Dataset Information

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan.

ABSTRACT: The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O?), sulfur dioxide (SO?), and nitrogen dioxide (NO?) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O? exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO? exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO? exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

SUBMITTER: Martenies SE

PROVIDER: S-EPMC5664744 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan.

Martenies Sheena E SE Milando Chad W CW Williams Guy O GO Batterman Stuart A SA

International journal of environmental research and public health 20171019 10

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various g ...[more]

PMID: 29048385

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Project description:Studies in a number of countries have reported associations between exposure to ambient air pollutants and adverse birth outcomes, including low birth weight, preterm birth (PTB) and, less commonly, small for gestational age (SGA). Despite their growing number, the available studies have significant limitations, e.g., incomplete control of temporal trends in exposure, modest sample sizes, and a lack of information regarding individual risk factors such as smoking. No study has yet examined large numbers of susceptible individuals. We investigated the association between ambient air pollutant concentrations and term SGA and PTB outcomes among 164,905 singleton births in Detroit, Michigan occurring between 1990 and 2001. SO(2), CO, NO(2), O(3) and PM(10) exposures were used in single and multiple pollutant logistic regression models to estimate odds ratios (OR) for these outcomes, adjusted for the infant's sex and gestational age, the mother's race, age group, education level, smoking status and prenatal care, birth season, site of residence, and long-term exposure trends. Term SGA was associated with CO levels exceeding 0.75ppm (OR=1.14, 95% confidence interval=1.02-1.27) and NO(2) exceeding 6.8ppb (1.11, 1.03-1.21) exposures in the first month, and with PM(10) exceeding 35?g/m(3) (1.22, 1.03-1.46) and O(3) (1.11, 1.02-1.20) exposure in the third trimester. PTB was associated with SO(2) (1.07, 1.01-1.14) exposure in the last month, and with (hourly) O(3) exceeding 92ppb (1.08, 1.02-1.14) exposure in the first month. Exposure to several air pollutants at modest concentrations was associated with adverse birth outcomes. This study, which included a large Black population, suggests the importance of the early period of pregnancy for associations between term SGA with CO and NO(2), and between O(3) with PTB; and the late pregnancy period for associations between term SGA and O(3) and PM(10), and between SO(2) with PTB. It also highlights the importance of accounting for individual risk factors such as maternal smoking, maternal race, and long-term trends in air pollutant levels and adverse birth outcomes in evaluating relationships between pollutant exposures and adverse birth outcomes.

Project description:Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into "low", "medium" or "high" traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments.

Project description:Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification.

Project description:UNLABELLED:Geostatistical interpolation methods to estimate individual exposure to outdoor air pollutants can be used in pregnancy cohorts where personal exposure data are not collected. Our objectives were to a) develop four assessment methods (citywide average (CWA); nearest monitor (NM); inverse distance weighting (IDW); and ordinary Kriging (OK)), and b) compare daily metrics and cross-validations of interpolation models. We obtained 2008 hourly data from Mexico City's outdoor air monitoring network for PM10, PM2.5, O3, CO, NO2, and SO2 and constructed daily exposure metrics for 1,000 simulated individual locations across five populated geographic zones. Descriptive statistics from all methods were calculated for dry and wet seasons, and by zone. We also evaluated IDW and OK methods' ability to predict measured concentrations at monitors using cross validation and a coefficient of variation (COV). All methods were performed using SAS 9.3, except ordinary Kriging which was modeled using R's gstat package. Overall, mean concentrations and standard deviations were similar among the different methods for each pollutant. Correlations between methods were generally high (r=0.77 to 0.99). However, ranges of estimated concentrations determined by NM, IDW, and OK were wider than the ranges for CWA. Root mean square errors for OK were consistently equal to or lower than for the IDW method. OK standard errors varied considerably between pollutants and the computed COVs ranged from 0.46 (least error) for SO2 and PM10 to 3.91 (most error) for PM2.5. OK predicted concentrations measured at the monitors better than IDW and NM. Given the similarity in results for the exposure methods, OK is preferred because this method alone provides predicted standard errors which can be incorporated in statistical models. The daily estimated exposures calculated using these different exposure methods provide flexibility to evaluate multiple windows of exposure during pregnancy, not just trimester or pregnancy-long exposures. IMPLICATIONS:Many studies evaluating associations between outdoor air pollution and adverse pregnancy outcomes rely on outdoor air pollution monitoring data linked to information gathered from large birth registries, and often lack residence location information needed to estimate individual exposure. This study simulated 1,000 residential locations to evaluate four air pollution exposure assessment methods, and describes possible exposure misclassification from using spatial averaging versus geostatistical interpolation models. An implication of this work is that policies to reduce air pollution and exposure among pregnant women based on epidemiologic literature should take into account possible error in estimates of effect when spatial averages alone are evaluated.

Dataset Information

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan.

Publications

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan.

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