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

Abstract

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.

Constructing Within-City Neighborhood Health Rankings in Philadelphia by Using Data From the 500 Cities Project

“…The 2018 County Health Rankings Key Findings Report noted similar patterns nationwide, with many of their identified health gaps resulting from lack of opportunity and structural obstacles to good health in certain geographic areas (28). Philadelphia neighborhoods with low health outcome rankings in our study had the highest rates of child poverty, which is strongly tied to adverse health outcomes in childhood and beyond (29,30). Similarly, many of the Philadelphia neighborhoods in our study with low health factor and outcome rankings were more adversely affected than neighborhoods with better rankings by the COVID-19 pandemic, which began after completion of this project.…”

A narrative review on the validity of electronic health record-based research in epidemiology

“…In the population health paradigm whereby the EHR is used as a surveillance tool to identify community health disparities [ 13 ], one also needs to be concerned about representativeness. There are emerging approaches for producing such small area community estimates from large observational datasets [ 22 , 23 ]. Conceivably, these approaches may also be useful for identifying issues of representativeness, for example by comparing stratified estimates across sociodemographic or other factors that may relate to catchment.…”

Socioeconomic Disparities in Hypertension by Levels of Green Space Availability: A Cross-Sectional Study in Philadelphia, PA

“…The final analytic sample included 3887 adults (18+ years old) that had valid responses on all relevant variables residing in 377 census tracts in Philadelphia County ( Figure 1 ). Philadelphia is the poorest large US city and has wide health disparities [ 53 , 54 , 55 , 56 ]. Furthermore, we decided to restrict the sample to Philadelphia to narrow the focus to an urban area, as green space availability (and its management) differs between the surrounding suburban and exurban counties.…”