Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado.

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by âˆ¼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to âˆ¼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

An environmental justice analysis of air pollution in India.

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 µg/m3 (95% CI 0.062 µg/m3, 0.192 µg/m3) and 0.199 µg/m3 (95% CI 0.116 µg/m3, 0.283 µg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.

Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings.

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.

Hyperlocal environmental data with a mobile platform in urban environments.

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking.

Key Themes, Trends, and Drivers of Mobile Ambient Air Quality Monitoring: A Systematic Review and Meta-Analysis.

Mobile ambient air quality monitoring is rapidly changing the current paradigm of air quality monitoring and growing as an important tool to address air quality and climate data gaps across the globe. This review seeks to provide a systematic understanding of the current landscape of advances and applications in this field. We observe a rapidly growing number of air quality studies employing mobile monitoring, with low-cost sensor usage drastically increasing in recent years. A prominent research gap was revealed, highlighting the double burden of severe air pollution and poor air quality monitoring in low- and middle-income regions. Experiment-design-wise, the advances in low-cost monitoring technology show great potential in bridging this gap while bringing unique opportunities for real-time personal exposure, large-scale deployment, and diversified monitoring strategies. The median value of unique observations at the same location in spatial regression studies is ten, which can be used as a rule-of-thumb for future experiment design. Data-analysis-wise, even though data mining techniques have been extensively employed in air quality analysis and modeling, future research can benefit from exploring air quality information from nontabular data, such as images and natural language.

The neighborhood built environment and COVID-19 hospitalizations.

Research on the associations between the built environment and COVID-19 outcomes has mostly focused on incidence and mortality. Also, few studies on the built environment and COVID-19 have controlled for individual-level characteristics across large samples. In this study, we examine whether neighborhood built environment characteristics are associated with hospitalization in a cohort of 18,042 individuals who tested positive for SARS-CoV-2 between May and December 2020 in the Denver metropolitan area, USA. We use Poisson models with robust standard errors that control for spatial dependence and several individual-level demographic characteristics and comorbidity conditions. In multivariate models, we find that among individuals with SARS-CoV-2 infection, those living in multi-family housing units and/or in places with higher particulate matter (PM2.5) have a higher incident rate ratio (IRR) of hospitalization. We also find that higher walkability, higher bikeability, and lower public transit access are linked to a lower IRR of hospitalization. In multivariate models, we did not find associations between green space measures and the IRR of hospitalization. Results for non-Hispanic white and Latinx individuals highlight substantial differences: higher PM2.5 levels have stronger positive associations with the IRR of hospitalization for Latinx individuals, and density and overcrowding show stronger associations for non-Hispanic white individuals. Our results show that the neighborhood built environment might pose an independent risk for COVID-19 hospitalization. Our results may inform public health and urban planning initiatives to lower the risk of hospitalization linked to COVID-19 and other respiratory pathogens.

An analysis of degradation in low-cost particulate matter sensors.

Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter (PM2.5) concentrations in cities around the world. One of the most commonly deployed LCS is the PurpleAir with ~ 15,000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM2.5 levels in their neighborhoods. PurpleAir measurements are also increasingly being integrated into models by researchers to develop large-scale estimates of PM2.5. However, the change in sensor performance over time has not been well studied. It is important to understand the lifespan of these sensors to determine when they should be serviced or replaced, and when measurements from these devices should or should not be used for various applications. This paper fills this gap by leveraging the fact that: (1) Each PurpleAir sensor is comprised of two identical sensors and the divergence between their measurements can be observed, and (2) There are numerous PurpleAir sensors within 50 meters of regulatory monitors allowing for the comparison of measurements between these instruments. We propose empirically derived degradation outcomes for the PurpleAir sensors and evaluate how these outcomes change over time. On average, we find that the number of 'flagged' measurements, where the two sensors within each PurpleAir sensor disagree, increases with time to ~ 4% after 4 years of operation. Approximately 2 percent of all PurpleAir sensors were permanently degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to be in the hot and humid climate zone, suggesting that sensors in these locations may need to be replaced more frequently. We also find that the bias of PurpleAir sensors, or the difference between corrected PM2.5 levels and the corresponding reference measurements, changed over time by -0.12 µg/m3(95% CI: -0.13 µg/m3, -0.10 µg/m3) per year. The average bias increases dramatically after 3.5 years. Further, climate zone is a significant modifier of the association between degradation outcomes and time.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Air Pollution and Mortality at the Intersection of Race and Social Class.

