A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures.

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population's exposure. Software for implement is provided in the R package nbRegQF.

Time-series analysis of temperature variability and cardiovascular emergency department visits in Atlanta over a 27-year period.

BACKGROUND: Short-term temperature variability, defined as the temperature range occurring within a short time span at a given location, appears to be increasing with climate change. Such variation in temperature may influence acute health outcomes, especially cardiovascular diseases (CVD). Most research on temperature variability has focused on the impact of within-day diurnal temperature range, but temperature variability over a period of a few days may also be health-relevant through its impact on thermoregulation and autonomic cardiac functioning. To address this research gap, this study utilized a database of emergency department (ED) visits for a variety of cardiovascular health outcomes over a 27-year period to investigate the influence of three-day temperature variability on CVD. METHODS: For the period of 1993-2019, we analyzed over 12 million CVD ED visits in Atlanta using a Poisson log-linear model with overdispersion. Temperature variability was defined as the standard deviation of the minimum and maximum temperatures during the current day and the previous two days. We controlled for mean temperature, dew point temperature, long-term time trends, federal holidays, and day of week. We stratified the analysis by age group, season, and decade. RESULTS: All cardiovascular outcomes assessed, except for hypertension, were positively associated with increasing temperature variability, with the strongest effects observed for stroke and peripheral vascular disease. In stratified analyses, adverse associations with temperature variability were consistently highest in the moderate-temperature season (October and March-May) and in the 65 + age group for all outcomes. CONCLUSIONS: Our results suggest that CVD morbidity is impacted by short-term temperature variability, and that patients aged 65 and older are at increased risk. These effects were more pronounced in the moderate-temperature season and are likely driven by the Spring season in Atlanta. Public health practitioners and patient care providers can use this knowledge to better prepare patients during seasons with high temperature variability or ahead of large shifts in temperature.

Differential DNA methylation in the brain as potential mediator of the association between traffic-related PM2.5 and neuropathology markers of Alzheimer's disease.

INTRODUCTION: Growing evidence indicates that fine particulate matter (PM2.5) is a risk factor for Alzheimer's disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as a potential mediator of this association. METHODS: We assessed genome-wide DNAm (Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD-related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors' residential traffic-related PM2.5 exposure 1, 3, and 5 years prior to death. We used a combination of the Meet-in-the-Middle approach, high-dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs. RESULTS: PM2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty-four CpG sites were identified as mediators of the association between PM2.5 exposure and neuropathology markers, several located in genes related to neuroinflammation. DISCUSSION: Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic-related PM2.5 and AD. HIGHLIGHTS: First study to evaluate the potential mediation effect of DNA methylation for the association between PM2.5 exposure and neuropathological changes of Alzheimer's disease. Study was based on brain tissues rarely investigated in previous air pollution research. Cg10495669, assigned to RBCK1 gene playing a role in inflammation, was associated consistently with 1-year, 3-year, and 5-year traffic-related PM2.5 exposures prior to death. Meet-in-the-middle approach and high-dimensional mediation analysis were used simultaneously to increase the potential of identifying the differentially methylated CpGs. Differential DNAm related to neuroinflammation was found to mediate the association between traffic-related PM2.5 and Alzheimer's disease.

Are children with sickle cell disease at particular risk from the harmful effects of air pollution? Evidence from a large, urban/peri-urban cohort.

INTRODUCTION: Pathophysiologic pathways of sickle cell disease (SCD) and air pollution involve inflammation, oxidative stress, and endothelial damage. It is therefore plausible that children with SCD are especially prone to air pollution's harmful effects. METHODS: Patient data were collected from a single-center, urban/peri-urban cohort of children with confirmed SCD. Daily ambient concentrations of particulate matter (PM2.5 ) were collected via satellite-derived remote-sensing technology, and carbon monoxide (CO), nitrogen dioxide (NO2 ), and ozone from local monitoring stations. We used multivariable regression to quantify associations of pollutant levels and daily counts of emergency department (ED) visits, accounting for weather and time trends. For comparison, we quantified the associations of pollutant levels with daily all-patient (non-SCD) ED visits to our center. RESULTS: From 2010 to 2018, there were 17,731 ED visits by 1740 children with SCD (64.8% HbSS/HbSß0 ). Vaso-occlusive events (57.8%), respiratory illness (17.1%), and fever (16.1%) were the most common visit diagnoses. Higher 3-day (lags 0-2) rolling mean PM2.5 and CO levels were associated with daily ED visits among those with SCD (PM2.5 incident rate ratio [IRR] 1.051 [95% confidence interval: 1.010-1.094] per 9.4 µg/m3 increase; CO 1.088 [1.045-1.132] per 0.5 ppm). NO2 showed positive associations in secondary analyses; ozone levels were not associated with ED visits. The comparison, all-patient ED visit analyses showed lower IRR for all pollutants. CONCLUSIONS: Our results suggest short-term air pollution levels as triggers for SCD events and that children with SCD may be more vulnerable to air pollution than those without SCD. Targeted pollution-avoidance strategies could have significant clinical benefits in this population.

