The impact of exposure assessment on associations between air pollution and cardiovascular mortality risks in the city of Rio de Janeiro, Brazil.

Despite a growing literature for complex air quality models, scientific evidence lacks of the influences of varying exposure assessments and air quality data sources on the estimated mortality risks. This case-crossover study estimated cardiovascular mortality risks from fine particulate matter (PM2.5) and ozone (O3) exposures, using varying exposure methods, to aid understanding of the impact of exposure methods in the health risk estimation. We used individual-level cardiovascular mortality data in the city of Rio de Janeiro, 2012-2016. PM2.5 and O3 exposure levels (from the date of death to seven prior days [lag0-7]) were estimated at the individual level or district level using either the WRF-Chem modeling data or monitoring data, resulting in a total of 10 exposure methods. The exposure-response relationships were estimated using multiple logistic regressions. The changes in cardiovascular mortality were represented as an odds ratio (OR) and 95% confidence intervals (CIs) for an interquartile range (IQR) increase in the exposures. Results showed that socioeconomically more advantaged populations had lower access to the stationary monitoring networks. Higher variance in the estimated exposure levels across the 10 exposure methods was found for PM2.5 than O3. PM2.5 exposure was not associated with mortality risk in any exposure methods. WRF-Chem-based O3 exposure estimated for each individual of the entire population found a significant mortality risk (OR = 1.06, 95% CI: 1.01, 1.11), but not the other exposure methods. Higher risks for females and older populations were suggested for O3 estimates estimated for each individual using the WRF-Chem data. Findings indicate that decisions on exposure methods and data sources can lead to substantially varying implications for air pollution risks and highlight the need for comprehensive exposure and health impact assessments to aid local decision-making for air pollution and public health.

Delineating Urbanicity and Rurality: Impact on Environmental Exposure Assessment.

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.

Temperature, Crime, and Violence: A Systematic Review and Meta-Analysis.

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.

Impacts of land-use and land-cover changes on temperature-related mortality.

Background: Land-use and land-cover change (LULCC) can substantially affect climate through biogeochemical and biogeophysical effects. Here, we examine the future temperature-mortality impact for two contrasting LULCC scenarios in a background climate of low greenhouse gas concentrations. The first LULCC scenario implies a globally sustainable land use and socioeconomic development (sustainability). In the second LULCC scenario, sustainability is implemented only in the Organisation for Economic Cooperation and Development countries (inequality). Methods: Using the Multi-Country Multi-City (MCC) dataset on mortality from 823 locations in 52 countries and territories, we estimated the temperature-mortality exposure-response functions (ERFs). The LULCC and noLULCC scenarios were implemented in three fully coupled Earth system models (ESMs): Community Earth System Model, Max Planck Institute Earth System Model, and European Consortium Earth System Model. Next, using temperature from the ESMs' simulations and the estimated location-specific ERFs, we assessed the temperature-related impact on mortality for the LULCC and noLULCC scenarios around the mid and end century. Results: Under sustainability, the multimodel mean changes in excess mortality range from -1.1 to +0.6 percentage points by 2050-2059 across all locations and from -1.4 to +0.5 percentage points by 2090-2099. Under inequality, these vary from -0.7 to +0.9 percentage points by 2050-2059 and from -1.3 to +2 percentage points by 2090-2099. Conclusions: While an unequal socioeconomic development and unsustainable land use could increase the burden of heat-related mortality in most regions, globally sustainable land use has the potential to reduce it in some locations. However, the total (cold and heat) impact on mortality is very location specific and strongly depends on the underlying climate change scenario due to nonlinearity in the temperature-mortality relationship.

Rainfall events and daily mortality across 645 global locations: two stage time series analysis.

