The link between air pollution exposure and increased mortality and morbidity is firmly supported by a broad evidence base. The 2015 Global Burden of Diseases Study, a 195-country investigation, found fine particulate matter (ie, particles with an aerodynamic diameter 2.5 μm [PM 2.5 ]) to be the fifth ranking worldwide mortality risk factor, with an attributed 4.2 million premature deaths. 1 Yet these impacts are not equally distributed. Worsening air quality, in conjunction with population growth, has driven absolute increases in air pollution-associated illness in low-and middle-income nations. Furthermore, while air quality has improved in high-income nations, geographic disparities persist among economically and racially disparate communities where air pollution remains stubbornly elevated. 2 Prioritizing air quality improvements in the most affected areas will yield the greatest public health benefits, but quantifying the level of improvement needed is an evolving question. Improvement in air quality can be attributed to policies that set regulatory air pollution standards or guidelines protective of human health. Epidemiological data and evidence are used by environmental agencies, such as the World Health Organization, the US Environmental Protection Agency, and the European Union, to determine appropriate regulatory targets for criteria air pollutants, including PM 2.5 , ozone, and nitrogen dioxide (NO 2). However, the decision-making behind regulatory standards relies on up-to-date exposure and health information derived using appropriate methods. In the US, the Clean Air Act requires an independent review of the most relevant air pollution and health literature assessed through a weight-of-evidence approach. Weight-of-evidence relies on the examination of multiple evidentiary lines crossing different scientific disciplines, including epidemiological cohorts, animal-based toxicologic studies, and controlled human exposures or clinical findings, to determine causality. 3 The strength of this approach is to ensure that identified air pollution and health effects are not restricted to a single line of study and consider the full body of scientific inquiry. So, why should we pursue studies of air pollution and health when the existing literature is so robust? The identification of appropriate regulatory thresholds depends on up-to-date investigations that support, dispute, or identify new air pollution and health associations. This observational study by Ma and colleagues 4 evaluated the association between acute PM 2.5 and NO 2 air pollution exposure with 8.96 million mortality events in 4 separate global regions: Jiangsu, China; California, US; south-central Italy; and Germany. The findings in the study by Ma et al 4 confirm previously identified associations of increased mortality with greater PM 2.5 and NO 2 exposure. 5 Ma et al 4 also observed heterogeneity in the strength of associations of mortality with the highest air pollution risks identified in south-central Italy for PM 2.5 and Germany for NO 2. However, the novel aspect of their work was the methodological application of a 2-way interactive fixed-effect model used to infer the air pollution and mortality association. Ma et al 4 argued that this econometric-derived approach has advantages over more traditional time-series modeling by controlling for both measured and unmeasured time-varying spatial unit-specific confounders. Ma and colleagues 4 further compared their findings with a more familiar time-series model and demonstrated robustness in the air pollution and mortality association using both approaches. This methodological comparison not only validates prior air pollution and health studies that use time-series methods 6 but provides evidentiary rigor for the validity of both methods when reconsidering regulatory standards. While the study by Ma et al 4 presents an alternative statistical approach for estimating air pollution health effects, there are other noteworthy advances in the air pollution and health literature. First, there have been new developments in air pollution exposure modeling. Initial

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. Identifying both individual and community risk factors associated with higher mortality is essential to improve outcomes. Few population-based studies of mortality in COPD include both individual characteristics and community risk factors. Objective: We used geocoded, patient-level data to describe the associations between individual demographics, neighborhood socioeconomic status, and all-cause mortality. Methods: We performed a nationally representative retrospective cohort analysis of all patients enrolled in the Veteran Health Administration with at least one ICD-9 or ICD-10 code for COPD in 2016-2019. We obtained demographic characteristics, comorbidities, and geocoded residential address. Area Deprivation Index and rurality were classified using individual geocoded residential addresses. We used logistic regression models to assess the association between these characteristics and age-adjusted all-cause mortality. Results: Of 1,106,163 COPD patients, 33.4% were deceased as of January 2021. In age-adjusted models, having more comorbidities, Black/African American race (OR 1.09 [95% CI: 1.08-1.11]), and higher neighborhood disadvantage (OR 1.30 [95% CI: 1.28-1.32]) were associated with all-cause mortality. Female sex (OR 0.67 [95% CI: 0.65-0.69]), Asian race (OR 0.64, [95% CI: 0.59-0.70]), and living in a more rural area were associated with lower odds of all-cause mortality. After adjusting for age, comorbidities, neighborhood socioeconomic status, and rurality, the association with Black/African American race reversed. Conclusion: All-cause mortality in COPD patients is disproportionately higher in patients living in poorer neighborhoods and urban areas, suggesting the impact of social determinants of health on COPD outcomes. Black race was associated with higher age-adjusted all-cause mortality, but this association was abrogated after adjusting for gender, socioeconomic status, comorbidities, and urbanicity. Future studies should focus on exploring mechanisms by which disparities arise and developing interventions to address these.

