Jason D. Sacks

Particulate Matter―Induced Health Effects: Who Is Susceptible?

Background Epidemiological, controlled human exposure, and toxicological studies have demonstrated a variety of health effects in response to particulate matter (PM) exposure with some of these studies indicating that populations with certain characteristics may be disproportionately affected. Objective To identify populations potentially at greatest risk for PM-related health effects, we evaluated epidemiological studies that examined various characteristics that may influence susceptibility, while using results from controlled human exposure and toxicological studies as supporting evidence. Additionally, we formulated a definition of susceptibility, building from the varied and inconsistent definitions of susceptibility and vulnerability used throughout the literature. Data synthesis We evaluated recent epidemiological studies to identify characteristics of populations potentially susceptible to PM-related health effects. Additionally, we evaluated controlled human exposure and toxicological studies to provide supporting evidence. We conducted a comprehensive review of epidemiological studies that presented stratified results (e.g., < 65 vs. ≥ 65 years of age), controlled human exposure studies that examined individuals with underlying disease, and toxicological studies that used animal models of disease. We evaluated results for consistency across studies, coherence across disciplines, and biological plausibility to assess the potential for increased susceptibility to PM-related health effects in a specific population or life stage. Conclusions We identified a diverse group of characteristics that can lead to increased risk of PM-related health effects, including life stage (i.e., children and older adults), preexisting cardiovascular or respiratory diseases, genetic polymorphisms, and low-socioeconomic status. In addition, we crafted a comprehensive definition of susceptibility that can be used to encompass all populations potentially at increased risk of adverse health effects as a consequence of exposure to an air pollutant.

The Effect of Ambient Air Pollution on Sperm Quality

Background Research has suggested an association with ambient air pollution and sperm quality. Objectives We investigated the effect of exposure to ozone (O3) and particulate matter < 2.5 μm in aerodynamic diameter (PM2.5) on sperm quality. Methods We reexamined a previous cohort study of water disinfection by-products to evaluate sperm quality in 228 presumed fertile men with different air pollution profiles. Outcomes included sperm concentration, total sperm per ejaculate (count), and morphology, as well as DNA integrity and chromatin maturity. Exposures to O3 and PM2.5 were evaluated for the 90–day period before sampling. We used multivariable linear regression, which included different levels of adjustment (i.e., without and with season and temperature) to assess the relationship between exposure to air pollutants during key periods of sperm development and adverse sperm outcomes. Results Sperm concentration and count were not associated with exposure to PM2.5, but there was evidence of an association (but not statistically significant) with O3 concentration and decreased sperm concentration and count. Additionally, a significant increase in the percentage of sperm cells with cytoplasmic drop [β = 2.64; 95% confidence interval (CI), 0.21–5.06] and abnormal head (β = 0.47; 95% CI, 0.03–0.92) was associated with PM2.5 concentration in the base model. However, these associations, along with all other sperm outcomes, were not significantly associated with either pollutant after controlling for season and temperature. Overall, although we found both protective and adverse effects, there was generally no consistent pattern of increased abnormal sperm quality with elevated exposure to O3 or PM2.5. Conclusions Exposures to O3 or PM2.5 at levels below the current National Ambient Air Quality Standards were not associated with statistically significant decrements in sperm outcomes in this cohort of fertile men. However, some results suggested effects on sperm concentration, count, and morphology.

Influence of urbanicity and county characteristics on the association between ozone and asthma emergency department visits in North Carolina.

Background: Air pollution epidemiologic studies, often conducted in large metropolitan areas because of proximity to regulatory monitors, are limited in their ability to examine potential associations between air pollution exposures and health effects in rural locations. Methods: Using a time-stratified case-crossover framework, we examined associations between asthma emergency department (ED) visits in North Carolina (2006–2008), collected by a surveillance system, and short-term ozone (O3) exposures using predicted concentrations from the Community Multiscale Air Quality (CMAQ) model. We estimated associations by county groupings based on four urbanicity classifications (representative of county size and urban proximity) and county health. Results: O3 was associated with asthma ED visits in all-year and warm season (April–October) analyses [odds ratio (OR) = 1.019; 95% CI: 0.998, 1.040; OR = 1.020; 95% CI: 0.997, 1.044, respectively, for a 20-ppb increase in lag 0–2 days O3]. The association was strongest in Less Urbanized counties, with no evidence of a positive association in Rural counties. Associations were similar when adjusted for fine particulate matter in copollutant models. Associations were stronger for children (5–17 years of age) compared with other age groups, and for individuals living in counties identified with poorer health status compared with counties that had the highest health rankings, although estimated associations for these subgroups had larger uncertainty. Conclusions: Associations between short-term O3 exposures and asthma ED visits differed by overall county health and urbanicity, with stronger associations in Less Urbanized counties, and no positive association in Rural counties. Results also suggest that children are at increased risk of O3-related respiratory effects. Citation: Sacks JD, Rappold AG, Davis JA Jr, Richardson DB, Waller AE, Luben TJ. 2014. Influence of urbanicity and county characteristics on the association between ozone and asthma emergency department visits in North Carolina. Environ Health Perspect 122:506–512; http://dx.doi.org/10.1289/ehp.1306940

