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Article

Independent and Joint Contributions of Fine Particulate Matter Exposure and Population Vulnerability to Mortality in the Detroit Metropolitan Area

by
Amy J. Schulz
1,*,
Graciela B. Mentz
1,
Natalie Sampson
2,
Melanie Ward
1,
J. Timothy Dvonch
3,
Ricardo De Majo
1,
Barbara A. Israel
1,
Angela G. Reyes
4 and
Donele Wilkins
5
1
Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
2
Department of Health and Human Services, University of Michigan-Dearborn, Dearborn, MI 48128, USA
3
Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
4
Detroit Hispanic Development Corporation, Detroit, MI 48216, USA
5
Green Door Initiative, Detroit, MI 48213, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(6), 1209; https://doi.org/10.3390/ijerph15061209
Submission received: 19 April 2018 / Revised: 25 May 2018 / Accepted: 6 June 2018 / Published: 8 June 2018
(This article belongs to the Special Issue Achieving Environmental Health Equity: Great Expectations)
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Abstract

:
Fine particulate matter is associated with adverse health outcomes. Exposure to fine particulate matter may disproportionately affect urban communities with larger numbers of vulnerable residents. We used multilevel logistic regression models to estimate the joint effects of fine particulate matter (PM2.5) and population vulnerabilities on cardiopulmonary mortality (CPM). We estimated the health benefits of reductions in PM2.5 across census tracts in the Detroit metropolitan area with varying levels of population vulnerability, using cluster-specific odds ratios scaled to reflect PM2.5-attributable cardiopulmonary risk. PM2.5 and population vulnerability were independently associated with odds of CPM. Odds of CPM and the number of deaths attributable to PM2.5 were greatest in census tracts with both high PM2.5 exposures and population vulnerability. Reducing PM2.5 in census tracts with high PM2.5 would lead to an estimated 18% annual reduction in PM2.5-attributable CPM. Between 78–79% of those reductions in CPM would occur within census tracts with high population vulnerabilities. These health benefits of reductions in PM2.5 occurred at levels below current U.S. reference concentrations. Focusing efforts to reduce PM2.5 in the Detroit metropolitan area in census tracts with currently high levels would also lead to greater benefits for residents of census tracts with high population vulnerabilities.

1. Introduction

Exposure to ambient air pollution is associated with increased risk of multiple adverse health outcomes, including cardiovascular mortality [1,2,3], respiratory hospitalization [4], asthma exacerbation [5,6,7], incidence and duration of respiratory symptoms [8,9], declines in lung function [10,11,12], preterm delivery and low birth weights [13,14,15], and restricted activity [16,17]. Evidence suggests that fine particulate matter (PM2.5) is more strongly associated with morbidity and mortality than coarse particulate matter (PM10) [18], with adverse health effects observed at pollution levels below current U.S. National Ambient Air Quality Standards (NAAQS) and common to many U.S. cities [19,20].
There is substantial evidence that non-Hispanic Black (NHB) and Hispanic communities, and those with low to moderate incomes, are disproportionately exposed to air pollutants [21,22,23,24]. NHBs and Hispanics are also more likely to reside in urban communities with reduced access to quality education and employment opportunities [25,26]. A sizeable literature documents increased vulnerability of residents of low to moderate income urban communities and communities with greater race-based residential segregation to air pollution and associated health risks [21,27,28,29]. Low to moderate economic status contributes to challenges avoiding exposure and may exacerbate adverse health effects of exposures [30] through mechanisms including, for example, poorer quality housing [31,32] or reduced access to health care [33]. Children and older adults are more susceptible to adverse health effects of exposure [34,35,36,37,38]. Yet, relatively few studies have examined combined effects of exposure to air pollution in conjunction with population vulnerabilities, or quantified the health impacts associated with reductions in air pollution in communities as these may vary with characteristics of exposed populations. Using data from the Detroit metropolitan area (DMA), we examine the independent and joint contributions of air pollution exposure and population vulnerabilities to cardiopulmonary mortality and estimate the number of deaths averted by focusing efforts on places with varying constellations of characteristics.
The Detroit metropolitan area (DMA), comprised of Macomb, Oakland, and Wayne Counties, is among the most racially segregated regions in the nation [39]. Historically, numerous factors contributed to this segregation, including restrictive agreements prominent prior to 1948 that prohibited racial and ethnic groups from living in parts of the city of Detroit [40]. Continued discrimination in the real estate market, including federally-backed home mortgages available to non-Hispanic Whites (NHWs) but not NHBs, perpetuated segregation and reduced NHBs’ access to suburban employment opportunities [40,41]. Today’s DMA reflects these historical and contemporary processes (see Figure 1), with NHB populations in Wayne, Oakland, and Macomb counties clustered in urban communities [42]. Eighty-five percent of Wayne County’s NHB population is located in Detroit city [42], also home to an Hispanic community whose roots extend back nearly a century [43]. NHBs and Hispanics across the DMA are disproportionately represented in households below the federal poverty line [44]. Over 57% of Detroit’s children [45,46], compared to 37%, 20%, and 13% of children in Wayne, Macomb, and Oakland counties, respectively, live in households below the poverty line [42].
Segregation contributes to health inequities through economic divestment, limited educational and employment opportunities, as well as foreclosure risk in racially segregated urban communities [34,40,47,48,49].
Segregation may also contribute to unequal exposure to air pollution. For example, in the DMA, census tracts with greater concentrations of NHB and Hispanic residents, households below the poverty line, and residents who have not completed high school are more likely to have higher levels of exposure to diesel PM (a component of PM2.5) and associated cancer and respiratory health risks [22]. This study extends previous research by examining the independent and joint effects attributable to fine particulate matter (PM2.5) and population vulnerabilities on three health outcomes previously found to be associated with air pollution: mortality attributable to ischemic heart disease [1,2], cardiovascular disease [1,50], and cardiopulmonary disease [3]. In addition, it quantifies the health impacts of reductions in PM2.5 in communities with high and low population vulnerability scores.

2. Materials and Methods

The study was conducted as part of Community Action to Promote Healthy Environments, a community-based participatory research partnership designed to equitably engage community, academic, and health service provider organizations in research, and aid the translation of results from that research into action to promote health equity (see Acknowledgments). The University of Michigan granted institutional review board approval for this study on 24 January 2013.

2.1. Data

Data for the analyses described below were drawn from multiple sources, informed by methods developed by a California-based team [30,51]. Information on fine particulate matter was drawn from the EPA’s National-Scale Fused Air Quality Surfaces Using Downscaling Tool and the Community Multiscale Air Quality (CMAQ) model, for the calendar year 2013. The downscaler model combines air quality monitoring and modeling data to provide better fine-scale predictions of air pollutant levels at local and community scales, and is downloadable from the EPA website [52,53,54]. Demographic data were drawn from the 2009–2013 American Community Survey [55]. Mortality data were provided by the Michigan Department of Health and Human Services (MDHHS) and include records of all deaths that occurred in the Detroit metropolitan area (Wayne, Oakland, and Macomb Counties) in 2013.

