibits spatial heterogeneity across different 3-D built environments while maintaining a consistent "U-shape" diurnal shade fraction during the day; (2) Solar geometry, regardless of solar zenith and azimuth angle, is negatively associated with aggregated metrics of shade landscape and positively associated with fragmented metrics. Moreover, solar zenith angles exert stronger controls over the citywide shade landscape patterns; and (3) Persistent shade significantly reduces the sunlight hours (i.e., the accumulated sunshine hours per day) across administrative districts, local climate zones (LCZs), and seasons. Due to shade effects originating from 3-D urban structures, Hong Kong typically experiences an average of 4-8 sunlight hours per day within a one-year cycle, which is substantially lower than the 11-14 h of natural daylength. This study offers a practical protocol for large-scale urban shade mapping, enhancing our understanding of urban shade, its spatiotemporal dynamics, and benefits in a broader spatiotemporal context. The associated datasets and findings from this study can inform urban planners and policymakers in developing effective strategies to create healthier and sustainable urban environments in Hong Kong.

Rising natural hazards amid a warming climate increasingly threaten human health and sustainable development. While infrastructure serves as a critical buffer against these risks, the relationships between hazard exposure, infrastructure access, and health outcomes remain unclear. Here, we assess global human exposure to joint flooding and extreme heat risks since 2000 and examine their interactions with infrastructure access in shaping human health. Results reveal stark inequalities, with 48% of the global population (~ 4 billion people) living in areas highly exposed to both hazards. Low-income countries are disproportionately affected, facing 5.37 times the flood exposure and 1.98 times the extreme heat exposure of other income groups, yet having only 30% of their critical infrastructure access. Enhanced infrastructure access is significantly associated with longer life expectancy (p < 0.05), but high levels of hazard exposure diminish these benefits. Future projections under the Shared Socioeconomic Pathway (SSP) 1-2.6, 2-4.5, and 5-8.5 scenarios indicate substantial increases in country-level hazard exposure by 2100 relative to baseline levels: 40%–393% for floods and 22%–161% for extreme heat. The share of areas simultaneously exposed to high levels of both hazards rises from 15%–18% in 2030 to 22%–51% by 2100, potentially widening existing gaps in infrastructure access and health disparities. These findings highlight the urgent need for targeted interventions that enhance infrastructure equity in climate adaptation and health strategies, especially for vulnerable populations.

Ambient temperature is a well-known environmental factor affecting gamete production in mammals. Despite a growing interest in biological mechanisms and epidemiologic studies for ambient temperature and human reproductive outcomes, systematic evidence synthesis is limited. Mapping human infertility outcomes in relation to ambient temperature is important to inform public health and identify research gaps. This scoping review systematically searched (n = 21,332) and included eligible epidemiologic studies (n = 135) on temperature and fertility in human populations to identify the types of temperature metrics and outcomes examined. Included studies used a variety of temperature exposure metrics: ambient temperature measurements, thermal indices, extreme temperatures (e.g., heatwaves), season, occupational heat exposure, climate region, and outdoor activities. Seven types of outcomes were identified across various geographical regions: sperm parameters, outcomes of assisted reproductive technologies, pregnancy loss, population-level reproductive outcomes (e.g., birth rate), infertility diagnosis (yes/no), testicular torsion, and ovarian function. The largest number of studies was for sperm parameters, followed by outcomes of assisted reproductive technologies. Results consistently indicated that higher temperature exposure was associated with reduced sperm parameters (e.g., motility) and increased odds of infertility, while other outcome types showed heterogeneous exposure-response associations. While the methodologies vary by the type of exposures and outcomes, most studies lacked longitudinal or prospective study designs, detailed description of exposure assessments, and consideration for non-monotonic exposure-response associations. This review maps research on temperature and human infertility, highlighting methodological limitations and knowledge gaps to guide future epidemiologic studies and systematic evidence synthesis for evidence-based public health and clinical decision making.