Background: Despite similar incidence rates among Black and White women, breast cancer mortality rates are 40% higher among Black women. More than half

Lead poisoning remains a persistent health issue for both humans and wildlife, despite strides to reduce lead contamination in the environment. Using Geographic Information Systems (GIS), this study explores the associations between blood lead levels (BLLs) in wildlife sentinels and possible built environment lead exposure risk factors in the Minneapolis-Saint Paul, Minnesota urban area. Results show a high-level of heterogeneity in animal BLLs (n = 472) across our urban environment and suggest that each kilometer increase in road density is associated with a 17.07% (95% CI: 1.48%, 35.05%) increase in BLL in our study species of Virginia opossums and Eastern gray squirrels, and a 14.28% (95% CI: 1.16%, 29.09%) increase in BLL of rock pigeons. For squirrels and opossums, we see an additional 5.72% (95% CI: 0.59%, 10.85%) increased risk of BLL for every 1000 people per square-mile. The relationship between animal sentinels and environmental hazards can give us an insight into the potential lead exposure risks for humans. The use of wildlife sentinel data to explore environmental risk factors supports a One Health approach to better address public health questions and aid in wildlife rehabilitation related to residual lead poisoning from ambient environmental exposures.

<h2>Abstract</h2><h3>Background</h3><p>Air pollution contributes to premature mortality, but potential impacts differ in populations with existing disease, particularly for individuals with multiple risk factors. While chronic obstructive pulmonary disease (COPD) increases vulnerability to air pollution, individuals with COPD and other individual risk factors are at the intersection of multiple risks and may be especially susceptible to the effect of acute outdoor air pollution.</p><h3>Research Question</h3><p>What is the association between wintertime air pollution and mortality in patients with COPD and the modifying role of individual risk factors?</p><h3>Study Design and Methods</h3><p>We evaluated 19,243 deceased Veterans with prior COPD diagnosis residing in 25 U.S. metropolitan regions (2016-2019). Electronic health records include patient demographics, smoking status, and comorbidities, such as asthma, coronary artery disease (CAD), obesity, and diabetes. Using geocoded addresses, individuals were assigned wintertime fine particulate matter (PM_2.5) and NO₂ air pollution exposures. We estimated associations between acute air pollution and mortality using a time-stratified case-crossover design with a conditional logistic model and assessed individual risk differences by stratified analysis.</p><h3>Results</h3><p>We estimated a 1.05 (95% CI: 1.02-1.09) mortality risk for each 10 <i>μ</i>g/m³ increase in daily wintertime PM_2.5. Older patients and African Americans showed elevated risk. Obesity was a substantial air pollution-related mortality risk factor (OR: 1.11; 95% CI: 1.01-1.23) and the estimated risk for individuals with obesity plus CAD or obesity plus diabetes was 16% higher.</p><h3>Interpretation</h3><p>Wintertime PM_2.5 exposure was associated with elevated mortality risk in people with COPD, but individuals with multiple comorbidities, notably obesity, had high vulnerability. Our study suggests that obesity, CAD, and diabetes are understudied modifiers of air pollution-related risks for people with existing COPD.</p>

