Food environment, diet quality, and disparities in obesity

描述（由申请人提供）：该项目将研究物理，社会和经济环境如何影响饮食选择，从而导致肥胖率的差异。目前的假设是，低成本和容易获得的能量密集的食物可能是一个独立的预测较高的肥胖率。这项研究将联合收割机调查研究技术与空间分析的新方法相结合，以获得有关参与者的物理和食物环境及其获得食物来源的深入信息。对华盛顿州金县2,000名成年居民进行分层抽样的电话调查，将提供参与者的社会经济地位（SEP）、购物和饮食习惯、食品支出、肥胖和健康的自我报告数据。所有地址都将进行地理编码，以便利空间分析和绘图。SEP的客观措施将基于现有最详细分辨率的数据-在税收包裹一级的县税务评估员。客观措施的物理访问三个关键的食物来源，定义为每个答卷人的质量，距离和时间，将计算在个人层面上使用定制的GIS为基础的应用程序和新的物理环境的指标。地理信息系统软件中的定制程序将在调查参与者的家庭环境及其主要食物来源附近捕获并执行地理空间数据的初步几何分析。将使用当地食品价格和将邮寄给所有2000名研究受访者的额外饮食问卷调查工具来估计个人饮食的饮食能量密度和货币成本。将使用市场篮子技术评估粮食价格的地方差异。空间分析和多层次回归模型将被用来测试的假设，物理和经济获得的食物来源可以预测膳食能量密度和肥胖率。

DESCRIPTION (provided by applicant): This project will examine how the physical, social, and economic environments influence dietary choices and, thereby, contribute to disparities in obesity rates. The present hypothesis is that the low cost and easy access to energy-dense foods may be an independent predictor of higher obesity rates. This study will combine survey research techniques with novel methods of spatial analysis to obtain in-depth information about the participants' physical and food environment and their access to food sources. A telephone survey of a stratified sample of 2,000 adult residents of King County, WA, will provide self-reported data on the participants' socioeconomic position (SEP), shopping and eating habits, food expenditures, obesity, and health. All addresses will be geocoded to facilitate spatial analysis and mapping. Objective measures of SEP will be based on data at the most detailed resolution available -- the County tax assessor at the tax parcel level. Objective measures of physical access to three key food sources, defined for each respondent in terms of quality, distance and time, will be calculated at the individual level using custom GIS based applications and novel metrics of the physical environment. Custom programs within GIS software will capture and perform preliminary geometric analysis of geospatial data within the proximity of the survey participants' home environment and their chief food sources. Dietary energy density and monetary cost of individual diets will be estimated using local food prices and an additional diet questionnaire instrument that will be mailed to all 2000 study respondents. Local variation in food prices will be assessed using a market basket technique. Spatial analyses and multilevel regression models will be used to test the hypothesis that physical and economic access to foods sources may predict dietary energy density and obesity rates.