OBJECTIVE: To examine the associations between characteristics of daily rainfall (intensity, duration, and frequency) and all cause, cardiovascular, and respiratory mortality. DESIGN: Two stage time series analysis. SETTING: 645 locations across 34 countries or regions. POPULATION: Daily mortality data, comprising a total of 109 954 744 all cause, 31 164 161 cardiovascular, and 11 817 278 respiratory deaths from 1980 to 2020. MAIN OUTCOME MEASURE: Association between daily mortality and rainfall events with return periods (the expected average time between occurrences of an extreme event of a certain magnitude) of one year, two years, and five years, with a 14 day lag period. A continuous relative intensity index was used to generate intensity-response curves to estimate mortality risks at a global scale. RESULTS: During the study period, a total of 50 913 rainfall events with a one year return period, 8362 events with a two year return period, and 3301 events with a five year return period were identified. A day of extreme rainfall with a five year return period was significantly associated with increased daily all cause, cardiovascular, and respiratory mortality, with cumulative relative risks across 0-14 lag days of 1.08 (95% confidence interval 1.05 to 1.11), 1.05 (1.02 to 1.08), and 1.29 (1.19 to 1.39), respectively. Rainfall events with a two year return period were associated with respiratory mortality only, whereas no significant associations were found for events with a one year return period. Non-linear analysis revealed protective effects (relative risk <1) with moderate-heavy rainfall events, shifting to adverse effects (relative risk >1) with extreme intensities. Additionally, mortality risks from extreme rainfall events appeared to be modified by climate type, baseline variability in rainfall, and vegetation coverage, whereas the moderating effects of population density and income level were not significant. Locations with lower variability of baseline rainfall or scarce vegetation coverage showed higher risks. CONCLUSION: Daily rainfall intensity is associated with varying health effects, with extreme events linked to an increasing relative risk for all cause, cardiovascular, and respiratory mortality. The observed associations varied with local climate and urban infrastructure.

Association of holidays and the day of the week with suicide risk: multicounty, two stage, time series study.

OBJECTIVES: To assess the short term temporal variations in suicide risk related to the day of the week and national holidays in multiple countries. DESIGN: Multicountry, two stage, time series design. SETTING: Data from 740 locations in 26 countries and territories, with overlapping periods between 1971 and 2019, collected from the Multi-city Multi-country Collaborative Research Network database. PARTICIPANTS: All suicides were registered in these locations during the study period (overall 1 701 286 cases). MAIN OUTCOME MEASURES: Daily suicide mortality. RESULTS: Mondays had peak suicide risk during weekdays (Monday-Friday) across all countries, with relative risks (reference: Wednesday) ranging from 1.02 (95% confidence interval (CI) 0.95 to 1.10) in Costa Rica to 1.17 (1.09 to 1.25) in Chile. Suicide risks were lowest on Saturdays or Sundays in many countries in North America, Asia, and Europe. However, the risk increased during weekends in South and Central American countries, Finland, and South Africa. Additionally, evidence suggested strong increases in suicide risk on New Year's day in most countries with relative risks ranging from 0.93 (95% CI 0.75 to 1.14) in Japan to 1.93 (1.31 to 2.85) in Chile, whereas the evidence on Christmas day was weak. Suicide risk was associated with a weak decrease on other national holidays, except for Central and South American countries, where the risk generally increased one or two days after these holidays. CONCLUSIONS: Suicide risk was highest on Mondays and increased on New Year's day in most countries. However, the risk of suicide on weekends and Christmas varied by country and territory. The results of this study can help to better understand the short term variations in suicide risks and define suicide prevention action plans and awareness campaigns.

Do relationships between ambient temperature and serious adverse health outcomes vary among users of different antidiabetes drugs? A retrospective cohort study of US Medicaid beneficiaries with type 2 diabetes.