Short-term associations between warm-season ambient temperature and emergency department visits for Alzheimer's disease and related dementia in five US states.

BACKGROUND: Ambient temperatures are projected to increase in the future due to climate change. Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) affect millions of individuals and represent substantial health burdens in the US. High temperature may be a risk factor for AD/ADRD outcomes with several recent studies reporting associations between temperature and AD mortality. However, the link between heat and AD morbidity is poorly understood. METHODS: We examined short-term associations between warm-season daily ambient temperature and AD/ADRD emergency department (ED) visits for individuals aged 45 years or above during the warm season (May to October) for up to 14 years (2005-2018) in five US states: California, Missouri, North Carolina, New Jersey, and New York. Daily ZIP code-level maximum, average and minimum temperature exposures were derived from 1 km gridded Daymet products. Associations are assessed using a time-stratified case-crossover design using conditional logistic regression. RESULTS: We found consistent positive short-term effects of ambient temperature among 3.4 million AD/ADRD ED visits across five states. An increase of the 3-day cumulative temperature exposure of daily average temperature from the 50th to the 95th percentile was associated with a pooled odds ratio of 1.042 (95% CI: 1.034, 1.051) for AD/ADRD ED visits. We observed evidence of the association being stronger for patients 65-74 years of age and for ED visits that led to hospital admissions. Temperature associations were also stronger among AD/ADRD ED visits compared to ED visits for other reasons, particularly among patients aged 65-74 years. CONCLUSION: People with AD/ADRD may represent a vulnerable population affected by short-term exposure to high temperature. Our results support the development of targeted strategies to reduce heat-related AD/ADRD morbidity in the context of global warming.

Neighborhood characteristics as confounders and effect modifiers for the association between air pollution exposure and subjective cognitive functioning.

BACKGROUND: Air pollution has been associated with cognitive function in the elderly. Previous studies have not evaluated the simultaneous effect of neighborhood-level socioeconomic status (N-SES), which can be an essential source of bias. OBJECTIVES: We explored N-SES as a confounder and effect modifier in a cross-sectional study of air pollution and subjective cognitive function. METHODS: We included 12,058 participants age 50+ years from the Emory Healthy Aging Study in Metro Atlanta using the Cognitive Function Instrument (CFI) score as our outcome, with higher scores representing worse subjective cognitive function. We estimated 9-year average ambient carbon monoxide (CO), nitrogen oxides (NOx), and fine particulate matter (PM2.5) concentrations at residential addresses using a fusion of dispersion and chemical transport models. We collected census-tract level N-SES indicators and created two composite measures via principal component analysis and k-means clustering. Associations between pollutants and CFI and effect modification by N-SES were estimated via linear regression models adjusted for age, education, race and N-SES. RESULTS: N-SES confounded the association between air pollution and CFI, independent of individual characteristics. We found significant effect modifications by N-SES for the association between air pollution and CFI (p-values<0.001) suggesting that effects of air pollution differ depending on N-SES. Participants living in areas with low N-SES were most vulnerable to air pollution. In the lowest N-SES urban areas, interquartile range (IQR) increases in CO, NOx, and PM2.5 were associated with 5.4% (95%-confidence interval, -0.2,11.3), 4.9% (-0.4,10.4), and 9.8% (2.2,18.0) changes in CFI, respectively. In lowest N-SES suburban areas, IQR increases in CO, NOx, and PM2.5 were associated with higher changes in CFI, namely 13.0% (0.9,26.5), 13.0% (-0.1,27.8), and 17.3% (2.5,34.2), respectively. DISCUSSION: N-SES is an important confounder and effect modifier in our study. This finding could have implications for studying health effects of air pollution and identifying susceptible populations.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation.

