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. Author manuscript; available in PMC: 2015 Aug 1.
Published in final edited form as: Environ Res. 2014 Jun 24;133:195–203. doi: 10.1016/j.envres.2014.06.001

Fine particulate matter air pollution and blood pressure: The modifying role of psychosocial stress

Margaret T Hicken 1, J Timothy Dvonch 2, Amy J Schulz 3, Graciela Mentz 4, Paul Max 5
PMCID: PMC4137402  NIHMSID: NIHMS609618  PMID: 24968081

Abstract

Background

Consensus is growing on the need to investigate the joint effects of psychosocial stress and environmental hazards on health. Some evidence suggests that psychosocial stress may be an important modifier of the association between air pollution respiratory outcomes, but few have examined cardiovascular outcomes.

Objectives

We examined the modifying effect of psychosocial stress on the association between fine particulate matter air pollution (PM2.5) and blood pressure (BP).

Methods

Our data came from the Detroit Healthy Environments Partnership (HEP) 2002–2003 survey. Of 919 participants, BP was collected at two time points for a subset of 347. Building on previous work reporting associations between PM2.5 and BP in this sample, we regressed systolic (SBP) and diastolic (DBP) BP and pulse pressure (PP), in separate linear models, on the interaction among psychosocial stress, PM2.5, and HEP neighborhood (Southwest, Eastside, Northwest).

Results

The association between PM2.5 and SBP was stronger for those who reported high levels of stress, but this interaction was significant only in the Southwest Detroit neighborhood. Southwest Detroit residents who reported low stress showed a 2.94 mmHg (95%CI: −0.85, 6.72) increase in SBP for each 10μg/m3 increase in 2-day prior PM2.5 exposure. Those who reported high stress showed a 9.05 mmHg (95%CI: 3.29, 14.81) increase in SBP for each 10μg/m3 increase in PM2.5 exposure.

Conclusions

These results suggest that psychosocial stress may increase vulnerability to the hypertensive effects of PM2.5. This work contributes to an understanding of the ways in which the social and physical environments may jointly contribute to poor health and to health disparities.

Keywords: disease susceptibility, air pollution, stress, psychological, blood pressure

Introduction

A growing literature has documented the health impacts of psychosocial stress, and environmental hazards – although generally in separate disciplinary literatures. However, consensus is growing on the need to investigate their joint effects as they are often spatially correlated, may operate through common biological mechanisms, and may act synergistically to affect health (Clougherty and Kubzansky 2009; Evans and Pilyoung 2010; Gee and Payne-Sturges 2004; Morello-Frosch and Lopez 2006; Pope and Dockery 2006; Schulz et al. 2005). Indeed, the US Clean Air Act requires that the National Ambient Air Quality Standards (NAAQS) protect populations that may be particularly vulnerable to the health effects of air pollution ([Anonymous] 1970). As such, it is a public health imperative to understand the factors that increase vulnerability to the health effects of air pollution.

Specifically, there is some evidence suggesting that psychosocial stress may be an important modifier of associations between air pollution and health (Clougherty et al. 2007; Clougherty and Kubzansky 2009). For example, some report that the association between air pollution and asthma is stronger in children who either have high exposure to violence or whose parents report high levels of stress (Clougherty et al. 2007; Shankardass et al. 2009). Others report that the association between air pollution and clinical asthma symptoms is stronger among asthmatic children who also report high levels of chronic family stress (Chen et al. 2008). Moreover, animal models support the notion that stress increases susceptibility to the respiratory effects of air pollution (Clougherty et al. 2010).

To date, most of the research on the modifying effects of psychosocial stress on the association between air pollution and health has focused on respiratory health outcomes. Yet, a growing body of work has documented positive associations between fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) (Brook et al. 2004; Brook 2008; Pope and Dockery 2006; Sun et al. 2010), and blood pressure in particular (Auchincloss et al. 2008; Brook and Rajagopalan 2009; Dvonch et al. 2009). For example, previous results from the Healthy Environments Partnership (HEP) Detroit-based study reported that a 10μg/m3 increase in PM2.5 was associated with a 3.25 mmHg increase in systolic blood pressure (SBP) (p<0.05) two days later (Dvonch et al. 2009). This association was particularly pronounced in the Southwest Detroit neighborhood, which experienced a 4.66 mmHg increase in systolic blood pressure (p=0.01) two days later – and up to an 8.6 mmHg increase in systolic blood pressure (p=0.01) four days later (Dvonch et al. 2009).

As with respiratory outcomes, it may be that the impact of air pollution on cardiovascular outcomes is modified by social stressors or psychosocial stress. One study showed that proximity to high-traffic roads is associated with coronary artery calcification, but only in those who live in neighborhoods with high unemployment (Dragano et al. 2009), suggesting that social factors and air pollution act synergistically to affect cardiovascular health. However, to our knowledge, there are no studies that have examined modifying effects of psychosocial stress on the association between PM2.5 and cardiovascular outcomes, including blood pressure.