2.2. Measures

Dependent variables include ischemic heart disease mortality, cardiovascular mortality, and cardiopulmonary mortality, coded using the following ICD-10 codes: Ischemic heart disease, I20–I25 [56]; cardiovascular, I10–I70 [56]; and cardiopulmonary, I10–I70 and J00–J99 [56]. Mortality was coded as a dichotomous measure (i.e., ‘1’ if cause of death was ischemic heart disease, cardiovascular disease, or cardiopulmonary disease, respectively), and geocoded to individuals’ residential addresses.
Census tract level-independent variables include census tract PM2.5 concentrations and social, economic, and age-related vulnerabilities [30,51]. A base map was constructed that indicates residential areas and land uses where sensitive populations may be present (e.g., child care facilities, health care facilities, schools). Strictly industrial or commercial areas are not included. Areas (polygons) are roughly the size of a city block. The base map geographically links residential and sensitive land use polygons with census tract-level metrics in each of the categories of exposure and population vulnerability described below, to create a risk score for each polygon. As described below, the risk score is used to rank neighborhoods according to their level of risk (e.g., PM2.5 concentration, population vulnerability) across categories. Polygons are aggregated to the census tract level for this analysis.
The PM2.5 concentration measure was calculated at the census tract level, using annual averages. Measures were weighted by the proportion of the census tract that was residential or sensitive land use. Scores were calculated based on quintile distribution rankings, ranging from 1 (low) to 5 (high), and applied to each tract in the study area.
The vulnerability index includes the following indicators associated with increased vulnerability to environmental exposures, calculated at the census tract level [27,29,30,51,57]: percent of households below poverty; median home value (reverse coded); percent of homes occupied by renters; percent of population aged >24 years with a less than high-school-level of education (<high school); linguistic isolation (percent of residents who live in households with no adults who speak English); percent people of color; percent of population aged <5 years; and percent population aged ≥60 years. Census tracts were rank-ordered based on their scores for each of the above items and scored as quintiles from 1 (low) to 5 (high). The scores for each item were then summed, using equal weights, to create a cumulative vulnerability index reflecting population vulnerability across all included dimensions at the census tract level. Census tracts were once again rank-ordered based on these cumulative scores and divided into quintiles to create the vulnerability index score, ranging from 1 (low) to 5 (high).
Individual level control variables include age (continuous); gender (male = referent); race/ethnicity (NHB, Hispanic, NHW (referent)); education (<high school, high school graduation, >high school (referent)); marital status (not currently married (referent)); and whether the cause of death was coded on the death certificate as related to tobacco use (yes, likely, no (referent)).

2.3. Analysis

We used multilevel, multivariate, longitudinal analyses with indicators of mortality for 2013. The use of hierarchical linear models accounts for the longitudinal nature of the data (e.g., mortality over a one-year period), accommodating changes over time in mortality patterns and providing unbiased estimates of exposure effects. Separate models were run using ischemic heart disease, cardiovascular, and cardiopulmonary disease mortality as dependent variables. Predictor variables were grand mean centered, the logit link was used, and an exchangeable correlation matrix was used to estimate standard errors.
Random intercept only multilevel logistic models were used to assess the independent and joint associations between PM2.5 exposure and the vulnerability index at the census tract level with each indicator of mortality at the individual level. Models examine dynamic exposure–response relationships between exposures and each dependent variable (e.g., cardiopulmonary mortality). For each dependent variable, models examined effects of PM2.5 exposure and vulnerabilities, each alone, and the joint effects of the two components when included together in a model, controlling for age, gender, race, and ethnicity. We also ran models with PM2.5 concentration and each individual indicator included in the vulnerability index (e.g., percent of households below the poverty line). In testing joint effects, we considered multiplicative models assessing interactions between PM2.5 and vulnerability. Interaction terms were not statistically significant (p < 0.05), and thus the final models reported in this paper do not include interaction terms. To assess the sensitivity of models to varying specifications of the variables, we ran models using the rank-ordered quintile versions of each variable as described above and using continuous versions of each variable. Patterns were similar: findings are reported using the rank-ordered quintile versions, with statistical significance reported as p < 0.05, p < 0.01, and p < 0.001.
Finally, we estimated the health impact of reductions in PM2.5 in the DMA on mortality rates attributable to this exposure. To do this, we converted the original five-point scale for each index to a dichotomous variable: census tracts falling in quintiles 1–2 were collapsed and coded as “low” and those in quintiles 3–5 were collapsed and coded as “high” census tracts. We calculated odds ratios for each level (high, low) using standard statistical methods [58,59], then calculated odds ratios for each of four resulting clusters—combinations of PM2.5 and population vulnerability (Cluster 1 = low/low; Cluster 2 = low/high; Cluster 3 = high/low; and Cluster 4 = high/high)—by multiplying odds ratios for their respective components. Odds ratios were then scaled to reflect PM2.5-attributable cardiopulmonary mortality risk. Based on estimates previously reported in the literature and recognizing geospatial variations in attributable risk, we estimated reductions in cardiopulmonary mortality using 3%, 5%, 10%, and 15% PM2.5-attributable risk [16,58,59,60,61]. Resulting odds ratios were multiplied by the total population in respective clusters to calculate the number of deaths due to ischemic heart disease, cardiovascular disease, and cardiopulmonary disease that would be averted under each of the above scenarios. All analyses were performed using HLM 7.03 for Windows (HLM 7.03 Scientific Software International (SSI), Stokie, IL, USA).