OBJECTIVE: Prior studies demonstrate that some untoward clinical outcomes vary by outdoor temperature. This is true of some endpoints common among persons with diabetes, a population vulnerable to climate change-associated health risks. Yet, prior work has been agnostic to the antidiabetes drugs taken by such persons. We examined whether relationships between ambient temperature and adverse health outcomes among persons with type 2 diabetes (T2D) varied by exposure to different antidiabetes drugs. DESIGN: Retrospective cohort. SETTING: Healthcare and meteorological data from five US states, 1999-2010. PARTICIPANTS: US Medicaid beneficiaries with T2D categorised by use of antidiabetes drugs. EXPOSURE: Maximum daily ambient temperature (t-max). OUTCOMES: Hospital presentation for serious hypoglycaemia, diabetic ketoacidosis (DKA) or sudden cardiac arrest (examined separately). METHODS: We linked US Medicaid to US Department of Commerce data that permitted us to follow individuals longitudinally and examine health plan enrolment, healthcare claims, and meteorological exposures-all at the person-day level. We mapped daily temperature from weather stations to Zone Improvement Plan (ZIP) codes, then assigned a t-max to each person-day based on the residential ZIP code. Among prespecified subcohorts of users of different pharmacologic classes of antidiabetes drugs, we calculated age and sex-adjusted occurrence rates for each outcome by t-max stratum. We used modified Poisson regression to assess relationships between linear and quadratic t-max terms and each outcome. We examined effect modification between t-max and a covariable for current exposure to a specific antidiabetes drug and assessed significance via Wald tests. RESULTS: We identified ∼3 million persons with T2D among whom 713 464 used sulfonylureas (SUs), dipeptidyl peptidase-4 inhibitors (DPP-4is), meglitinides, or glucagon-like peptide 1 receptor agonists (GLP1RAs). We identified a positive linear association between t-max and serious hypoglycaemia among non-insulin users of glimepiride and of glyburide but not glipizide (Wald p value for interaction among SUs=0.048). We identified an inverse linear association between t-max and DKA among users of the DPP-4i sitagliptin (p=0.016) but not the GLP1RA exenatide (p=0.080). We did not identify associations between t-max and sudden cardiac arrest among users of SUs, meglitinides, exenatide, or DPP-4is. CONCLUSIONS: We identified some antidiabetes drug class-specific and agent-specific differences in the relationship between ambient temperature and untoward glycaemic but not arrhythmogenic, safety outcomes.

Long-term exposure to wildland fire smoke PM2.5 and mortality in the contiguous United States.

Despite the substantial evidence on the health effects of short-term exposure to ambient fine particles (PM2.5), including increasing studies focusing on those from wildland fire smoke, the impacts of long-term wildland fire smoke PM2.5 exposure remain unclear. We investigated the association between long-term exposure to wildland fire smoke PM2.5 and nonaccidental mortality and mortality from a wide range of specific causes in all 3,108 counties in the contiguous United States, 2007 to 2020. Controlling for nonsmoke PM2.5, air temperature, and unmeasured spatial and temporal confounders, we found a nonlinear association between 12-mo moving average concentration of smoke PM2.5 and monthly nonaccidental mortality rate. Relative to a month with the long-term smoke PM2.5 exposure below 0.1 µg/m3, nonaccidental mortality increased by 0.16 to 0.63 and 2.11 deaths per 100,000 people per month when the 12-mo moving average of PM2.5 concentration was of 0.1 to 5 and 5+ µg/m3, respectively. Cardiovascular, ischemic heart disease, digestive, endocrine, diabetes, mental, and chronic kidney disease mortality were all found to be associated with long-term wildland fire smoke PM2.5 exposure. Smoke PM2.5 contributed to approximately 11,415 nonaccidental deaths/y (95% CI: 6,754, 16,075) in the contiguous United States. Higher smoke PM2.5-related increases in mortality rates were found for people aged 65 and above. Positive interaction effects with extreme heat were also observed. Our study identified the detrimental effects of long-term exposure to wildland fire smoke PM2.5 on a wide range of mortality outcomes, underscoring the need for public health actions and communications that span the health risks of both short- and long-term exposure.

Temporal variations in the short-term effects of ambient air pollution on cardiovascular and respiratory mortality: a pooled analysis of 380 urban areas over a 22-year period.