We used data from the HEP Community Survey to investigate the extent to which short-term exposures to PM2.5 and psychosocial exposures may act together to affect blood pressure in an urban sample. This work builds on findings of an association between PM2.5 and blood pressure in HEP, that was particularly strong in the Southwest neighborhood of Detroit (Dvonch et al. 2009) by exploring effect modification by psychosocial stress. Specifically we examined whether psychosocial stress modified the association between PM2.5 and blood pressure within each of the three Detroit neighborhoods.

Methods

Dataset

We used data from the 2002–2003 Community Survey of the Detroit HEP (Schulz et al. 2005). The details of this survey are found elsewhere (Schulz et al. 2005). Briefly, a stratified probability sample of 919 residents, ages 25 and older, of the three HEP neighborhoods participated in the survey with blood pressure measurement (community survey = time 1 (t1)). Of that 919 at t1, 347 completed a follow-up survey (t2) with an additional blood pressure measurement. The mean time between the first and second blood pressure measurements was four weeks. We excluded those with missing information on any of the key variables, which reduced our sample size to 313.

Variables

Our outcome variables, systolic (SBP) and diastolic blood pressure (DBP) were measured from the right arm of seated participants using a portable cuff device (Omron model HEM 711AC). Three measurements were collected with approximately one minute between measures. Blood pressure variables were calculated as the average of the second and third measurements. Blood pressure measurements were taken at both t1 and t2. Pulse pressure (PP) was calculated as SBP-DBP.

Our air pollution exposure variables, 24-hour averaged particulate matter ambient air pollution ≥ 2.5 microns in aerodynamic diameter (PM2.5), were collected in the three HEP communities, with each monitor located within a five-kilometer radius of all study participants in each of the three communities (Dvonch et al. 2009). Published results on these data showed that mean PM2.5 levels across all three neighborhoods for the study period (2000–2003) was 15.0μg/m3 (SD=8.2μg/m3) (Dvonch et al. 2009). Furthermore, the mean PM2.5 level for the Eastside and Northwest neighborhoods were nearly identical at approximately 15μg/m3, while the mean PM2.5 level for the Southwest neighborhood was approximately 20% higher (Dvonch et al. 2009). Following from previous work (Dvonch et al. 2009), we examined PM2.5 exposures at 2, 3, and 4 days prior to blood pressure measurement as a proxy for acute exposure to ambient air pollution. While the literature outlining the biological mechanisms linking PM2.5 exposure and blood pressure indicate that blood pressure responses be seen on the same day as exposure, the epidemiological literature using population-level samples suggests that the error in ambient air pollution measurement provides for effects seen with longer exposure times (Auchincloss et al. 2008; Hicken et al. 2013).

The psychosocial stress variable was created as an index of six psychosocial stress variables created from information collected at t1, as has been done previously in the literature (Evans and Pilyoung 2010; Lee and Hicken 2013; Sternthal et al. 2011). Scores were created for each so that higher scores represented higher stress. The neighborhood environment stress measure was composed of 13 questions pertaining to perceptions of the physical (e.g., litter, noise) and social (e.g., gang activity) aspects of one’s neighborhood. Regarding perceptions of the physical characteristics, participants were asked to respond on a Likert scale the extent to which they agreed (1-strongly agree to 5-strongly disagree) with a series of statements such as “The houses in my neighborhood are generally well maintained.” Regarding perceptions of the social characteristics, participants were asked to respond on a Likert scale about the frequency (1-never to 5-always) of such activities as “Gang activity in your neighborhood.” A score was developed as the mean of all responses (α=0.75). We used an adapted version of the Duke acute life events scale (Hughes et al. 1988) that incorporated additional items based on focus group results in Detroit (Israel et al. 2002; Schulz et al. 2001). Participants were asked if, in the last 12 months, they had experienced any of nine events, such as death of a loved one, a relative or close friend going to jail. A score was developed as the sum of affirmative responses. The family caregiving stress measure included responses, on a Likert scale, to three questions about the frequency (1- never to 5-always) over the past 12 months of caregiving to adult family members. Participants were asked how often they were responsible for care, were burdened by caregiving problems, and were worried about caregiving problems. A score was developed as the mean of all responses (α=0.70). The financial vulnerability measure was created as the mean of two questions on financial strain (Kessler et al. 1987; Schulz et al. 2008; Vinokur and Caplan 1987). Participants were asked how long they could live at their current address and standard of living if they lost all sources of income (1-less than one month to 5-more than one year). Participants were also asked how difficult it is to provide for basics such as food, clothing, medical care, and housing (1-very difficult to 4-not at all difficult). The responses to the latter question were reverse-coded and the responses to both questions were standardized before combining. For major unfair treatment, participants were asked if they had experienced any of seven situations in which they had received unfair treatment, such as in school, by police, or at work (Krieger 1990). A score was developed as the sum of affirmative responses. For everyday unfair treatment, participants were asked to respond on a Likert scale regarding the frequency (1-never to 5- always) of five situations including: less courteous treatment by others, poorer service as restaurants and stores, people acting as if participant is not smart, people acting as if afraid of participant, and threats or harassment from others (Williams et al. 1997). A score was created as the mean of the responses (α=0.77). To create the overall index, each scale was transformed in to a z-score and the dichotomized into the high quartile and lower three quartiles. The index is the sum of the number of high scores of each of the six transformed scales.