To explore the capabilities of race/ethnicity and gender prediction algorithms in uncovering patterns of authorship distribution in scientific paper submissions to a major peer-reviewed scientific journal (AJPH), we analyzed 17 667 manuscript submissions from the United States between 2013 and 2022. We used machine-learning algorithms to predict corresponding authors' race/ethnicity (Asian, Black, Hispanic, White) and gender categories based on name-derived probabilities to compare the predictive performance of these algorithms and their impact on disparity analysis. Predicted White authors dominated submissions and had the highest acceptance rates (21.1%), while predicted Asian authors faced the lowest (14.9%). Predicted women, despite being the majority, had lower acceptance rates (17.9%) than men (20.5%), a trend consistent across most racial/ethnic groups. Different algorithms revealed similar disparities but were limited by biases and inaccuracies in predicting race and ethnicity. Manuscript acceptance rates revealed disparities by race/ethnicity and gender; predicted White and male authors had the highest rates. While machine-learning algorithms can identify such patterns, their limitations necessitate combining them with self-identified demographic data for greater accuracy. (Am J Public Health. 2025;115(7):1129-1136. https://doi.org/10.2105/AJPH.2025.308017).

Strategies to reduce greenhouse gas emissions may provide health benefits through improved air quality, yet these benefits might not be equitably distributed. Understanding these cobenefits and who receives them can aid policymakers in prioritizing mitigation strategies. We investigated four future energy scenarios (port electrification, electric vehicles, natural gas, energy efficiency) and a business-as-usual scenario to determine how changes to ambient fine particle (PM2.5) levels impact health within the contiguous United States (U.S.) by region, race/ethnicity, urbanicity, and income. We also investigated how methodological assumptions affect findings. Our projections of avoided mortalities from energy transition policies range from 67,011 (95% CI: 45,692, 82,397) to 81,003 (55,286, 99,532) in 2050. The monetized health benefits from avoided mortalities and hospitalizations range from $785.8 billion to $949.9 billion/year. These benefits vary by region and subpopulation, with Black, suburban, and less wealthy Americans experiencing higher percent avoided mortality across scenarios. Results were sensitive to assumptions for future concentration-response functions relating pollution levels to health, baseline incidence rates, and population projections. Our findings indicate energy policies transitioning from fossil fuel production in the U.S. provide substantial health and economic benefits that vary across populations and help reduce environmental health inequities in exposure and associated morality.

Background:Acute respiratory infections (ARIs) are a leading cause of morbidity and mortality among children. Air pollution may play a role in the exacerbation of ARIs via inflammation, immunosuppression, and oxidative stress, yet this effect has been infrequently evaluated among children. Objectives:Evaluate the impact of short-term exposure to fine particulate matter (PM2.5) to ARI severity among US children aged <5 years. Methods:We analyzed data from a claims-based cohort of children included in a private health insurance plan (Merative™ MarketScan® Commercial Claims and Encounters database) who were diagnosed with an ARI between January 2018 and March 2020. We use daily monitored PM2.5 concentrations at the metropolitan statistical area level to estimate the short-term weekly PM2.5 exposure. We evaluated the association between short-term PM2.5 exposure and the risk of prescription claim for antiviral medication, hospital admissions and readmissions for an ARI, intensive care unit (ICU) admission for an ARI, mechanical ventilation, and length of stay among hospital-admitted and ICU-admitted children using generalized linear models. Results:The risk of an antiviral prescription claim increased by 11% (95% confidence interval, 1.07-1.15) per interquartile range increase in PM2.5 exposure (3.34 µg/m3); this association was consistent regardless of age, biological sex, and influenza vaccination status. We observed a 6% increased risk of ICU admission (95% confidence interval, 1.02-1.10) among children not vaccinated against influenza and no increase among vaccinated children. Conclusions:Short-term PM2.5 exposure may contribute to ARI severity among children. Influenza vaccination may modify the risk of severe ARI-associated outcomes.

Urban greenspace has a profound impact on public health by purifying the air, blocking bacteria, and creating activity venues. Due to people’s different position, the greenspace exposure to different age groups changes at various times. In this study, we combined NDVI (normalized difference vegetation index) and GVI (green view index) green indices with mobile signaling big data to evaluate the greenspace exposure of 3 age groups in Shanghai at different times. A dynamic assessment model for greenspace exposure has been adopted in this study. April 2021 and April 2022 were selected as the study periods, representing the non-lockdown period and the lockdown period, respectively. The results indicate that greenspace exposure changes slightly during the lockdown period. During lockdown, the NDVI exposure in the age groups of 31 to 50, 51, and above was higher than that during non-lockdown. However, the NDVI exposure of people aged 0 to 30 during lockdown is lower than that during non-lockdown. The GVI exposure of people aged 51 and above is lower than that of the other age group. Whether it is under lockdown or not, from 8:00 to 17:00, the NDVI exposure showed a slightly higher value than at other hours. The value of GVI exposure fluctuates steadily during 6:00 to 24:00. This study enriches the evaluation dimensions of urban greenspace exposure.

