Food environment, diet quality, and disparities in obesity

食物环境、饮食质量和肥胖差异

• 批准号: 7558516
• 负责人: ADAM DREWNOWSKI
• 金额: $ 53.83万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7558516
• 关键词: Address; Adult; Affect; Area; Clinical; Code; Communities; Computer software; County; Custom; Data; Databases; Diabetes Mellitus; Diet; Diet Habits; Economics; Educational Background; Environment; Epidemiology; Ethnic group; Expenditure; Food; Food Access; Food Assistance Programs; Gender; Geographic Information Systems; Geographic Locations; Geography; Health; Health Planning; Health Services Accessibility; Healthcare Systems; Home environment; Income; Individual; Lead; Link; Low Income Population; Mails; Maps; Marketing; Measures; Medicine; Methods; Metric; Minority Groups; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Participant; Patient Self-Report; Physical activity; Physical environment; Preventive; Price; Public Health; Questionnaires; Race; Research; Research Personnel; Research Technics; Resolution; Respondent; Sampling; Series; Social Environment; Socioeconomic Status; Source; Subgroup; Surveys; Taxes; Techniques; Telephone; Testing; Time; United States Public Health Service; Variant; Work; base; burden of illness; cost; design; economic impact; energy density; evidence base; falls; fast food; improved; instrument; land use; novel; nutrition; nutrition education; programs; racial and ethnic; social; social capital; social health determinants; socioeconomics; statistics

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.

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，低成本且容易获得高能量食品可能是肥胖率较高的独立预测因素。这项研究将调查研究技术与新颖的空间分析方法相结合，以获得有关参与者的物理和食物环境及其获取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、肥胖和健康状况的自我报告数据。所有地址都将进行地理编码，以方便空间分析和绘图。 SEP 的客观衡量标准将基于可用的最详细分辨率的数据——税包级别的县税务评估员。将使用基于定制 GIS 的应用程序和物理环境的新颖指标，在个人层面上计算对三种主要食物来源的实际获取情况的客观衡量标准，根据质量、距离和时间为每个受访者定义。 GIS 软件中的定制程序将捕获调查参与者的家庭环境及其主要食物来源附近的地理空间数据并对其进行初步几何分析。膳食能量密度和个人饮食的货币成本将使用当地食品价格和额外的饮食问卷工具进行估算，该工具将邮寄给所有 2000 名研究受访者。将使用市场篮子技术来评估当地食品价格的变化。空间分析和多级回归模型将用于检验以下假设：获得食物来源的物理和经济机会可以预测膳食能量密度和肥胖率。