Neighborhood Structure and Cardiovascular Disease
NIH Grant #1R03HL088622-01A2
to Charles Drew University
PI: Paul L. Robinson, Ph.D.
Status: Funded- Aug 2009-July 2011
This project will utilize geographic analysis techniques to create measures that detect the influence of key aspects of the local social and built environmental on cardiovascular disease management at the individual level. The specific community characteristics that these measures will be constructed for are 1) geographic access to primary care, 2) local nutritional contexts, and 3) proximity to public opportunities for exercise. The study has the following specific aims:
Specific Aim 1:
To determine whether self reported cardiovascular disease related behaviors and outcomes (physical activity, nutritional behaviors, and health outcomes) are independently influenced by geographic relationships to
local nutritional outlets, and proximity to public opportunities for exercise even after controlling for known predisposing, enabling and need based factors, along with perceptions of public safety.Specific Aim 2:
To determine whether the independent effects of local food and exercise opportunity environments will be differentially distributed, with some age groups, gender groups, income groups and or racial/ethnic groups health related
behaviors and outcomes being disproportionately impacted by the local environmental situation.
The analysis will proceed as follows: a) nesting Los Angeles Health Survey respondents within their local health care accessibility contexts by utilizing street network based gravity models as indicators of geographic accessibility to primary care providers, b) nesting Los Angeles Health Survey respondents within their local food availability contexts by utilizing street network based gravity models to characterize local access to health promoting and health inhibiting food outlets (fresh produce vs. fast food), derived from geo-coded food license data, c) nesting Los Angeles Health Survey respondents
within their local public exercise space contexts using geographic data on parks and open space to derive street network based gravity models of geographic accessibility to land uses that have been shown to influence levels of physical activity within a community. These predictor variables will be incorporated within a hierarchical analytical model that accounts for the known influences on health care utilization and the use of public facilities.