Environmental inequality in exposures to airborne particulate matter components in the United States

The OATH Syndemic: opioids and other substances, aging, alcohol, tobacco, and HIV

PURPOSE OF REVIEW

Persons living with HIV (PLWH) are aging, continue to use alcohol and other substances, and experience age-associated adverse effects. We explore a new syndemic: OATH (opioids and other substances, aging, alcohol, tobacco, and HIV).

RECENT FINDINGS

Frailty and falls are important problems that affect the health status of PLWH who continue to use alcohol and other substances. HIV, alcohol and other substance use, and aging each contributes to inflammaging. Multimorbidity and polypharmacy are also important pathways as alcohol and other substances interact with prescribed medications resulting in adverse-drug interactions leading to potentially serious consequences. Social conditions including racism, poverty, sex bias, stress, and stigma contribute to the existence and persistence of this syndemic.

SUMMARY

Substance use, HIV, and aging are linked in a new syndemic (OATH) that drives age-related outcomes such as frailty and falls. We need to expand our understanding of the 'healthcare team' so that we include social and political advocates who can support necessary structural change. Treatment of substance use should be better incorporated into the management of HIV, including a focus on potential medication/substance interactions. Finally, we need to explore treatment of frailty rather than individual manifestations of frailty (e.g., atherosclerosis, neurodegeneration).

Optimizing Emissions Reductions from the U.S. Power Sector for Climate and Health Benefits

Improved air quality and human health are often discussed as "co-benefits" of mitigating climate change, yet they are rarely considered when designing or implementing climate policies. We analyze the implications of integrating health and climate when determining the best locations for replacing power plants with new wind, solar, or natural gas to meet a CO₂ reduction target in the United States. We employ a capacity expansion model with integrated assessment of climate and health damages, comparing portfolios optimized for benefits to climate alone or both health and climate. The model estimates county-level health damages and accounts for uncertainty by using a range of air quality models (AP3, EASIUR, and InMAP) and concentration-response functions (American Cancer Society and Harvard Six Cities). We find that reducing CO₂ by 30% yields $21-68 billion in annual health benefits, with an additional $9-36 billion possible when co-optimizing for climate and health benefits. Additional benefits accrue from prioritizing emissions reductions in counties with high population exposure. Total health benefits equal or exceed climate benefits across a wide range of modeling assumptions. Our results demonstrate the value of considering health in climate policy design and the need for interstate cooperation to achieve additional health benefits equitably.

A Framework to Classify Environmental Inequity in Absolute and Relative Terms, and Its Application in Beijing

Although reducing environmental inequities is widely recognized as an essential step towards sustainable cities, decision-makers frequently lack the tools to identify inequity distribution patterns and designing effective intervention policies. This study seeks to present a framework that can help decision-making processes by classifying environmental inequity districts in multiple perspectives, especially in absolute and relative terms. This framework includes four steps: (A) variable selection, (B) data normalization, (C) ranking indicators, (D) summarizing inequity classification, which then assign results to selected areas. The framework aims to classify and compare environmental inequities in multiple perspectives, and can be applied in various environmental problems, with advantages such as high acceptability and clear comprehensibility. To show the potential use of this framework, a case application in Beijing, China, was conducted to evaluate the environmental inequity of air pollution. The results suggest that decision-makers should focus on the central urban area and some southern regions of Beijing to implement various improvement policies. Based on the results from Beijing, how the framework can be used to help decision-makers, the future roles of this framework with the government and the public, as well as the framework’s limitations are further discussed.

Disparities in Distribution of Particulate Matter Emissions from US Coal-Fired Power Plants by Race and Poverty Status After Accounting for Reductions in Operations Between 2015 and 2017

Objectives. To investigate potential changes in burdens from coal-fired electricity-generating units (EGUcfs) that emit fine particulate matter (PM2.5, defined as matter with a nominal mean aerodynamic diameter of ≤ 2.5 µm) among racial/ethnic and economic groups after reduction of operations in 92 US EGUcfs.Methods. PM2.5 burdens calculated for EGUs listed in the 2008, 2011, and 2014 National Emissions Inventory were recalculated for 2017 after omitting emissions from 92 EGUcfs. The combined influence of race/ethnicity and poverty on burden estimates was characterized.Results. Omission of 92 EGUcfs decreased PM2.5 burdens attributable to EGUs by 8.6% for the entire population and to varying degrees for every population subgroup. Although the burden decreased across all subgroups, the decline was not equitable. After omission of the 92 EGUcfs, burdens were highest for the below-poverty and non-White subgroups. Proportional disparities between White and non-White subgroups increased. In our combined analysis, the burden was highest for the non-White-high-poverty subgroup.Conclusions. Our results indicate that subgroups living in poverty experience the greatest absolute burdens from EGUcfs. Changes as a result of EGUcf closures suggest a shift in burden from White to non-White subgroups. Policymakers could use burden analyses to jointly promote equity and reduce emissions.

Environmental predictors of survival in a cohort of U.S. military veterans: A multi-level spatio-temporal analysis stratified by race

We analyzed racial differences in all-cause mortality rates associated with air pollution in a cohort of military veterans in which 37% of the 70,000 members identified as African-American (black). In this comprehensive analysis, spatial levels comprised individuals, zip-codes, and counties. Temporal levels comprised the 26-y follow-up period (1976-2001) and 4 subperiods. Proportional hazard regression models were used, controlling for individual age, race (white, black), smoking (current, ever), education, height, body-mass index, and systolic and diastolic blood pressure; zipcode-average socioeconomic indicators; and county-average climate. County-level air quality measures included vehicular traffic density as a surrogate for all traffic-related pollutants including noise. The model accounted for nonlinear mortality relationships with age, body-mass index, blood pressure and zip-code racial composition. Relative to whites, more of the black veterans smoked, had slightly higher blood pressure, and lived in predominately black zip-codes that had more poverty than whites. The black veterans lived in counties that had slightly worse ambient air quality and substantially higher levels of vehicular traffic density. We analyzed all-cause mortality associations with county-level average ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide for 1975-81, and subsequent data on particulates by particle size. We also considered sulfate and elemental carbon particles, benzene, SO2, and NOx based on nationwide modeling for 2002. We had no information on indoor air quality or personal exposures; our risk estimates should thus be regarded as characterizing the counties of residence rather than individual exposures of inhabitants. In addition to age, the strongest predictors of veterans' survival were residence in high-poverty zip-codes, smoking, and diastolic blood pressure, to all of which black veterans were less sensitive than whites. Black veterans had significantly lower mortality risks from aging, smoking, and elevated diastolic blood pressure, but larger risks from excessive body-mass index. They were less at risk from living a high-poverty zip-code than whites. We assumed these risk factors to be stable during follow-up and thus applicable to chronic health effects. After controlling for them, the all-cause mortality risk for black veterans was 10% lower than whites. In an effort to reduce random scatter we computed mean risks associated with overlapping groups of similar pollutants. These means were statistically significant for both black and white veterans for traffic-related, gaseous, and NOx-O3 pollutants, for which the overall mean relative risk was 1.076 (1.057-1.090). Grouped mean risks for particulate pollutants, sulfur compounds, and non-traffic pollutants were not significant for either race. Black veterans carried more of the traffic-related risks than whites because of their greater exposures and risk coefficients. PM2.5 risk estimates were negative for black veterans (0.82 [0.75-0.89]) but positive for whites (1.05 [1.005-1.10]) which is consistent with regional differences in overall mortality. The temporal analyses compared mortality rates by follow-up subperiod for the pollutants measured at enrollment. We expected increasing (cumulative) risks for chronic effects and decreasing risks for delayed acute effects, but found no significant trend for either race. We concluded that the higher exposures and mortality risks associated with vehicular traffic posed environmental injustice for the black veterans.