Background Despite growing literature on animal feeding operations (AFOs) including concentrated animal feeding operations (CAFOs), research on disproportionate exposure and associated health burden is relatively limited and shows inconclusive findings. Objective We systematically reviewed previous literature on AFOs/CAFOs, focusing on exposure assessment, associated health outcomes, and variables related to environmental justice (EJ) and potentially vulnerable populations. Methods We conducted a systematic search of databases (MEDLINE/PubMed and Web of Science) and performed citation screening. Screening of titles, abstracts, and full-text articles and data extraction were performed independently by pairs of reviewers. We summarized information for each study (i.e., study location, study period, study population, study type, study design, statistical methods, and adjusted variables (if health association was examined), and main findings), AFO/CAFO characteristics and exposure assessment (i.e., animal type, data source, measure of exposure, and exposure assessment), health outcomes or symptoms (if health association was examined), and information related to EJ and potentially vulnerable populations (in relation to exposure and/or health associations, vulnerable populations considered, related variables, and main findings in relation to EJ and vulnerable populations). Results After initial screening of 10,963 papers, we identified 76 eligible studies. This review found that a relatively small number of studies (20 studies) investigated EJ and vulnerability issues related to AFOs/CAFOs exposure and/or associated health outcomes (e.g., respiratory diseases/symptoms, infections). We found differences in findings across studies, populations, the metrics used for AFO/CAFO exposure assessment, and variables related to EJ and vulnerability. The most commonly used metric for AFO/CAFO exposure assessment was presence of or proximity to facilities or animals. The most investigated variables related to disparities were race/ethnicity and socioeconomic status. Conclusion Findings from this review provide suggestive evidence that disparities exist with some subpopulations having higher exposure and/or health response in relation to AFO/CAFO exposure, although results varied across studies.

BACKGROUND:Heat is known to affect many health outcomes, but more evidence is needed on the impact of rising temperatures on crime and/or violence. OBJECTIVES:We conducted a systematic review with meta-analysis regarding the influence of hot temperatures on crime and/or violence. METHODS:In this systematic review and meta-analysis, we evaluated the relationship between increase in temperature and crime and/or violence for studies across the world and generated overall estimates. We searched MEDLINE and Web of Science for articles from the available database start year (1946 and 1891, respectively) to 6 November 2023 and manually reviewed reference lists of identified articles. Two investigators independently reviewed the abstracts and full-text articles to identify and summarize studies that analyzed the relationship between increasing temperature and crime, violence, or both and met a priori eligibility criteria. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used to extract information from included articles. Some study results were combined using a profile likelihood random-effects model for meta-analysis for a subset of outcomes: violent crime (assault, homicide), property crime (theft, burglary), and sexual crime (sexual assault, rape). This review is registered at PROSPERO, CRD42023417295. RESULTS:We screened 16,634 studies with 83 meeting the inclusion criteria. Higher temperatures were significantly associated with crime, violence, or both. A 10°C (18°F) increase in short-term mean temperature exposure was associated with a 9% [95% confidence interval (CI): 7%, 12%] increase in the risk of violent crime (I2=30.93%; eight studies). Studies had differing definitions of crime and/or violence, exposure assessment methods, and confounder assessments. DISCUSSION:Our findings summarize the evidence supporting the association between elevated temperatures, crime, and violence, particularly for violent crimes. Associations for some categories of crime and/or violence, such as property crimes, were inconsistent. Future research should employ larger spatial/temporal scales, consistent crime and violence definitions, advanced modeling strategies, and different populations and locations. https://doi.org/10.1289/EHP14300.