3. Results

Table 1 shows descriptive statistics for each of the indicators included in the CI. Mean PM2.5 for census tracts in the DMA is 9.6 µg/m3 (SD = 0.3). For indicators of population vulnerability, on average at the census tracts level, about two-fifths of the population were of color, one in ten households had incomes below the poverty line, and about one-third of households were occupied by renters. Also, at the census tract level, on average, one in eight residents had less than a high school education level and about one in twenty residents were linguistically isolated. The average census tract had about one in 15 residents under the age of five and about one in five aged 60 or older. At the census tract level, on average, about one-fifth of deaths were attributable to ischemic heart disease, about one-third to cardiovascular disease, and just over four in 10 were attributable to cardiopulmonary causes.
Table 2 presents results from analyses examining the first research question: what are the associations between PM2.5 and population vulnerability and each of the three mortality indicators? Odds ratios were significantly greater for those residing in census tracts with the higher exposure quintiles. Specifically, as shown in Model 1, likelihood of death due to ischemic heart disease was significantly greater in census tracts ranked in the third (p = 0.01), fourth (p = 0.004), and fifth (p < 0.001) quintiles compared to the first. Likelihood of death due to cardiovascular disease was significantly greater for those in census tracts in the fifth quintile (p = 0.01), and deaths attributed to cardiopulmonary disease were more likely in the third (p = 0.07), fourth (p < 0.001), and fifth (p < 0.001) quintiles compared to those in the lowest exposure quintile.
Similarly, as shown in Table 2, in Model 2, odds ratios of ischemic heart disease were significantly higher for those living in census tracts in the third (p = 0.05), fourth (p = 0.01), and fifth (p < 0.001) quintiles of population vulnerability, compared to those in the lowest vulnerability census tracts. For cardiovascular disease, likelihood of mortality was significantly higher for those in the second (p = 0.04), third (p < 0.001), fourth (p < 0.001), and fifth (p = 0.01) quintiles of vulnerability; and likelihood of mortality due to cardiopulmonary disease was significantly higher in the third (p < 0.001), fourth (p < 0.001), and fifth (p < 0.001) quintiles of population vulnerability.
Because census tracts with greater population vulnerability scores have been previously demonstrated to have higher air pollutant exposures [22], our third research question assessed joint contributions of PM2.5 exposure and population vulnerabilities. Results are shown in Table 3. After accounting for population vulnerabilities, effects of PM2.5 on mortality remained significantly higher for ischemic heart disease in the third (p = 0.02), fourth (p = 0.03), and fifth (p < 0.001) quintiles of PM2.5 exposure, and for cardiopulmonary disease in census tracts, in the fourth (p = 0.03) and fifth (p = 0.01) quintiles of PM2.5 exposure.
Similarly, after including PM2.5 in the models, odds ratios for cardiovascular disease remained significantly higher in the second (p = 0.04), third (p = 0.001), and fourth (p = 0.001) and approached significance in the fifth (p = 0.07) quintiles of vulnerability, and for cardiopulmonary disease, in the third (p < 0.001), fourth (p < 0.001), and fifth (p < 0.001) quintiles of vulnerability, compared with census tracts with the lowest vulnerability scores. Models using continuous versions of PM2.5 and the population vulnerability index yielded similar results (see Supplementary Materials Table S1). Models testing for potential modifications of effects of PM2.5 by population vulnerabilities found no significant interactions for any of our three dependent variables (results not shown).
Table 4 shows the population of the DMA living in census tracts within each of four clusters: low population vulnerability/low PM2.5; low population vulnerability/high PM2.5; high population vulnerability/low PM2.5; and high population vulnerability/high PM2.5 concentration. An estimated 1,659,342 (39%) residents of the Detroit metropolitan area live in census tracts with both high PM2.5 concentrations and high vulnerability, with 50% (2400 of 4800) of estimated total cardiopulmonary mortality occurring in census tracts with those characteristics (Table 4). We calculated PM2.5-attributable scaled odds ratios for each of the four clusters. Results from tests of contrasts indicate that residents of census tracts with high vulnerability and high PM2.5 concentrations experience significantly greater probability of cardiopulmonary mortality compared with residents of the other three clusters (p < 0.001) (full calculations are shown in Supplementary Materials Table S2).
Also shown in Table 4, reductions in PM2.5 in census tracts with high PM2.5 concentrations to levels currently experienced in the low concentration census tracts would avert an estimated 15 to 75 PM2.5-attributable cardiopulmonary deaths each year in census tracts with high population vulnerability. An additional 4–20 PM2.5-attributable cardiopulmonary deaths would be averted by reducing PM2.5 in census tracts with high PM2.5 concentration and low vulnerability. This smaller number is due both to the lower probability of cardiopulmonary mortality and the lower population in census tracts with these characteristics. Overall, these estimates suggest that reducing PM2.5 to the levels encountered in the low exposure census tracts would avert between 19 and 95 cardiopulmonary deaths each year, approximately 13.5% of total cardiopulmonary mortality attributable to PM2.5 in the DMA (between 144 and 721, as shown in Table 4). (See Supplementary Materials Table S2 for greater detail on this analysis.)
Census tracts with high PM2.5 concentration and population vulnerability are concentrated within and adjacent to Detroit city, Dearborn, River Rouge, Southfield, Inkster, Taylor, and Trenton. Based on estimates presented in Table 4, approximately 79% of the averted PM2.5-attributable cardiopulmonary mortality would occur in census tracts with high levels of population vulnerability, and with 40% of the population (See Figure 2).

4. Discussion

Findings reported here indicate that, when included in models controlling for individual demographic characteristics, census tract level indicators of exposure to PM2.5 air pollution and indicators of population vulnerability are associated with increased likelihood of death attributable to ischemic, cardiovascular, and cardiopulmonary causes in the DMA. Consistent with findings previously reported in the literature [62,63,64], associations of PM2.5 and mortality remained significant after accounting for population vulnerability (e.g., census tract socioeconomic status, racial composition). While not statistically significant, our finding suggesting that adverse effects of PM2.5 on mortality may be heightened in neighborhoods with higher levels of population vulnerability is consistent with results reported elsewhere [63,65,66].
Estimated reductions in mortality that would be realized with reductions in PM2.5 suggest that 79% of cardiopulmonary deaths averted would occur in census tracts with high levels of population vulnerability. These results add to a limited literature examining differential health impacts of reductions in air pollution by neighborhood indicators of population vulnerability. In 2014, Kheirbek and colleagues estimated health benefits associated with reductions in PM2.5 associated with cooking fuels in New York City, and reported the greatest benefits in high poverty neighborhoods [67]. Similarly, in 2011, Fann and colleagues [66] reported that air pollution reduction strategies that focus in areas with more susceptible/vulnerable populations yield greater reductions in mortality and in risk inequality compared with more traditional approaches used in regulatory frameworks.
These results have implications for efforts to reduce health inequities linked to unequal environmental exposures. Detroit’s industrial history, combined with historical and contemporary patterns of segregation and economic divestment from predominantly NHB and Hispanic urban communities, have contributed to both disproportionate exposure and vulnerability for NHB and Hispanic urban communities in the DMA [22]. Census tracts with both high PM2.5 exposure and high population vulnerability tend to be clustered in and adjacent to major urban communities, including areas with major industrial sources located in the southern, southwestern, and central areas of the Detroit metropolitan area. Effects are visible at levels of PM2.5 that are below the current National Ambient Air Quality Standards (NAAQS) level of 12 µg/m3 [68] (see Table 1), and at levels common in many urban communities in the U.S. Focusing efforts to reduce PM2.5 pollution in the high exposure census tracts would not only reduce population mortality, but contribute to reductions in racial, ethnic, and socioeconomic health inequities. Such efforts could include, for example, reductions in mobile sources of PM2.5 (e.g., reducing diesel traffic through urban residential neighborhoods); reductions in industrial pollutants (e.g., conversion to clean sources of power such as wind and solar); increases in green infrastructure, such as spatial or vegetative buffers between, for example, residential neighborhoods and heavily trafficked freeways [69,70]; or permitting for air pollution emissions that accounts for cumulative risks [70].
These analyses join similar studies [67] that illustrate the potential for using health impact assessments to inform policy and planning decisions. Nearly 40% of DMA residents live in census tracts with high vulnerability and high PM2.5 exposure. Residents of these census tracts experience higher probability of cardiopulmonary mortality compared with residents of census tracts with all other combinations of exposure and vulnerability. Assessments of differential health impacts of reductions in PM2.5 in census tracts with differential population vulnerabilities can help inform regulatory and land use decisions by incorporating analysis of implications for population health and health equity. These findings join a substantial literature indicating that particulate matter is associated with mortality (and morbidity), and that exposure to it disproportionately impacts the health of vulnerable populations [71,72]. The finding of the significant impacts of PM2.5 exposure on mortality despite a fairly narrow distribution of average PM2.5 exposure suggests the importance of continued attention to the composition as well as mass levels of PM2.5 [73].

