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Trends in Tract-Level Prevalence of Obesity in Philadelphia by Race-Ethnicity, Space, and Time
Journal article   Peer reviewed

Trends in Tract-Level Prevalence of Obesity in Philadelphia by Race-Ethnicity, Space, and Time

Harrison Quick, Dina Terloyeva, Yaxin Wu, Kari Moore and Ana V Diez Roux
Epidemiology (Cambridge, Mass.), v 31(1), pp 15-21
Jan 2020
PMID: 31688128
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

African Americans - statistics & numerical data Bayes Theorem Cities - epidemiology European Continental Ancestry Group - statistics & numerical data Female Hispanic Americans - statistics & numerical data Humans Male Obesity - ethnology Philadelphia - epidemiology Prevalence Self Report Spatio-Temporal Analysis
The growing recognition of often substantial neighborhood variation in health within cities has motivated greater demand for reliable data on small-scale variations in health outcomes. The goal of this article is to explore temporal changes in geographic disparities in obesity prevalence in the City of Philadelphia by race and sex over the period 2000-2015. Our data consist of self-reported survey responses of non-Hispanic whites, non-Hispanic blacks, and Hispanics from the Southeastern Pennsylvania Household Health Survey. To analyze these data-and to obtain more reliable estimates of the prevalence of obesity-we apply a Bayesian model that simultaneously accounts for spatial-, temporal-, and between-race/ethnicity dependence structures. This approach yields estimates of the obesity prevalence by age, race/ethnicity, sex, and poverty status for each census tract at all time-points in our study period. While the data suggest that the prevalence of obesity has increased at the city-level for men and women of all three race/ethnicities, the magnitude and geographic distribution of these increases differ substantially by race/ethnicity and sex. The method can be flexibly used to describe and visualize spatial heterogeneities in levels, trends, and in disparities. This is useful for targeting, surveillance, and etiologic research.

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22 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#10 Reduced Inequalities
#3 Good Health and Well-Being

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Web of Science research areas
Public, Environmental & Occupational Health
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