Abstract
Epidemiologic evidence consistently links urban air pollution exposures to health, even after adjustment for potential spatial confounding by socioeconomic position (SEP), given concerns that air pollution sources may be clustered in and around lower-SEP communities. SEP, however, is often measured with less spatial and temporal resolution than are air pollution exposures (i.e., census-tract socio-demographics vs. fine-scale spatio-temporal air pollution models). Although many questions remain regarding the most appropriate, meaningful scales for the measurement and evaluation of each type of exposure, we aimed to compare associations for multiple air pollutants and social factors against cardiovascular disease (CVD) event rates, with each exposure measured at equal spatial and temporal resolution. We found that, in multivariable census-tract-level models including both types of exposures, most pollutant–CVD associations were non-significant, while most social factors retained significance. Similarly, the magnitude of association was higher for an IQR-range difference in the social factors than in pollutant concentrations. We found that when offered equal spatial and temporal resolution, CVD was more strongly associated with social factors than with air pollutant exposures in census-tract-level analyses in New York City.
Highlights
Evidence from environmental epidemiology consistently links urban air pollution to a variety of health risks including increased mortality, cardiovascular disease and respiratory disease [1]
From January 1, 2005 to December 31, 2011 there were 1,113,185 acute cardiovascular disease (CVD) events presented at hospitals in New York City (NYC)
In sensitivity analyses (Tables S1–S4), we found that implementing the Moran eigenvector filtering function in negative binomial generalized linear models (GLMs) models provided slightly better model fit and reduced spatial autocorrelation, but associations among air pollutants, social factors, and CVD rates did not differ substantially from negative binomial regression models
Summary
Evidence from environmental epidemiology consistently links urban air pollution to a variety of health risks including increased mortality, cardiovascular disease and respiratory disease [1]. It has become standard practice, in this field, to adjust models for potential confounding by socioeconomic position (SEP), because air pollution is often higher in lower-SEP communities [2,3,4] and because many factors associated with lower SEP (i.e., poverty, lower education, violence, poor diet) may directly impact health. Res. Public Health 2019, 16, 4621; doi:10.3390/ijerph16234621 www.mdpi.com/journal/ijerph
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