Cluster 2 had the greatest annual mean PM2.5 concentration from the hybrid model (13.2 µg/m3) and based on EPA monitors (13.3 µg/m3; Table 3). Cluster 6 had the lowest annual mean PM2.5 concentrations from the hybrid model (11.9 µg/m3) and based on EPA monitors (12.6 µg/m3). Participants in cluster 2 lived closest to A1 or A2 roads (mean 498.2 m), and participants in cluster 5 lived furthest (mean 2,076.6 m). Similarly, participants in clusters 1 and 2 resided in the highest TEZs, while those in cluster 5 resided in the lowest.
After adjustment for age, sex, BMI, race, and smoking status, an increase of 1 µg/m3 annual average PM2.5 concentration was associated with a greater odds of CAD in cluster 3 (OR = 1.15, 95% CI = 1.00, 1.31) and overall (OR = 1.07, 95% CI = 0.98, 1.17) (Figure and eTable S1; http://links.lww.com/EE/A31). Annual PM2.5 concentrations were positively associated with the history of MI in all clusters, although the 95% confidence intervals for clusters 2 and 6 included the null. In an analysis of all clusters combined, an increase of 1 µg/m3 annual PM2.5 concentration was associated with an increase in odds of MI (OR = 1.29, 95% CI = 1.16, 1.42); cluster-specific ORs ranged from 1.25 (95% CI = 1.07, 1.46) in cluster 3 to 1.50 (95% CI = 0.81, 2.76) in cluster 6. We did not observe substantial differences in the association between annual average PM2.5 concentration and MI across clusters.
Greater annual average PM2.5 was associated with greater odds of hypertension in cluster 2 (OR = 1.64, 95% CI = 1.16, 2.32) and cluster 1 (OR = 1.22, 95% CI = 0.99, 1.50) and lower odds of hypertension in clusters 5 (OR = 0.81, 95% CI = 0.66, 1.00) and 6 (OR = 0.63, 95% CI = 0.39, 1.01). Furthermore, the associations between annual PM2.5 concentration and hypertension in clusters 1 and 2 were significantly different from that in cluster 3 (OR = 0.93, 95% CI = 0.82, 1.07; P for interaction 0.03, 0.003, respectively). We did not observe associations between annual PM2.5 concentrations and diabetes mellitus status overall or within clusters.
In sensitivity analyses, cluster-specific and overall associations between mean annual PM2.5 concentrations were not substantially different after adjustment for DTR or TEZ (eTable S1; http://links.lww.com/EE/A31). Cluster-specific and the overall association between mean annual PM2.5 concentrations at the nearest EPA air quality monitor and cardiometabolic outcomes were similar to those generated by the hybrid model of PM2.5 concentrations (eTable S2; http://links.lww.com/EE/A31). Ambient PM2.5 concentrations were highly correlated between the hybrid model and EPA monitors (ρ = 0.87; eTable S3; http://links.lww.com/EE/A31). Annual PM2.5 concentrations were only weakly correlated with inverse log distance to A1 or A2 roads. As expected, concentrations of PM2.5 increased as TEZ increased, while distance to A1 or A2 roads decreased (eTable S4; http://links.lww.com/EE/A31). As shown in eTable S5 (http://links.lww.com/EE/A31), most Census-derived variables had null or weak associations with cardiovascular and diabetes mellitus outcomes. The strongest associations were between percent urban and CAD (OR = 0.69, 95% CI = 0.50, 0.96), between percent receiving public assistance income and hypertension (OR = 1.07, 95% CI = 1.03, 1.11) and diabetes mellitus (OR = 1.04, 95% CI = 1.00, 1.07), between percent unemployed and hypertension (OR = 1.04, 95% CI = 1.02, 1.07) and diabetes mellitus (OR = 1.04, 95% CI = 1.02, 1.05). Similar results, albeit of lesser magnitude, were observed between percent income below poverty level, percent black, percent in nonmanagerial occupations, and percent in single-parent housing with hypertension and diabetes mellitus. We observed interactions between PM2.5 and the following Census-derived variables in adjusted models for hypertension at the P < 0.05 level: Bachelor’s degree or more, income below poverty level, black race, nonmanagerial occupation, and single-parent housing.