4.1. Limitations

There are several limitations associated with this study. The models reported here examine exposure and mortality over a single year. They are neither able to examine the impacts of long-term exposures over the life course nor to disentangle the effects of exposure at particularly vulnerable periods (e.g., prenatal development, early life, puberty). The focus on mortality omits substantial contributions of both PM2.5 and social and economic vulnerabilities to morbidity throughout the life course (e.g., asthma exacerbations, missed work days). Finally, the relatively brief period of time covered does not allow us to disentangle, for example, the extent to which individuals with greater health challenges may move to areas with higher exposure risks and lower socioeconomic indicators due to, for example, loss of income through inability to work.

4.2. Strengths

These analyses also have a number of strengths. The statistical models used enable us to separate the effects of neighborhood or environmental exposures from individual characteristics, and to assess the independent contributions of PM2.5 exposure and population vulnerabilities at the census tract level. The use of multilevel models combined with the spatial scale of the PM2.5-level data (census tract level) allows us to examine variations at a relatively fine spatial scale. We are also able to estimate the number of deaths that could be averted by focusing efforts to reduce PM2.5 in areas with high versus low population vulnerabilities, providing valuable information to aid in decision-making processes. The use of analytical models that examine independent and joint effects of social, economic, and environmental exposures on health contributes to the ability to examine the implications of environmental rulemaking, policy, and planning decisions, and to inform those decisions by quantifying their health implications.

5. Conclusions

We find significant effects of environmental exposures and population vulnerabilities, assessed at the census tract level, on the likelihood of mortality due to ischemic heart, cardiovascular, and cardiopulmonary disease in the Detroit metropolitan area. Disentangling the effects of vulnerability and exposure allowed us to determine that this excess mortality disproportionately affects residents of communities with high vulnerability scores, with the greatest benefits realized within those same communities with reductions in PM exposure.
These findings extend a critical body of evidence documenting not only excess environmental exposures, but also more substantial health risks associated with those exposures in vulnerable communities, including communities of color and those with lower socioeconomic status. Combined with previously reported research demonstrating that communities with higher vulnerability experience heightened exposure to PM2.5, these results support the critical importance of a national research agenda focused on examining cumulative exposure and associated health risks [38,74,75], and more importantly, on the application of findings to focus efforts to reduce exposures in areas with the most vulnerable populations.
Recognizing the limitations and uncertainties associated with the current science of cumulative risk assessment, continued efforts to develop the science and apply it to analyses of risk are critical for identifying communities that experience disproportionate risk. Paying particular attention to reducing environmental exposures in communities already overburdened with multiple risks, combined with efforts to reduce the social and economic vulnerabilities experienced within those communities, stand to make important contributions to reductions in health inequities.

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/15/6/1209/s1. Table S1: Ischemic Heart Disease, Cardiovascular and Cardiopulmonary Mortality Regressed on PM2.5 and Population Vulnerability As Continuous Variables, Table S2: Number of cardiopulmonary deaths averted annually by reducing PM2.5 to low in all census tracts, by high and low vulnerability scores* under scenarios with 3–15% attributable risk.

Author Contributions

A.J.S., G.B.M., N.S., A.G.R. and D.W. conceived and designed the study; G.B.M. analyzed the data; M.W. contributed to the literature review and organization of data and wrote portions of the manuscript; B.A.I. contributed to the writing of the manuscript; J.T.D. provided expertise in analysis of air quality data; R.d.M. contributed data analysis, geocoding and mapping.

