Abstract

Features of the environment may modify the effect of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) on health. Therefore, we investigated how neighborhood sociodemographic and land-use characteristics may modify the association between PM2.5 and cardiovascular mortality. We obtained residence-level geocoded cardiovascular mortality cases from the Massachusetts Department of Public Health (n = 179,986), and PM2.5 predictions from a satellite-based model (2001–2011). We appended census block group-level information on sociodemographic factors and walkability, and calculated neighborhood greenness within a 250 m buffer surrounding each residence. We found a 2.54% (1.34%; 3.74%) increase in cardiovascular mortality associated with a 10 µg/m3 increase in two-day average PM2.5. Walkability or greenness did not modify the association. However, when stratifying by neighborhood sociodemographic characteristics, smaller PM2.5 effects were observed in greener areas only among cases who resided in neighborhoods with a higher population density and lower percentages of white residents or residents with a high school diploma. In conclusion, the PM2.5 effects on cardiovascular mortality were attenuated by higher greenness only in areas with sociodemographic features that are highly correlated with lower socioeconomic status. Previous evidence suggests health benefits linked to neighborhood greenness may be stronger among lower socioeconomic groups. Attenuation of the PM2.5–mortality relationship due to greenness may explain some of this evidence.

Highlights

  • Cardiovascular disease (CVD) is the most common cause of death in the United States [1]

  • We included 179,986 persons who resided in Massachusetts and died of CVD between 2001 and

  • The interquartile range (IQR) of the two-day average concentration of PM2.5 was between 6.8 μg/m3 and 12.3 μg/m3, and the mean value was 10.2 μg/m3

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Summary

Objectives

We aim to assess the joint modification of the PM2.5 related cardiovascular risk by land-use characteristics and neighborhood sociodemographic characteristics while controlling for individual sociodemographic characteristics

Methods
Results
Discussion
Conclusion

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