The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time

Major improvements in air quality since 1990, observed through reductions in fine particulate matter (PM2.5), have been associated with reduced cardiovascular mortality rates (CMR). However, it is not well understood whether the health benefit attributed to PM2.5 reductions has been similar across strata of socioeconomic deprivation (SED). Using mixed effect regression models, we estimated the PM2.5-related change in the CMR across 2,132 US counties in five SED strata between 1990 and 2010. The analysis included annual county CMR (deaths/100,000 person-year), annual county PM2.5 (μg/m3), and an index of county SED based on socioeconomic factors from the 1990 US Census. The contribution of PM2.5 reductions to decreased CMR varied by SED strata and over time. Yearly differences resulted from varying rates of PM2.5 reduction and because of the non-linear relationship between CMR and PM2.5 concentration. In early years, PM2.5-related CMR reductions were smallest in the most deprived counties compared to all other counties (range: 0.4-0.6 vs 0.7-1.6 fewer deaths/100,000 person-year), due to slower rates of PM2.5 reduction in these counties. However, in later years, PM2.5-related CMR reductions were highest counties with moderate to high deprivation, compared to counties with the least deprivation (range: 1.0-2.2 vs 0.5-0.9 fewer deaths/100,000 person-year) due to larger CMR reductions per decrease in PM2.5. We identified that CMR reductions related to air quality improvements have become more similar over time between socioeconomic strata.

Characterization of urinary concentrations of heavy metals among socioeconomically disadvantaged black pregnant women

The objective of this study was to characterize exposures to metals using biological samples collected on socioeconomically disadvantaged black pregnant women. We obtained 131 anonymous urine samples provided by black pregnant women visiting a Medicaid-serving prenatal clinic in Houston, TX, from March 27, 2017 to April 11, 2017. We analyzed urine samples for 15 metals including cadmium (Cd), arsenic (As), lead (Pb), and nickel (Ni) and for creatinine and cotinine. We found that median concentrations of zinc (Zn), selenium (Se), and aluminum (Al) among black pregnant women in this study were 1.5 to 3 times higher than levels reported among a cohort of well-educated non-Hispanic white pregnancy planners. We also observed elevated levels of urinary Cd and antimony (Sb) as compared with those reported for a nationally representative sample of adult women in the USA. Based on the results of an exploratory factor analysis, potential sources of metal exposures in this population may arise in home environments or be due to diet, industrial and natural sources, or traffic.

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

The fine particulate matter baseline (PMB), which includes PM_2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM_2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM_2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km² CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM_2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM_2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17.

Association between cumulative social risk, particulate matter environmental pollutant exposure, and cardiovascular disease risk

Background

Long-term exposure to pollution has been shown to increase risk of cardiovascular disease (CVD) and mortality, and may contribute to the increased risk of CVD among individuals with higher social risk.

Methods

Data from the community-based Heart Strategies Concentrating on Risk Evaluation (HeartSCORE) study were used to quantify Cumulative Social Risk (CSR) by assigning a score of 1 for the presence of each of 4 social risk factors: racial minority, single living, low income, and low educational status. 1-year average air pollution exposure to PM_2.5 was estimated using land-use regression models. Associations with clinical outcomes were assessed using Cox models, adjusting for traditional CVD risk factors. The primary clinical outcome was combined all-cause mortality and nonfatal CVD events.

Results

Data were available on 1933 participants (mean age 59 years, 66% female, 44% Black). In a median follow up time of 8.3 years, 137 primary clinical outcome events occurred. PM_2.5 exposure increased with higher CSR score. PM_2.5 was independently associated with clinical outcome (adjusted hazard ratio [HR]: 1.19 [95% CI: 1.00, 1.41]). Participants with ≥2 CSR factors had an adjusted HR of 2.34 (1.48–3.68) compared to those with CSR = 0. The association was attenuated after accounting for PM_2.5 (HR: 2.16; [1.34, 3.49]). Mediation analyses indicate that PM_2.5 explained 13% of the risk of clinical outcome in individuals with CSR score ≥ 2.

Conclusion

In a community-based cohort study, we found that the association of increasing CSR with higher CVD and mortality risks is partially accounted for by exposure to PM_2.5 environmental pollutants.

Ancestry-based intracategorical injustices in carcinogenic air pollution exposures in the United States

Previous studies of US environmental inequalities have focused on racial/ethnic differences in air pollutant exposure. Few have applied an intracategorical framework, which enables the identification of within-group differences through the examination of subgroups. We applied this framework to examine exposure disparities between 26 ancestry/ethnic origin groups within five US racial/ethnic categories. Data come from the US Census, American Community Survey, and National Air Toxics Assessment. We calculated national population-weighted lifetime cancer risk (LCR) scores from residential exposure to hazardous air pollutants. Results showed that Americans of Dominican, Ethiopian, and Somalian descent have the highest total LCR scores at 53.1, 49.2, and 48.3 estimated excess cases of cancer per one million people, respectively. Use of the intracategorical framework enabled characterization of disparate risks that would be overlooked based on the conventional assumption that racial/ethnic environmental inequalities conform to broad, homogenous categories. Intracategorical studies can inform interventions by identifying environmentally-disadvantaged socio-demographic groups.

The legacy of the Home Owners' Loan Corporation and the political ecology of urban trees and air pollution in the United States

This study examines the persistent impacts of historical racebased discriminatory housing policies on contemporary urban environments in the United States. Specifically, we examine the relationships between Home Owners' Loan Corporation (HOLC) grades assigned to neighborhoods in the 1930s and the current distribution of tree canopy and level of exposure to air pollution hazards. Our results indicate a clear gradient in tree canopy by HOLC grade, with better neighborhood grades associated with significantly higher percentage of tree canopy coverage. The pattern also exists for airborne carcinogens and respiratory hazards, with worse neighborhood grades associated with significantly higher hazards exposure. Our findings indicate that early 20th century discriminatory housing policies exert a contemporary influence on patterns of green space exposure in American cities, with implications for health and health inequities. Our findings suggest that, in order to achieve equitable access to the benefits of urban greenspace, we must acknowledge these historical influences and consider policies and practices that directly counter these influences, for example, through targeted greenspace development in areas historically identified as unfit for investment.

Outdoor Air Pollution and Brain Structure and Function From Across Childhood to Young Adulthood: A Methodological Review of Brain MRI Studies

Outdoor air pollution has been recognized as a novel environmental neurotoxin. Studies have begun to use brain Magnetic Resonance Imaging (MRI) to investigate how air pollution may adversely impact developing brains. A systematic review was conducted to evaluate and synthesize the reported evidence from MRI studies on how early-life exposure to outdoor air pollution affects neurodevelopment. Using PubMed and Web of Knowledge, we conducted a systematic search, followed by structural review of original articles with individual-level exposure data and that met other inclusion criteria. Six studies were identified, each sampled from 3 cohorts of children in Spain, The Netherlands, and the United States. All studies included a one-time assessment of brain MRI when children were 6–12 years old. Air pollutants from traffic and/or regional sources, including polycyclic aromatic hydrocarbons (PAHs), nitrogen dioxide, elemental carbon, particulate matter (<2.5 or <10 μm), and copper, were estimated prenatally (n = 1), during childhood (n = 3), or both (n = 2), using personal monitoring and urinary biomarkers (n = 1), air sampling at schools (n = 4), or a land-use regression (LUR) modeling based on residences (n = 2). Associations between exposure and brain were noted, including: smaller white matter surface area (n = 1) and microstructure (n = 1); region-specific patterns of cortical thinness (n = 1) and smaller volumes and/or less density within the caudate (n = 3); altered resting-state functional connectivity (n = 2) and brain activity to sensory stimuli (n = 1). Preliminary findings suggest that outdoor air pollutants may impact MRI brain structure and function, but limitations highlight that the design of future air pollution-neuroimaging studies needs to incorporate a developmental neurosciences perspective, considering the exposure timing, age of study population, and the most appropriate neurodevelopmental milestones.