Information on sociodemographics and other potential confounders was collected at t1 and include: age, gender, race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, other), education (<high school (HS), HS, >HS), and poverty income ratio (PIR).

Analytic approach

We examined sample descriptive statistics by estimating the means and standard deviations for continuous variable and percentages within categories for categorical variables. We examined these descriptive statistics in the total sample and by HEP neighborhood.

We built on published results using these HEP data, where blood pressure at t2 was regressed on the interaction between neighborhood and PM2.5 at t2 (Dvonch et al. 2009). First, we regressed BP at t2 on the interaction between neighborhood and PM2.5 at t2, adjusting for psychosocial stress. Second, we regressed BP at t2 on the three-way interaction between psychosocial stress, neighborhood and PM2.5 at t2. All models were adjusted for age, gender, race/ethnicity, education, PIR, BP at t1 and PM2.5 at t1.

Each blood pressure outcome (SBP, DBP, PP) was analyzed in separate models. Each of the PM2.5 exposure measures (2-, 3-, and 4-days prior to blood pressure measurement) was analyzed in separate models. PM2.5 was mean-centered before all interactions. Temperature and season were not included due to multicollinearity resulting in nonconvergence of those models (Dvonch et al. 2009). Because the sociodemographic, stress, and air pollution measures may be highly correlated particularly by neighborhood, we computed and examined the variance inflation factors (VIF) for each of these measures and the models overall. High multicollinearity was defined as an average VIF greater than or equal to five (Belsley et al. 2005).

We performed several sensitivity analyses. First, we examined different average PM2.5 exposure estimates, including: the maximum one-hour average for the 2-, 3-, and 4-day lag periods, the maximum 8-hour average for the 2-, 3-, and 4-day lag periods, and 48-, 72-, and 96-hour periods. We also examined models that further adjusted for anti-hypertensive medication use, body mass index (BMI), current smoking status, sodium intake, and diabetes diagnosis. While these are likely mediators in the linkages among neighborhood, stress, and blood pressure, they may also account for some confounding between neighborhood and blood pressure. Because there are racial/ethnic or sex composition differences among the neighborhoods, we examined the possibility that race/ethnicity or sex either modified the association between PM2.5 and blood pressure independently of neighborhood or that race/ethnicity mediated the modifying effects of neighborhood using approaches outlined in the literature (Muller et al. 2005).

All analyses were conducted in STATA MP 11.0 (StataCorp) and include survey weights developed to account for the complex survey design of the HEP Community Survey (Dvonch et al. 2009).

Results

While there were neighborhood differences in PM2.5 (discussed in the Methods section), there were no neighborhood differences in blood pressure, as shown in Table 1. While there were no neighborhood differences in the mean stress index, there were differences in the percent in each neighborhood that were in the high stress quartile. Forty-two percent of those in the Eastside neighborhood were in the high stress quartile of the overall stress index distribution. In general, each neighborhood differed from the others in sociodemographic characteristics.

Table 1.

Characteristics of the Healthy Environments Partnership survey sample, by neighborhood

Total sample (n=313) Southwest (n=127) Eastside (n=106) Northwest (n=80) Neighborhood differencea
mean SD Mean SD mean SD mean SD S-E S-N E-N
SBP, mmHg 128.7 20.8 128.5 20.5 129.7 21.3 127.7 20.9
DBP, mmHg 78.7 11.9 77.9 11.9 79.3 12.0 79.3 11.7
PP, mmHg 50.0 15.8 50.6 17.4 50.5 17.4 48.1 13.3
Stress 1.9 1.5 1.7 1.4 2.1 1.7 2.0 1.5
High stress, % 32 25 42 30 *
Age, years 46.1 13.6 45.2 13.1 46.4 13.4 47.0 14.6
Female, % 79 59 79 80 *** **
Race
 White, % 20 33 1 24 *** ***
 Black, % 62 26 97 73 *** *** ***
 Hispanic, % 17 39 1 1 *** ***
 Other, % 2 2 1 3
Education
 <HS, % 34 45 27 25 ** **
 =HS, % 39 28 33 26
 >HS, % 37 28 40 49 * **
Poverty income ratio 1.7 2.0 1.5 1.5 1.8 2.7 1.8 1.5
Open in a new tab

Results reported are means and standard deviations, SD, unless otherwise noted. Category percentages of a single variable may not add to 100 due to rounding. Blood pressure values are for the second time point. Abbreviations: SD, standard deviation; HS, high school; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; PM2.5, 2.5micron diameter particulate matter air pollution; S, southwest; E, east; N, northwest.

a

Tests for differences among neighborhoods:

*

p<0.05;

**

p<0.01;

***

p<0.001

In Tables 2 through 5, we provide the parameter estimates for the components of the interactions and post-estimation calculations for the association between PM2.5 and blood pressure in each neighborhood at low and high stress for SBP, DBP, and PP, respectively. In Table 2, Model 1 under each of the PM2.5 exposure periods is the association between PM2.5 and SBP, adjusting for covariates, location, and stress. For example, for each ten μg/m3 increase in PM2.5 three days prior, there was a 1.18 mmHg (95% CI: −1.21, 3.57) increase in SBP for the entire sample.

