TY - JOUR
T1 - Race/ethnicity, socioeconomic status, residential segregation, and spatial variation in noise exposure in the contiguous United States
AU - Casey, Joan A.
AU - Morello-Frosch, Rachel
AU - Mennitt, Daniel J.
AU - Fristrup, Kurt
AU - Ogburn, Elizabeth L.
AU - James, Peter
N1 - Funding Information:
We thank B. Jesdale for assembling the segregation data and the University of California, Berkeley Statistical Consulting Service for statistical support. This work was supported by the Robert Wood Johnson Foundation Health & Society Scholars program (J.A.C.), the National Cancer Institute Award K99CA201542 (P.J.), and the Hewlett and Kellogg and Foundations (R. M.-F.).
Publisher Copyright:
© 2017, Public Health Services, US Dept of Health and Human Services. All rights reserved.
PY - 2017/7
Y1 - 2017/7
N2 - BACKGROUND: Prior research has reported disparities in environmental exposures in the United States, but, to our knowledge, no nationwide studies have assessed inequality in noise pollution. OBJECTIVES: We aimed to a) assess racial/ethnic and socioeconomic inequalities in noise pollution in the contiguous United States; and b) consider the modifying role of metropolitan level racial residential segregation. METHODS: We used a geospatial sound model to estimate census block group–level median (L50) nighttime and daytime noise exposure and 90th percentile (L10) daytime noise exposure. Block group variables from the 2006–2010 American Community Survey (ACS) included race/ethnicity, education, income, poverty, unemployment, homeownership, and linguistic isolation. We estimated associations using polynomial terms in spatial error models adjusted for total population and population density. We also evaluated the relationship between race/ethnicity and noise, stratified by levels of metropolitan area racial residential segregation, classified using a multigroup dissimilarity index. RESULTS: Generally, estimated nighttime and daytime noise levels were higher for census block groups with higher proportions of nonwhite and lower-socioeconomic status (SES) residents. For example, estimated nighttime noise levels in urban block groups with 75% vs. 0% black residents were 46.3 A-weighted decibels (dBA) [interquartile range (IQR): 44:3–47:8 dBA] and 42:3 dBA (IQR: 40:4–45:5 dBA), respectively. In urban block groups with 50% vs. 0% of residents living below poverty, estimated nighttime noise levels were 46:9 dBA (IQR: 44:7–48:5 dBA) and 44:0 dBA (IQR: 42:2–45:5 dBA), respectively. Block groups with the highest metropolitan area segregation had the highest estimated noise exposures, regardless of racial composition. Results were generally consistent between urban and suburban/rural census block groups, and for daytime and nighttime noise and robust to different spatial weight and neighbor definitions. CONCLUSIONS: We found evidence of racial/ethnic and socioeconomic differences in model-based estimates of noise exposure throughout the United States. Additional research is needed to determine if differences in noise exposure may contribute to health disparities in the United States.
AB - BACKGROUND: Prior research has reported disparities in environmental exposures in the United States, but, to our knowledge, no nationwide studies have assessed inequality in noise pollution. OBJECTIVES: We aimed to a) assess racial/ethnic and socioeconomic inequalities in noise pollution in the contiguous United States; and b) consider the modifying role of metropolitan level racial residential segregation. METHODS: We used a geospatial sound model to estimate census block group–level median (L50) nighttime and daytime noise exposure and 90th percentile (L10) daytime noise exposure. Block group variables from the 2006–2010 American Community Survey (ACS) included race/ethnicity, education, income, poverty, unemployment, homeownership, and linguistic isolation. We estimated associations using polynomial terms in spatial error models adjusted for total population and population density. We also evaluated the relationship between race/ethnicity and noise, stratified by levels of metropolitan area racial residential segregation, classified using a multigroup dissimilarity index. RESULTS: Generally, estimated nighttime and daytime noise levels were higher for census block groups with higher proportions of nonwhite and lower-socioeconomic status (SES) residents. For example, estimated nighttime noise levels in urban block groups with 75% vs. 0% black residents were 46.3 A-weighted decibels (dBA) [interquartile range (IQR): 44:3–47:8 dBA] and 42:3 dBA (IQR: 40:4–45:5 dBA), respectively. In urban block groups with 50% vs. 0% of residents living below poverty, estimated nighttime noise levels were 46:9 dBA (IQR: 44:7–48:5 dBA) and 44:0 dBA (IQR: 42:2–45:5 dBA), respectively. Block groups with the highest metropolitan area segregation had the highest estimated noise exposures, regardless of racial composition. Results were generally consistent between urban and suburban/rural census block groups, and for daytime and nighttime noise and robust to different spatial weight and neighbor definitions. CONCLUSIONS: We found evidence of racial/ethnic and socioeconomic differences in model-based estimates of noise exposure throughout the United States. Additional research is needed to determine if differences in noise exposure may contribute to health disparities in the United States.
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U2 - 10.1289/EHP898
DO - 10.1289/EHP898
M3 - Article
C2 - 28749369
AN - SCOPUS:85032837959
VL - 125
JO - Environmental Health Perspectives
JF - Environmental Health Perspectives
SN - 0091-6765
IS - 7
M1 - 077017
ER -