Synthesis of Harvard Environmental Protection Agency (EPA) Center studies on traffic-related particulate pollution and cardiovascular outcomes in the Greater Boston Area

Figures & data

Table 1. Description of roadway proximity studies assessing cardiovascular mortality risk.

Study	Cohort	Exposure	Reference group	Follow-up period
Medina-Ramón et al. (2008)	1,389 acute HF patients in Worcester, MA	US Census feature class A1 or public bus route	>100 m of highway or >50 m of bus route	5 years
Wilker et al. (2013)	1,683 ischemic stroke pts at BIDMC	Distance to the nearest high-traffic roadway (>10,000 vehicles/day)	>400 m	4.6 years (median)
Rosenbloom et al. (2012)	3,547 post-AMI patients from 64 US hospitals (82% in New England)	US Census feature class A1 or A2	>1,000 m	10 years

Figure 1. Post-hospitalization mortality risk of roadway proximity among cardiovascular patients. Risk estimates are derived from Medina-Ramón et al. (2008) (post-heart failure), Wilker et al. (2013) (post-ischemic stroke), and Rosenbloom et al. (2012) (post-acute MI).

Figure 2. Effect of traffic exposure on heart rate variability (HRV). Risk estimates reported per interquartile range (IQR) increase in PM_2.5 or BC.

Table 2. Summary of fibrinogen risk estimates associated with PM_2.5, BC, and PN exposure.

Study	Cohort	Study Period	Moving averages	Significant Findings*
Zeka et al. (2006)	NAS (n = 710) Mean age: 73.0; 100% men	2000–2004	2-, 7-, 28-d	PM2.5: none (+) BC: 28-d (+) PN: 2-, 7-d
Bind et al. (2012)	NAS (n = 704) Mean age: 73.2; 100% men	2000–2009	4-h, 1-, 3-, 7-, 14-, 21-, 28-d	PM2.5: none (+) BC: 3-, 7-d (+) PN: 3-, 14-, 21-, 28-d
Bind et al. (2014)	NAS (n = 822) Mean age: 72.4; 100% men	1999–2011	7-d	PM2.5: none (+) BC: 7-d (+) PN: 7-d
Li et al. (2017)	Framingham (n = 3,996) Mean age: 53.6; 46% men	1998–2011	1- to 7-d	PM2.5: none (-) BC: 2-d, 3-d

Notes: *Bind et al. (2014): results for high oxidative stress, endothelial dysfunction, and metal processing dysfunction

(+): positive associations reported

(-): negative associations reported

Figure 3. Effect of traffic exposure on inflammatory markers–-C reactive protein (CRP) and fibrinogen. Risk estimates reported per 1 μg/m³ increase in BC, 10 μg/m³ increase in PM_2.5, and 15,000 particles/cm³ increase in PNC.

Table 3. Summary of VCAM-1 risk estimates associated with PM_2.5, BC, and PN exposure.

Study	Cohort	Study Period	Findings*
Madrigano et al. (2010)	NAS (n = 809; 1,819 measures) Mean age: 72.3; 100% men	1999–2008	2-d 1 μg/m^3 BC, 4.5%, [1.1%, 8.0%] 2-d 10 μg/m^3 PM2.5, 1.7% [−0.9%, 4.3%]
Bind et al. (2012)	NAS (n = 704; up to 4 measures per participant) Mean age: 73.2; 100% men	2000–2009	3-d IQR PN, 10.3% [6.6%, 14.2%] 3-d IQR BC, 3.0% [0.5%, 5.6%] 3-d IQR PM2.5, 2.7% [0.6%, 4.8%]
O’Neil et al. (2007)	92 clinical trial participants at Joslin Diabetes Center and BIDMC Mean age: 56.6; 60% men	1998–2002	5-d IQR BC, 23.8% [8.4%, 41.4%] 5-d IQR PM2.5, 8.6% [0.1%, 17.8%]
Garshick et al. (2018)	COPD patients from Eastern MA (n = 88) Mean age: 72.7; 100% men	Nov 2012 -Dec 2014	2-d IQR BC, −0.03% [−1.48%, 1.44%] 3-d IQR BC, −0.07% [−1.71%, 1.59%] 5-d IQR BC, −0.09% [−1.68%, 1.53%]

Note: * 95% confidence interval reported

Figure 4. Effect of traffic exposure on inflammatory markers ICAM and VCAM. Risk estimates reported per 1 μg/m³ increase in BC, 10 μg/m³ increase in PM_2.5, and 15,000 particles/cm³ increase in PNC.

Table 4. Risk estimates derived from measured versus modeled BC concentrations (risk estimates reported per 1 µg/m³ increase in BC).

Study	Outcome	Moving Avg	Risk (monitored BC)	Risk (modeled* BC)
Bind et al. (2012)	VCAM ICAM	28-d	6.4% (1.3, 11.8%) 8.8% (5.3, 12.2%)
Alexeeff et al. (2011)	VCAM ICAM	28-d		3.4% (−2.2, 9.2%) 5.1% (0.7, 9.6%)
Wilker et al. (2010)	DBP (mmHg) SBP (mmHg)	7-d	5.5 mmHg (4.5, 6.4) 7.3 mmHg (5.5, 9.1)
Schwartz et al. (2012)	DBP (mmHg) SBP (mmHg)	7-d		2.6 mmHg (1.0, 4.2) 3.1 mmHg (0.2, 6.0)
Wellenius et al. (2012)	Ischemic Stroke	1-d	21.0% (4.0, 41.6%)	13.7% (1.7, 28.1%)
Mostofsky et al. (2012)	Ischemic Stroke	1-d	52.8% (17.2, 99.5%)

Note: *Land use regression models were used to derive BC concentrations in these studies.

Figure 5. Effect of BC exposure on ICAM and VCAM using land use regression models. Risk estimates reported per 1 μg/m³ increase in BC.

Table 5. Summary of blood pressure risk estimates associated with PM_2.5, BC, and PN exposure.

Study	Cohort	Study Period	Moving averages	Significant Findings*
Mordukhovich et al. (2009)	NAS (n = 461) Mean age: 71.1; 100% men	1999–2007	7-d	PM2.5: none BC: 7-d (DBP & SBP)
Schwartz et al. (2012)	NAS (n = 853) Mean age: 72.6; 100% men	1996–2008	1-, 7-, 28-d, 3-mo, 12-mo	BC: 1-, 7-, 28-d, 3-mo, 12-mo (DBP) 7-, 28-d, 3-mo, 12-mo (SBP)
Wilker et al. (2010)	NAS (n = 789) Mean age: 72.3; 100% men	1995–2008	7-d	BC: 7-d (DBP & SBP)
Zhong et al. (2016)	NAS (n = 675) Age 55–100; 100% men	1999–2012	2-, 5-, 7-, 14-, 21-, 28-d	BC: 2-, 5-, 7-, 14-, 21-, 28-d (DBP & SBP)
Bind et al. (2016)	NAS (n = 1,112) Median age: 69; 100% men	1995–2013	28-d	PM2.5: 28-d (DBP & SBP) BC: 28-d (DBP & SBP) PNC: 28-d (DBP & SBP)

Notes: DBP: diastolic blood pressure, SBP: systolic blood pressure.

Figure 6. Effect of traffic exposure on systolic and diastolic blood pressure. Risk estimates reported per 1 μg/m³ increase in BC and 10 μg/m³ increase in PM_2.5.

Figure 7. Estimated effects of traffic exposure on ischemic stroke risk. Risk estimates reported per IQR increase in BC and PM_2.5.

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