Authors: Konty Kevin*, New York City department of Health and Mental Hygiene, Sophia E Day, New York City Department of Health and Mental Hygiene, Stuart H Sweeney, University of California, Santa Barbara
Topics: Population Geography, Geography and Urban Health, Medical and Health Geography
Keywords: Gentrification, American Community Survey, Public Health, Population Health
Session Type: Paper
Start / End Time: 1:30 PM / 2:45 PM
Room: Century, Sheraton, IM Pei Tower, Majestic Level
Presentation File: No File Uploaded
There has been increasing interest in the effect of gentrification on health. The crucial first step in establishing this relationship is the identification of gentrifying areas and appropriate comparison areas. This raises important questions about geographic and temporal scale. Although there is an extensive literature on the identification of these areas, in most jurisdictions no ‘official’ designations exist nor are there readily available, transparent, updateable classifications for use by public health researchers. One seemingly convenient data source is the American Community Survey (ACS) 5-year estimates, as used in a recent high-profile study linking health to gentrification in New York City [Dragan et al., Health Affairs September 2019]. ACS-based approaches are particularly appealing because such designation can be extended to cover all U.S. cities. Using New York City, we review three important considerations when measuring change with ACS: uncertainty, geographic scale, and updating rules. We find that census tract-based ACS gentrification designations should be expected to have high levels of misclassification leading to additional uncertainty when characterizing the gentrification/health relationship. These misclassification issues arise from sampling error and corresponding low levels of precision of ACS estimates at the census tract level. We explore the use of simple clustering measure based on join counts as a possible approach to discerning genuine change. While we focus on poverty, income, and level of education, the approach is relevant to monitoring change in other social determinants with repeated surveys.