BACKGROUND: Ambient air pollution, including particulate matter (such as PM10 and PM2·5) and nitrogen dioxide (NO2), has been linked to increases in mortality. Whether populations' vulnerability to these pollutants has changed over time is unclear, and studies on this topic do not include multicountry analysis. We evaluated whether changes in exposure to air pollutants were associated with changes in mortality effect estimates over time. METHODS: We extracted cause-specific mortality and air pollution data collected between 1995 and 2016 from the Multi-Country Multi-City (MCC) Collaborative Research Network database. We applied a two-stage approach to analyse the short-term effects of NO2, PM10, and PM2·5 on cause-specific mortality using city-specific time series regression analyses and multilevel random-effects meta-analysis. We assessed changes over time using a longitudinal meta-regression with time as a linear fixed term and explored potential sources of heterogeneity and two-pollutant models. FINDINGS: Over 21·6 million cardiovascular and 7·7 million respiratory deaths in 380 cities across 24 countries over the study period were included in the analysis. All three air pollutants showed decreasing concentrations over time. The pooled results suggested no significant temporal change in the effect estimates per unit exposure of PM10, PM2·5, or NO2 and mortality. However, the risk of cardiovascular mortality increased from 0·37% (95% CI -0·05 to 0·80) in 1998 to 0·85% (0·55 to 1·16) in 2012 with a 10 µg/m3 increase in PM2·5. Two-pollutant models generally showed similar results to single-pollutant models for PM fractions and indicated temporal differences for NO2. INTERPRETATION: Although air pollution levels decreased during the study period, the effect sizes per unit increase in air pollution concentration have not changed. This observation might be due to the composition, toxicity, and sources of air pollution, as well as other factors, such as socioeconomic determinants or changes in population distribution and susceptibility. FUNDING: None.

Short-term effects of wildfire-specific fine particulate matter and its carbonaceous components on perinatal outcomes: A multicentre cohort study in New South Wales, Australia.

BACKGROUND: Epidemiological evidence on the association between wildfire-specific fine particulate matter (PM2.5) and its carbonaceous components with perinatal outcomes is limited. We aimed to examine the short-term effects of wildfire-specific PM2.5 and its carbonaceous components on perinatal outcomes. METHODS: A multicentre cohort of 9743 singleton births during the wildfire seasons from 1 September 2009 to 31 December 2015 across six cities in New South Wales, Australia were linked with daily wildfire-specific PM2.5 and carbonaceous components (organic carbon and black carbon). Adjusted distributed lag Cox regression models with spatial clustering were performed to estimate daily and cumulative adjusted hazard ratios (aHRs) during the last four gestational weeks for preterm birth, stillbirth, nonvertex presentation, low 5-min Apgar score, special care nursery/neonatal intensive care unit (SCN/NICU) admission, and caesarean section. RESULTS: Daily aHRs per 10 µg/m3 PM2.5 showed nearly inverted 'U'-shaped positive associations and daily cumulative aHRs that increased with increasing duration of the exposures. The aHRs for lag 0-6 days were 1.17 (95 % CI: 1.04, 1.32) for preterm birth, 1.40 (95 % CI: 1.11, 1.78) for stillbirth, 1.20 (95 % CI: 1.08, 1.33) for nonvertex presentation, 1.12 (95 % CI: 0.93, 1.35) for low 5-min Apgar score, 0.99 (95 % CI: 0.83, 1.19) for SNC/NICU admission, and 1.01 (95 % CI: 0.94, 1.08) for caesarean section. Organic carbon and black carbon components for lag 0-6 days showed positive associations. The highest component-specific aHRs were 1.09 (95 % CI: 1.03, 1.15) and 4.57 (95 % CI: 1.96, 10.68) for stillbirth per 1 µg/m3 organic carbon and black carbon, respectively. The subgroups identified as most vulnerable were female births, births to mothers with low socioeconomic status, and births to mothers with high biothermal exposure. CONCLUSIONS: Positive associations of short-term wildfire-specific PM2.5 exposure and its carbonaceous components with adverse perinatal outcomes suggest that policies to reduce exposure would benefit public health.

Regional variation in the role of humidity on city-level heat-related mortality.

