Proximity to Food Establishments and BMI in the Framingham Heart Study

弗雷明汉心脏研究中与食品企业的接近程度和体重指数

• 批准号: 8776508
• 负责人: Alistair James O'MALLEY
• 金额: $ 40.98万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8776508
• 关键词: Proximity; Food; Establishments; BMI; Framingham

DESCRIPTION (provided by applicant): While body weight has multiple determinants, from biological to environmental, the development of an obesity epidemic over the last several decades implicates changes in environmental conditions above other factors. An environmental factor that has received considerable attention is the role of the food environment in obesity and, specifically, the role of fast-food establishments. Evidence regarding the association between an individual's food environment and his or her BMI remains inconclusive. Prior studies are limited by cross- sectional design, by incomplete or poor ascertainment of proximity to food establishments, or by a narrow focus on the residential food environment rather than a more comprehensive assessment that accounts for exposures at home and work. Using a sample that combines subjects from the Framingham Heart Study (FHS) Offspring and Omni Cohorts, we will build on prior work by 1) examining nearly 40-year longitudinal data on both BMI and the exposure to food establishments; 2) using multiple sources of data on food establishments; 3) including residential and workplace neighborhoods as areas of exposure with longitudinal data to account for residential and workplace mobility; 4) precisely measuring distance between residential and workplace addresses and food establishments over time (accounting for the opening and closing of establishments across time); and 5) identifying food establishments along common routes to and from work. Data on food establishments will come from multiple sources, including local Boards of Health, historical Yellow and White Pages, and a commercial database. For the primary analyses, the sample will include subjects who were living in a 4 town, geographically-concentrated area with 17,446 observations from 3,160 FHS Offspring Cohort subjects and additional subjects and observations from the FHS Omni Cohort. We will examine associations of BMI with proximity to 6 types of food establishments, including fast-food restaurants, full-service restaurants, bakeries/coffee shops, convenience stores, grocery stores, and chain supermarkets, from 1971 to 2008. We also will determine the proportion of the variation in BMI over time that arises from clustering at the level of residential or workplace neighborhoods (in this case, census tracts) for both this geographically-concentrated sample as well as for a sample that combines the entire FHS Offspring and Omni Cohorts. To facilitate these analyses, we will develop new statistical software capable of handling multilevel data where repeated observations are made on individuals whose group membership (in our case, residential and workplace neighborhoods) may change over time. This software will accommodate a family of cross- classified multilevel random effect models using Bayesian methods for model estimation. Overall, the work from this grant will provide important answers to questions regarding the association of the food environment with BMI. The results will help determine whether creating healthier food environments is a strategy that has the potential to improve health. We will disseminate the methods and software for use in studies with similarly complex data.

描述（由申请人提供）：虽然体重具有从生物到环境的多种决定因素，但过去几十年肥胖流行的发展表明环境条件的变化高于其他因素。受到广泛关注的一个环境因素是食品环境在肥胖中的作用，特别是快餐店的作用。关于个人的食物环境与其体重指数之间关系的证据仍然没有定论。先前的研究受到横断面设计、对食品场所邻近程度的不完整或较差确定、或者狭隘地关注住宅食品环境而不是对家庭和工作中的暴露进行更全面的评估的限制。使用结合了弗雷明汉心脏研究 (FHS) 后代和 Omni 队列受试者的样本，我们将在之前的工作基础上通过以下方式进行：1) 检查近 40 年的 BMI 和食品场所暴露情况的纵向数据； 2) 使用有关食品企业的多个来源的数据； 3) 将住宅和工作场所社区作为暴露区域，并提供纵向数据，以考虑住宅和工作场所的流动性； 4）随着时间的推移，精确测量住宅和工作场所地址与食品场所之间的距离（考虑到随时间推移场所的开业和关闭）； 5) 确定上下班共同路线沿线的食品店。有关食品企业的数据将来自多个来源，包括当地卫生委员会、历史黄页和白页以及商业数据库。对于初步分析，样本将包括居住在 4 个城镇、地理集中区域的受试者，其中包含来自 3,160 名 FHS 后代队列受试者的 17,446 个观察值以及来自 FHS Omni 队列的其他受试者和观察值。我们将研究 1971 年至 2008 年间 BMI 与 6 种类型食品场所附近的关联，包括快餐店、全方位服务餐厅、面包店/咖啡店、便利店、杂货店和连锁超市。我们还将确定 BMI 随时间变化的比例，该变化是由居住区或工作场所附近（在本例中为人口普查区）层面的聚类引起的， 既包括这个地理上集中的样本，也包括结合了整个 FHS 后代和 Omni 队列的样本。为了促进这些分析，我们将开发能够处理多级数据的新统计软件，其中对群体成员（在我们的例子中是住宅和工作场所社区）可能随时间变化的个人进行重复观察。该软件将容纳一系列使用贝叶斯方法进行模型估计的交叉分类多级随机效应模型。总的来说，这项资助的工作将为有关食物环境与体重指数之间关系的问题提供重要答案。研究结果将有助于确定创造更健康的食品环境是否是一种有可能改善健康的策略。我们将传播用于具有类似复杂数据的研究的方法和软件。