Table 2.

Linear regression estimates for systolic blood pressure regressed on the interaction among neighborhood, stress, and 24-hour averaged PM2.5 exposure

24-hour average PM2.5 exposure, number of days prior to exam
2 days prior 3 days prior 4 days prior
--1-- --2-- --1-- --2-- --1-- --2--
Neighborhood
 Southwest ref ref
 East −3.90 (−9.05,1.26) −4.71 (−9.87,0.45) −4.14 (−9.76,1.49) −3.58 (−8.96,1.80) −4.17 (−10.13,1.79) −4.96 (−11.29,1.37)
 Northwest −4.51* (−8.76,−0.25) −1.57 (−5.52,2.39) −4.20 (−9.18,0.78) −1.58 (−6.10,2.94) −5.54* (−11.00,−0.08) −2.36 (−7.88,3.17)
PM2.5 3.06* (0.00,6.12) 2.94 (−0.85,6.72) 1.18 (−1.21,3.57) 1.69 (−1.31,4.70) 3.72 (−0.29,7.73) 5.84* (0.45,11.24)
Stress level
 Low stress ref ref ref ref ref ref
 High stress 0.37 (−3.28,4.01) 3.76 (−2.90,10.42) 0.94 (−2.65,4.53) 6.67* (1.23,12. 10) 0.43 (−3.26,4.12) 9.79*** (4.68,14.89)
NeighborhoodXstress
 SouthwestXstress ref ref ref
 EastXstress −0.78 (−9.62,8.06) −4.53 (−11.75,2.68) −7.21 (−15.03,0.61)
 NorthwestXstress −8.76* (−16.16,−1.36) −11.64*** (−18.18,−5.11) −16.41** (−25.95,−6.86)
NeighborhoodXPM2.5
 SouthwestXPM2.5 ref ref ref
 EastXPM2.5 4.33 (−4.89,1.355) 0.11 (−4.86,5.08) −4.39 (−13.45,4.67)
 NorthwestXPM2.5 −4.23 (−14.68,6.22) −6.08 (−16.24,4.09) −8.01 (−19.29,3.27)
StressXPM2.5
 Low stressXPM2.5 ref ref ref
 High stressXPM2.5 6.11 (−2.41,14.63) 7.16* (0.21,14. 11) 14.85*** (0.74,2.23)
NeighborhoodXstressXPM2.5
SouthwestXstressXPM2.5
 EastXstressXPM2.5 −12.22 (−26.45,2.01) −7.83 (−18.02,2.36) −6.94 (−18.48,4.60)
NorthwestXstressXPM2.5 −11.20 (−29.03,6.63) −8.33 (−22.49,5.83) −11.57 (−34.94,11.81)
Southwest neighborhood
 Low stress 2.94 (−0.85,6.72) 1.69 (−1.31,4.70) 5.84* (0.45,11.24)
 High stress 9.05** (3.29,14. 81) 8.86** (3.20,14. 51) 20.69*** (14.47,26. 92)
East neighborhood
 Low stress 7.26 (−1.01,15.53) 1.80 (−2.03,5.63) 1.45 (−5.20,8.10)
 High stress 1.16 (−7.91,10.22) 1.14 (−5.12,7.39) 9.36 (−1.97,20.68)
Northwest neighborhood
 Low stress −1.29 (−11.27,8.68) −4.39 (−14.10,5.33) −2.17 (−12.27,7.94)
 High stress −6.38 (−19.26,6.50) −5.55 (−15.49,4.39) 1.12 (−13.00,15.23)
Open in a new tab

Results reported in the upper panel are regression coefficients (95% confidence intervals) for PM2.5, stress, and PM2.5-by-stress interaction terms. Results reported in the lower panel are post-estimation coefficients (95% confidence intervals) for the association between PM2.5 and systolic blood pressure at low (lower three quartiles of the stress distribution) and high (high quartile of the stress distribution) variable. PM2.5coefficients are interpreted as change in systolic blood pressure per 10 μg/m3 increase in PM2.5. All estimates are adjusted for: age, sex, race, education, and poverty income ratio. PM2.5 was mean-centered before interaction.

*

p<0.05;

**

p<0.01;

***

p<0.001 for difference from zero.