The Washington State Environmental Health Disparities Map: Development of a Community-Responsive Cumulative Impacts Assessment Tool

Communities across Washington State have expressed the need for neighborhood-level information on the cumulative impact of environmental hazards and social conditions to illuminate disparities and address environmental justice issues. Many existing mapping tools have not explicitly integrated community voice and lived experience as an integral part of their development. The goals of this project were to create a new community–academic–government partnership to collect and summarize community concerns and to develop a publicly available mapping tool that ranks relative environmental health disparities for populations across Washington State. Using a community-driven framework, we developed the Washington Environmental Health Disparities Map, a cumulative environmental health impacts assessment tool. Nineteen regularly updated environmental and population indicators were integrated into the geospatial tool that allows for comparisons of the cumulative impacts between census tracts. This interactive map provides critical information for the public, agencies, policymakers, and community-based organizations to make informed decisions. The unique community–academic–government partnership and the community-driven framework can be used as a template for other environmental and social justice mapping endeavors.

Air pollution and lung cancer incidence in China: Who are faced with a greater effect?

BACKGROUND

Whether socioeconomic indicators modify the relationship between air pollution exposure and health outcomes remains uncertain, especially in developing countries.

OBJECTIVE

This work aims to examine modification effects of socioeconomic indicators on the association between PM2.5 and annual incidence rate of lung cancer for males in China.

METHODS

We performed a nationwide analysis in 295 counties (districts) from 2006 to 2014. Using multivariable linear regression models controlling for weather conditions and socioeconomic indicators, we examined modification effects in the stratified and combined datasets according to the tertile and binary divisions of socioeconomic indicators. We also extensively investigated whether the roles of socioeconomic modifications were sensitive to the further adjustment of demographic factors, health and behaviour covariates, household solid fuel consumption, the different operationalization of socioeconomic indicators and PM2.5 exposure with single and moving average lags.

RESULTS

We found a stronger relationship between PM2.5 and incidence rate of male lung cancer in urban areas, in the lower economic or lower education counties (districts). If PM2.5 changes by 10 μg/m³, then the shift in incidence rate relative to its mean was significantly higher by 3.97% (95% CI: 2.18%, 4.96%, p = 0.000) in urban than in rural areas. With regard to economic status, if PM2.5 changes by 10 μg/m³, then the change in incidence rate relative to its mean was significantly lower by 0.99% (95% CI: -2.18%, 0.20%, p = 0.071) and 1.39% (95% CI: -2.78%, 0.00%, p = 0.037) in the middle and high economic groups than in the low economic group, respectively. The change in incidence rate relative to its mean was significantly lower by 1.98% (95% CI: -3.18%, -0.79%, p = 0.001) and 2.78% (95% CI: -4.17%, -1.39%, p = 0.000) in the middle and high education groups compared with the low education group, respectively, if PM2.5 changes by 10 μg/m³. We found no robust modification effects of employment rate and urbanisation growth rate.

CONCLUSION

Male residents in urban areas, in the lower economic or lower education counties are faced with a greater effect of PM2.5 on the incidence rate of lung cancer in China. The findings emphasize the need for public health intervention and urban planning initiatives targeting the urban-rural, educational or economic disparities in health associated with air pollution exposure. Future prediction on air pollution-induced health effects should consider such socioeconomic disparities, especially for the dominant urban-rural disparity in China.

Housing as a determinant of health equity: A conceptual model

Housing is a major pathway through which health disparities emerge and are sustained over time. However, no existing unified conceptual model has comprehensively elucidated the relationship between housing and health equity with attention to the full range of harmful exposures, their cumulative burden and their historical production. We synthesized literature from a diverse array of disciplines to explore the varied aspects of the relationship between housing and health and developed an original conceptual model highlighting these complexities. This holistic conceptual model of the impact of housing on health disparities illustrates how structural inequalities shape unequal distribution of access to health-promoting housing factors, which span four pillars: 1) cost (housing affordability); 2) conditions (housing quality); 3) consistency (residential stability); and 4) context (neighborhood opportunity). We further demonstrate that these four pillars can lead to cumulative burden by interacting with one another and with other structurally-rooted inequalities to produce and reify health disparities. We conclude by offering a comprehensive vision for healthy housing that situates housing's impact on health through a historical and social justice lens, which can help to better design policies and interventions that use housing to promote health equity.

Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions

PURPOSE OF REVIEW

Fine particle (PM2.5) levels have been decreasing in the USA over the past decades. Our goal was to assess the current literature to characterize the association between PM2.5 and adverse health at low exposure levels.

RECENT FINDINGS

We reviewed 26 papers that examined the association between short- and long-term exposure to PM2.5 and cardio-respiratory morbidity and mortality. There is evidence suggesting that these associations are stronger at lower levels. However, there are certain methodological and interpretational limitations specific to studies of low PM2.5 levels, and further methodological development is warranted. There is strong agreement across studies that air pollution effects on adverse health are still observable at low concentrations, even well below current US standards. These findings suggest that US standards need to be reevaluated, given that further improving air quality has the potential of benefiting public health.

Fossil fuels are harming our brains: identifying key messages about the health effects of air pollution from fossil fuels

BACKGROUND

Previous research suggests that providing generalized information about the health implications of air pollution from fossil fuels may be effective at promoting public support for a transition to cleaner sources of energy. We sought to extend that work by identifying the specific messages about the health implications of air pollution from fossil fuels that are most and least concerning to people, and whether rankings of concern vary among different audiences. We also hypothesized that reading the statements would influence people's attitudes and behavioral intentions in a manner supportive of a transition to cleaner sources of energy.

METHODS

We conducted a survey with a diverse sample of U.S. adults (n = 1644) from a non-probability internet panel. Using maximum difference scaling, participants ranked a set of ten statements that revealed which statements were the most and least concerning to them. We also measured attitudes about air pollution and energy use before and after the ranking exercise to assess changes in opinion caused by cumulative exposure to the messages.

RESULTS

Across all sub-groups examined, participants were most concerned by a message about the neurological impacts of air pollution on babies and children. After the ranking exercise, participants expressed increases in perceived health harm of air pollution and fossil fuels, a desire for more clean energy, and intention to engage in consumer advocacy to support clean energy.

CONCLUSIONS

To our knowledge, this study is the first to assess how people respond to information about the neurological health harms of air pollution from fossil fuels. While efforts to communicate the cardio-pulmonary health harms of air pollution are well established, our study suggests that efforts should now be organized to communicate the neurological effects of air pollution from fossil fuels, especially the neuro-developmental effects on babies and children.