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), and three speciated PM2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO4)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO2 and PM2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO2, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta.

BACKGROUND: Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations. METHODS: Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area. RESULTS: For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient. CONCLUSION: Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.

The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru.

BACKGROUND: Asthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM2.5 (particulate matter that are 2.5 µm or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM2.5 estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM2.5. OBJECTIVE: We conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM2.5 levels in Lima, Peru from 2010 to 2016. METHODS: We used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM2.5. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent poverty and gender variables to assess effect modification. RESULTS: There were 103,974 cases of asthma ED visits during the study period across 39 districts in Lima. We found a 3.7% (95% CI: 1.7%-5.8%) increase in ED visits for every interquartile range (IQR, 6.02 µg/m3) increase in PM2.5 same day exposure with no age stratification. For the 0-18 years age group, we found a 4.5% (95% CI: 2.2%-6.8%) increase in ED visits for every IQR increase in PM2.5 same day exposure. For the 19-64 years age group, we found a 6.0% (95% CI: 1.0%-11.0%) increase in ED visits for every IQR in average 3-day exposure. For the 65 years and up age group, we found a 16.0% (95% CI: 7.0%-24.0%) decrease in ED visits for every IQR increase in PM2.5 average 3-day exposure, although the number of visits in this age group was low (4,488). We found no effect modification by SES or gender. DISCUSSION: Results from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low- and middle-income (LMIC) country.

Application of high-resolution metabolomics to identify biological pathways perturbed by traffic-related air pollution.

BACKGROUND: Substantial research has investigated the adverse effects of traffic-related air pollutants (TRAP) on human health. Convincing associations between TRAP and respiratory and cardiovascular diseases are known, but the underlying biological mechanisms are not well established. High-resolution metabolomics (HRM) is a promising platform for untargeted characterization of molecular mechanisms between TRAP and health indexes. OBJECTIVES: We examined metabolic perturbations associated with short-term exposures to TRAP, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), organic carbon (OC), and elemental carbon (EC) among 180 participants of the Center for Health Discovery and Well-Being (CHDWB), a cohort of Emory University-affiliated employees. METHODS: A cross-sectional study was conducted on baseline visits of 180 CHDWB participants enrolled during 2008-2012, in whom HRM profiling was determined in plasma samples using liquid chromatography-high-resolution mass spectrometry with positive and negative electrospray ionization (ESI) modes. Ambient pollution concentrations were measured at an ambient monitor near downtown Atlanta. Metabolic perturbations associated with TRAP exposures were assessed following an untargeted metabolome-wide association study (MWAS) framework using feature-specific Tobit regression models, followed by enriched pathway analysis and chemical annotation. RESULTS: Subjects were predominantly white (76.1%) and non-smokers (95.6%), and all had at least a high school education. In total, 7821 and 4123 metabolic features were extracted from the plasma samples by the negative and positive ESI runs, respectively. There are 3421 features significantly associated with at least one air pollutant by negative ion mode, and 1691 features by positive ion mode. Biological pathways enriched by features associated with the pollutants are primarily involved in nucleic acids damage/repair (e.g., pyrimidine metabolism), nutrient metabolism (e.g., fatty acid metabolism), and acute inflammation (e.g., histidine metabolism and tyrosine metabolism). NO2 and EC were associated most consistently with these pathways. We confirmed the chemical identity of 8 metabolic features in negative ESI and 2 features in positive ESI, including metabolites closely linked to oxidative stress and inflammation, such as histamine, tyrosine, tryptophan, and proline. CONCLUSIONS: We identified a range of ambient pollutants, including components of TRAP, associated with differences in the metabolic phenotype among the cohort of 180 subjects. We found Tobit models to be a robust approach to handle missing data among the metabolic features. The results were encouraging of further use of HRM and MWAS approaches for characterizing molecular mechanisms underlying exposure to TRAP.

Estimating climate change-related impacts on outdoor air pollution infiltration.