Model 2 under each of the PM2.5 exposure periods is the association between the interaction among neighborhood, PM2.5, and stress with SBP. Furthermore in the lower panel, for Model 2, we include the post-estimation calculations for the association between PM2.5 and SBP for both the low and high stress groups in each of the neighborhoods. For example, for those in the low stress group in the Southwest neighborhood, there was a 1.69 mmHg (95% CI: −1.31, 4.70) increase in SBP for each ten μg/m3 increase in PM2.5 three days prior. However, for those in the high stress group in the Southwest neighborhood, there was an additional 7.16 mmHg (95% CI: 0.21, 14.11) increase in SBP for each ten μg/m3 in PM2.5 three days prior. In the lower panel, we show that this means that for the high stress group in the Southwest neighborhood, there is a total 8.86 mmHg increase in SBP for each ten μg/m3 in PM2.5 three days prior. This difference in PM2.5-SBP association between the low and high stress groups is statistically significant as shown by the interaction term. This pattern of results was observed at each of the PM2.5 exposure periods, but the interaction was statistically significant only with a PM2.5 exposure 3- and 4-days prior to the exam.

All information in Tables 2 through 5 is presented in the same manner. A similar pattern of results was observed for DBP, although the interaction was statistically significant only with a PM2.5 exposure 4 days prior to the exam. Finally, a similar pattern of results was observed for PP, with significant interaction terms with a PM2.5 exposure 3 days prior to the exam.

We did not find evidence of problematic multicollinearity in our models. Average VIFs ranged from 2.46 for 2-day lagged exposure with DBP to 3.42 for the 4-day lagged exposure with SBP. Our model results were similar when we used alternate exposure averaging periods. When we adjusted for anti-hypertensive medication use, BMI, current smoking status, sodium intake, and diabetes diagnosis, the pattern of results did not change, with several of the interaction terms increasing, suggesting a stronger modifying effect of stress after adjustment for these additional hypertension risk factors. We did not find evidence that race/ethnicity modified the association between PM2.5 and blood pressure or that race/ethnicity mediated the modifying effects of neighborhood on the association between PM2.5 and blood pressure.

We did find, however, that men but not women exhibited an association between PM2.5 and blood pressure. For example, for each 10 μg/m3 increase in PM2.5 4 days prior, men showed a 11.07 mmHg increase in SBP (95%CI: 5.25,16.89) while women showed a 0.87 decrease in SBP (95%CI: −4.04,2.30; results not shown in table form). However, we did not find evidence that the differences in the sex composition mediated the modifying effect of neighborhood.

Discussion

We set out to examine the potentially modifying role of psychosocial stress on the association between PM2.5 and blood pressure. We found evidence to suggest that there is, in fact, a modifying effect of stress on the association between PM2.5 and SBP, but this modifying effect varied by location. While the pattern of results was similar across the time-lagged exposure measures in the Southwest area, the most pronounced associations were for exposure four days prior to blood pressure measurement. The literature suggests that PM2.5 would increase blood pressure within the first day (Brook et al. 2002; Brook et al. 2009) rather than four days later. It is likely that this apparent delayed reaction is really due to measurement issues. Others who have examined ambient PM2.5 in relation to blood pressure have reported similar associations with longer-term acute measures of PM2.5 (Auchincloss et al. 2008; Hicken et al. 2013).

Researchers have theorized that psychosocial stress increases vulnerability to the health effects of environmental hazards. However, there is still a dearth of empirical evidence. In most of the empirical work to date, researchers have examined the modifying role of stress on the association between air pollution and respiratory outcomes. For example, some report that the association between air pollution and asthma is stronger in children who either have high exposure to violence or whose parents report high levels of stress (Clougherty et al. 2007; Shankardass et al. 2009). Others report that the association between air pollution and clinical asthma symptoms is stronger among asthmatic children who also report high levels of chronic family stress (Chen et al. 2008). Although this study is one of the first examination of a modifying effect of stress on the association between PM2.5 and blood pressure, one study showed that the association between proximity to high-traffic roads and coronary artery calcification was stronger for those in neighborhoods with high unemployment, which can be considered a social source of stress (Dragano et al. 2009). (Babisch et al. 2001; Babisch 2002, 2003; Combined Environmental Exposures: Noise Air Pollutants and Chemicals participants 2007; Huang et al. 2013)However, another recent study examined the modifying effect of social and psychosocial adversity on the association between PM2.5 and BP and found no evidence of effect modification (Hicken et al. 2013). However, in that study, the authors did not use an index of multiple sources and types of stress. Alternatively, noise has been framed as a source of stress (Babisch 2002) and has been shown to activate the stress response systems (Babisch et al. 2001; Babisch 2003). Furthermore, noise been shown to enhance the association between air pollution and heart rate variability (Huang et al. 2013).

Some have found evidence of a modifying effect of psychosocial stress on the association between other environmental hazards and hypertension. For example, researchers reported that men who report high levels of perceived stress showed a stronger association between bone lead and blood pressure compared to those who reported low levels of perceived stress (Peters et al. 2007). Similarly, researchers found the association between blood lead and hypertension is stronger in adults with high, compared to low, allostatic load scores, a measure of the wear and tear on the physiological systems due to psychosocial stress (McEwen 1998; Zota et al. 2010). Our work extends this literature by examining the modifying effect of psychosocial stress on the association between PM2.5 and blood pressure.