Elderly Health Inequality in China and its Determinants: A Geographical Perspective

Inter-regional health differences and apparent inequalities in China have recently received significant attention. By collecting health status data and individual socio-economic information from the 2015 fourth sampling survey of the elderly population in China (4th SSEP), this paper uses the geographical differentiation index to reveal the spatial differentiation of health inequality among Chinese provinces. We test the determinants of inequalities by multilevel regression models at the provincial and individual levels, and find three main conclusions: 1) There were significant health differences on an inter-provincial level. For example, provinces with a very good or good health rating formed a good health hot-spot region in the Yangtze River Delta, versus elderly people living in Gansu and Hainan provinces, who had a poor health status. 2) Nearly 2.4% of the health differences in the elderly population were caused by inter-provincial inequalities; access (or lack of access) to economic, medical and educational resources was the main reason for health inequalities. 3) At the individual level, inequalities in annual income served to deepen elderly health differences, and elderly living in less developed areas were more vulnerable to urban vs. rural-related health inequalities.

Estimating the causal effect of annual PM2.5 exposure on mortality rates in the Northeastern and mid-Atlantic states

Background

Dozens of cohort studies have associated particulate matter smaller than 2.5 µm in diameter (PM_2.5) exposure with early deaths, and the Global Burden of Disease identified PM_2.5 as the fifth-ranking mortality risk factor in 2015. However, few studies have used causal modeling techniques. We assessed the effect of annual PM_2.5 exposure on all-cause mortality rates among the Medicare population in the Northeastern and mid-Atlantic states, using the difference-in-differences approach for causal modeling.

Methods

We obtained records of Medicare beneficiaries 65 years of age or more who reside in the Northeastern or mid-Atlantic states from 2000 to 2013 and followed each participant from the year of enrollment to the last year of follow-up. We estimated the causal effect of annual PM_2.5 exposure on mortality rates using the difference-in-differences approach in the Poisson survival analysis. We controlled for individual confounders, for spatial differences using dummy variables for each ZIP code and for time trends using a penalized spline of year.

Results

We included 112,376,805 person-years from 15,401,064 people, of whom 37.4% died during the study period. The interquartile range (IQR) of the annual PM_2.5 concentration was 3 µg/m³, and the mean annual PM_2.5 concentration ranged between 6.5 and 14.5 µg/m³ during the study period. An IQR incremental increase in PM_2.5 was associated with a 4.04% increase (95% CI = 3.49%, 4.59%) in mortality rates.

Conclusions

Assuming no omitted predictors changing differently across ZIP codes over time in correlation with PM_2.5, we found a causal effect of PM_2.5 on mortality incidence rate.

Maternal and early-life area-level characteristics and childhood adiposity: A systematic review

There is a cross-sectional evidence that physical and social environments are linked to childhood adiposity. Evidence is scarce for the role of preconception, pregnancy, and early-life area-level characteristics in shaping childhood adiposity. We aimed to systematically review evidence for associations between physical and social environmental conditions experienced in these periods and childhood adiposity. Published literature was identified from the CINAHL, Embase, MEDLINE, and PsycINFO databases. Longitudinal studies linking an area-level environmental exposure in the preconception, pregnancy, or early-life (less than 1 year) periods and a measure of adiposity between the ages of 2 and 12 years were examined. Eight studies in the United States, Denmark, South Korea, United Kingdom, and Canada satisfied the inclusion criteria. Storm-induced maternal stress, nitrogen oxides exposure, traffic noise, and proximity were associated with greater childhood adiposity. Frequent neighbourhood disturbances were associated with lower adiposity, while particulate matter exposure was associated with both higher and lower adiposity in childhood. Area-level characteristics may play a role in the ongoing obesity epidemic. There is a limited evidence of longitudinal associations between preconception, pregnancy, and early-life area-level characteristics with childhood adiposity. Numerous factors that appear important in cross-sectional research have yet to be assessed longitudinally, both individually and in combination.

Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM_2.5) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM_2.5 and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m³ increase in PM_2.5 and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R² < 1%), multivariate models began to explain some of the observed heterogeneity (R² = 13%).

The impact of air exchange rate on ambient air pollution exposure and inequalities across all residential parcels in Massachusetts

Individual housing characteristics can modify outdoor ambient air pollution infiltration through air exchange rate (AER). Time and labor-intensive methods needed to measure AER has hindered characterization of AER distributions across large geographic areas. Using publicly-available data and regression models associating AER with housing characteristics, we estimated AER for all Massachusetts residential parcels. We conducted an exposure disparities analysis, considering ambient PM_2.5 concentrations and residential AERs. Median AERs (h^-1) with closed windows for winter and summer were 0.74 (IQR: 0.47-1.09) and 0.36 (IQR: 0.23-0.57), respectively, with lower AERs for single family homes. Across residential parcels, variability of indoor PM_2.5 concentrations of ambient origin was twice that of ambient PM_2.5 concentrations. Housing parcels above the 90th percentile of both AER and ambient PM_2.5 (i.e., the leakiest homes in areas of highest ambient PM_2.5)-vs. below the 10 percentile-were located in neighborhoods with higher proportions of Hispanics (20.0% vs. 2.0%), households with an annual income of less than $20,000 (26.0% vs. 7.5%), and individuals with less than a high school degree (23.2% vs. 5.8%). Our approach can be applied in epidemiological studies to estimate exposure modifiers or to characterize exposure disparities that are not solely based on ambient concentrations.

Visualizing Air Pollution: Communication of Environmental Health Information in a Chinese Immigrant Community

This study developed and evaluated a visual approach to promoting environmental health literacy about highway pollution. The Interactive Map of Chinatown Traffic Pollution was the centerpiece of a communication approach designed to make complex scientific information about traffic-related air pollution comprehensible to Chinese immigrants with limited English proficiency. The map enabled visualization of the spatial distribution of ultrafine particles (less than 100 nanometers in diameter), a toxic and invisible form of air pollution, in Boston Chinatown. A university-community partnership enabled design of intergenerational training sessions aimed toward empowering community members to take health-promoting actions that reduce exposure to ultrafine particulate pollution. A mixed methods approach was taken to evaluation. Nine high school youth learned to use the map and then tutored adults recruited from English as a Second Language (ESL) classes and from a community workshop. Seventy-three of these adults completed a pre-post survey measuring change in three domains: pollution knowledge, attitudes toward environmental issues, and self-efficacy in using maps. Adult participants demonstrated statistically significant improvements in all three domains (Wilcoxon signed-rank test, all p < 0.01). Seventeen adults and nine youth participated in interviews. Interview participants reported adjusting daily routines to reduce exposure to pollution.

Trimester specific PM2.5 exposure and fetal growth in Ohio, 2007-2010

BACKGROUND

Exposure to particulate matter, particularly with aerodynamic diameter < 2.5 µm (PM_2.5), may increase inflammation and oxidative stress in pregnant women and affect fetal growth. We examined trimester specific PM_2.5 exposure levels and small for gestational age (SGA) using the statewide birth registry of Ohio from 2007 to 2010.

METHODS

Exposure to PM_2.5 in each trimester and for each gestational week was determined using data from 57 Environmental Protection Agency network monitoring stations across the state of Ohio. We restricted the data to 224,921 singleton live births, with a gestational age of 20-42 weeks, no genetic disorders or congenital abnormalities, and who had home addresses within a 10 km radius of any PM_2.5 monitoring station. We estimated odds ratios of SGA using Generalized Linear Models (GLMs) and Distributed Lag Models (DLMs), and adjustment for maternal age, race, education, parity, body mass index, insurance type, tobacco use, prenatal care initiation, birth year, season of birth, and sex of the baby.