Background: Emerging research has suggested a link between ambient temperature and mental and neurological conditions such as depression and dementia. This systematic review aims to summarize the epidemiological evidence on the effects of ambient temperature on mental and neurological conditions in older adults, who may be more vulnerable to temperature-related health effects compared to younger individuals. Methods: A systematic search was conducted in PubMed, Ovid/Embase, Web of Science, and Ovid/PsycINFO on July 17, 2023, and updated on July 31, 2024. We included epidemiological studies investigating the association between ambient temperature exposures and numerous mental and neurological conditions in populations aged 60 years and older. Exclusions were made for studies on indoor or controlled exposure, suicide, substance abuse, those not published as peer-reviewed journal articles, or those not written in English. The risk of bias of included studies was assessed using a tool developed by the World Health Organization (WHO). Qualitative synthesis was performed on all eligible studies, and random-effects meta-analyses were conducted on groups of at least four studies sharing similar study design, exposure metric, and health outcome. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework modified by the WHO. Results: From 16,786 screened articles, 76 studies were deemed eligible, primarily from mainland China and North America. There was notable heterogeneity in study variables and methodologies. The most commonly used exposure metrics were daily absolute temperature and heat waves, and time-series and case-crossover analyses were the most frequently employed study designs. Meta-analysis of four studies on the effect of a 1 degrees C increase in temperature on hospital admissions/visits for mental disorders showed a pooled risk ratio (RR) of 1.014 (95 % Confidence Interval, CI: 1.001, 1.026). Comparing heat wave days to non-heat wave days, pooled effect estimates showed increased risk in hospital admissions/visits (RR: 1.269; 95 % CI: 1.030, 1.564; six studies) and mortality related to mental disorders (RR: 1.266; 95 % CI: 0.956, 1.678; four studies). Despite the limited number of studies on cold exposures, they consistently reported that lower temperatures were associated with an increased risk of various mental and neurological conditions. Conclusions: This review presents epidemiological evidence of the adverse impacts of ambient temperature ex- posures, such as high temperatures and heat waves, on mental and neurological conditions among the older adult population, with overall moderate certainty. The findings highlight the need for greater attention to the mental and neurological health of older adults in the context of climate change and population aging. Registration number (PROSPERO ID): CRD42023428137.

Greenspace, offering multifaceted ecological and socioeconomic benefits to the nature system and human society, is integral to the 11th Sustainable Development Goal pertaining to cities and communities. Spatially and temporally explicit information on greenspace is a premise to gauge the balance between its supply and demand. However, existing efforts on urban greenspace mapping primarily focus on specific time points or baseline years without well considering seasonal fluctuations, which obscures our knowledge of greenspace’s spatiotemporal dynamics in urban settings. Here, we combined spectral unmixing approach, time-series phenology modeling, and Sentinel-2 satellite images with a 10-m resolution and nearly 5-day revisit cycle to generate a four-year (2019–2022) 10-m and 10-day resolution greenspace dynamic data cube over 1028 global major cities (with an urbanized area >100 km2). This data cube can effectively capture greenspace seasonal dynamics across greenspace types, cities, and climate zones. It also can reflect the spatiotemporal dynamics of the cooling effect of greenspace with Landsat land surface temperature data. The developed data cube provides informative data support to investigate the spatiotemporal interactions between greenspace and human society.

Loneliness may contribute to chronic diseases, while neighbourhood green space is increasingly understood to benefit health. However, whether green space is associated with loneliness is less understood, especially for an ageing population. This study aims to explore the relationship between different measures of green space and loneliness among middle-aged and older adults (N = 8,383) based on a national cohort. Loneliness was measured with a yes or no (binary) self-reported question, while the availability of residential green space was assessed with the normalised difference vegetation index (NDVI) and proportion of neighbourhood public parks. Multilevel logistic regression models, stratified and mediation analysis were used to test whether green space availability was associated with loneliness. The results showed that both forms of residential green space were negatively associated with the risk of loneliness, even after adjustments for covariates. These associations were partially mediated by social cohesion and modified by socioeconomic status and age; residents who were males, at least 60 years old, had lower incomes, or had no high school degree showed protective associations of residential green space on loneliness in some stratified models. These findings indicate that residential green space may play an important role in loneliness risk in middle-aged and older adults, so policymakers may consider urban greening as part of their comprehensive plans to support the mental health of ageing adults.