Acknowledgments

The study was conducted as part of Community Approaches to Promoting Healthy Environments (CAPHE), a community-based participatory research partnership that includes community-based organizations, health practice agencies, environmental organizations, and academic researchers (www.caphedetroit.sph.umich.edu). We thank the members of the CAPHE Steering Committee for their contributions to the work presented here, including representatives from: Community Action Against Asthma (CAAA), the Detroit Healthy Environments Partnership (www.hepdetroit.org), the Detroit Community-Academic Urban Research Center (www.detroiturc.org), Detroit Hispanic Development Corporation, Detroit Future Cities, Detroiters Working for Environmental Justice, Green Door Initiative, Michigan Department of Environmental Quality, Sierra Club Detroit, Southwest Detroit Environmental Vision, University of Michigan School of Public Health, and the Wayne State University Transnational Environmental Law Clinic. The study and analysis were supported by the National Institute of Environmental Health Sciences (NIEHS) (R01ES022616 and P30ES017885) and the Fred A. and Barbara M. Erb Family Foundation (http://www.erbff.org/). The results presented here are solely the responsibility of the authors and do not necessarily represent the views of NIEHS or the Erb Family Foundation, their directors, officers, or staff. We thank our funders for support for this manuscript, and also thank Katie Carew for critical support in its preparation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brook, R.D.; Rajagopalan, S.; Pope, C.A.; Brook, J.R.; Bhatnagar, A.; Diez-Roux, A.V.; Holguin, F.; Hong, Y.; Luepker, R.V.; Mittleman, M.A.; et al. On behalf of the American Heart Association Council on Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement From the American Heart Association. Circulation 2010, 121, 2331–2378. [Google Scholar] [CrossRef] [PubMed]
  2. Jerrett, M.; Burnett, R.T.; Beckerman, B.S.; Turner, M.C.; Krewski, D.; Thurston, G.; Martin, R.V.; van Donkelaar, A.; Hughes, E.; Shi, Y.; et al. Spatial Analysis of Air Pollution and Mortality in California. Am. J. Respir. Crit. Care Med. 2013, 188, 593–599. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Ostro, B.; Hu, J.; Goldberg, D.; Reynolds, P.; Hertz, A.; Bernstein, L.; Kleeman, M.J. Associations of mortality with long-term exposures to fine and ultrafine particles, species and sources: Results from the California Teachers Study Cohort. Environ. Health Perspect. 2015, 123, 549–556. [Google Scholar] [CrossRef] [PubMed]
  4. Neupane, B.; Jerrett, M.; Burnett, R.T.; Marrie, T.; Arain, A.; Loeb, M. Long-term exposure to ambient air pollution and risk of hospitalization with community-acquired pneumonia in older adults. Am. J. Respir. Crit. Care Med. 2010, 181, 47–53. [Google Scholar] [CrossRef] [PubMed]
  5. Fan, J.; Li, S.; Fan, C.; Bai, Z.; Yang, K. The impact of PM2.5 on asthma emergency department visits: A systematic review and meta-analysis. Environ. Sci. Pollut. Res. Int. 2016, 23, 843–850. [Google Scholar] [CrossRef] [PubMed]
  6. Li, S.; Batterman, S.; Wasilevich, E.; Wahl, R.; Wirth, J.; Su, F.-C.; Mukherjee, B. Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: Time-series and time-stratified case-crossover analyses with threshold effects. Environ. Res. 2011, 111, 1137–1147. [Google Scholar] [CrossRef] [PubMed]
  7. Strickland, M.J.; Darrow, L.A.; Klein, M.; Flanders, W.D.; Sarnat, J.A.; Waller, L.A.; Sarnat, S.E.; Mulholland, J.A.; Tolbert, P.E. Short-term associations between ambient air pollutants and pediatric asthma emergency department visits. Am. J. Respir. Crit. Care Med. 2010, 182, 307–316. [Google Scholar] [CrossRef] [PubMed]
  8. Johannson, K.A.; Vittinghoff, E.; Lee, K.; Balmes, J.R.; Ji, W.; Kaplan, G.G.; Kim, D.S.; Collard, H.R. Acute exacerbation of idiopathic pulmonary fibrosis associated with air pollution exposure. Eur. Respir. J. 2014, 43, 1124–1131. [Google Scholar] [CrossRef] [PubMed]
  9. Stern, G.; Latzin, P.; Röösli, M.; Fuchs, O.; Proietti, E.; Kuehni, C.; Frey, U. A prospective study of the impact of air pollution on respiratory symptoms and infections in infants. Am. J. Respir. Crit. Care Med. 2013, 187, 1341–1348. [Google Scholar] [CrossRef] [PubMed]
  10. Gehring, U.; Gruzieva, O.; Agius, R.M.; Beelen, R.; Custovic, A.; Cyrys, J.; Eeftens, M.; Flexeder, C.; Fuertes, E.; Heinrich, J.; et al. Air pollution exposure and lung function in children: The ESCAPE project. Environ. Health Perspect. 2013, 121, 1357–1364. [Google Scholar] [CrossRef] [PubMed]
  11. Rice, M.B.; Ljungman, P.L.; Wilker, E.H.; Gold, D.R.; Schwartz, J.D.; Koutrakis, P.; Washko, G.R.; O’Connor, G.T.; Mittleman, M.A. Short-term exposure to air pollution and lung function in the Framingham Heart Study. Am. J. Respir. Crit. Care Med. 2013, 188, 1351–1357. [Google Scholar] [CrossRef] [PubMed]
  12. Urman, R.; McConnell, R.; Islam, T.; Avol, E.L.; Lurmann, F.W.; Vora, H.; Linn, W.S.; Rappaport, E.B.; Gilliland, F.D.; Gauderman, W.J. Associations of children’s lung function with ambient air pollution: Joint effects of regional and near-roadway pollutants. Thorax 2014, 69, 540–547. [Google Scholar] [CrossRef] [PubMed]
  13. Pedersen, M.; Giorgis-Allemand, L.; Bernard, C.; Aguilera, I.; Andersen, A.-M.N.; Ballester, F.; Beelen, R.M.J.; Chatzi, L.; Cirach, M.; Danileviciute, A.; et al. Ambient air pollution and low birthweight: A European cohort study (ESCAPE). Lancet Respir. Med. 2013, 1, 695–704. [Google Scholar] [CrossRef]
  14. Shah, P.S.; Balkhair, T. Knowledge Synthesis Group on Determinants of Preterm/LBW births Air pollution and birth outcomes: A systematic review. Environ. Int. 2011, 37, 498–516. [Google Scholar] [CrossRef] [PubMed]
  15. Stieb, D.M.; Chen, L.; Eshoul, M.; Judek, S. Ambient air pollution, birth weight and preterm birth: A systematic review and meta-analysis. Environ. Res. 2012, 117, 100–111. [Google Scholar] [CrossRef] [PubMed]
  16. Fann, N.; Lamson, A.D.; Anenberg, S.C.; Wesson, K.; Risley, D.; Hubbell, B.J. Estimating the national public health burden associated with exposure to ambient PM2.5 and ozone. Risk Anal. 2012, 32, 81–95. [Google Scholar] [CrossRef] [PubMed]
  17. Nam, K.-M.; Selin, N.E.; Reilly, J.M.; Paltsev, S. Measuring welfare loss caused by air pollution in Europe: A CGE analysis. Energy Policy 2010, 38, 5059–5071. [Google Scholar] [CrossRef] [Green Version]
  18. U.S. Environmental Protection Agency (EPA). Health Assessment Document for Diesel Engine Exhaust; National Center for Environmental Assessment: Washington, DC, USA, 2002.
  19. Shi, L.; Zanobetti, A.; Kloog, I.; Coull, B.A.; Koutrakis, P.; Melly, S.J.; Schwartz, J.D. Low-Concentration PM2.5 and Mortality: Estimating Acute and Chronic Effects in a Population-Based Study. Environ. Health Perspect. 2016, 124, 46–52. [Google Scholar] [CrossRef] [PubMed]
  20. Lepeule, J.; Laden, F.; Dockery, D.; Schwartz, J. Chronic exposure to fine particles and mortality: An extended follow-up of the Harvard Six Cities study from 1974 to 2009. Environ. Health Perspect. 2012, 120, 965–970. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Mohai, P.; Lantz, P.M.; Morenoff, J.; House, J.S.; Mero, R.P. Racial and socioeconomic disparities in residential proximity to polluting industrial facilities: Evidence from the Americans’ Changing Lives Study. Am. J. Public Health 2009, 99 (Suppl. 3), S649–S656. [Google Scholar] [CrossRef] [PubMed]
  22. Schulz, A.J.; Mentz, G.; Sampson, N.; Ward, M.; Anderson, R.; de Majo, R.; Israel, B.A.; Lewis, T.C.; Wilkins, D. Race and the distribution of social and physical environmental risk: A case example from the Detroit metropolitan area. Du Bois Rev. Soc. Sci. Res. Race 2016, 13, 285–304. [Google Scholar] [CrossRef] [PubMed]
  23. Jones, M.R.; Diez-Roux, A.V.; Hajat, A.; Kershaw, K.N.; O’Neill, M.S.; Guallar, E.; Post, W.S.; Kaufman, J.D.; Navas-Acien, A. Race/ethnicity, residential segregation, and exposure to ambient air pollution: The Multi-Ethnic Study of Atherosclerosis (MESA). Am. J. Public Health 2014, 104, 2130–2137. [Google Scholar] [CrossRef] [PubMed]
  24. Gray, S.C.; Edwards, S.E.; Miranda, M.L. Race, socioeconomic status, and air pollution exposure in North Carolina. Environ. Res. 2013, 126, 152–158. [Google Scholar] [CrossRef] [PubMed]
  25. Williams, D.R.; Jackson, P.B. Social sources of racial disparities in health. Health Aff. 2005, 24, 325–334. [Google Scholar] [CrossRef] [PubMed]
  26. Darling-Hammond, L. The Color Line in American Education: Race, Resources, and Student Achievement. Du Bois Rev. Soc. Sci. Res. Race 2004, 1, 213–246. [Google Scholar] [CrossRef]