BACKGROUND: Rising temperatures due to climate change are expected to impact human adaptive response, including changes to home cooling and ventilation patterns. These changes may affect air pollution exposures via alteration in residential air exchange rates, affecting indoor infiltration of outdoor particles. We conducted a field study examining associations between particle infiltration and temperature to inform future studies of air pollution health effects. METHODS: We measured indoor fine particulate matter (PM2.5) in Atlanta in 60 homes (810 sampling-days). Indoor-outdoor sulfur ratios were used to estimate particle infiltration, using central site outdoor sulfur concentrations. Linear and mixed-effects models were used to examine particle infiltration ratio-temperature relationships, based on which we incorporated projected meteorological values (Representative Concentration Pathways intermediate scenario RCP 4.5) to estimate particle infiltration ratios in 20-year future (2046-2065) and past (1981-2000) scenarios. RESULTS: The mean particle infiltration ratio in Atlanta was 0.70 ± 0.30, with a 0.21 lower ratio in summer compared to transition seasons (spring, fall). Particle infiltration ratios were 0.19 lower in houses using heating, ventilation, and air conditioning (HVAC) systems compared to those not using HVAC. We observed significant associations between particle infiltration ratios and both linear and quadratic models of ambient temperature for homes using natural ventilation and those using HVAC. Future temperature was projected to increase by 2.1 °C in Atlanta, which corresponds to an increase of 0.023 (3.9%) in particle infiltration ratios during cooler months and a decrease of 0.037 (6.2%) during warmer months. DISCUSSION: We estimated notable changes in particle infiltration ratio in Atlanta for different 20-year periods, with differential seasonal patterns. Moreover, when stratified by HVAC usage, increases in future ambient temperature due to climate change were projected to enhance seasonal differences in PM2.5 infiltration in Atlanta. These analyses can help minimize exposure misclassification in epidemiologic studies of PM2.5, and provide a better understanding of the potential influence of climate change on PM2.5 health effects.

Time-series analysis of daily ambient temperature and emergency department visits in five US cities with a comparison of exposure metrics derived from 1-km meteorology products.

BACKGROUND: Ambient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error. METHODS: We conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution. RESULTS: Across cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center. CONCLUSION: Epidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.

Fine Particle Iron in Soils and Road Dust Is Modulated by Coal-Fired Power Plant Sulfur.

Transition metal ions, such as water-soluble iron (WS-Fe), are toxic components of fine particles (PM2.5). In Atlanta, from 1998 to 2013, a previous study found that WS-Fe was the PM2.5 species most associated with adverse cardiovascular outcomes. We examined this data set to investigate the sources of WS-Fe and the effects of air quality regulations on ambient levels of WS-Fe. We find that insoluble forms of iron in mineral and road dust combined with sulfate from coal-fired electrical generating units were converted into soluble forms by sulfate-driven acid dissolution. Sulfate produced both the highly acidic aerosol (summer pH 1.5-2) and liquid water required for the aqueous phase acid dissolution, but variability in WS-Fe was mainly driven by particle liquid water. These processes were more pronounced in summer when particles were most acidic, whereas in winter the relative importance of WS-Fe from combustion emissions increased. Although WS-Fe constituted a minute fraction of PM2.5 mass (0.15%), the WS-Fe-PM2.5 mass correlation was high (r = 0.67) and may be explained by these formation routes, which, in part, could account for observed associations between PM2.5 mass and adverse health seen in past studies. Similar processes are expected in many regions, implying that these unexpected benefits from coal-burning reduction may be widespread.

Air Quality Index and air quality awareness among adults in the United States.

BACKGROUND: Information about local air quality is reported across the United States using air quality alerts such as the Environmental Protection Agency's Air Quality Index. However, the role of such alerts in raising awareness of air quality is unknown. We conducted this study to evaluate associations between days with Air Quality Index ≥101, corresponding to a categorization of air quality as unhealthy for sensitive groups, unhealthy, very unhealthy, or hazardous, and air quality awareness among adults in the United States. METHODS: Data from 12,396 respondents to the 2016-2018 ConsumerStyles surveys were linked by geographic location and survey year to daily Air Quality Index data. We evaluated associations between the number of days in the past year with Air Quality Index ≥101 and responses to survey questions about awareness of air quality alerts, perception of air quality, and changes in behavior to reduce air pollution exposure using logistic regression. RESULTS: Awareness of air quality alerts (prevalence ratio [PR] = 1.23; 95% confidence interval [CI] = 1.15, 1.31), thinking/being informed air quality was bad (PR = 2.02; 95% CI = 1.81, 2.24), and changing behavior (PR = 2.27; 95% CI = 1.94, 2.67) were higher among respondents living in counties with ≥15 days with Air Quality Index ≥101 than those in counties with zero days in the past year with Air Quality Index ≥101. Each aspect of air quality awareness was higher among adults with than without asthma, but no differences were observed by heart disease status. Across quintiles of the number of days with Air Quality Index ≥101, air quality awareness increased among those with and without selected respiratory and cardiovascular diseases. CONCLUSIONS: Among U.S. adults, air quality awareness increases with increasing days with alerts of unhealthy air. These findings improve our understanding of the extent to which air quality alerts prompt people to take actions to protect their health amidst poor air quality.