Background The short-term association between increasing temperatures and injury has been described in high-income countries, but less is known for low-income and-middle-income countries, including Vietnam. Methods We used emergency injury visits (EIV) data for 2017–2019 from 733 hospitals and clinics in Hanoi, Vietnam to examine the effects of daily temperature on EIV. Time-series analysis with quasi-Poisson models was used to estimate a linear relative risk increase (RRI) for overall populations and ones stratified by age and sex. Exposure–response curves estimated non-linear associations as an RR between daily temperature and injury. Models were adjusted for the day of week, holidays, daily relative humidity, daily particulate matter, and long-term and seasonal trends. Results and conclusions A total of 39 313 EIV were recorded averaging 36 injuries daily. Injuries more likely occurred in males and those aged 15–44, and aged 44–60. For linear effects, a 5°C increase in same day mean temperature was associated with an overall increased EIV (RRI 4.8; 95% CI 2.3 to 7.3) with males (RRI 5.9; 95% CI 3.0 to 8.9) experiencing a greater effect than females (RRI 3.0; 95% CI −0.5 to 6.5). Non-linear effects showed an increase in EIV at higher temperatures compared with the threshold temperature of 15°C, with the greatest effect at 33°C (RR 1.3; 95% CI 1.2 to 1.6). Further research to investigate temperature-injury among different populations and by the cause of injury is warranted. Data may be obtained from a third party and are not publicly available. Data were provided by Hanoi Social Security.

Research on the public health significance of Ixodes scapularis ticks in the Midwest seldom focuses on extreme weather conditions that can modulate their population dynamics and ability to transmit pathogenic organisms. In this study, we assessed whether the distributional abundance of I. scapularis immatures is associated with current and time-lagged climatic determinants either directly or indirectly. We analyzed a 20-year longitudinal small mammal live-trapping dataset within a seven-county metropolitan area in Minnesota (1998–2016) using yearly tick counts at each site to assess whether inter- and intra-annual variation in immature I. scapularis counts is associated with climate and land-use conditions. We found that (1) immature I. scapularis ticks infesting mammals expanded southwesterly over the study period, (2) eastern chipmunks, Tamias striatus, supplied a substantial proportion of nymphal blood meals, (3) a suite of climatological variables are demonstrably associated with I. scapularis presence, and abundance across sites, most notably summer vapor pressure deficit, and (4) immature I. scapularis display an affinity for deciduous forests in metro areas. Our results suggest that climatic and land-type conditions may impact host-seeking I. scapularis ticks through numerous mechanistic avenues. These findings extend our understanding of the abiotic factors supporting I. scapularis populations in metro areas of the upper Midwest with strong implications for discerning future tick-borne pathogen risk.

<p>Background: Triple negative breast cancer (TNBC) is an aggressive subtype of invasive breast cancer that disproportionately affects Black women and contributes to racial disparities in breast cancer mortality. Prior research has found evidence of a link between TNBC risk and neighborhood/area-level measures of disadvantage, including socioeconomic status and racial segregation. This study evaluated additional area-level exposures as potential mediators of the relationship between neighborhood disadvantage and TNBC risk in New Castle County, DE, a region with elevated rates of TNBC. Methods: The study population included 3,316 Black and White adult female residents of New Castle County who were diagnosed with invasive breast cancer at the Helen F. Graham Cancer Center &amp; Research Institute (HFGCCRI) in Newark, DE from 2012-2020. Patient addresses were geocoded, and demographic and clinical measures were abstracted from the HFGCCRI cancer registry. Neighborhood and environmental exposure measures were generated from electronic health records (EHR), the US Census Bureau American Community Survey, and Environmental Protection Agency (EPA) Risk-Screening Environmental Indicator (RSEI) data; these measures were aggregated to the census tract and linked to patient geocodes. Included neighborhood exposure measures were factors that may influence patient-level exposure to well-known breast cancer risk factors, including alcohol use disorder (AUD) and diabetes prevalence, breastfeeding (assessed with the proxy measure ‘% single female-headed households with children’), and toxic environmental indicators (e.g., toxic release inventory sites). The relationships between these exposures and the odds of TNBC (relative to other subtypes of invasive breast cancer) were first evaluated on a univariate basis. Next, to estimate the relationship between cumulative exposure and TNBC odds, census tract risk scores were calculated by dichotomizing each exposure into low- and high-risk categories and summing the total of high-risk exposures. A multilevel logistic regression model was used to test the association between this risk score and TNBC odds while adjusting for patient-level age and race.Results: TNBC cases accounted for 14% (n = 453) of invasive breast cancer cases. Significantly higher odds of TNBC were observed for patients who reside in census tracts characterized by higher rates of AUD, diabetes, % single female-headed households with children, and RSEI scores. In the multilevel logistic regression model that adjusted for patient demographics, the cumulative exposure risk score was significantly associated with greater odds of TNBC. Conclusion: Results provide preliminary support for a link between TNBC odds and cumulative, area-level exposure to disordered alcohol use, metabolic disorders, lower breastfeeding rates, and toxic environmental indicators. Additional research evaluating the cumulative exposure risk score as a potentially modifiable mediator of the relationship between neighborhood disadvantage and TNBC risk will be presented.</p>