That the evidence of a modifying effect was isolated to the Southwest neighborhood is consistent with research suggesting that location is important for the health effects of air pollution (Brook et al. 2009). It may be that the chemical composition of PM2.5 is important to this effect of stress. While in our particular study the Southwest neighborhood did not experience greater PM2.5 levels due to seasonal confounding with regard to data collection in each neighborhood, others have shown that the southwest area of Detroit has consistently elevated levels of PM2.5 compared to other areas of Detroit (Keeler et al. 2002). It has been observed that the PM2.5 in the Southwest neighborhood is derived from numerous nearby emission sources, including traffic-related (particularly diesel) and industrial point sources (Hammond et al. 2008; Morishita et al. 2006; Pancras et al. 2013) and that the chemical composition is different between neighborhoods (Hammond et al. 2008). It may be that the chemical composition of PM2.5 in Southwest Detroit is particularly harmful to cardiovascular health. The Southwest area of Detroit is at the nexus of numerous high-traffic roadways and at the point of the largest commercial border crossing between the U.S. and Canada. This suggests that motor vehicle, including diesel exhaust pollution, may be higher in this area of Detroit compared to the other two neighborhoods (Morishita et al. 2006). Authors recently reported that, at the time of the HEP Community Survey, 35% of the PM2.5 mass in the area was due to motor vehicle exhaust – with 12% alone due to diesel exhaust (Hammond et al. 2008). Research has shown that diesel exhaust pollution results in increased blood pressure (Cosselman et al. 2012) inflammation and oxidative stress in both animal and human studies (Lodovici and Bigagli 2011). Furthermore, using animal models, researchers have shown elevated levels of an endogenous NO synthase inhibitor (Dvonch et al. 2004) and inflammatory responses (Morishita et al. 2004) after exposure to the particulate matter (PM) air pollution in Southwest Detroit. They further showed that the inflammatory responses were not due simply to the level but the chemical composition of the PM in Southwest Detroit (Morishita et al. 2004). These pathways through oxidative stress and inflammatory processes are thought to be an important mechanism linking PM2.5 and cardiovascular outcomes (Brook and Rajagopalan 2009). Future research should focus, not only on PM2.5 level, but also on the chemical composition in relation to health.

Similarly, it may be that the proximity of the Southwest neighborhood to unique forms of traffic-related noise (i.e., the movement and idling of thousands of commercial trucks per day) is responsible for the modifying effect of PM2.5. As we mentioned earlier, research suggests that noise may modifying the cardiovascular health effects of air pollution (Huang et al. 2013).

Finally, a growing literature documents the link between overactivation of the biological stress process and oxidative stress and inflammatory processes (Dimsdale 2008; McEwen 1998). While there have been no animal studies specifically on the causal mechanistic linkages among PM2.5, stress, and blood pressure, there are animal studies to support these linkages among PM2.5, stress, and respiratory outcomes (Clougherty et al. 2010). Our understanding of the pathways through which PM2.5 and stress synergistically affect blood pressure would benefit from future research in animal models.

While we had hypothesized that stress would modify the association between PM2.5 and blood pressure, our results in the Southwest neighborhood may be due to differences in neighborhood composition. Some studies suggest that Black and White adults exhibit different mechanisms in the development of hypertension (Morris et al. 1999). In general, however, there are not racial/ethnic differences in the association between PM2.5 and cardiovascular outcomes, including blood pressure (Adar et al. 2010; Adar et al. 2013; Hicken et al. 2013), although one study showed a stronger association between air pollution and self-reported cardiovascular outcomes for White compared to Black or Hispanic adults (Johnson and Parker 2009). It may also be that the differences in the sex composition of the neighborhoods are driving the differences in the modifying effects of stress, as there are fewer women in the Southwest neighborhood than in the other two. Estrogen has been shown to be cardioprotective and may provide a resiliency to the joint cardiovascular effects of PM2.5 and stress. While we found that sex (but not race/ethnicity) modified the association between PM2.5 and blood pressure, we did not find that either sex or race/ethnicity mediated the modifying effect of neighborhood.

While our research is one of the first contributions to the literature on the modifying effects of psychosocial stress on the association between PM2.5 and blood pressure, it is not without limitations. The HEP sample is of relatively low socioeconomic status (SES) and psychosocial stress, PM2.5 exposure, and hypertension have been shown to be related to SES (Adler et al. 1994; Kaplan and Nunes 2003; O’Neill et al. 2003). As such, our results may provide a conservative estimate of the associations among these factors. Future research should examine these associations in samples with broader ranges of socioeconomic characteristics.

Conclusion

We show evidence to suggest that psychosocial stress increases vulnerability to the hypertensive effects of PM2.5. Our work expands the empirical literature on the synergistic effects of psychosocial stress and environmental hazards on health. More importantly, our work contributes to an understanding of the ways in which the social and physical environments may jointly contribute to poor health and, potentially, health disparities.

Table 3.