Ambient air pollution and sickle cell disease-related emergency department visits in Atlanta, GA.

BACKGROUND: Sickle cell disease (SCD) is an inherited, autosomal recessive blood disorder, among the most prevalent genetic diseases, globally. While the genetic and hemolytic dynamics of SCD have been well-characterized, the etiology of SCD-related pathophysiological processes is unclear. Although limited, observational evidence suggests that environmental factors, including urban air pollution, may play a role. OBJECTIVES: We assessed whether daily ambient air pollution concentrations are associated with corresponding emergency department (ED) visit counts for acute SCD exacerbations in Atlanta, Georgia, during a 9-year (2005-2013) period. We also examined heterogeneity in response by age and sex. METHODS: ED visit data were from 41 hospitals in the 20-county Atlanta, GA area. Associations between daily air pollution levels for 8 urban air pollutants and counts of SCD related ED visits were estimated using Poisson generalized linear models. RESULTS: We observed positive associations between pollutants generally indicative of traffic emissions and corresponding SCD ED visits [e.g., rate ratio of 1.022 (95% CI: 1.002, 1.043) per interquartile range increase in carbon monoxide]. Age stratified analyses indicated stronger associations with traffic pollutants among children (0-18 years), as compared to older age strata. Associations involving other pollutants, including ozone and particulate matter and for models of individuals >18 years old, were consistent a null hypothesis of no association. DISCUSSION: This analysis represents the first North American study to examine acute risk among individuals with SCD to urban air pollution and provide evidence of urban air pollution, especially from traffic sources, as a trigger for acute exacerbations. These findings are consistent with a hypothesis that biological pathways, including several centrally associated with oxidative stress, may contribute towards enhanced susceptibility in individuals with SCD.

Evaluating a multipollutant metric for use in characterizing traffic-related air pollution exposures within near-road environments.

Accurately characterizing human exposures to traffic-related air pollutants (TRAPs) is critical to public health protection. However, quantifying exposure to this single source is challenging, given its extremely heterogeneous chemical composition. Efforts using single-species tracers of TRAP are, thus, lacking in their ability to accurately reflect exposures to this complex mixture. There have been recent discussions centered on adopting a multipollutant perspective for sources with many emitted pollutants to maximize the benefits of control expenditures as well as to minimize population and ecosystem exposure. As part of a larger study aimed to assess a complete emission-to-exposure pathway of primary traffic pollution and understand exposure of individuals in the near-road environment, an intensive field campaign measured TRAPs and related data (e.g., meteorology, traffic counts, and regional air pollutant levels) in Atlanta along one of the busiest highway corridors in the US. Given the dynamic nature of the near-road environment, a multipollutant exposure metric, the Integrated Mobile Source Indicator (IMSI), which was generated based on emissions-based ratios, was calculated and compared to traditional single-species methods for assessing exposure to mobile source emissions. The current analysis examined how both traditional and non-traditional metrics vary spatially and temporally in the near-road environment, how they compare with each other, and whether they have the potential to offer more accurate means of assigning exposures to primary traffic emissions. The results indicate that compared to the traditional single pollutant specie, the multipollutant IMSI metric provided a more spatially stable method for assessing exposure, though variations occurred based on location with varying results among the six sites within a kilometer of the highway.

Temporal changes in short-term associations between cardiorespiratory emergency department visits and PM2.5 in Los Angeles, 2005 to 2016.