  27. Clark, N.M.; Quinn, M.; Dodge, J.A.; Nelson, B.W. Alliance system and policy change: Necessary ingredients for improvement in diabetes care and reduction of disparities. Health Promot. Pract. 2014, 15, 11S–22S. [Google Scholar] [CrossRef] [PubMed]
  28. Collins, T.W.; Grineski, S.E.; Chakraborty, J.; Montgomery, M.C.; Hernandez, M. Downscaling Environmental Justice Analysis: Determinants of Household-Level Hazardous Air Pollutant Exposure in Greater Houston. Ann. Assoc. Am. Geogr. 2015, 105, 684–703. [Google Scholar] [CrossRef]
  29. Huang, G.; London, J. Mapping cumulative environmental effects, social vulnerability, and health in the San Joaquin Valley, California. Am. J. Public Health 2012, 102, 830–832. [Google Scholar] [CrossRef] [PubMed]
  30. Sadd, J.L.; Pastor, M.; Morello-Frosch, R.; Scoggins, J.; Jesdale, B. Playing it safe: Assessing cumulative impact and social vulnerability through an environmental justice screening method in the South Coast Air Basin, California. Int. J. Environ. Res. Public Health 2011, 8, 1441–1459. [Google Scholar] [CrossRef] [PubMed]
  31. Adamkiewicz, G.; Spengler, J.D.; Harley, A.E.; Stoddard, A.; Yang, M.; Alvarez-Reeves, M.; Sorensen, G. Environmental conditions in low-income urban housing: Clustering and associations with self-reported health. Am. J. Public Health 2014, 104, 1650–1656. [Google Scholar] [CrossRef] [PubMed]
  32. Jacobs, D.E. Environmental Health Disparities in Housing. Available online: http://ajph.aphapublications.org/ (accessed on 22 August 2017).
  33. Forno, E.; Celedon, J.C. Asthma and ethnic minorities: Socioeconomic status and beyond. Curr. Opin. Allergy Clin. Immunol. 2009, 9, 154–160. [Google Scholar] [CrossRef] [PubMed]
  34. Bell, J.; Lee, M.M. Why Place and Race Matter: Impacting Health through a Focus on Race and Place; Report; Policy Link: Oakland, CA, USA, 2011; Available online: http://www.policylink.org/atf/cf/%7B97c6d565-bb43-406d-a6d5-eca3bbf35af0%7D/WPRM%20FULL%20REPORT (accessed on 4 June 2018).
  35. Kelishadi, R.; Poursafa, P. Air pollution and non-respiratory health hazards for children. Arch. Med. Sci. 2010, 6, 483–495. [Google Scholar] [CrossRef] [PubMed]
  36. Sacks, J.D.; Stanek, L.W.; Luben, T.J.; Johns, D.O.; Buckley, B.J.; Brown, J.S.; Ross, M. Particulate matter-induced health effects: Who is susceptible? Environ. Health Perspect. 2011, 119, 446–454. [Google Scholar] [CrossRef] [PubMed]
  37. Solomon, G.M.; Morello-Frosch, R.; Zeise, L.; Faust, J.B. Cumulative Environmental Impacts: Science and Policy to Protect Communities. Annu. Rev. Public Health 2016, 37, 83–96. [Google Scholar] [CrossRef] [PubMed]
  38. National Environmental Justice Advisory Council. Recommendations for Integrating Environmental Justice into the EPA’s Research Enterprise; National Environmental Justice Advisory Council: Washington, DC, USA, 2014.
  39. Fry, R.; Taylor, P. The Rise of Residential Segregation by Income; Pew Research Center’s Social & Demographic Trends Project: Washington, DC, USA, 2012. [Google Scholar]
  40. Sugrue, T.J. The Origins of the Urban Crisis: Race and Inequality in Postwar Detroit; Princeton University Press: Princeton, NJ, USA, 2014; ISBN 978-1-4008-5121-8. [Google Scholar]
  41. Farley, R.; Danziger, S.; Holzer, H.J. Detroit Divided; Russell Sage Foundation: New York, NY, USA, 2000; ISBN 978-1-61044-198-8. [Google Scholar]
  42. 2015 Data Release New and Notable. Available online: https://www.census.gov/programs-surveys/acs/news/data-releases/2015/release.html (accessed on 24 August 2017).
  43. Rodriguez, E.M.; Bowie, J.V.; Frattaroli, S.; Gielen, A. A qualitative exploration of the community partner experience in a faith-based breast cancer educational intervention. Health Educ. Res. 2009, 24, 760–771. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Jargowsky, P.A. Architecture of Segregation: Civil Unrest, the Concentration of Poverty, and Public Policy; The Century Foundation: New York, NY, USA, 2015; Volume 7. [Google Scholar]
  45. Kids Count Data Book; Annie E. Casey Foundation: Baltimore: MD, USA, 2015.
  46. Metropolitan Detroit Race Equity Report; New Detroit Coalition: Detroit, MI, USA, 2014.
  47. Rugh, J.S.; Massey, D.S. Racial Segregation and the American Foreclosure Crisis. Am. Sociol. Rev. 2010, 75, 629–651. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Morello-Frosch, R.; Jesdale, B.M. Separate and Unequal: Residential Segregation and Estimated Cancer Risks Associated with Ambient Air Toxics in U.S. Metropolitan Areas. Environ. Health Perspect. 2006, 114, 386–393. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Morello-Frosch, R.; Lopez, R. The riskscape and the color line: Examining the role of segregation in environmental health disparities. Environ. Res. 2006, 102, 181–196. [Google Scholar] [CrossRef] [PubMed]
  50. Chen, H.; Goldberg, M.S.; Burnett, R.T.; Jerrett, M.; Wheeler, A.J.; Villeneuve, P.J. Long-Term Exposure to Traffic-Related Air Pollution and Cardiovascular Mortality. Epidemiology 2013, 24, 35–43. [Google Scholar] [CrossRef] [PubMed]
  51. Morello-Frosch, R.; Zuk, M.; Jerrett, M.; Shamasunder, B.; Kyle, A.D. Understanding the cumulative impacts of inequalities in environmental health: Implications for policy. Health Aff. 2011, 30, 879–887. [Google Scholar] [CrossRef] [PubMed]
  52. Berrocal, V.J.; Gelfand, A.E.; Holland, D.M. Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality. Biometrics 2012, 68, 837–848. [Google Scholar] [CrossRef] [PubMed]
  53. Berrocal, V.; Gelfand, A.E.; Holland, D. A spatiotemporal downscaler for output from numerical models. J. Agric. Biol. Environ. Stat. 2010, 15, 176–197. [Google Scholar] [CrossRef] [PubMed]
  54. Berrocal, V.; Gelfand, A.E.; Holland, D. A bivariate space-time downscaler under space and time misalignment. Ann. Appl. Stat. 2010, 4, 1942–1975. [Google Scholar] [CrossRef] [PubMed]
  55. U.S. Census Bureau American Community Survey (ACS). Available online: https://www.census.gov/programs-surveys/acs (accessed on 22 December 2016).
  56. WHO|International Classification of Diseases. Available online: http://www.who.int/classifications/icd/en/ (accessed on 22 August 2017).
  57. Bullard, R.D.; Mohai, P.; Saha, R.; Wright, B. Toxic Wastes and Race at Twenty 1987–2007: Grassroots Struggles to Dismantle Environmental Racism in the United States; United Church of Christ Justice and Witness Ministries: Washington, DC, USA, 2007. [Google Scholar]
  58. Lim, S.S.; Vos, T.; Flaxman, A.D.; Danaei, G.; Shibuya, K.; Adair-Rohani, H.; AlMazroa, M.A.; Amann, M.; Anderson, H.R.; Andrews, K.G.; et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012, 380, 2224–2260. [Google Scholar] [CrossRef]
  59. Evans, J.; van Donkelaar, A.; Martin, R.V.; Burnett, R.; Rainham, D.G.; Birkett, N.J.; Krewski, D. Estimates of global mortality attributable to particulate air pollution using satellite imagery. Environ. Res. 2013, 120, 33–42. [Google Scholar] [CrossRef] [PubMed]
  60. Cohen, A.; Anderson, H.; Ostro, B.; Pandey, K.; Krzyzanowski, M.; Kunzli, N.; Gutschmidt, K.; Pope, A., III; Romieu, I.; Samet, J.; et al. Urban Air Pollution. In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors; Ezzati, M., Lopez, A., Rodgers, A., Murray, C., Eds.; World Health Organization: Geneva, Switzerland, 2004; Volume 2, pp. 1353–1434. [Google Scholar]
  61. Giannadaki, D.; Lelieveld, J.; Pozzer, A. Implementing the US air quality standard for PM2.5 worldwide can prevent millions of premature deaths per year. Environ. Health 2016, 15, 88. [Google Scholar] [CrossRef] [PubMed]
  62. Chi, G.C.; Hajat, A.; Bird, C.E.; Cullen, M.R.; Griffin, B.A.; Miller, K.A.; Shih, R.A.; Stefanick, M.L.; Vedal, S.; Whitsel, E.A.; et al. Individual and Neighborhood Socioeconomic Status and the Association between Air Pollution and Cardiovascular Disease. Environ. Health Perspect. 2016, 124, 1840–1847. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Wang, Y.; Shi, L.; Lee, M.; Liu, P.; Di, Q.; Zanobetti, A.; Schwartz, J.D. Long-term exposure to PM2.5 and mortality among older adults in the Southeastern US. Epidemiology 2017, 28, 207–214. [Google Scholar] [CrossRef] [PubMed]
  64. Parker, J.D.; Kravets, N.; Vaidyanathan, A. Particulate Matter Air Pollution Exposure and Heart Disease Mortality Risks by Race and Ethnicity in the United States. Circulation 2018, 137, 1688–1697. [Google Scholar] [CrossRef] [PubMed]
  65. Kioumourtzoglou, M.-A.; Schwartz, J.; James, P.; Dominici, F.; Zanobetti, A. PM2.5 and mortality in 207 US cities: Modification by temperature and city characteristics. Epidemiology 2016, 27, 221–227. [Google Scholar] [CrossRef] [PubMed]