The rising humid heat is regarded as a severe threat to human survivability, but the proper integration of humid heat into heat-health alerts is still being explored. Using state-of-the-art epidemiological and climatological datasets, we examined the association between multiple heat stress indicators (HSIs) and daily human mortality in 739 cities worldwide. Notable differences were observed in the long-term trends and timing of heat events detected by HSIs. Air temperature (Tair) predicts heat-related mortality well in cities with a robust negative Tair-relative humidity correlation (CT-RH). However, in cities with near-zero or weak positive CT-RH, HSIs considering humidity provide enhanced predictive power compared to Tair. Furthermore, the magnitude and timing of heat-related mortality measured by HSIs could differ largely from those associated with Tair in many cities. Our findings provide important insights into specific regions where humans are vulnerable to humid heat and can facilitate the further enhancement of heat-health alert systems.

Disparities in exposure to concentrated animal feeding operations (CAFOs) and risk of adverse birth outcomes in Pennsylvania, USA.

Background: Previous studies have linked exposure to concentrated animal feeding operations (CAFOs) with various health outcomes. However, relatively few studies evaluated the impacts of CAFOs on adverse birth outcomes, despite significant public health concerns regarding maternal and child health. Objectives: This cross-sectional study investigated the risk of adverse birth outcomes associated with CAFOs exposure and evaluated disparities in exposure to CAFOs and associated health outcomes. Methods: We obtained individual-level birth records from 2003 to 2020 from the Pennsylvania Department of Health. We considered two adverse birth outcomes: (1) preterm birth (PTB); and (2) low birth weight (LBW). Exposure was considered as a binary indicator (presence or absence of CAFO) and as categories based on level of exposure. Logistic regression was applied to estimate the association between CAFOs exposure and adverse birth outcomes. Models were adjusted for infant's sex, maternal demographics (age, race/ethnicity, education), prenatal BMI, prenatal care, smoking status, marital status, plurality, WIC status, and urban/rural indicator. We examined both disparities in exposure and in health response. Results: Presence of CAFOs was associated with higher risk of PTB, with an increasing trend with higher levels of CAFOs exposure. Compared to the no CAFO exposure group, the odds ratios for PTB were 1.022 (95 % confidence interval 1.003, 1.043), 1.066 (1.034, 1.100), 1.069 (1.042, 1.097) for low, medium, and high CAFOs exposure groups, respectively. Some maternal characteristics were associated with a higher CAFO-related risk of PTB. Similar associations were observed for LBW for some characteristics such as mother's race/ethnicity, education, WIC status, and urbanicity, although some findings were not statistically significant. Conclusions: Our findings suggest that presence of CAFOs increases risk of preterm birth. Our results indicate that some maternal characteristics may be associated with higher risk of CAFO-related PTB or LBW. This study can inform future research on disparities in CAFO exposure and associated health burden.

All-cause, cardiovascular, and respiratory mortality and wildfire-related ozone: a multicountry two-stage time series analysis.

BACKGROUND: Wildfire activity is an important source of tropospheric ozone (O3) pollution. However, no study to date has systematically examined the associations of wildfire-related O3 exposure with mortality globally. METHODS: We did a multicountry two-stage time series analysis. From the Multi-City Multi-Country (MCC) Collaborative Research Network, data on daily all-cause, cardiovascular, and respiratory deaths were obtained from 749 locations in 43 countries or areas, representing overlapping periods from Jan 1, 2000, to Dec 31, 2016. We estimated the daily concentration of wildfire-related O3 in study locations using a chemical transport model, and then calibrated and downscaled O3 estimates to a resolution of 0·25°â€ˆ× 0·25° (approximately 28 km2 at the equator). Using a random-effects meta-analysis, we examined the associations of short-term wildfire-related O3 exposure (lag period of 0-2 days) with daily mortality, first at the location level and then pooled at the country, regional, and global levels. Annual excess mortality fraction in each location attributable to wildfire-related O3 was calculated with pooled effect estimates and used to obtain excess mortality fractions at country, regional, and global levels. FINDINGS: Between 2000 and 2016, the highest maximum daily wildfire-related O3 concentrations (≥30 µg/m3) were observed in locations in South America, central America, and southeastern Asia, and the country of South Africa. Across all locations, an increase of 1 µg/m3 in the mean daily concentration of wildfire-related O3 during lag 0-2 days was associated with increases of 0·55% (95% CI 0·29 to 0·80) in daily all-cause mortality, 0·44% (-0·10 to 0·99) in daily cardiovascular mortality, and 0·82% (0·18 to 1·47) in daily respiratory mortality. The associations of daily mortality rates with wildfire-related O3 exposure showed substantial geographical heterogeneity at the country and regional levels. Across all locations, estimated annual excess mortality fractions of 0·58% (95% CI 0·31 to 0·85; 31 606 deaths [95% CI 17 038 to 46 027]) for all-cause mortality, 0·41% (-0·10 to 0·91; 5249 [-1244 to 11 620]) for cardiovascular mortality, and 0·86% (0·18 to 1·51; 4657 [999 to 8206]) for respiratory mortality were attributable to short-term exposure to wildfire-related O3. INTERPRETATION: In this study, we observed an increase in all-cause and respiratory mortality associated with short-term wildfire-related O3 exposure. Effective risk and smoke management strategies should be implemented to protect the public from the impacts of wildfires. FUNDING: Australian Research Council and the Australian National Health and Medical Research Council.

