Using simulated data to investigate the spatial patterns of obesity prevalence at the census tract level in metropolitan Detroit

Abstract

© 2015 Elsevier Ltd. Obesity is a serious public health problem in the United States. It is important to estimate obesity prevalence at the local level to target programmatic and policy interventions. It is challenging, however, to obtain local estimates of obesity prevalence because national health surveys such as the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) are not designed to produce direct estimates at the local levels (e.g. census tracts) due to small population samples and the need to preserve individual confidentiality. In this study we address the problem of estimating local obesity prevalence rates by implementing a spatial microsimulation modeling technique to proportionally replicate the demographic characteristics of BRFSS respondents to census tract populations in metropolitan Detroit. Obesity prevalence rates are examined for high and low spatial clusters and studied in relation to the U.S. Department of Agriculture's (USDA) measures of low-income neighborhoods and local food deserts and CDC's measure of healthy and less healthy food environments currently used to target obesity reduction initiatives. This study found that obesity prevalence was largely clustered in the City of Detroit extending north into contiguous suburbs. The spatial patterns of highest obesity prevalence tracts were most similarly aligned with USDA-defined low-income tracts and CDC's less healthy food tracts. The locations of USDA's food desert tracts rarely overlapped with the highest obesity prevalence tracts. This study demonstrated a new methodology by which to assess local areas in need of future obesity interventions.