Kellee White1, Luisa N Borrell. 1. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
Abstract
OBJECTIVES: To examine the relationship between racial/ethnic neighborhood concentration and self-reported health before and after adjustment of individual- and neighborhood-level characteristics and to determine whether this association varies by race/ethnicity and perception of neighborhood. DESIGN: The data are derived from the 1999 and 2002 New York City Social Indicator Survey, a cross-sectional survey. Logistic regression was used to assess the strength of the association between racial/ethnic neighborhood concentration and self-reported health before and after controlling for other covariates. SETTING: The survey was conducted in New York City in 1999 and 2002. PARTICIPANTS: A final sample of 2,845 individuals who self-identified as White, Black, Hispanic, or Asian was linked by zip code to the 2000 US Census. MAIN OUTCOME MEASURE: Self-reported health was used as a dichotomous variable, good health status (including responses of excellent, very good, pretty good, or good) and poor health status (including the responses fair or poor). RESULTS: Overall, 21.8% of respondents rated their health as poor, and those who live in neighborhoods with a high concentration of Blacks reported poorer health (27.2%) than those who live in neighborhoods with a low concentration of Blacks (17.3%, P<.001). Our findings suggest that individuals living in the most concentrated neighborhoods were almost two times more likely (odds ratio 1.77, 95% confidence interval 1.12-2.79) to perceive their health as poor compared to their counterparts living in less concentrated neighborhoods. CONCLUSIONS: This study demonstrates that poor self-reported health varies with patterns of concentration of Blacks in a neighborhood, after adjusting for individual- and neighborhood-level characteristics and perception of neighborhood. The results underscore the need for elucidating the pathways by which racial/ethnic neighborhood concentration affects health.
OBJECTIVES: To examine the relationship between racial/ethnic neighborhood concentration and self-reported health before and after adjustment of individual- and neighborhood-level characteristics and to determine whether this association varies by race/ethnicity and perception of neighborhood. DESIGN: The data are derived from the 1999 and 2002 New York City Social Indicator Survey, a cross-sectional survey. Logistic regression was used to assess the strength of the association between racial/ethnic neighborhood concentration and self-reported health before and after controlling for other covariates. SETTING: The survey was conducted in New York City in 1999 and 2002. PARTICIPANTS: A final sample of 2,845 individuals who self-identified as White, Black, Hispanic, or Asian was linked by zip code to the 2000 US Census. MAIN OUTCOME MEASURE: Self-reported health was used as a dichotomous variable, good health status (including responses of excellent, very good, pretty good, or good) and poor health status (including the responses fair or poor). RESULTS: Overall, 21.8% of respondents rated their health as poor, and those who live in neighborhoods with a high concentration of Blacks reported poorer health (27.2%) than those who live in neighborhoods with a low concentration of Blacks (17.3%, P<.001). Our findings suggest that individuals living in the most concentrated neighborhoods were almost two times more likely (odds ratio 1.77, 95% confidence interval 1.12-2.79) to perceive their health as poor compared to their counterparts living in less concentrated neighborhoods. CONCLUSIONS: This study demonstrates that poor self-reported health varies with patterns of concentration of Blacks in a neighborhood, after adjusting for individual- and neighborhood-level characteristics and perception of neighborhood. The results underscore the need for elucidating the pathways by which racial/ethnic neighborhood concentration affects health.
Authors: Susan M Mason; Jay S Kaufman; Julie L Daniels; Michael E Emch; Vijaya K Hogan; David A Savitz Journal: Ann Epidemiol Date: 2011-08 Impact factor: 3.797
Authors: Laia Bécares; Richard Shaw; James Nazroo; Mai Stafford; Christo Albor; Karl Atkin; Kathleen Kiernan; Richard Wilkinson; Kate Pickett Journal: Am J Public Health Date: 2012-10-18 Impact factor: 9.308