Linear regression estimates for diastolic blood pressure regressed on the interaction among neighborhood, stress, and 24-hour averaged PM2.5 exposure

24-hour average PM2.5 exposure, number of days prior to exam
2 days prior 3 days prior 4 days prior
--1-- --2-- --1-- --2-- --1-- --2--
Neighborhood
 Southwest ref ref
 East −2.22 (−7.04,2.60) −3.65 (−8.10,0.80) −2.08 (−7.37,3.21) −2.84 (−7.93,2.25) −2.48 (−7.96,3.00) −3.86 (−9.87,2.15)
 Northwest −0.73 (−4.49,3.02) 0.45 (−3.31,4.20) −0.68 (−5.07,3.71) 0.99 (−3.56,5.53) −1.48 (−6.29,3.32) 0.41 (−4.76,5.58)
PM2.5 −1.51 (−3.79,0.77) −2.32 (−5.24,0.60) −0.68 (−2.81,1.45) 0.21 (−2.83,3.25) 1.23 (−1.93,4.38) 1.49 (−5.08,8.06)
Stress level
 Low stress ref ref ref
 High stress 2.19 (−2.60,6.99) 4.80 (−0.64,10.24) 8.08** (3.19,12.96)
NeighborhoodXstress
 SouthwestXstress ref ref ref
 EastXstress 1.77 (−4.91,8.45) −0.75 (−7.33,5.83) −3.07 (−9.86,3.73)
 NorthwestXstress −4.82 (−11.65,2.01) −7.37 (−14.96,0.22) −12.28*** (−17.95,−6.62)
NeighborhoodXPM2.5
 SouthwestXPM2.5 ref ref ref
 EastXPM2.5 2.03 (−2.04,6.09) −2.04 (−5.89,1.82) −3.20 (−10.67,4.28)
 NorthwestXPM2.5 1.15 (−4.90,7.19) −1.71 (−7.59,4.17) −2.20 (−9.63,5.22)
StressXPM2.5
 Low stressXPM2.5 ref ref ref
 High stressXPM2.5 3.16 (−3.49,9.81) 1.87 (−5.40,9.14) 10.31* (0.14,20.49)
NeighborhoodXstressXPM2.5
SouthwestXstressXPM2.5
 EastXstressXPM2.5 −4.52 (−13.62,4.58) −0.23 (−9.09,8.62) −3.16 (−17.21,10.89)
NorthwestXstressXPM2.5 −4.89 (−15.75,5.97) −3.18 (−12.40,6.04) −7.04 (−20.96,6.89)
Southwest neighborhood
 Low stress −2.32 (−5.24,0.60) 0.21 (−2.83,3.25) 1.49 (−5.08,8.06)
 High stress 0.84 (−5.05,6.73) 2.08 (−5.53,9.69) 11.80** (3.80,19.80)
East neighborhood
 Low stress −0.30 (−3.27,2.68) −1.83 (−4.27,0.62) −1.71 (−4.68,1.26)
 High stress −1.66 (−8.45,5.13) −0.19 (−5.17,4.79) 5.45 (−1.99,12.88)
Northwest neighborhood
 Low stress −1.18 (−6.61,4.26) −1.50 (−6.44,3.44) −0.71 (−4.23,2.80)
 High stress −2.90 (−10.43,4.62) −2.81 (−7.74,2.12) 2.56 (−5.42,10.55)
Open in a new tab

Results reported in the upper panel are regression coefficients (95% confidence intervals) for PM2.5, stress, and PM2.5-by-stress interaction terms. Results reported in the lower panel are post-estimation coefficients (95% confidence intervals) for the association between PM2.5 and diastolic blood pressure at low (lower three quartiles of the stress distribution) and high (high quartile of the stress distribution) variable. PM2.5coefficients are interpreted as change in diastolic blood pressure per 10 μg/m3 increase in PM2.5. All estimates are adjusted for: age, sex, race, education, and poverty income ratio. PM2.5 was mean-centered before interaction.

*

p<0.05;

**

p<0.01;

***

p<0.001 for difference from zero.

Table 4.

Linear regression estimates for pulse pressure regressed on the interaction among neighborhood, stress, and 24-hour averaged PM2.5 exposure