BACKGROUND: Emissions control programs targeting certain air pollution sources may alter PM2.5 composition, as well as the risk of adverse health outcomes associated with PM2.5. OBJECTIVES: We examined temporal changes in the risk of emergency department (ED) visits for cardiovascular diseases (CVDs) and asthma associated with short-term increases in ambient PM2.5 concentrations in Los Angeles, California. METHODS: Poisson log-linear models with unconstrained distributed exposure lags were used to estimate the risk of CVD and asthma ED visits associated with short-term increases in daily PM2.5 concentrations, controlling for temporal and meteorological confounders. The models were run separately for three predefined time periods, which were selected based on the implementation of multiple emissions control programs (EARLY: 2005-2008; MIDDLE: 2009-2012; LATE: 2013-2016). Two-pollutant models with individual PM2.5 components and the remaining PM2.5 mass were also considered to assess the influence of changes in PM2.5 composition on changes in the risk of CVD and asthma ED visits associated with PM2.5 over time. RESULTS: The relative risk of CVD ED visits associated with a 10 µg/m3 increase in 4-day PM2.5 concentration (lag 0-3) was higher in the LATE period (rate ratio = 1.020, 95% confidence interval = [1.010, 1.030]) compared to the EARLY period (1.003, [0.996, 1.010]). In contrast, for asthma, relative risk estimates were largest in the EARLY period (1.018, [1.006, 1.029]), but smaller in the following periods. Similar temporal differences in relative risk estimates for CVD and asthma were observed among different age and season groups. No single component was identified as an obvious contributor to the changing risk estimates over time, and some components exhibited different temporal patterns in risk estimates from PM2.5 total mass, such as a decreased risk of CVD ED visits associated with sulfate over time. CONCLUSIONS: Temporal changes in the risk of CVD and asthma ED visits associated with short-term increases in ambient PM2.5 concentrations were observed. These changes could be related to changes in PM2.5 composition (e.g., an increasing fraction of organic carbon and a decreasing fraction of sulfate in PM2.5). Other factors such as improvements in healthcare and differential exposure misclassification might also contribute to the changes.

Associations of pollen and cardiovascular disease morbidity in Atlanta during 1993-2018.

Background: Pollen exposure is associated with substantial respiratory morbidity, but its potential impact on cardiovascular disease (CVD) remains less understood. This study aimed to investigate the associations between daily levels of 13 pollen types and emergency department (ED) visits for eight CVD outcomes over a 26-year period in Atlanta, GA. Methods: We acquired pollen data from Atlanta Allergy & Asthma, a nationally certified pollen counting station, and ED visit data from individual hospitals and the Georgia Hospital Association. We performed time-series analyses using quasi-Poisson distributed lag models, with primary analyses assessing 3-day (lag 0-2 days) pollen levels. Models controlled for temporally varying covariates, including air pollutants. Results: During 1993-2018, there were 1,573,968 CVD ED visits. Most pairwise models of the 13 pollen types and eight CVD outcomes showed no association, with a few exceptions potentially due to chance. Conclusion: We found limited evidence of the impact of pollen on cardiovascular morbidity in Atlanta. Further study on pollen exposures in different climactic zones and exploration of pollen-pollution mixture effects is warranted.

Assessment of census-tract level socioeconomic position as a modifier of the relationship between short-term PM2.5 exposure and cardiovascular emergency department visits in Missouri.

INTRODUCTION: Ambient particulate matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) exposure elevates the risk for cardiovascular disease morbidity (CVDM). The aim of this study is to characterise which area-level measures of socioeconomic position (SEP) modify the relationship between PM2.5 exposure and CVDM in Missouri at the census-tract (CT) level. METHODS: We use individual level Missouri emergency department (ED) admissions data (n=3 284 956), modelled PM2.5 data, and yearly CT data from 2012 to 2016 to conduct a two-stage analysis. Stage one uses a case-crossover approach with conditional logistic regression to establish the baseline risk of ED visits associated with IQR changes in PM2.5. In the second stage, we use multivariate metaregression to examine how CT-level SEP modifies the relationship between ambient PM2.5 exposure and CVDM. RESULTS: We find that overall, ambient PM2.5 exposure is associated with increased risk for CVDM. We test effect modification in statewide and urban CTs, and in the warm season only. Effect modification results suggest that among SEP measures, poverty is most consistently associated with increased risk for CVDM. For example, across Missouri, the highest poverty CTs are at an elevated risk for CVDM (OR=1.010 (95% CI 1.007 to 1.014)) compared with the lowest poverty CTs (OR=1.004 (95% CI 1.000 to 1.008)). Other SEP modifiers generally display an inconsistent or null effect. CONCLUSION: Overall, we find some evidence that area-level SEP modifies the relationship between ambient PM2.5 exposure and CVDM, and suggest that the relationship between air-pollution, area-level SEP and CVDM may be sensitive to spatial scale.