Intensifying wildfires and human settlement expansion have placed more people and infrastructure at the wildland–urban interface (WUI) areas under risk. Effective wildfire management and policy response are needed to protect ecosystems and residential communities; however, maps containing spatially and temporally explicit information on the distribution of WUI areas are limited to certain countries or local regions, and global WUI patterns and associated wildfire exposure risk therefore remain unclear. Here we generated the global WUI data layers for the 2020 baseline and the 1985–2020 time series by integrating fine-resolution housing and vegetation mapping. We estimated the total global WUI area to be 6.62 million km2. Time-series analysis revealed that global WUI areas increased by 12.56% between 1985 and 2020. By overlapping 2001–2020 wildfire burned area maps and fine-resolution population datasets, our analysis uncovered that globally, 7.07% (12.54%) of WUI areas housing 4.47 million (10.11 million) people are within a 2,400 m (4,800 m) buffer zone of wildfire threat. Regionally, we found that the United States, Brazil, China, India and Australia account for the majority of WUI areas, but African countries experience higher wildfire risk. Our quantification of global WUI spatiotemporal patterns and the associated wildfire risk could support improvement of wildfire management. Using decades of high-resolution mapping, this study tracks the land area of the wildland–urban interface that is exposed to fire risk, finding increases in both area and risk in multiple locations globally.

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Environmental exposures and their health impacts can vary substantially between urban and rural areas. However, different methods for classifying these areas could lead to inconsistencies in environmental exposure and health studies, which are often overlooked. We constructed different urban/rural classification systems based on multiple population-based (e.g., total population, population density, and commuting) and built-environment-based (e.g., nighttime light intensity, building density, road density, distance to urban centers, point of interest density, and urban area coverage) indicators and various classification schemes. These classification systems were applied to Virginia and West Virginia, United States. We compared differences in urban/rural spatial patterns, demographic compositions, and exposures of particulate matter (PM2.5), greenspace, and land surface temperature using these urban/rural classification systems to understand their impacts on environmental exposure and health research. Our findings reveal clear differences in spatial patterns and demographic compositions across various systems. We also observed that different systems can lead to changes in the magnitude and direction of urban/rural disparities in environmental exposure assessment. Addressing the complexities in delineating urbanicity and rurality may include careful consideration of classification systems to reflect those aspects of urbanicity and rurality that are relevant to the research question or the use of multiple, complementary systems.

Background and aims: The disease burden attributable to metabolic risk factors is rapidly increasing in China, especially in older people. The objective of this study was to (i) estimate the pattern and trend of six metabolic risk factors and attributable causes in China from 1990 to 2019, (ii) ascertain its association with societal development, and (iii) compare the disease burden among the Group of 20 (G20) countries. Methods: The main outcome measures were disability-adjusted life-years (DALYs) and mortality (deaths) attributable to high fasting plasma glucose (HFPG), high systolic blood pressure (HSBP), high low-density lipoprotein (HLDL) cholesterol, high body-mass index (HBMI), kidney dysfunction (KDF), and low bone mineral density (LBMD). The average annual percent change (AAPC) between 1990 and 2019 was analyzed using Joinpoint regression. Results: For all sixmetabolic risk factors, the rate of DALYs and death increased with age, accelerating for individuals older than 60 and 70 for DALYs and death, respectively. The AAPC value in rate of DALYs and death were higher in male patients than in female patients across 20 age groups. A double-peak pattern was observed for AAPC in the rate of DALYs and death, peaking at age 20-49 and at age 70-95 plus. The age-standardized rate of DALYs increased for HBMI and LBMD, decreased for HFPG, HSBP, KDF, and remained stable for HLDL from 1990 to 2019. In terms of age-standardized rate of DALYs, there was an increasing trend of neoplasms and neurological disorders attributable to HFPG; diabetes and kidney diseases, neurological disorders, sense organ diseases, musculoskeletal disorders, neoplasms, cardiovascular diseases, digestive diseases to HBMI; unintentional injuries to LBMD; and musculoskeletal disorders to KDF. Among 19 countries of Group 20, in 2019, the age-standardized rate of DALYs and death were ranked fourth to sixth for HFPG, HSBP, and HLDL, but ranked 10th to 15th for LBMD, KDF, and HBMI, despite the number of DALYs and death ranked first to second for sixmetabolic risk factors. Conclusions: Population aging continuously accelerates the metabolic risk factor driven disease burden in China. Comprehensive and tight control of metabolic risk factors before 20 and 70 may help to mitigate the increasing disease burden and achieve healthy aging, respectively.