24-hour average PM2.5 exposure, number of days prior to exam
2 days prior 3 days prior 4 days prior
--1-- --2-- --1-- --2-- --1-- --2--
Neighborhood
 Southwest ref ref ref ref ref ref
 East −1.39 (−5.28,2.51) −0.89 (−5.20,3.41) −1.69 (−6.38,3.01) −0.58 (−5.86,4.69) −1.44 (−6.16,3.28) −0.99 (−6.40,4.43)
 Northwest −3.58 (−7.63,0.47) −1.83 (−6.08,2.42) −3.34 (−8.02,1.35) −2.37 (−7.46,2.72) −3.94 (−9.08,1.20) −2.65 (−8.63,3.34)
PM2.5 4.47** (1.82,7.12) 5.30* (1.22,9.37) 1.84* (0.34,3.34) 1.56 (−0.60,3.73) 2.62 (−0.18,5.43) 4.49 (−0.18,9.15)
Stress level
 Low stress ref ref ref
 High stress 2.04 (−1.49,5.57) 2.39 (−2.03,6.81) 2.07 (−3.38,7.51)
NeighborhoodXstress
 SouthwestXstress ref ref ref
 EastXstress −2.71 (−6.92,1.50) −3.96 (−9.05,1.13) −4.216 (−10.19,1.76)
 NorthwestXstress −4.14 (−11.95,3.67) −4.56 (−12.16,3.04) −4.22 (−15.81,7.38)
NeighborhoodXPM2.5
 SouthwestXPM2.5 ref ref ref
 EastXPM2.5 1.85 (−5.95,9.66) 2.05 (−1.22,5.31) −1.23 (−8.84,6.38)
 NorthwestXPM2.5 −5.50 (−13.94,2.94) −4.57 (−10.66,1.53) −5.76 (−14.48,2.96)
StressXPM2.5
 Low stressXPM2.5 ref ref ref
 High stressXPM2.5 3.41 (−1.16,7.98) 5.47* (0.83,10.10) 4.42 (−4.24,13.08)
NeighborhoodXstressXPM2.5
SouthwestXstressXPM2.5
 EastXstressXPM2.5 −7.88 (−16.72,0.96) −7.80* (−13.89,−1.71) −3.95 (−13.53,5.63)
NorthwestXstressXPM2.5 −7.04 (−21.47,7.38) −5.42 (−17.65,6.81) −4.68 (−22.08,12.73)
Southwest neighborhood
 Low stress 5.30* (1.22,9.37) 1.56 (−0.60,3.73) 4.49 (−0.18,9.15)
 High stress 8.71*** (4.74,12.67) 7.03** (2.37,11.69) 8.91* (2.02,15.80)
East neighborhood
 Low stress 7.15* (0.36,13.94) 3.61** (1.17,6.05) 3.26 (−2.50,9.01)
 High stress 2.68 (−0.83,6.20) 1.28 (−0.87,3.43) 3.73 (−2.15,9.61)
Northwest neighborhood
 Low stress −0.20 (−7.66,7.26) −3.00 (−8.71,2.70) −1.28 (−9.10,6.55)
 High stress −3.83 (−15.59,7.93) −2.96 (−12.13,6.21) −1.53 (−10.57,7.52)
Open in a new tab

Results reported in the upper panel are regression coefficients (95% confidence intervals) for PM2.5, stress, and PM2.5-by-stress interaction terms. Results reported in the lower panel are post-estimation coefficients (95% confidence intervals) for the association between PM2.5 and pulse pressure at low (lower three quartiles of the stress distribution) and high (high quartile of the stress distribution) variable. PM2.5coefficients are interpreted as change in pulse pressure per 10 μg/m3 increase in PM2.5. All estimates are adjusted for: age, sex, race, education, and poverty income ratio. PM2.5 was mean-centered before interaction.

*

p<0.05;

**

p<0.01;

***

p<0.001 for difference from zero.

Highlights.

  • Work suggests that psychosocial stress increases vulnerability to the effects of air pollution in certain contexts.

  • We examined the modifying role of psychosocial stress on the hypertensive effects of PM2.5.

  • In Southwest Detroit, high stress was associated with stronger PM2.5-BP associations.

Acknowledgments

Funding sources: Partial funding to support this effort was from the National Institute of Environmental Health Sciences (grants P01-ES09589-01, R826710-01, R01ES10936, and R01ES014234), the US Environmental Protection Agency (grant ES10688-03) and the Robert Wood Johnson Foundation Health & Society Scholars program.

Healthy Environments Partnership is a community-based participatory research partnership dedicated to understanding and addressing environmental contributions to cardiovascular health disparities (www.hepdetroit.org). We thank members of the Healthy Environments Partnership Steering Committee, including the Brightmoor Community Center, Detroit Hispanic Development Corporation, Detroit Institute for Population Health, Friends of Parkside, Henry Ford Health System, University of Michigan School of Public Health, and Warren/Conner Development Coalition, for their contribution to the work presented here. We also thank Community Action Against Asthma, members of the University of Michigan Air Quality Laboratory for assistance with particulate matter data collection and the University of Michigan Nutritional Biomarkers Laboratory for assistance with blood pressure measures.

Abbreviations

BMI

body mass index

DBP

diastolic blood pressure

HEP

Healthy Environments Partnership

NHANES

National Health and Nutrition Examination Survey

PIR

poverty income ratio

PM2.5

particulate matter air pollution of 2.5 microns in diameter

SBP

systolic blood pressure

SES

socioeconomic status

Footnotes

The Community Survey of Detroit protocol was approved by the Institutional Review Board of the University of Michigan.

Competing financial information: The authors declare no competing financial interests.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errorsmaybe discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Margaret T. Hicken, Department of Epidemiology, University of Michigan

J. Timothy Dvonch, Department of Environmental Health Sciences, University of Michigan.

Amy J. Schulz, Department of Health Behavior and Health Education, University of Michigan

Graciela Mentz, Department of Health Behavior and Health Education, University of Michigan.

Paul Max, Environmental Affairs Unit, Building, Safety Engineering, and Environmental Department, City of Detroit.

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