BACKGROUND: Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 µm [PM2.5]) than White Americans and may be more susceptible to its health effects. Low-income Americans may also be more susceptible to PM2.5 pollution than high-income Americans. Because information is lacking on exposure-response curves for PM2.5 exposure and mortality among marginalized subpopulations categorized according to both race and socioeconomic position, the Environmental Protection Agency lacks important evidence to inform its regulatory rulemaking for PM2.5 standards. METHODS: We analyzed 623 million person-years of Medicare data from 73 million persons 65 years of age or older from 2000 through 2016 to estimate associations between annual PM2.5 exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible). RESULTS: Lower PM2.5 exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM2.5 levels decreased. For example, the hazard ratio associated with decreasing PM2.5 from 12 µg per cubic meter to 8 µg per cubic meter for the White higher-income subpopulation was 0.963 (95% confidence interval [CI], 0.955 to 0.970), whereas equivalent hazard ratios for marginalized subpopulations were lower: 0.931 (95% CI, 0.909 to 0.953) for the Black higher-income subpopulation, 0.940 (95% CI, 0.931 to 0.948) for the White low-income subpopulation, and 0.939 (95% CI, 0.921 to 0.957) for the Black low-income subpopulation. CONCLUSIONS: Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM2.5 levels than higher-income White persons. These findings underscore the importance of considering racial identity and income together when assessing health inequities. (Funded by the National Institutes of Health and the Alfred P. Sloan Foundation.).

Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information.

U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.

Evaluation of Model-Based PM2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S.

Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.

Impact of air pollution on stunting among children in Africa.

BACKGROUND: Undernutrition is a global public health crisis, causing nearly half of deaths for children under age 5 years. Little is known regarding the impact of air pollution in-utero and early childhood on health outcomes related to undernutrition. The aim of our study is to evaluate the association of prenatal and early-life exposure to PM2.5 and child malnutrition as captured by the height-for-age z-score (HAZ), and stunting in 32 countries in Africa. We also evaluated critical windows of susceptibility during pregnancy to each environmental risk. METHODS: We linked nationally representative anthropometric data from 58 Demographic and Health Surveys (DHS) (n = 264,207 children < 5 years of age) with the average in-utero PM2.5 concentrations derived from satellite imagery. We then estimated associations between PM2.5 and stunting and HAZ after controlling for child, mother and household factors, and trends in time and seasonality. RESULTS: We observed lower HAZ and increased stunting with higher in-utero PM2.5 exposure, with statistically significant associations observed for stunting (OR: 1.016 (95% CI: 1.002, 1.030), for a 10 µg/m3 increase). The associations observed were robust to various model specifications. Wald tests revealed that sex, wealth quintile and urban/rural were not significant effect modifiers of these associations. When evaluating associations between trimester-specific PM2.5 levels, we observed that associations between PM2.5 and stunting was the largest. CONCLUSIONS: This is one of the first studies for the African continent to investigate in-utero and early-life exposure to PM2.5 is an important marker of childhood undernutrition. Our results highlight that PM2.5 concentrations need to be urgently mitigated to help address undernutrition in children on the continent.

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.

Robust relationship between ambient air pollution and infant mortality in India.

BACKGROUND: Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. We estimate the associations between in-utero and perinatal exposure to PM2.5 and infant, neonatal and postneonatal mortality in India. We evaluate the sensitivity of this association to two widely-used exposure assessments. METHOD: We linked nationally representative anthropometric data from India's 2015-2016 Demographic and Health Survey (n = 259,627 children under five across 640 districts of India) with satellite-based PM2.5 concentrations during the month of birth of each child. We then estimated the associations between PM2.5 from each dataset and child mortality, after controlling for child, mother and household factors including trends in time and seasonality. We examined if factors: urban/rural, sex, wealth quintile and state modified the associations derived from the two datasets using Wald tests. RESULTS: We found evidence that PM2.5 impacts infant mortality primarily through neonatal mortality. The estimated association between neonatal mortality and PM2.5 in trimester 3 was OR: 1.016 (95% CI: 1.003, 1.030) for every 10 µg/m3 increase in exposure. This association was robust to the exposure assessment used. Child sex was a significant effect modifier, with PM2.5 impacting mortality in infant girls more than boys. CONCLUSIONS: Our results revealed a robust association between ambient exposure to PM2.5 in the latter period of pregnancy and early life with infant and neonatal mortality in India. Urgent air pollution management plans are needed to improve infant mortality in India.

Evaluating the Meteorological Effects on the Urban Form-Air Quality Relationship Using Mobile Monitoring.

Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC10 (number concentration for particles with diameters of 1 µm-10 µm) than the control group (R = 0.01), and modest improvements for PM2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC10 and 14.90% for PM2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels.

Structural Racism and COVID-19 in the USA: a County-Level Empirical Analysis.

Substantial health disparities exist across race/ethnicity in the USA, with Black Americans often most affected. The current COVID-19 pandemic is no different. While there have been ample studies describing racial disparities in COVID-19 outcomes, relatively few have established an empirical link between these disparities and structural racism. Such empirical analyses are critically important to help defuse "victim-blaming" narratives about why minority communities have been badly hit by COVID-19. In this paper, we explore the empirical link between structural racism and disparities in county-level COVID-19 outcomes by county racial composition. Using negative binomial regression models, we examine how five measures of county-level residential segregation and racial disparities in socioeconomic outcomes as well as incarceration rates are associated with county-level COVID-19 outcomes. We find significant associations between higher levels of measured structural racism and higher rates of COVID-19 cases and deaths, even after adjusting for county-level population sociodemographic characteristics, measures of population health, access to healthcare, population density, and duration of the COVID-19 outbreak. One percentage point more Black residents predicted a 1.1% increase in county case rate. This association decreased to 0.4% when structural racism indicators were included in our model. Similarly, one percentage point more Black residents predicted a 1.8% increase in county death rates, which became non-significant after adjustment for structural racism. Our findings lend empirical support to the hypothesis that structural racism is an important driver of racial disparities in COVID-19 outcomes, and reinforce existing calls for action to address structural racism as a fundamental cause of health disparities.

On the distribution of low-cost PM2.5 sensors in the US: demographic and air quality associations.

BACKGROUND: Low-cost sensors have the potential to democratize air pollution information and supplement regulatory networks. However, differentials in access to these sensors could exacerbate existing inequalities in the ability of different communities to respond to the threat of air pollution. OBJECTIVE: Our goal was to analyze patterns of deployments of a commonly used low-cost sensor, as a function of demographics and pollutant concentrations. METHODS: We used Wilcoxon rank sum tests to assess differences between socioeconomic characteristics and PM2.5 concentrations of locations with low-cost sensors and those with regulatory monitors. We used Kolomogorov-Smirnov tests to examine how representative census tracts with sensors were of the United States. We analyzed predictors of the presence, and number of, sensors in a tract using regressions. RESULTS: Census tracts with low-cost sensors were higher income more White and more educated than the US as a whole and than tracts with regulatory monitors. For all states except for California they are in locations with lower annual-average PM2.5 concentrations than regulatory monitors. The existing presence of a regulatory monitor, the percentage of people living above the poverty line and PM2.5 concentrations were associated with the presence of low-cost sensors in a tract. SIGNIFICANCE: Strategies to improve access to low-cost sensors in less-privileged communities are needed to democratize air pollution data.

Nationwide Study of Short-term Exposure to Fine Particulate Matter and Cardiovascular Hospitalizations Among Medicaid Enrollees.

BACKGROUND: Fine particulate matter (PM2.5) has been consistently linked to cardiovascular disease (CVD). Although studies have reported modification by income, to our knowledge, no study to date has examined this relationship among adults in Medicaid, which provides health coverage to low-income and/or disabled Americans. METHODS: We estimated the association between short-term PM2.5 exposure (average of PM2.5 on the day of hospitalization and the preceding day) and CVD admissions rates among adult Medicaid enrollees in the continental United States (2000-2012) using a time-stratified case-crossover design. We repeated this analysis at PM2.5 concentrations below the World Health Organization daily guideline of 25 µg/m. We compared the PM2.5-CVD association in the Medicaid ≥65 years old versus non-Medicaid-eligible Medicare enrollees (≥65 years old). RESULTS: Using information on 3,666,657 CVD hospitalizations among Medicaid adults, we observed a 0.9% (95% CI = 0.6%, 1.1%) increase in CVD admission rates per 10 µg/m PM2.5 increase. The association was stronger at low PM2.5 levels (1.3%; 95% CI = 0.9%, 1.6%). Among Medicaid enrollees ≥65 years old, the association was 0.9% (95% CI = 0.6%, 1.3%) vs. 0.8% (95% CI = 0.6%, 0.9%) among non-Medicaid-eligible Medicare enrollees ≥65 years old. CONCLUSION: We found robust evidence of an association between short-term PM2.5 and CVD hospitalizations among the vulnerable subpopulation of adult Medicaid enrollees. Importantly, this association persisted even at PM2.5 levels below the current national standards.