RESULTS

Mean PM_2.5 levels during the entire pregnancy were 13.03 µg/m³ with a standard deviation of 1.57 µg/m³. Covariates adjusted odds ratios and 95% confidence intervals of a 10 µg/m³ increase in PM_2.5 levels with a 10 km buffer radius for SGA and trimesters modeled separately were 0.94 (0.88, 1.00) for the first trimester, 0.93 (0.86, 1.00) for the second trimester, 1.07 (1.00, 1.15) for the third trimester, and 0.92 (0.81, 1.06) for the entire pregnancy. When a 5 km buffer radius was used, adjusted odds ratios and 95% confidence intervals for SGA were 0.97 (0.89, 1.05) for the first trimester, 0.96 (0.88, 1.05) for the second trimester, 1.09 (1.02, 1.17) for the third trimester, and 0.99 (0.85, 1.14) for the overall pregnancy, indicating sensitivity to buffer choice. DLMs showed gestational weeks 30-35 to be a particular window of vulnerability.

CONCLUSION

Increasing exposure to PM_2.5 during the third trimester of pregnancy was associated with a small increase in risk of SGA in this population-based study. Selection of a buffer radius significantly impacted our results in the first trimester, but not in the third trimester.

Public Perceptions of Environmental Public Health Risks in the United States

Understanding public perceptions about environmental health hazards, exposures, and health impacts can help environmental public health practitioners to target and prioritize community activities, policy needs, and communication strategies. The online cross-sectional 2013 summer wave of the ConsumerStyles survey sampled U.S. adults and used questions from the Centers for Disease Control’s Environmental Public Health Tracking Program to measure public awareness of governmental efforts to track environmental exposures and links to health impacts, as well as perceptions of environmental health issues. Unadjusted and adjusted logistic regressions examined the associations between demographic characteristics and level of awareness of government environmental public health efforts or level of concern about health risks associated with environmental pollutants. Responses were received from 4033 participants, yielding a response rate of 66.0%. More than half of respondents (57.8%) noted concerns about health risks from environmental pollutants. More than one-third (40.0%) of respondents reported awareness of government efforts. Nearly 40% of respondents felt that none of the health impacts listed in the survey were related to environmental issues. Multiple logistic regression models showed that non-Hispanic blacks, other races, females, people with a college or higher education, and people living in the Midwest or South regions were more likely than their counterparts to be concerned about how the environment affects their health. Future work should focus on improving risk communication, filling the information gap on environmental health issues, and understanding how perceptions change over time.

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors

PURPOSE OF REVIEW

The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health?

RECENT FINDINGS

There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Advancing environmental exposure assessment science to benefit society

Awareness of the human health impacts of exposure to air pollution is growing rapidly. For example, it has become evident that the adverse health effects of air pollution are more pronounced in disadvantaged populations. Policymakers in many jurisdictions have responded to this evidence by enacting initiatives that lead to lower concentrations of air pollutants, such as urban traffic restrictions. In this review, we focus on the interplay between advances in environmental exposure assessment and developments in policy. We highlight recent progress in the granular measurement of air pollutants and individual-level exposures, and how this has enabled focused local policy actions. Finally, we detail an illustrative study designed to link individual-level health-relevant exposures with economic, behavioral, biological, familial, and environmental variables.

Neighborhood Greenness Attenuates the Adverse Effect of PM2.5 on Cardiovascular Mortality in Neighborhoods of Lower Socioeconomic Status

Features of the environment may modify the effect of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) on health. Therefore, we investigated how neighborhood sociodemographic and land-use characteristics may modify the association between PM2.5 and cardiovascular mortality. We obtained residence-level geocoded cardiovascular mortality cases from the Massachusetts Department of Public Health (n = 179,986), and PM2.5 predictions from a satellite-based model (2001⁻2011). We appended census block group-level information on sociodemographic factors and walkability, and calculated neighborhood greenness within a 250 m buffer surrounding each residence. We found a 2.54% (1.34%; 3.74%) increase in cardiovascular mortality associated with a 10 µg/m³ increase in two-day average PM2.5. Walkability or greenness did not modify the association. However, when stratifying by neighborhood sociodemographic characteristics, smaller PM2.5 effects were observed in greener areas only among cases who resided in neighborhoods with a higher population density and lower percentages of white residents or residents with a high school diploma. In conclusion, the PM2.5 effects on cardiovascular mortality were attenuated by higher greenness only in areas with sociodemographic features that are highly correlated with lower socioeconomic status. Previous evidence suggests health benefits linked to neighborhood greenness may be stronger among lower socioeconomic groups. Attenuation of the PM2.5⁻mortality relationship due to greenness may explain some of this evidence.

Preterm birth and economic benefits of reduced maternal exposure to fine particulate matter

Preterm birth (PTB) is a predictor of infant mortality and later-life morbidity. Despite recent declines, PTB rates remain high in the United States. Growing research suggests a possible relationship between a mother's exposure to common air pollutants, including fine particulate matter (PM_2.5), and PTB of her baby. Many policy actions to reduce exposure to common air pollutants require benefit-cost analysis (BCA), and it's possible that PTB will need to be included in BCA in the future. However, an estimate of the willingness to pay (WTP) to avoid PTB risk is not available, and a comprehensive alternative valuation of the health benefits of reducing pollutant-related PTB currently does not exist. This paper demonstrates an approach to assess potential economic benefits of reducing PTB resulting from environmental exposures when an estimate of WTP to avoid PTB risk is unavailable. We utilized a recent meta-analysis, county-level air quality data and county-level PTB prevalence data to estimate the potential health and economic benefits of a reduction in air pollution-related PTB, with PM_2.5 as our case study pollutant. Using this method, a simulated nationwide 10% decrease from 2008 PM_2.5 levels resulted in an estimated reduction of 5016 PTBs and benefits of at least $339 million, potentially reaching over one billion dollars when considering later-life effects of PTB.

Prenatal Exposure to an Acute Stressor and Children's Cognitive Outcomes

Exposure to environmental stressors is highly prevalent and unequally distributed along socioeconomic lines and may have enduring negative consequences, even when experienced before birth. Yet, estimating the consequences of prenatal stress on children's outcomes is complicated by the issue of confounding (i.e., unobserved factors correlated with stress exposure and with children's outcomes). I combine a natural experiment-a strong earthquake in Chile-with a panel survey to capture the effect of prenatal exposure on acute stress and children's cognitive ability. I find that stress exposure in early pregnancy has no effect on children's cognition among middle-class families, but it has a strong negative influence among disadvantaged families. I then examine possible pathways accounting for the socioeconomic stratification in the effect of stress, including differential exposure across socioeconomic status, differential sensitivity, and parental responses. Findings suggest that the interaction between prenatal exposures and socioeconomic advantage provides a powerful mechanism for the intergenerational transmission of disadvantage.

Associations of Source-apportioned Fine Particles with Cause-specific Mortality in California

BACKGROUND

Exposure to ambient fine particulate matter (PM2.5) has been linked with premature mortality, but sources of PM2.5 have been less studied.

METHODS

We evaluated associations between source-specific PM2.5 exposures and cause-specific short-term mortality in eight California locations from 2002 to 2011. Speciated PM2.5 measurements were source-apportioned using Positive Matrix Factorization into eight sources and combined with death certificate data. We used time-stratified case-crossover analysis with conditional logistic regression by location and meta-analysis to calculate pooled estimates.