Evaluating Potential Response-Modifying Factors for Associations between Ozone and Health Outcomes: A Weight-of-Evidence Approach

Background: Epidemiologic and experimental studies have reported a variety of health effects in response to ozone (O3) exposure, and some have indicated that certain populations may be at increased or decreased risk of O3-related health effects. Objectives: We sought to identify potential response-modifying factors to determine whether specific groups of the population or life stages are at increased or decreased risk of O3-related health effects using a weight-of-evidence approach. Methods: Epidemiologic, experimental, and exposure science studies of potential factors that may modify the relationship between O3 and health effects were identified in U.S. Environmental Protection Agency’s 2013 Integrated Science Assessment for Ozone and Related Photochemical Oxidants. Scientific evidence from studies that examined factors that may influence risk were integrated across disciplines to evaluate consistency, coherence, and biological plausibility of effects. The factors identified were then classified using a weight-of-evidence approach to conclude whether a specific factor modified the response of a population or life stage, resulting in an increased or decreased risk of O3-related health effects. Discussion: We found “adequate” evidence that populations with certain genotypes, preexisting asthma, or reduced intake of certain nutrients, as well as different life stages or outdoor workers, are at increased risk of O3-related health effects. In addition, we identified other factors (i.e., sex, socioeconomic status, and obesity) for which there was “suggestive” evidence that they may increase the risk of O3-related health effects. Conclusions: Using a weight-of-evidence approach, we identified a diverse group of factors that should be considered when characterizing the overall risk of health effects associated with exposures to ambient O3. Citation: Vinikoor-Imler LC, Owens EO, Nichols JL, Ross M, Brown JS, Sacks JD. 2014. Evaluating potential response-modifying factors for associations between ozone and health outcomes: a weight-of-evidence approach. Environ Health Perspect 122:1166–1176; http://dx.doi.org/10.1289/ehp.1307541

Examining the effects of air pollution composition on within region differences in PM 2.5 mortality risk estimates

Multi-city population-based epidemiological studies have observed significant heterogeneity in both the magnitude and direction of city-specific risk estimates, but tended to focus on regional differences in PM2.5 mortality risk estimates. Interpreting differences in risk estimates is complicated by city-to-city heterogeneity observed within regions due to city-to-city variations in the PM2.5 composition and the concentration of gaseous pollutants. We evaluate whether variations in PM2.5 composition and gaseous pollutant concentrations have a role in explaining the heterogeneity in PM2.5 mortality risk estimates observed in 27 US cities from 1997 to 2002. Within each region, we select the two cities with the largest and smallest mortality risk estimate. We compare for each region the within- and between-city concentrations and correlations of PM2.5 constituents and gaseous pollutants. We also attempt to identify source factors through principal component analysis (PCA) for each city. The results of this analysis indicate that identifying a PM constituent(s) that explains the differences in the PM2.5 mortality risk estimates is not straightforward. The difference in risk estimates between cities in the same region may be attributed to a group of pollutants, possibly those related to local sources such as traffic.

Contribution of Particle-Size-Fractionated Airborne Lead to Blood Lead during the National Health and Nutrition Examination Survey, 1999–2008

The objective of this work is to examine associations between blood lead (PbB) and air lead (PbA) in particulate matter measured at different size cuts by use of PbB concentrations from the National Health and Nutrition Examination Survey and PbA concentrations from the U.S. Environmental Protection Agency for 1999-2008. Three size fractions of particle-bound PbA (TSP, PM10, and PM2.5) data with different averaging times (current and past 90-day average) were utilized. A multilevel linear mixed effect model was used to characterize the PbB-PbA relationship. At 0.15 μg/m(3), a unit decrease in PbA in PM10 was significantly associated with a decrease in PbB of 0.3-2.2 μg/dL across age groups and averaging times. For PbA in PM2.5 and TSP, slopes were generally positive but not significant. PbB levels were more sensitive to the change in PbA concentrations for children (1-5 and 6-11 years) and older adults (≥ 60 years) than teenagers (12-19 years) and adults (20-59 years). For the years following the phase-out of Pb in gasoline and a resulting upward shift in the PbA particle size distribution, PbA in PM10 was a statistically significant predictor of PbB. The results also suggest that age could affect the PbB-PbA association, with children having higher sensitivity than adults.

Regional variations in particulate matter composition and the ability of monitoring data to represent population exposures.