Even early in the COVID-19 pandemic, adherence to physical distancing measures was variable, exposing some communities to elevated risk. While cognitive factors from the Health Belief Model (HBM) and resilience correlate with compliance with physical distancing, external conditions may preclude full compliance with physical distancing guidelines. Our objective was to identify HBM and resilience constructs that could be used to improve adherence to physical distancing even when full compliance is not possible. We examined adherence as expressed through 7-day non-work, non-household contact rates in two cohorts: 1) adults in households with children from Minnesota and Iowa; and 2) adults ≥50 years-old from Minnesota, one-third of whom had Parkinson’s disease. We identified multiple cognitive factors associated with physical distancing adherence, specifically perceived severity, benefits, self-efficacy, and barriers. However, the magnitude, and occasionally the direction, of these associations was population-dependent. In Cohort 1, perceived self-efficacy for remaining 6-feet from others was associated with a 29% lower contact rate (RR 0.71; 95% CI 0.65, 0.77). This finding was consistent across all race/ethnicity and income groups we examined. The barriers to adherence of having a child in childcare and having financial concerns had the largest effects among individuals from marginalized racial and ethnic groups and high-income households. In Cohort 2, self-efficacy to quarantine/isolate was associated with a 23% decrease in contacts (RR 0.77; 95% CI 0.66, 0.89), but upon stratification by education level, the association was only present for those with at least a Bachelor’s degree. Education also modified the effect of the barrier to adherence leaving home for work, increasing contacts among those with a Bachelor’s degree and reducing contacts among those without. Our findings suggest that public health messaging tailored to the identified cognitive factors has the potential to improve physical distancing adherence, but population-specific needs must be considered to maximize effectiveness.

Recent evidence has shown that fine particulate matter (PM2.5) may be an important environmental risk factor for chronic kidney disease (CKD), but few studies have examined this association for individual patients using fine spatial data. To investigate the association between PM2.5 and CKD (estimated glomerular filtration rate [eGFR]<45 ml/min/1.73 m2) in the Twin-Cities area in Minnesota using a large electronic health care database (2012–2019). We estimated the previous 1-year average PM2.5 from the first eGFR (measured with the CKD Epidemiology Collaboration equation using the first available creatinine measure during the baseline period [2012–2014]) using Environmental Protection Agency downscaler modeling data at the census tract level. We evaluated the spatial relative risk and clustering of CKD prevalence using a K-function test statistic. We assessed the prevalence ratio of the PM2.5 association with CKD incidence using a mixed effect Cox model, respectively. Patients (n = 20,289) in the fourth (PM2.5 > 10.4), third (10.3 < PM2.5 < 10.8) and second quartile (9.9 < PM2.5 < 10.3) vs. the first quartile (<9.9 μg/m3) had a 2.52[2.21, 2.87], 2.18[1.95, 2.45], and 1.72[1.52, 1.97] hazard rate of developing CKD in the fully adjusted models, respectively. We identified spatial heterogeneities and evidence of CKD clustering across our study region, but this spatial variation was accounted for by air pollution and individual covariates. Exposure to higher PM2.5 is associated with a greater risk for incident CKD. Improvements in air quality, specifically at hotspots, may reduce CKD.

BACKGROUND AND AIM: Studies have shown an acute association between the occurrence of thunderstorm conditions in the presence of high pollen and severe asthma, but minimal work has studied these ev...

BACKGROUND AND AIM: Drought represents a globally relevant natural disaster linked to adverse health. Evidence has shown agricultural communities to be particularly susceptible to drought condition...

The COVID-19 global pandemic has likely affected air quality due to extreme changes in human behavior. We assessed air quality during the COVID-19 pandemic for fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the continental United States from January 8th-April 21st in 2017–2020. We considered pollution during the COVID-19 period (March 13–April 21st) and the pre-COVID-19 period (January 8th-March 12th) with 2020 representing ‘current’ data and 2017–2019 representing ‘historical’ data. County-level pollution concentrations were compared between historical versus current periods, and counties were stratified by institution of early or late non-essential business closures. Statistically significant NO2 declines were observed during the current COVID-19 period compared to historical data: a 25.5% reduction with absolute decrease of 4.8 ppb. PM2.5 also showed decreases during the COVID-19 period, and the reduction is statistically significant in urban counties and counties from states instituting early non-essential business closures. Understanding how air pollution is affected during COVID-19 pandemic will provide important clues regarding health effects and control of emissions. Further investigation is warranted to link this finding with health implications.