RESULTS

Biomass burning was associated with all-cause mortality lagged 2 days after exposure (lag2) (% changelag2 in odds per interquartile range width increase in biomass burning PM2.5 = 0.8, 95% confidence interval [CI] = 0.2, 1.4), cardiovascular (% changelag2 = 1.3, 95% CI = 0.3, 2.4), and ischemic heart disease (% changelag2 = 2.0, 95% CI = 0.6, 3.5). Vehicular emissions were associated with increases in cardiovascular mortality (% changelag0 = 1.4, 95% CI = 0.0, 2.9). Several other sources exhibited positive associations as well. Many findings persisted during the cool season. Warm season biomass burning was associated with respiratory/thoracic cancer mortality (% changelag1 = 5.9, 95% CI = 0.7, 11.3), and warm season traffic was associated with all-cause (% changelag0 = 1.9, 95% CI = 0.1, 3.6) and cardiovascular (% changelag0 = 2.9, 95% CI = 0.1, 5.7) mortality.

CONCLUSIONS

Our results suggest that acute exposures to biomass burning and vehicular emissions are linked with cardiovascular mortality, with additional sources (i.e., soil, secondary nitrate, secondary sulfate, aged sea salt, and chlorine sources) showing associations with other specific mortality types.

Neighborhood sociodemographic effects on the associations between long-term PM2.5 exposure and cardiovascular outcomes and diabetes

Supplemental Digital Content is available in the text.

Exposure to PM_2.5 air pollution and neighborhood-level sociodemographic characteristics are associated with cardiovascular disease and possibly diabetes mellitus. However, the joint effect of sociodemographics and PM_2.5 on these outcomes is uncertain.

People, place and pollution: Investigating relationships between air quality perceptions, health concerns, exposure, and individual- and area-level characteristics

An extensive body of research has demonstrated that air pollution exposure is associated with adverse health outcomes. Urban air quality remains a major concern for both public health officials and the general public. In the United States, air quality public awareness campaigns are major efforts of governments at every level. Yet, our understanding of relationships between ambient air pollution exposure, public perceptions of air quality, and concerns about associated health risks is incomplete. We examined 2869 individual responses to annual air quality public awareness surveys administered between 2009 and 2012 in the Kansas City metropolitan area. Our study had three objectives: 1) examine the spatial distribution of PM_2.5 and O₃ exposures, pollution perceptions, and pollution health concerns; 2) explore relationships between individual- and area-level characteristics and PM_2.5 and O₃ exposure; 3) and examine cross-sectional associations between individual- and area-level characteristics, as well as PM_2.5 and O₃ exposures, and pollution perception and pollution health concerns. We found inverse spatial patterns between the distribution of O₃ and PM_2.5 exposure levels as well as between areas where respondents perceived air pollution as worsening and areas where residents had higher concern about the health effects of pollution exposure. We also found inverse relationships between individual- and area-level demographic and socioeconomic characteristics and O₃ and PM_2.5 exposure. Individual-level characteristics were significantly related to pollution perceptions, while both individual- and area-level characteristics were significantly related to pollution health concerns. Public awareness campaigns should be combined with education on ways the public can protect themselves and tailored explicitly for targeting vulnerable populations (e.g., elderly, and persons with respiratory problems) and areas both socioeconomically vulnerable (e.g., higher racial segregation and poverty) and experiencing higher pollution exposure (e.g., O₃ and PM_2.5).

Unequal residential exposure to air pollution and noise: A geospatial environmental justice analysis for Ghent, Belgium

Following the growing empirical evidence on the health effects of air pollution and noise, the fair distribution of these impacts receives increasing attention. The existing environmental inequality studies often focus on a single environmental impact, apply a limited range of covariates or do not correct for spatial autocorrelation. This article presents a geospatial data analysis on Ghent (Belgium), combining residential exposure to air pollution and noise with socioeconomic variables and housing variables. The global results show that neighborhoods with lower household incomes, more unemployment, more people of foreign origin, more rental houses, and higher residential mobility, are more exposed to air pollution, but not to noise. Multiple regression models to explain exposure to air pollution show that residential mobility and percentage of rental houses are the strongest predictors, stressing the role of the housing market in explaining which people are most at risk. Applying spatial regression models leads to better models but reduces the importance of all covariates, leaving income and residential mobility as the only significant predictors for air pollution exposure. While traditional multiple regression models were not significant for explaining noise exposure, spatial regression models were, and also indicate the significant contribution of income to the model. This means income is a robust predictor for both air pollution and noise exposure across the whole urban territory. The results provide a good starting point for discussions about environmental justice and the need for policy action. The study also underlines the importance of taking spatial autocorrelation into account when analyzing environmental inequality.

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We found socioeconomic inequalities in exposure to air pollution and noise.

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Median household income was the most significant predictor overall.

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Residential mobility was an important predictor for air pollution exposure.

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Spatial autocorrelation distorts traditional multiple regression results.

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The results contribute to environmental justice debates.

Diverse segments of the US public underestimate the environmental concerns of minority and low-income Americans

In a nationally representative survey experiment, diverse segments of the US public underestimated the environmental concerns of nonwhite and low-income Americans and misperceived them as lower than those of white and more affluent Americans. Moreover, both whites and nonwhites and higher- and lower-income respondents associated the term "environmentalist" with whites and the well-educated, suggesting that shared cultural stereotypes may drive these misperceptions. This environmental belief paradox-a tendency to misperceive groups that are among the most environmentally concerned and most vulnerable to a wide range of environmental impacts as least concerned about the environment-was largely invariant across demographic groups and also extended to the specific issue of climate change. Suggesting these beliefs are malleable, exposure to images of a racially diverse (vs. nondiverse) environmental organization in an embedded randomized experiment reduced the perceived gap between whites' and nonwhites' environmental concerns and strengthened associations between nonwhites and the category "environmentalists" among minority respondents. These findings suggest that stereotypes about others' environmental attitudes may pose a barrier to broadening public engagement with environmental initiatives, particularly among populations most vulnerable to negative environmental impacts.

Neighborhood and Social Environmental Influences on Child Chronic Disease Prevalence

We investigate how distinct residential environments uniquely influence chronic child disease. Aggregating over 200,000 pediatric geocoded medical records to the census tract of residence and linking them to neighborhood-level measures, we use multiple data analysis techniques to assess how heterogeneous exposures of social and environmental neighborhood conditions influence an index of child chronic disease (CCD) prevalence for the neighborhood. We find there is a graded relationship between degree of overall neighborhood disadvantage and children's chronic disease such that the highest neighborhood CCD scores reside in communities with the highest concentrated disadvantage. Finally, results show that higher levels of neighborhood concentrated disadvantage and air pollution exposure associate with higher risks of having at least one chronic condition for children after also considering their individual- and family-level characteristics. Overall, our analysis serves as a comprehensive start for future researchers interested in assessing which neighborhood factors matter most for child chronic health conditions.

Fine Particle Sources and Cognitive Function in An Older Puerto Rican Cohort in Greater Boston

Supplemental Digital Content is available in the text.

Background:

Puerto Ricans living in the mainland US have substantially higher rates of impairment to cognitive performance as compared to non-Hispanic Whites, with air pollutant exposures a potential risk factor. We investigated whether exposures to specific air pollution sources were associated with performance across several cognitive domains in a cohort of Puerto Rican older adults.

Objectives:

To investigate the association between sources of fine particulate matter (PM_2.5) and cognitive performance in each of five cognitive domains.