Epidemiologic studies have demonstrated that relative risks for mortality associated with ambient particulate matter (PM) concentrations vary with location in the U.S. with larger associations in both magnitude and strength observed in the East compared to the West. Two factors potentially contributing to the regional heterogeneity in PM-mortality associations observed are regional variations in PM composition and the ability of a single PM concentration estimate to represent the community-average exposure for an entire study area, which may lead to regional differences in exposure error. Variations in PM composition and the proportion of the population living in proximity to ambient monitors, an indicator of potential exposure error, are examined for the 20 most populated and 10 mid-size study areas included in the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Clear differences in PM and in the proportion of the population living in proximity to ambient monitors are found for some of these cities. Differences in these exposure parameters may be interpreted more reasonably in terms of north-south differences compared to east-west differences, and may need to be considered when conducting future epidemiologic studies that aim to examine the factors that influence the regional variability in PM-mortality associations.

Systematic review of differential inorganic arsenic exposure in minority, low-income, and indigenous populations in the United States

Inorganic arsenic (iAs) is a human carcinogen and associated with cardiovascular, respiratory, and skin diseases. Natural and anthropogenic sources contribute to low concentrations of iAs in water, food, soil, and air. Differential exposure to environmental hazards in minority, indigenous, and low income populations is considered an environmental justice (EJ) concern, yet it is unclear if higher iAs exposure occurs in these populations. A systematic review was conducted to evaluate evidence for differential iAs exposure in the United States (US). The peer-reviewed literature was searched for studies that (1) estimated iAs exposure based on environmental concentrations of iAs in water, food, soil, or iAs biomarkers and (2) examined iAs exposure in minority, indigenous, and low income US populations. Five studies were identified that estimated exposures and provided demographic information about EJ populations. These studies reported arsenic concentrations in water, soil, or food to estimate exposure, with varied evidence of differential exposure. Additionally, six studies were identified that suggested potential arsenic exposure from environmental sources including soil, rice, private well-water, and fish, but did not report data stratified by demographic information. Evidence across these 11 studies was qualitatively integrated to draw conclusions about differential iAs exposure. The total body of evidence is limited by lack of individual exposure measures, lack of iAs concentration data, and insufficient comparative demographic data. Based upon these data gaps, there is inadequate evidence to conclude whether differential exposure to iAs is an EJ concern in the US.

Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures

PURPOSE Air pollution epidemiology traditionally focuses on the relationship between individual air pollutants and health outcomes (e.g., mortality). To account for potential copollutant confounding, individual pollutant associations are often estimated by adjusting or controlling for other pollutants in the mixture. Recently, the need to characterize the relationship between health outcomes and the larger multipollutant mixture has been emphasized in an attempt to better protect public health and inform more sustainable air quality management decisions. METHODS New and innovative statistical methods to examine multipollutant exposures were identified through a broad literature search, with a specific focus on those statistical approaches currently used in epidemiologic studies of short-term exposures to criteria air pollutants (i.e., particulate matter, carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone). RESULTS Five broad classes of statistical approaches were identified for examining associations between short-term multipollutant exposures and health outcomes, specifically additive main effects, effect measure modification, unsupervised dimension reduction, supervised dimension reduction, and nonparametric methods. These approaches are characterized including advantages and limitations in different epidemiologic scenarios. DISCUSSION By highlighting the characteristics of various studies in which multipollutant statistical methods have been used, this review provides epidemiologists and biostatisticians with a resource to aid in the selection of the most optimal statistical method to use when examining multipollutant exposures.

Clustering cities with similar fine particulate matter exposure characteristics based on residential infiltration and in-vehicle commuting factors.

Epidemiological studies have observed between city heterogeneity in PM2.5-mortality risk estimates. These differences could potentially be due to the use of central-site monitors as a surrogate for exposure which do not account for an individuals activities or ambient pollutant infiltration to the indoor environment. Therefore, relying solely on central-site monitoring data introduces exposure error in the epidemiological analysis. The amount of exposure error produced by using the central-site monitoring data may differ by city. The objective of this analysis was to cluster cities with similar exposure distributions based on residential infiltration and in-vehicle commuting characteristics. Factors related to residential infiltration and commuting were developed from the American Housing Survey (AHS) from 2001 to 2005 for 94 Core-Based Statistical Areas (CBSAs). We conducted two separate cluster analyses using a k-means clustering algorithm to cluster CBSAs based on these factors. The first only included residential infiltration factors (i.e. percent of homes with central air conditioning (AC) mean year home was built, and mean home size) while the second incorporated both infiltration and commuting (i.e. mean in-vehicle commuting time and mean in-vehicle commuting distance) factors. Clustering on residential infiltration factors resulted in 5 clusters, with two having distinct exposure distributions. Cluster 1 consisted of cities with older, smaller homes with less central AC while homes in Cluster 2 cities were newer, larger, and more likely to have central AC. Including commuting factors resulted in 10 clusters. Clusters with shorter in-vehicle commuting times had shorter in-vehicle commuting distances. Cities with newer homes also tended to have longer commuting times and distances. This is the first study to employ cluster analysis to group cities based on exposure factors. Identifying cities with similar exposure distributions may help explain city-to-city heterogeneity in PM2.5 mortality risk estimates.