Proximity to Food Establishments and BMI in the Framingham Heart Study

描述(申请人提供)：虽然体重有多种决定因素，从生物到环境，但过去几十年肥胖症流行的发展意味着环境条件的变化高于其他因素。一个备受关注的环境因素是食物环境在肥胖中的作用，特别是快餐店的作用。关于一个人的饮食环境和他或她的BMI之间的联系的证据仍然没有定论。以前的研究受到横断面设计的限制，由于不完全或不能很好地确定与食品机构的接近程度，或者受到对住宅食品环境的狭隘关注，而不是考虑到家庭和工作场所暴露的更全面的评估。使用结合了弗雷明翰心脏研究(FHS)后代和OMNI队列受试者的样本，我们将通过1)检查关于BMI和接触食肆的近40年的纵向数据；2)使用关于食肆的多种来源的数据；3)包括住宅和工作场所社区作为暴露区域，以考虑住宅和工作场所的流动性；4)精确测量住宅和工作场所地址与食肆之间的时间距离(考虑到不同时间食肆的开业和关闭)；以及5)识别上下班共同路线上的食肆。有关食品机构的数据将来自多个来源，包括地方卫生委员会、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个城镇的受试者，地理集中区域有来自3,160名FHS后代队列的17,446个观察结果，以及来自FHS Omni队列的其他受试者和观察结果。我们将研究从1971年到2008年，BMI与接近6种类型的食品机构的关系，包括快餐店、全服务餐厅、面包店/咖啡店、便利店、杂货店和连锁超市。我们还将为这一地理集中的样本以及结合了整个FHS子孙和OMNI队列的样本，确定由于住宅或工作场所社区(在本例中为人口普查区域)水平上的聚集而产生的BMI随时间变化的比例。为了促进这些分析，我们将开发新的统计软件，能够处理多层次的数据，其中重复观察其群体成员(在我们的情况下，住宅和工作场所社区)可能随着时间的推移而变化的个人。该软件将适用于使用贝叶斯方法进行模型估计的一系列交叉分类的多级随机效应模型。总体而言，这笔赠款的工作将为有关食物环境与BMI之间的联系的问题提供重要的答案。结果将有助于确定创造更健康的食物环境是否是一项有潜力改善健康的战略。我们将传播用于同样复杂数据的研究的方法和软件。 公共卫生相关性：在弗雷明翰心脏研究后代和OMNI队列的受试者中，我们建议研究1971年至2008年期间居住和工作场所与食品机构的接近程度与体重指数的关系。我们将开发关于食肆和雇主的新数据，并将创建新的统计软件，以促进对复杂的纵向和地理数据的分析。这项研究将提供重要信息，帮助政策制定者确定创造健康的食品环境是否可以改善健康。

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. PUBLIC HEALTH RELEVANCE: Among subjects in the Framingham Heart Study Offspring and Omni Cohorts, we propose to study the association of residential and workplace proximity to food establishments with body mass index, from 1971 to 2008. We will develop new data on food establishments and employers and will create new statistical software to facilitate analyses of complex longitudinal and geographic data. This study will provide important information to help policymakers determine whether creating healthy food environments can improve health.