Associations between gestational residential radon exposure and term low birthweight in Connecticut, USA.

BACKGROUND: Studies suggest biologic mechanisms for gestational exposure to radiation and impaired fetal development. We explored associations between gestational radon exposure and term low birthweight, for which evidence is limited. METHODS: We examined data for 68,159 singleton full-term births in Connecticut, USA, 2016-2018. Using a radon spatiotemporal model, we estimated ZIP code-level basement and ground-level exposures during pregnancy and trimesters for each participant's address at birth or delivery. We used logistic regression models, including confounders, to estimate odds ratios (ORs) for term low birth weight in four exposure quartiles (Q1 to Q4) with the lowest exposure group (Q1) as the reference. RESULTS: Exposure levels to basement radon throughout pregnancy (0.27-3.02 pCi/L) were below the guideline level set by the US Environmental Protection Agency (4 pCi/L). The ORs for term low birth weight in the second-highest (Q3; 1.01-1.33 pCi/L) exposure group compared to the reference (<0.79 pCi/L) group for basement radon during the first trimester was 1.22 (95% confidence interval [CI]: 1.02, 1.45). The OR in the highest (Q4; 1.34-4.43 pCi/L) quartile group compared to the reference group during the first trimester was 1.26 (95% CI: 1.05, 1.50). Risks from basement radon were higher for participants with lower income, lower maternal education levels, or living in urban regions. CONCLUSION: This study found increased term low birth weight risks for increases in basement radon. Results have implications for infants' health for exposure to radon at levels below the current national guideline for indoor radon concentrations and building remediations.

A systematic review of animal feeding operations including concentrated animal feeding operations (CAFOs) for exposure, health outcomes, and environmental justice.

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.

Air quality modeling in the metropolitan area of São Paulo, Brazil: A review.

Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O3) and fine particulate matter (PM2.5) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O3 and PM2.5 concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM2.5 and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models' physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning.

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.

Long-term exposure to PM2.5 and mortality in a national cohort in South Korea: effect modification by community deprivation, medical infrastructure, and greenness.

BACKGROUND: Long-term exposure to PM2.5 has been linked to increased mortality risk. However, limited studies have examined the potential modifying effect of community-level characteristics on this association, particularly in Asian contexts. This study aimed to estimate the effects of long-term exposure to PM2.5 on mortality in South Korea and to examine whether community-level deprivation, medical infrastructure, and greenness modify these associations. METHODS: We conducted a nationwide cohort study using the National Health Insurance Service-National Sample Cohort. A total of 394,701 participants aged 30 years or older in 2006 were followed until 2019. Based on modelled PM2.5 concentrations, 1 to 3-year and 5-year moving averages of PM2.5 concentrations were assigned to each participant at the district level. Time-varying Cox proportional-hazards models were used to estimate the association between PM2.5 and non-accidental, circulatory, and respiratory mortality. We further conducted stratified analysis by community-level deprivation index, medical index, and normalized difference vegetation index to represent greenness. RESULTS: PM2.5 exposure, based on 5-year moving averages, was positively associated with non-accidental (Hazard ratio, HR: 1.10, 95% Confidence Interval, CI: 1.01, 1.20, per 10 µg/m3 increase) and circulatory mortality (HR: 1.22, 95% CI: 1.01, 1.47). The 1-year moving average of PM2.5 was associated with respiratory mortality (HR: 1.33, 95% CI: 1.05, 1.67). We observed higher associations between PM2.5 and mortality in communities with higher deprivation and limited medical infrastructure. Communities with higher greenness showed lower risk for circulatory mortality but higher risk for respiratory mortality in association with PM2.5. CONCLUSIONS: Our study found mortality effects of long-term PM2.5 exposure and underlined the role of community-level factors in modifying these association. These findings highlight the importance of considering socio-environmental contexts in the design of air quality policies to reduce health disparities and enhance overall public health outcomes.