  66. Fann, N.; Roman, H.A.; Fulcher, C.M.; Gentile, M.A.; Hubbell, B.J.; Wesson, K.; Levy, J.I. Maximizing Health Benefits and Minimizing Inequality: Incorporating Local-Scale Data in the Design and Evaluation of Air Quality Policies. Risk Anal. 2011, 31, 908–922. [Google Scholar] [CrossRef] [PubMed]
  67. Kheirbeck, K.I.; Haney, J.; Douglas, S.; Ito, K.; Caputo, S.; Matte, S. The Public Health Benefits of Reducing Fine Particulate Matter through Conversion to Cleaner Heating Fuels in New York City. Available online: https://www.ncbi.nlm.nih.gov/pubmed/25365783/ (accessed on 21 May 2018).
  68. Hoyer, M.; Foureman, G.; Valcovic, L. Health Assessment Document for Diesel Engine Exhaust—Google Scholar; United States Environmental Protection Agency: Washington, DC, USA, 2002.
  69. Escobedo, F.J.; Kroeger, T.; Wagner, J.E. Urban forests and pollution mitigation: Analyzing ecosystem services and disservices. Environ. Pollut. 2011, 159, 2078–2087. [Google Scholar] [CrossRef] [PubMed]
  70. Ellickson, K.M.; Sevcik, S.M.; Burman, S.; Pak, S.; Kohlasch, F.; Pratt, G.C. Cumulative Risk Assessment and Environmental Equity in Air Permitting: Interpretation, Methods, Community Participation and Implementation of a Unique Statute. Int. J. Environ. Res. Public Health 2011, 8, 4140–4159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Phelan, J.; Link, B.; Tehranifar, P. Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. J. Health Soc. Behav. 2010, 51, S28–S40. [Google Scholar] [CrossRef] [PubMed]
  72. Williams, D.R.; Mohammed, S.A. Racism and Health I: Pathways and Scientific Evidence. Am. Behav. Sci. 2013, 57, 1152–1173. [Google Scholar] [CrossRef] [PubMed]
  73. Dvonch, J.T.; Kannan, S.S.; Schulz, A.J.; Keeler, G.J.; Mentz, G.; House, J.; Benjamin, A.; Max, P.; Bard, R.L.; Brook, R.D. Acute effects of ambient particulate matter on blood pressure: Differential effects across urban communities. Hypertension 2009, 53, 853–859. [Google Scholar] [CrossRef] [PubMed]
  74. Ensuring Risk Reduction in Communities with Multiple Stressors: Environmental Justice and Cumulative Risks; National Environmental Justice Advisory Council: Washington, DC, USA, 2004.
  75. Environmental Justice 2020 Action Agenda: The U.S. EPA’s Environmental Justice Strategic Plan for 2016–2020; United States Environmental Protection Agency: Washington, DC, USA, 2016.
Ijerph 15 01209 g001 550
Figure 1. Proportion of persons of color at the census tract level, Detroit metropolitan area.
Figure 1. Proportion of persons of color at the census tract level, Detroit metropolitan area.
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Figure 2. PM2.5 mapped at the census tract level, Detroit metropolitan area.
Figure 2. PM2.5 mapped at the census tract level, Detroit metropolitan area.
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Table
Table 1. Descriptive statistics for individual and census tract level indicators, Detroit metropolitan area.
Table 1. Descriptive statistics for individual and census tract level indicators, Detroit metropolitan area.
Individual Level (n = 171,000)PercentMean (SD)Range
Demographics
 Age 72.6 (18.7)(0.0, 99.2)
 Gender (Female = 1)51.2
 Race/ethnicity
  Hispanic1.4
  Non-Hispanic White71.9
  Non-Hispanic Black25.6
  Non-Hispanic Other1.1
 Education Attainment
  Less than high school17.3
  High school29.1
  More than high school17.2
  Not reported36.4
 Married33.6
 Smoking behavior
  Yes7.4
  No36.1
  Probable5.7
  Not reported50.8
Mortality rates
 Ischemic heart19.3
 Cardiovascular34.3
 Cardiopulmonary42.7
Tract Level (n = 1166)
 Pollution Exposure
  PM2.5 (µg/m3) 9.6 (0.3)(8.9, 10.0)
 Vulnerability (mean percent at the tract level)
  Percent people of color 40.3 (35.4)(0.0, 100)
  Percent households living below poverty line 19.9 (16.9)(0.0, 100)
  Median home value (in thousands) 121.5 (87.1)(10.0, 761.3)
  Percent renter-occupied housing 30.9 (20.9)(0.0, 100)
  Percent aged ≥24 with <high school diploma 13.7 (10.2)(0.0, 61.7)
  Percent linguistically isolated 0.4 (1.1)(0.0, 9.7)
  Percent aged <5 5.8 (2.7)(0.0, 17.8)
  Percent aged ≥60 19.9 (6.8)(0.0, 57.5)
Table
Table 2. Mortality due to ischemic heart disease, cardiovascular disease, and cardiopulmonary disease, respectively, regressed on PM2.5 and cumulative vulnerability, adjusted for individual characteristics 1.
Table 2. Mortality due to ischemic heart disease, cardiovascular disease, and cardiopulmonary disease, respectively, regressed on PM2.5 and cumulative vulnerability, adjusted for individual characteristics 1.
Tract Level PredictorsIschemic Health DiseaseCardiovascularCardiopulmonary
Model 1Model 2Model 1Model 2Model 1Model 2
PM2.5 (1 = low)OR 2 (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)
21.06 (0.95, 1.19) 1.00 (0.92, 1.08) 0.98 (0.90, 1.07)
31.17 (1.05, 1.31) b 1.03 (0.95, 1.12) 1.08 (0.99, 1.18) †
41.19 (1.06, 1.34) b 1.08 (0.99, 1.18) 1.18 (1.08, 1.29) c
51.31 (1.16, 1.48) c 1.14 (1.03, 1.26) b 1.22 (1.11, 1.34) c
Vulnerability (1 = low)
2 1.06 (0.94,1.19) 1.10 (1.01, 1.19) a 1.09 (1.00, 1.19)
3 1.12 (1.00,1.26) a 1.16 (1.07, 1.26) c 1.19 (1.09, 1.29) c
4 1.17 (1.04,1.31) b 1.17 (1.07, 1.27) c 1.22 (1.12, 1.33) c
5 1.24 (1.09,1.42) c 1.16 (1.05, 1.29) b 1.23 (1.11, 1.35) c
1 Models were adjusted by age, gender, race/ethnicity (NHB, Hispanic, NHW), educational attainment (less than high school, high school, more than high school), death attributable to smoking (yes, probably, no), and marital status. a p < 0.05. b p < 0.01, c p < 0.001. † p = 0.07. 2 OR= Odds Ratio.
Table
Table 3. Mortality due to ischemic heart disease, cardiovascular disease, and cardiopulmonary disease regressed on PM2.5 and population vulnerability at the census tract level, adjusted for individual demographic characteristics 1.
Table 3. Mortality due to ischemic heart disease, cardiovascular disease, and cardiopulmonary disease regressed on PM2.5 and population vulnerability at the census tract level, adjusted for individual demographic characteristics 1.
QuintileIschemic Heart
DiseaseCardiovascularCardiopulmonary
OR (95% CI)OR (95% CI)OR (95% CI)
PM2.5 (1 = low, 5 = high)
1refrefref
21.04 (0.92, 1.17)0.95 (0.87, 1.04)0.94 (0.86, 1.03)
31.15 (1.02, 1.29) a0.99 (0.91, 1.08)1.03 (0.94, 1.13)
41.15 (1.02, 1.31) a1.02 (0.93, 1.13)1.11 (1.01, 1.23) a
51.24 (1.08, 1.42) c1.10 (0.98, 1.22)1.15 (1.04, 1.28) b
Vulnerability (1 = low, 5 = high)
1refrefref
21.03 (0.91, 1.16)1.10 (1.00, 1.20) a1.08 (0.98, 1.18)
31.07 (0.95, 1.21)1.16 (1.06, 1.27) c1.17 (1.07, 1.28) c
41.09 (0.96, 1.24)1.15 (1.05, 1.26) c1.19 (1.08, 1.30) c
51.13 (0.98, 1.31)1.11 (0.99, 1.24) †1.15 (1.03, 1.28) b
1 Adjusted by age, gender (ref = female), race, and ethnicity (NHB, Hispanic, NHW), educational attainment (less then high school, high school (ref) and more than high school), mortality linked to smoking (yes, probable, no (ref)) and marital status. Ranks for PM2.5: 1 = (0.892–0.944); 2 = (0.945–0.964); 3 = (0.965–9.74); 4 = (0.975–0.982); and 5 = (0.983+). Ranks for vulnerability index: 1 = (0–16); 2 = (16.1–21.5); 3 = (21.6–25.5); 4 = (25.6–31); and 5 = (31.1+). 2 PM2.5 was standardized by 10. a p < 0.05, b p < 0.01, c p < 0.001. † p = 0.07.
Table
Table 4. Number of cardiopulmonary deaths averted annually by reducing PM2.5 to low in all census tracts, by high and low vulnerability scores 1 under scenarios with 3–15% attributable risk.
Table 4. Number of cardiopulmonary deaths averted annually by reducing PM2.5 to low in all census tracts, by high and low vulnerability scores 1 under scenarios with 3–15% attributable risk.
Percent Attributable RiskLow VulnerabilityHigh Vulnerability
Low PM2.5High PM2.5Low PM2.5High PM2.5
Population estimates1,301,007 (30.3%)657,199 (15.3%)677,435 (15.8%)1,659,342 (38.6%)
Cardiopulmonary Mortality Estimates (Total)1000 (20.8%)633 (13.2%)767 (16.0%)2400 (50.0%)
Attributable to PM2.5 (3%)30 (20.8%)19 (13.2%)23 (16.0%)72 (50%)
Attributable to PM2.5 (5%)50 (20.7%)32 (13.3%)39 (16.2%)120 (49.8%)
Attributable to PM2.5 (10%)100 (20.8%)64 (13.3%)77 (16.0%)239 (49.8%)
Attributable to PM2.5 (15%)150 (20.8%)96 (13.3%)116 (16.1%)359 (49.8%)
Cardiopulmonary deaths averted
If PM2.5 moves from High to Low (3%) 4 (21.1%) 15 (78.9%)
If PM2.5 moves from High to Low (5%) 7 (21.9%) 25 (78.1%)
If PM2.5 moves from High to Low (10%) 13 (20.6%) 50 (79.4%)
If PM2.5 moves from High to Low (15%) 20 (21.1%) 75 (78.9%)
1 Low includes census tracts that were in the first and second quintiles of risk; High includes census tracts in the third–fifth quintiles.

