Obesity and Neighborhood Characteristics

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DESCRIPTION (provided by applicant) The current obesity epidemic in the US is largely attributed to behavioral and social ecological factors. Limited efficacies of individual-level approaches to prevention highlight the need for modifications in the political, socioeconomic and physical environment to facilitate individual behavior change. A priority in this area of research is to develop measurement techniques that are applicable at the local level and would help evaluate the likely health effects of neighborhood environmental changes based on national and state data collection systems. This R21 project will develop measures of neighborhood environmental factors (NEFs) and quantify their relationship to individuals' obesity status. The operating concept of neighborhood is the township in Connecticut, Massachusetts and Rhode Island - the lowest level of municipal government at which civil services, health agencies and land use planning boards operate. Specifically, this project will: 1) Develop town-level measures of 5 key NEFs and assess their reliability. The five NEFs include restaurant accessibility, supermarket accessibility, exercise facility accessibility, safety and land use mix pattern; 2) Assess the association of the NEFs with individual's obesity status, level of physical activity and amount of fruit and vegetable consumption, after adjusting for individual- and town-level socioeconomic status; 3) Develop a town-level Neighborhood Quality Index (or indices) in the context of prevention of obesity using a parsimonious subset of the NEFs, and town-level socioeconomic status indicators. The neighborhood environmental variables will be derived using databases compiled from Info USA business registry, US Census, and state Geographic Information Systems centers. Using the Behavioral Risk Factors Surveillance System (BRFSS) data, their relations to individual obesity status, physical activity and fruit and vegetable consumption will be examined with hierarchical linear or generalized linear mixed models while accounting for sampling probability and measurement error. The feasibility of extending the research into a larger geographic region will be assessed, and a R01 grant application will be prepared.