Methods:

We obtained demographic, health, and cognitive function data for 1500 elderly participants of the Boston Puerto Rican Health Study. Cognitive function was assessed in each of two waves for five domains: verbal memory, recognition, mental processing, and executive and visuospatial function. To these data, we linked concentrations of PM_2.5 and its components, black carbon (BC), nickel, sulfur, and silicon, as tracers for PM_2.5 from traffic, oil combustion, coal combustion, and resuspended dust, respectively. Associations between each PM_2.5 component and cognitive domain were examined using linear mixed models.

Results:

One year moving average exposures to BC were significantly associated with decreased verbal memory (−0.38; 95% confidence interval [CI] = −0.46, −0.30), recognition (−0.35; 95% CI = −0.46, −0.25), mental processing (−1.14; 95% CI = −1.55, −0.74), and executive function (−0.94; 95% CI = −1.31, −0.56). Similar associations were found for nickel. Associations for sulfur, and silicon, and PM_2.5 were generally null, although sulfur (−0.51; 95% CI = −0.75, −0.28), silicon (−0.25; 95% CI = −0.36, −0.13), and PM_2.5 (−0.35; 95% CI = −0.57, −0.12) were associated with decreased recognition.

Conclusion:

Long-term exposures to BC and nickel, tracers of traffic and oil combustion, respectively, were associated with decreased cognitive function across all domains, except visuospatial function.

Retirements of Coal and Oil Power Plants in California: Association With Reduced Preterm Birth Among Populations Nearby

Coal and oil power plant retirements reduce air pollution nearby, but few studies have leveraged these natural experiments for public health research. We used California Department of Public Health birth records and US Energy Information Administration data from 2001-2011 to evaluate the relationship between the retirements of 8 coal and oil power plants and nearby preterm (gestational age of <37 weeks) birth. We conducted a difference-in-differences analysis using adjusted linear mixed models that included 57,005 births-6.3% of which were preterm-to compare the probability of preterm birth before and after power plant retirement among mothers residing within 0-5 km and 5-10 km of the 8 power plants. We found that power plant retirements were associated with a decrease in the proportion of preterm birth within 5 km (-0.019, 95% CI: -0.031, -0.008) and 5-10 km (-0.015, 95% CI: -0.024, -0.007), controlling for secular trends with mothers living 10-20 km away. For the 0-5-km area, this corresponds to a reduction in preterm birth from 7.0% to 5.1%. Subgroup analyses indicated a potentially larger association among non-Hispanic black and Asian mothers than among non-Hispanic white and Hispanic mothers and no differences in educational attainment. Future coal and oil power plant retirements may reduce preterm birth among nearby populations.

Retirements of Coal and Oil Power Plants in California: Association With Reduced Preterm Birth Among Populations Nearby

Coal and oil power plant retirements reduce air pollution nearby, but few studies have leveraged these natural experiments for public health research. We used California Department of Public Health birth records and US Energy Information Administration data from 2001-2011 to evaluate the relationship between the retirements of 8 coal and oil power plants and nearby preterm (gestational age of <37 weeks) birth. We conducted a difference-in-differences analysis using adjusted linear mixed models that included 57,005 births-6.3% of which were preterm-to compare the probability of preterm birth before and after power plant retirement among mothers residing within 0-5 km and 5-10 km of the 8 power plants. We found that power plant retirements were associated with a decrease in the proportion of preterm birth within 5 km (-0.019, 95% CI: -0.031, -0.008) and 5-10 km (-0.015, 95% CI: -0.024, -0.007), controlling for secular trends with mothers living 10-20 km away. For the 0-5-km area, this corresponds to a reduction in preterm birth from 7.0% to 5.1%. Subgroup analyses indicated a potentially larger association among non-Hispanic black and Asian mothers than among non-Hispanic white and Hispanic mothers and no differences in educational attainment. Future coal and oil power plant retirements may reduce preterm birth among nearby populations.

Cognitive Aging in Black and White Americans: Cognition, Cognitive Decline, and Incidence of Alzheimer Disease Dementia

BACKGROUND

US-based studies have reported that older blacks perform worse than older whites on cognitive tests and have higher risk of Alzheimer disease dementia (AD). It is unclear whether these findings reflect differences in cognitive decline.

METHODS

The Chicago Health and Aging Project followed individuals, 65+ years old (64% black, 36% white), for up to 18 years. Participants underwent triennial cognitive assessments; stratified randomized samples underwent assessments for AD. We compared black and white participants' cognitive performance, cognitive decline rate (N = 7,735), and AD incidence (N = 2,144), adjusting for age and sex.

RESULTS

Black participants performed worse than white participants on the cognitive tests; 441 participants developed AD. Black participants' incident AD risk was twice that of whites (RR = 1.9; 95% CI, 1.4, 2.7), with 58 excess cases/1,000 occurring among blacks (95% CI, 28, 88). Among noncarriers of APOE ε4, blacks had 2.3 times the AD risk (95% CI, 1.5, 3.6), but among carriers, race was not associated with risk (RR = 1.1; 95% CI, 0.6, 2.0; Pinteraction = 0.05). However, cognitive decline was not faster among blacks: the black-white difference in 5-year change in global cognitive score was 0.007 standard unit (95% CI, -0.034, 0.047). Years of education accounted for a sizable portion of racial disparities in cognitive level and AD risk, in analyses using a counterfactual approach.

CONCLUSIONS

The higher risk of AD among blacks may stem from lower level of cognitive test performance persisting throughout the observation period rather than faster rate of late-life cognitive decline. Disparities in educational attainment may contribute to these performance disparities. See video abstract at, http://links.lww.com/EDE/B299.

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology

The technological ability to make personal measurements of toxicant exposures is growing rapidly. While this can decrease measurement error and therefore help reduce attenuation of effect estimates, we argue that as measures of exposure or dose become more personal, threats to validity of study findings can increase in ways that more proxy measures may avoid. We use directed acyclic graphs (DAGs) to describe conditions where confounding is introduced by use of more personal measures of exposure and avoided via more proxy measures of personal exposure or target tissue dose. As exposure or dose estimates are more removed from the individual, they become less susceptible to biases from confounding by personal factors that can often be hard to control, such as personal behaviors. Similarly, more proxy exposure estimates are less susceptible to reverse causation. We provide examples from the literature where adjustment for personal factors in analyses that use more proxy exposure estimates have little effect on study results. In conclusion, increased personalized exposure assessment has important advantages for measurement accuracy, but it can increase the possibility of biases from personal factors and reverse causation compared with more proxy exposure estimates. Understanding the relation between more and less proxy exposures, and variables that could introduce confounding are critical components to study design.

Reconsidering the Relationship between Air Pollution and Deprivation

This paper critically examines the relationship between air pollution and deprivation. We argue that focusing on a particular economic or social model of urban development might lead one to erroneously expect all cities to converge towards a particular universal norm. A naive market sorting model, for example, would predict that poor households will eventually be sorted into high pollution areas, leading to a positive relationship between air pollution and deprivation. If, however, one considers a wider set of theoretical perspectives, the anticipated relationship between air pollution and deprivation becomes more complex and idiosyncratic. Specifically, we argue the relationship between pollution and deprivation can only be made sense of by considering processes of risk perception, path dependency, gentrification and urbanization. Rather than expecting all areas to eventually converge to some universal norm, we should expect the differences in the relationship between air pollution and deprivation across localities to persist. Mindful of these insights, we propose an approach to modeling which does not impose a geographically fixed relationship. Results for Scotland reveal substantial variations in the observed relationships over space and time, supporting our argument.

