A Direct Estimate of the Impact of PM2.5, NO2, and O3 Exposure on Life Expectancy Using Propensity Scores

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

Long-Term Exposure to Ambient PM2.5 and Hospitalizations for Myocardial Infarction Among US Residents: A Difference-in-Differences Analysis

Background Air pollution has been recognized as an untraditional risk factor for myocardial infarction (MI). However, the MI risk attributable to long-term exposure to fine particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) is unclear, especially in younger populations, and few studies have represented the general population or had power to examine comorbidities. Methods and Results We applied the difference-in-differences approach to estimate the relationship between annual PM2.5 exposure and hospitalizations for MI among US residents and further identified potential susceptible subpopulations. All hospital admissions for MI in 10 US states over the period 2002 to 2016 were obtained from the Healthcare Cost and Utilization Project State Inpatient Database. In total, 1 914 684 MI hospital admissions from 8106 zip codes were included in this study. We observed a 1.35% (95% CI, 1.11-1.59) increase in MI hospitalization rate for 1-μg/m3 increase in annual PM2.5 exposure. The estimate was robust to adjustment for surface pressure, relative humidity, and copollutants. In the population exposed to ≤12 μg/m3, there was a larger increment of 2.17% (95% CI, 1.79-2.56) in hospitalization rate associated with 1-μg/m3 increase in PM2.5. Young people (0-34 years of age) and elderly people (≥75 years of age) were the 2 most susceptible age groups. Residents living in more densely populated or poorer areas and individuals with comorbidities were observed to be at a greater risk. Conclusions This study indicates long-term residential exposure to PM2.5 could increase risk of MI among the general US population, people with comorbidities, and poorer individuals. The association persists below current standards.

Effects of low-level air pollution exposures on hospital admission for myocardial infarction using multiple causal models

Myocardial infarctions have been associated with PM2.5, and more recently with NO2 and O3, however counterfactual designs have been lacking and argument continues over the extent of confounding control. Here we introduce a doubly robust, counterfactual-based approach that deals with nonlinearity and interactions in associations between confounders and both outcome and exposure, as well as a double negative controls approach that capture omitted confounders. We used data from over 4 million admissions for myocardial infarction in the US Medicare population between 2000 and 2016 and linked them by ZIP code of residence to high resolution predictions of annual PM2.5, NO2, and O3. We computed the counts of admissions for each ZIP code-year. In the doubly robust approach, we divided each pollutant into deciles, and for each decile, we fitted a gradient boosting machine model to estimate the effects of covariates, including the co-pollutants, on the counts. We used these models to predict, for all ZIP code-years, the expected counts had everyone be exposed in that decile. We also estimated the probability of being in that decile given all covariates, again with a gradient boosting machine, and used inverse probability weights to compute the weighted average rate of MI admission in each decile. In the negative control approach, for each pollutant, we fitted a quasi-Poisson model to estimate the exposure effect, adjusting for covariates including the co-pollutants, and negative exposure and outcome controls to control for unmeasured confounding. Each 1-μg/m3 increase in annual PM2.5 increased the admission for MI by 1.37 cases per 10,000 person-years (95% CI: 1.20, 1.54) in the doubly robust approach, and by 0.69 cases (95% CI 0.60, 0.78) using the negative control approach. Elevated risks were seen even below annual PM2.5 level of 8 μg/m3. Results for NO2 and O3 were inconsistent.

Air quality and individual-level academic performance in Brazil: A nationwide study of more than 15 million students between 2000 and 2020

Studies have shown that living and studying in places with poor air quality is associated with cognitive deficits. However, there is still a limitation in the literature in terms of study design and geographic location. Also, only a few studies have looked at the effects of more than one air pollutant. To address this research gap, in this study we estimated the association between air pollution (considering three criteria air pollutants - PM_2.5, NO₂, and O₃) and academic performance (a proxy of cognitive performance) at the student level in Brazil between 2000 and 2020. We assessed academic performance data from a nationwide high school exam. The data included 15,443,772 students who took this national test between 2000 and 2020 in Brazil. Air pollution data was derived from satellite remote sensing observations. We fit mixed-effects regression models with a state-specific random intercept and adjusted for school characteristics, spatio-temporal factors, and socioeconomic status. We performed sub-group analyses by stratifying the analysis by type of school management (private or public), location of the school (urban or rural), sex, and periods. Our findings suggest air pollution exposure was associated with drops in the students' marks varying from 0.13% to 5.39%. To our knowledge, this is the first study that estimates the association between air pollution and individual-level academic performance in Brazil. This study is of substantial environmental and educational importance by supporting policymakers to improve the air quality surrounding schools.