On adjustment for temperature in heatwave epidemiology: a new method and toward clarification of methods to estimate health effects of heatwaves.

Defining the effect of exposure of interest and selecting an appropriate estimation method are prerequisite for causal inference. Understanding the ways in which association between heatwaves (i.e., consecutive days of extreme high temperature) and an outcome depends on whether adjustment was made for temperature and how such adjustment was conducted, is limited. This paper aims to investigate this dependency, demonstrate that temperature is a confounder in heatwave-outcome associations, and introduce a new modeling approach to estimate a new heatwave-outcome relation: E[R(Y)|HW=1, Z]/E[R(Y)|T=OT, Z], where HW is a daily binary variable to indicate the presence of a heatwave; R(Y) is the risk of an outcome, Y; T is a temperature variable; OT is optimal temperature; and Z is a set of confounders including typical confounders but also some types of T as a confounder. We recommend characterization of heatwave-outcome relations and careful selection of modeling approaches to understand the impacts of heatwaves under climate change. We demonstrate our approach using real-world data for Seoul, which suggests that the total effect of heatwaves may be larger than what may be inferred from the extant literature. An R package, HEAT (Heatwave effect Estimation via Adjustment for Temperature), was developed and made publicly available.

Global, regional, and national burden of heatwave-related mortality from 1990 to 2019: A three-stage modelling study.

BACKGROUND: The regional disparity of heatwave-related mortality over a long period has not been sufficiently assessed across the globe, impeding the localisation of adaptation planning and risk management towards climate change. We quantified the global mortality burden associated with heatwaves at a spatial resolution of 0.5°×0.5° and the temporal change from 1990 to 2019. METHODS AND FINDINGS: We collected data on daily deaths and temperature from 750 locations of 43 countries or regions, and 5 meta-predictors in 0.5°×0.5° resolution across the world. Heatwaves were defined as location-specific daily mean temperature ≥95th percentiles of year-round temperature range with duration ≥2 days. We first estimated the location-specific heatwave-mortality association. Secondly, a multivariate meta-regression was fitted between location-specific associations and 5 meta-predictors, which was in the third stage used with grid cell-specific meta-predictors to predict grid cell-specific association. Heatwave-related excess deaths were calculated for each grid and aggregated. During 1990 to 2019, 0.94% (95% CI: 0.68-1.19) of deaths [i.e., 153,078 cases (95% eCI: 109,950-194,227)] per warm season were estimated to be from heatwaves, accounting for 236 (95% eCI: 170-300) deaths per 10 million residents. The ratio between heatwave-related excess deaths and all premature deaths per warm season remained relatively unchanged over the 30 years, while the number of heatwave-related excess deaths per 10 million residents per warm season declined by 7.2% per decade in comparison to the 30-year average. Locations with the highest heatwave-related death ratio and rate were in Southern and Eastern Europe or areas had polar and alpine climates, and/or their residents had high incomes. The temporal change of heatwave-related mortality burden showed geographic disparities, such that locations with tropical climate or low incomes were observed with the greatest decline. The main limitation of this study was the lack of data from certain regions, e.g., Arabian Peninsula and South Asia. CONCLUSIONS: Heatwaves were associated with substantial mortality burden that varied spatiotemporally over the globe in the past 30 years. The findings indicate the potential benefit of governmental actions to enhance health sector adaptation and resilience, accounting for inequalities across communities.