We examined the effect of neighborhood-level sociodemographic factors on the association between PM2.5 and CVD and diabetes mellitus in a high-risk population. Our most notable result is that participants residing in clusters 1 and 2, which were urban and had high proportions of individuals who were black, impoverished, working in nonmanagerial positions, unemployed, and living in single-parent homes, had significantly greater associations between PM2.5 and hypertension compared with our reference cluster. The reference cluster, 3, was also urban but had low proportions of people who were black, impoverished, working in nonmanagerial occupations, unemployed, and living in single-parent homes. Racial distribution of clusters were based on Census data, and, as expected, reflected the racial distribution of CATHGEN participants across clusters, with clusters 1 and 2 having the highest proportion of participants who were black. Higher prevalence of hypertension among black Americans compared with white Americans has been well documented.1,30 Racial differences in the associations between PM2.5 exposure and hypertension is less well understood. In sensitivity analysis, we observed interaction between percent black population, as well as percent Bachelor’s degree or more, percent with income below the poverty level, percent in nonmanagerial occupations, and percent in single-parent housing and PM2.5 concentration on hypertension. This indicates that individuals living in neighborhoods with high proportions of black individuals, as well as those with lower socioeconomic indicators, have stronger associations between PM2.5 and hypertension compared with those living in more affluent neighborhoods and those with lower proportions of black individuals. Areas with high proportions of black Americans, unemployed people, people who have less than a high school education, and people with incomes below the poverty level are, on average, exposed to relatively high concentrations of PM2.5.31–34 PM2.5 exposure was somewhat higher in clusters 1 and 2 compared with other clusters, so greater exposure may be at least partially responsible for these results. Those who live in neighborhoods enriched for black individuals, single-parent homes, and those with relative socioeconomic disadvantage, may suffer from increased psychosocial stress, including perceived discrimination, which may, in turn, influence the development of hypertension.35,36 Indeed, in a recent study, Smith et al37 observed increased methylation in genes related to stress and methylation among those who lived in neighborhoods of socioeconomic disadvantage, indicating a biologic mechanism for neighborhood effects on chronic health outcomes.
We observed inverse associations between PM2.5 and hypertension in clusters 5 and 6. Cluster 5 was relatively rural compared with our overall study area and clusters 5 and 6 both had low population density, low proportions of poverty, unemployment, and residents who were black,12 as well as the lowest PM2.5 concentrations. Most studies of PM2.5 and cardiometabolic diseases are conducted in urban areas, with greater exposure levels. However, less is known about how PM2.5 influences cardiometabolic diseases in rural and suburban areas; it is possible that effects of PM2.5 on health may be different in urban and rural areas. Correia et al13 observed that, in contrast to urban and densely-populated counties, in counties with lower population densities and less than 90% urbanicity, a reduction in PM2.5 was associated with decreased life expectancy. Possible reasons for differential effects of PM2.5 on health in urban compared with rural areas include different health behaviors and different PM2.5 composition in urban compared with rural areas.13 Additionally, measurement error may be an issue, as PM2.5 monitors are located in urban areas, specifically Raleigh and Durham, which are further away from cluster 5 compared with other clusters. Although our satellite-based hybrid exposure model does not solely rely on monitoring data, it still trains on monitor data and may be less accurate in areas further form monitors. In our analyses, only estimates for clusters 1 and 2 were significantly different from those in cluster 3 (the reference cluster). It is possible that significant differences were not detectable given small sample sizes in clusters 5 and 6. Future studies should specifically examine PM2.5 associations in rural and suburban areas to determine if this inverse association holds.
When combining all clusters, PM2.5 was associated with greater odds of CAD; this effect was greatest in cluster 3, the largest cluster, though there was no significant interaction by cluster. In a larger study including all CATHGEN participants in NC, we observed a positive association between PM2.5 exposure and CAD (OR = 1.11, 95% CI = 1.04, 1.19).16 Our results largely agree with the results of this study, and the association between PM2.5, and CAD in cluster 3 was even stronger than in this previous study. However, the 95% confidence intervals for both the previous NC-wide estimates, current cluster-agnostic estimates, and cluster 3–specific estimates largely overlapped.
We observed associations between PM2.5 exposure and MI in all clusters, although 95% confidence intervals included the null in some clusters. These results are consistent with literature that generally shows an association between PM2.5 exposure and MI.15,16,38 Our point estimate, an OR of 1.29, was relatively high compared with past studies, possibly due to the fact that our study population had a high prevalence of MI. In addition, individuals with underlying cardiac disease may be more sensitive to air pollution exposure.15 Our estimates between PM2.5 and MI are somewhat elevated compared those observed in previous studies of all CATHGEN participants in NC, which also noted elevated associations.16,38 We did not observe substantial differences in odds ratios by cluster, indicating that the associations between PM2.5 and MI are independent of our sociodemographically defined clusters for this study area and patient population.