Sampling campaign design is a crucial aspect of air pollution exposure studies. Selection of both monitor numbers and locations is important for maximizing measured information, while minimizing bias and costs. We developed a two-stage geostatistical-based method using pilot NO 2 samples from Lanzhou, China with the goal of improving sample design decision-making, including monitor numbers and spatial pattern. In the first step, we evaluate how additional monitors change prediction precision through minimized kriging variance. This was assessed in a Monte Carlo fashion by adding up to 50 new monitors to our existing sites with assigned concentrations based on conditionally simulated NO 2 surfaces. After identifying a number of additional sample sites, a second step evaluates their potential placement using a similar Monte Carlo scheme. Evaluations are based on prediction precision and accuracy. Costs are also considered in the analysis. It was determined that adding 28-locations to the existing Lanzhou NO 2 sampling campaign captured 73.5% of the total kriged variance improvement and resulted in predictions that were on average within 10.9 μg/m 3 of measured values, while using 56% of the potential budget. Additional monitor sites improved kriging variance in a nonlinear fashion. This method development allows for informed sampling design by quantifying prediction improvement (accuracy and precision) against the costs of monitor deployment.

Indoor concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and carbon monoxide (CO) were measured across 16 urban public schools in three different seasons. Exceedance of the WHO guidelines for indoor air was observed, mainly for the hourly average NO2 concentrations. Seasonal variability was statistically significant for indoor NO2 and CO concentrations, with higher exposures in fall and winter. An extensive list of potential factors at the outdoor environment, school, and room level that may explain the variability in indoor exposure was examined. Factors with significant contributions to indoor exposure were mostly related to the outdoor pollution sources. This is evidenced by the strong associations between indoor concentration of CO and NO2 and factors including outdoor PM2.5 and NO2 concentrations, including length of the nearby roads and the number of nearby industrial facilities. Additionally, we found that poor conditions of the buildings (a prevalent phenomenon in the studied urban area), including physical defects and lack of proper ventilation, contributed to poor air quality in schools. The results suggest that improving building conditions and facilities as well as a consideration of the school surroundings may improve indoor air quality in schools.

BACKGROUND Violence is a leading cause of death and an important public health threat, particularly among adolescents and young adults. However, the environmental causes of violent behavior are not well understood. Emerging evidence suggests exposure to air pollution may be associated with aggressive or impulsive reactions in people. METHODS We applied a two-stage hierarchical time series model to estimate change in risk of violent and non-violent criminal behavior associated with short-term air pollution in U.S. counties (2000-2013). We used daily monitoring data for ozone and fine particulate matter (PM2.5) from the Environmental Protection Agency and daily crime counts from the Federal Bureau of Investigation. We evaluated the exposure-response relationship and assessed differences in risk by community characteristics of poverty, urbanicity, race, and age. RESULTS Our analysis spans 301 counties in 34 states, representing 86.1 million people and 721,674 days. Each 10µg/m change in daily PM2.5 was associated with a 1.17% (95% CI: 0.90, 1.43) and a 10ppb change in ozone with a 0.59% (95% CI: 0.41, 0.78) relative risk increase (RRI) for violent crime. However, we observed no risk increase for non-violent property crime due to PM2.5 (RRI: 0.11%; 95% CI: -0.09, 0.31) or ozone (RRI: -0.05%; 95% CI: -0.22, 0.12). Our results were robust across all community types, except rural regions. Exposure-response curves indicated increased violent crime risk at concentrations below regulatory standards. CONCLUSIONS Our results suggest that short-term changes in ambient air pollution may be associated with greater risk of violent behavior, regardless of community type.

© 2019 Elsevier Inc. Background: Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings. Objective: A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings. Methods: In each of four seasons in 2016–2017, NO2 was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO2 and PM2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models. Results: We developed robust LUR models at the ground level for estimated annual averages of NO2 (R2: 0.71, adjusted R2: 0.67, and Leave-One-Out Cross Validation (LOOCV) R2: 0.64) and PM2.5 (R2: 0.77, adjusted R2: of 0.73, and LOOCV R2: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO2 showed similar patterns. Vertical variation of NO2 and PM2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations. Conclusions: Ground-level NO2 and PM2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.