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Schulz, A.J.; Mentz, G.B.; Sampson, N.; Ward, M.; Dvonch, J.T.; De Majo, R.; Israel, B.A.; Reyes, A.G.; Wilkins, D. Independent and Joint Contributions of Fine Particulate Matter Exposure and Population Vulnerability to Mortality in the Detroit Metropolitan Area. Int. J. Environ. Res. Public Health 2018, 15, 1209. https://doi.org/10.3390/ijerph15061209

AMA Style

Schulz AJ, Mentz GB, Sampson N, Ward M, Dvonch JT, De Majo R, Israel BA, Reyes AG, Wilkins D. Independent and Joint Contributions of Fine Particulate Matter Exposure and Population Vulnerability to Mortality in the Detroit Metropolitan Area. International Journal of Environmental Research and Public Health. 2018; 15(6):1209. https://doi.org/10.3390/ijerph15061209

Chicago/Turabian Style

Schulz, Amy J., Graciela B. Mentz, Natalie Sampson, Melanie Ward, J. Timothy Dvonch, Ricardo De Majo, Barbara A. Israel, Angela G. Reyes, and Donele Wilkins. 2018. "Independent and Joint Contributions of Fine Particulate Matter Exposure and Population Vulnerability to Mortality in the Detroit Metropolitan Area" International Journal of Environmental Research and Public Health 15, no. 6: 1209. https://doi.org/10.3390/ijerph15061209

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