Short-term air pollution exposure and hospital admissions for cardiorespiratory diseases in Brazil: A nationwide time-series study between 2008 and 2018

The established evidence associating air pollution with health is limited to populations from specific regions. Further large-scale studies in several regions worldwide are needed to support the literature to date and encourage national governments to act. Brazil is an example of these regions where little research has been performed on a large scale. To address this gap, we conducted a study looking at the relationship between daily PM_2.5, NO₂, and O₃, and hospital admissions for circulatory and respiratory diseases across Brazil between 2008 and 2018. A time-series analytic approach was applied with a distributed lag modeling framework. We used a generalized conditional quasi-Poisson regression model to estimate relative risks (RRs) of the association of each air pollutant with the hospitalization for circulatory and respiratory diseases by sex, age group, and Brazilian regions. Our study population includes 23, 791, 093 hospital admissions for cardiorespiratory diseases in Brazil between 2008 and 2018. Among those, 53.1% are respiratory diseases, and 46.9% are circulatory diseases. Our findings suggest significant associations of ambient air pollution (PM_2.5, NO₂, and O₃) with respiratory and circulatory hospital admissions in Brazil. The national meta-analysis for the whole population showed that for every increase of PM_2.5 by 10 μg/m³, there is a 3.28% (95%CI: 2.61; 3.94) increase in the risk of hospital admission for respiratory diseases. For O₃, we found positive associations only for some sub-group analyses by age and sex. For NO₂, our findings suggest that a 10 ppb increase in this pollutant, there was a 35.26% (95%CI: 24.07; 46.44) increase in the risk of hospital admission for respiratory diseases. This study may better support policymakers to improve the air quality and public health in Brazil.

Health Outcomes in Children Associated with Prenatal and Early-Life Exposures to Air Pollution: A Narrative Review

(1) Background: The developmental origins of health and disease (DOHaD) hypothesis links adverse fetal exposures with developmental mal-adaptations and morbidity later in life. Short- and long-term exposures to air pollutants are known contributors to health outcomes; however, the potential for developmental health effects of air pollution exposures during gestation or early-childhood have yet to be reviewed and synthesized from a DOHaD lens. The objective of this study is to summarize the literature on cardiovascular and metabolic, respiratory, allergic, and neuropsychological health outcomes, from prenatal development through early childhood, associated with early-life exposures to outdoor air pollutants, including traffic-related and wildfire-generated air pollutants. (2) Methods: We conducted a search using PubMed and the references of articles previously known to the authors. We selected papers that investigated health outcomes during fetal or childhood development in association with early-life ambient or source-specific air pollution exposure. (3) Results: The current literature reports that prenatal and early-childhood exposures to ambient and traffic-related air pollutants are associated with a range of adverse outcomes in early life, including cardiovascular and metabolic, respiratory and allergic, and neurodevelopmental outcomes. Very few studies have investigated associations between wildfire-related air pollution exposure and health outcomes during prenatal, postnatal, or childhood development. (4) Conclusion: Evidence from January 2000 to January 2022 supports a role for prenatal and early-childhood air pollution exposures adversely affecting health outcomes during development. Future studies are needed to identify both detrimental air pollutants from the exposure mixture and critical exposure time periods, investigate emerging exposure sources such as wildfire, and develop feasible interventional tools.

Air Pollution, Socioeconomic Status, and Age-Specific Mortality Risk in the United States

IMPORTANCE

Prior studies on the association between fine particulate matter with diameters 2.5 μm or smaller (PM2.5) and probability of death have not applied multilevel analysis disaggregating data for US census tract, states, and counties, nor tested its interaction by socioeconomic status (SES). Such an approach could provide a more refined identification and targeting of populations exposed to increased risk from PM2.5.

OBJECTIVE

To assess the association between PM2.5 and age-specific mortality risk (ASMR) using disaggregated data at the census tract level and evaluate such association according to census tract SES.

DESIGN, SETTING, AND PARTICIPANTS

This nationwide cross-sectional study used a linkage of 3 different data sets. ASMR for the period of 2010 to 2015 was obtained from the National Center for Health Statistic, SES data covering a period from 2006 to 2016 came from the American Community Survey, and mean PM2.5 exposure levels from 2010 to 2015 were derived from well-validated atmospheric chemistry and machine learning models. Data were analyzed in April 2021.

EXPOSURES

The main exploratory variable was mean census tract-level long-term exposure to PM2.5 from 2010 to 2015.

MAIN OUTCOMES AND MEASURES

The primary outcome was census tract-level ASMR. Multilevel models were used to quantify the geographic variation in ASMR at levels of census tract, county, and state. Additional analysis explored the interaction of SES in the association of ASMR with PM2.5 exposure.

RESULTS

Data from 67 148 census tracts nested in 3087 counties and 50 states were analyzed. The association between exposure to PM2.5 and ASMR varied substantially across census tracts. The magnitude of such association also varied across age groups, being higher among adults and older adults. Census tracts accounted for most of the total geographic variation in mortality risk (range, 77.0%-94.2%). ASMR was higher in deciles with greater PM2.5 concentration. For example, ASMR for age 75 to 84 years was 54.6 per 1000 population higher in the decile with the second-highest PM2.5 concentration than in the decile with the lowest PM2.5 concentration. The ASMR, PM2.5 concentrations, and magnitude of the association between both were higher in the census tracts with the lowest SES.

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that census tracts with lower SES presented higher PM2.5 concentrations. ASMR and air pollution varied substantially across census tracts. There was an association between air pollution and ASMR across all age groups in the United States. These findings suggest that equitable public policies aimed at improving air quality are needed and important to increase life expectancy.