The last several years have witnessed growing interest in the potential contribution of the """"""""food environment"""""""" to the epidemic increase in obesity rates in the United States. Early empirical evidence suggests that there is significant variation across geographic areas in the availability of different kinds of foods. More economically and socially disadvantaged communities have poorer food environments, with healthy food less available and unhealthy food more available in those communities. There appears to be growing evidence that characteristics of the food environment are associated with dietary behaviors and outcomes, but there are still many unanswered questions in this area. In large part we don't yet know exactly which characteristics of the food environment are most influential, how influential they are, in which geospatial environments, and for which populations. Yet public policy is proceeding apace in this area, with calorie labeling, restrictive fast food zoning, financial incentives for supermarkets in underserved neighborhoods, """"""""healthy bodega"""""""" initiatives, and other regulatory levers already implemented in many communities. Further research in this area is thus required. We propose to combine New York City Department of Education FITNESSGRAMdata, which includes BMI, school and residential locations, for all New York City public school children from 2005 onward with detailed data on the food environment surrounding each child's home and school. Then, we will use a variety of enhanced methodological techniques to estimate the true relationship between food environment and obesity. The methodological enhancements we utilize over past work are: ? A large, detailed dataset, including data on over 1 million NYC public school children. ? Examining longitudinally the same children over time, examining what happens to BMI as healthy and less healthy food stores open and close. (Child fixed effects) ? Examining fine-grained differences in food resources, even within the same Census tract. For example, the relative influence of living within 500 feet, or two city blocks, versus farther than that. (Census tract fixed effects) ? Data on both the home and school food environment, modeled together. With these changes, we will be able to provide unbiased estimates on the influence of the food environmental on child BMI, substantially answering key health and policy-related questions.
|Lee, David C; Yi, Stella S; Fong, Hiu-Fai et al. (2017) Identifying Local Hot Spots of Pediatric Chronic Diseases Using Emergency Department Surveillance. Acad Pediatr 17:267-274|
|Elbel, Brian; Corcoran, Sean P; Schwartz, Amy Ellen (2016) Neighborhoods, Schools and Obesity: The Potential for Place-Based Approaches to Reduce Childhood Obesity. PLoS One 11:e0157479|
|Kiszko, Kamila; Cantor, Jonathan; Abrams, Courtney et al. (2015) Corner Store Purchases in a Low-Income Urban Community in NYC. J Community Health 40:1084-90|