We did not observe associations between PM2.5 and diabetes mellitus overall or by cluster. Prior studies on the associations between PM2.5 and diabetes mellitus have had mixed results, ranging from null17,20 to positive.19 Although Park et al19 observed an overall positive association between PM2.5 and diabetes mellitus prevalence (OR = 1.09, 95% CI = 1.00, 1.17) in the multisite multi-ethnic study of atherosclerosis study, the authors observed a null association at the North Carolina multi-ethnic study of atherosclerosis site,19 consistent with our findings.
In sensitivity analyses, we observed that our main findings were largely robust to adjustment for traffic indicators, indicating that PM2.5 concentrations, and not traffic alone, drive these results. Our results were also robust to an alternative method of assessing PM2.5 exposure, and these two measures were highly correlated. PM2.5 concentrations were only weakly correlated with distance to road but did increase by traffic exposure zone, as expected. We observed weak interactions between PM2.5 and hypertension with the following Census-derived indicators: Bachelor’s degree or more, income below poverty level, black race, nonmanagerial occupation, and single-parent housing. It is likely that the combination of these factors, rather than any one individual factor, contributes to the observed association.
As we used data from medical records, we did not have access to individual-level data on important demographic and socioeconomic indicators or other important risk factors for CVD and diabetes mellitus, such as nutrition and physical activity. However, we used BMI as a proxy measure. In addition, we did not have gradations of smoking and alcohol consumption, which are important potential confounders. Addition of some covariates to the model (eTable S2; http://links.lww.com/EE/A31) did not substantially change point estimates or 95% CI from crude estimates; it is not clear if addition of more detailed confounding information would substantially change point estimates. Diagnoses were made by physicians in a clinical setting; misclassification is possible, particularly for history of diabetes mellitus and hypertension. This is especially likely if a participant is not a regular user of health services and thus was not previously diagnosed, which is most likely for low-income populations. This would result in an underestimate of hypertension and diabetes mellitus in low-income areas, which had the highest prevalence of both in our study. It is unlikely, but possible, that there is some misclassification of outcome which could bias results toward the null.
Small sample sizes, particularly in clusters 5 and 6, may have hindered our efforts to observe associations. We assessed PM2.5 at the primary residence. As individuals do not spend the entirety of their day at their residence, this could lead to exposure misclassification. Additionally, we did not correct for air exchange rates of residences, so we do not have an exact measure of exposure. However, exposures at the primary residence likely capture the majority of exposure time for participants, are the primary means of exposure classification in the field and are potentially relevant for communicating exposure risks—particularly those risks tied to the joint effect of neighborhood and air pollution exposure. We assessed the sensitivity of our associations to our particular air pollution models by using annual average PM2.5 as measured at the nearest monitor (mean distance to monitor = 10.8 km). Results from this coarser exposure model were consistent with those from the 1 km2 resolution hybrid model. This study only included individuals who received a cardiac catheterization, were white or black, and lived in one of three largely urban counties in NC. This limits the generalizability of our study. However, when combining all neighborhood clusters, associations were largely similar to those observed for all of NC. All participants received a cardiac catheterization, thus while this study is not representative of the general population, it likely represents a population with high risk for CVD, more sensitive to the adverse health effects from PM2.5 exposure. This study was conducted at a single site, Duke University Medical Center in Durham, NC. This single-site sampling ensures a consistent quality of assessment of clinical variables, in particular the assessment of medical history and imaging of coronary arteries during the cardiac catheterization, which can reduce errors. However, the population is from a relatively small geographic area and is not generalizable to a larger area.
In a high-risk population, we observed elevated associations between PM2.5 and hypertension in urban neighborhood clusters defined by high proportions of people who were black, impoverished, unemployed, working in nonmanagerial positions, and living in single-parent homes, as compared with a neighborhood cluster defined by low proportions of people who were black, impoverished, unemployed, working in nonmanagerial occupations, and living in single-parent homes. Associations with CAD were most prominent in the neighborhood cluster defined by low proportions of people who were black, impoverished, unemployed, working in nonmanagerial occupations, and living in single-parent homes, while associations between annual average PM2.5 and MI were relatively consistent across all neighborhoods. We did not observe associations between PM2.5 and diabetes mellitus in any cluster. These results indicate that neighborhood residence may be an important contributor to air pollution sensitivity, which partially underlie differences in the prevalence of air pollution associated outcomes such as hypertension and CVD across neighborhoods.
We thank all the participants in the CATHGEN study, and we acknowledge the essential contributions of the faculty and staff of the Duke Cardiac Catheterization Lab, the Duke Databank for Cardiovascular Disease, and the Duke Center for Human Genetics for their contributions to this manuscript.
Conflicts of interest statement
The authors declare that they have no conflicts of interest with regard to the content of this report.
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Ambient air pollution; Cardiovascular disease; Community, Particulate matter; Socioeconomic status
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