Antenatal and Neonatal Antecedents of Executive Dysfunctions in Extremely Preterm Children

To find out why children born extremely preterm are at heightened risk of executive dysfunctions, the authors assessed 716 children who were 10 years old born extremely preterm whose IQ was ≥ 70. A working memory dysfunction (n = 169), an inhibition dysfunction (n = 360), a switching dysfunction (355), and all 3 (executive dysfunction; n = 107) were defined on the basis of Z-scores ≤ -1 on the Differential Ability Scales-II Working Memory composite, and/or on the NEPSY-II Inhibition-Inhibition and Inhibition-Switching subtests. All risk profiles include an indicator of socioeconomic disadvantage. The risk profile of each of the 3 individual dysfunctions includes an indicator of the newborn's immaturity, and the risk profiles of the inhibition dysfunction and switching dysfunction also include an indicator of inflammation. Only the switching dysfunction was associated with fetal growth restriction. The risk factors for executive dysfunction can be subsumed under the 4 themes of socioeconomic disadvantage, immaturity/vulnerability, inflammation, and fetal growth restriction.

Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status

OBJECTIVES

To quantify nationwide disparities in the location of particulate matter (PM)-emitting facilities by the characteristics of the surrounding residential population and to illustrate various spatial scales at which to consider such disparities.

METHODS

We assigned facilities emitting PM in the 2011 National Emissions Inventory to nearby block groups across the 2009 to 2013 American Community Survey population. We calculated the burden from these emissions for racial/ethnic groups and by poverty status. We quantified disparities nationally and for each state and county in the country.

RESULTS

For PM of 2.5 micrometers in diameter or less, those in poverty had 1.35 times higher burden than did the overall population, and non-Whites had 1.28 times higher burden. Blacks, specifically, had 1.54 times higher burden than did the overall population. These patterns were relatively unaffected by sensitivity analyses, and disparities held not only nationally but within most states and counties as well.

CONCLUSIONS

Disparities in burden from PM-emitting facilities exist at multiple geographic scales. Disparities for Blacks are more pronounced than are disparities on the basis of poverty status. Strictly socioeconomic considerations may be insufficient to reduce PM burdens equitably across populations.

Wildfire-specific Fine Particulate Matter and Risk of Hospital Admissions in Urban and Rural Counties

BACKGROUND

The health impacts of wildfire smoke, including fine particles (PM2.5), are not well understood and may differ from those of PM2.5 from other sources due to differences in concentrations and chemical composition.

METHODS

First, for the entire Western United States (561 counties) for 2004-2009, we estimated daily PM2.5 concentrations directly attributable to wildfires (wildfires-specific PM2.5), using a global chemical transport model. Second, we defined smoke wave as ≥2 consecutive days with daily wildfire-specific PM2.5 > 20 μg/m, with sensitivity analysis considering 23, 28, and 37 μg/m. Third, we estimated the risk of cardiovascular and respiratory hospital admissions associated with smoke waves for Medicare enrollees. We used a generalized linear mixed model to estimate the relative risk of hospital admissions on smoke wave days compared with matched comparison days without wildfire smoke.

RESULTS

We estimated that about 46 million people of all ages were exposed to at least one smoke wave during 2004 to 2009 in the Western United States. Of these, 5 million are Medicare enrollees (≥65 years). We found a 7.2% (95% confidence interval: 0.25%, 15%) increase in risk of respiratory admissions during smoke wave days with high wildfire-specific PM2.5 (>37 μg/m) compared with matched non smoke wave days. We did not observe an association between smoke wave days with wildfire-specific PM2.5 ≤ 37 μg/mand respiratory or cardiovascular admissions. Respiratory effects of wildfire-specific PM2.5 may be stronger than that of PM2.5 from other sources.

CONCLUSION

Short-term exposure to wildfire-specific PM2.5was associated with risk of respiratory diseases in the elderly population in the Western United States during severe smoke days. See video abstract at, http://links.lww.com/EDE/B137.

Temporal trends in air pollution exposure inequality in Massachusetts

Mounting evidence over the past several decades has demonstrated inequitable distribution of pollutants of ambient origin between sociodemographic groups in the United States. Most environmental inequality studies to date are cross-sectional and used proximity-based methods rather than modeled air pollution concentrations, limiting the ability to examine trends over time or the factors that drive exposure inequalities. In this paper, we use 1km² modeled PM_2.5 and NO₂ concentrations in Massachusetts over an 8-year period and Census demographic data to quantify inequality between sociodemographic groups and to develop a more nuanced understanding of the drivers and trends in longitudinal air pollution inequality. Annual-average population-weighted PM_2.5 and NO₂ concentrations were highest for urban non-Hispanic black populations (11.8µg/m³ in 2003 and 8.4µg/m³ in 2010, vs. 11.3µg/m³ and 8.1µg/m³ for urban non-Hispanic whites) and urban Hispanic populations (15.9 ppb in 2005 and 13.0 ppb in 2010, vs. 13.0 ppb and 10.2 ppb for urban non-Hispanic whites), respectively. While population groups experienced similar absolute decreases in exposure over time, disparities in population-weighted concentrations increased over time when quantified by the Atkinson Index, a relative inequality measure. Exposure inequalities were approximately one order of magnitude greater for NO₂ compared to PM_2.5, were more pronounced in urban compared to rural geographies, and between racial/ethnic groups compared to income and educational attainment groups. Our results also revealed similar longitudinal PM_2.5 and NO₂ inequality trends using Census 2000 and Census 2010 data, indicating that spatio-temporal shifts in air pollution may best explain observed trends in inequality. These findings enhance our understanding of factors that contribute to persistent inequalities and underscore the importance of targeted exposure reduction strategies aimed at vulnerable populations and neighborhoods.

Socio-economic inequalities in exposure to industrial air pollution emissions in Quebec public schools

OBJECTIVES

We aimed to assess the relationships between deprivation at Quebec public schools, their proximity to polluting industries, and their exposure to industrial air emission sources including ambient fine particulate matter (PM2.5), sulphur dioxide (SO2) and nitrogen dioxide (NO2).

METHODS

We obtained four indicators of school deprivation using data from the 2006 Canadian census called the low-income threshold indicator, the neighbourhood SES indicator, and the social and material deprivation indicators of Pampalon. Using proximity spatial tools, we constructed three buffers of 2.5, 5 and 7.5 km around each school and summed up total emissions of PM2.5, SO2 and NO2 for each school. Industrial air emissions were estimated using data from the 2006 Canadian National Pollutant Release Inventory. The Pearson correlations and LOESS regressions and natural log-transformed industrial air emissions were evaluated for Quebec public schools within the three buffers.

RESULTS

Of the 2189 public schools in Quebec, 608 (27.8%), 1108 (50.6%) and 1384 (63.2%) schools were located near at least one industry emitting one or more pollutants of interest in buffers of 2.5 km, 5 km and 7.5 km of schools respectively. Weak positive Pearson correlations (r) were found between log-transformed tons of industrial emissions of PM2.5, SO2 and NO2 and both the social deprivation (r = {0.23; 0.33}) and low-income threshold (r = {0.17; 0.29}) indicators in a buffer of 2.5 km. However, we found negative associations between emissions and the neighbourhood SES (r = {0.06; 0.16}) and material deprivation (r = {-0.04; 0.08}) indicators.

CONCLUSION

Our study suggests that schools in Quebec with higher rates of socio-economic deprivation among their students may be more likely to be exposed to higher emissions of industrial air pollutants.