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: 8292826
• 负责人: Alistair James O'MALLEY
• 金额: $ 44.78万
• 依托单位: HARVARD MEDICAL SCHOOL
• 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/8292826
• 关键词: Accounting; Address; Area; Attention; Bayesian Method; Biological; Body Weight; Body mass index; Build-it; Cardiovascular Diseases; Censuses; Coffee; Complex; Computer software; Computing Methodologies; Data; Data Sources; Databases; Development; Environment; Environmental Risk Factor; Epidemic; Exposure to; Family; Food; Framingham Heart Study; Grant; Health; Health Food; Home environment; Individual; Life; Link; Measurement; Measures; Methods; Modeling; Neighborhoods; Obesity; Outcome; Play; Public Health; Research Personnel; Restaurants; Role; Route; Sampling; Services; Source; Supermarket; Time; Variant; Woman; Work; Workplace; cohort; design; fast food; improved; longitudinal database; men; novel; offspring; residence

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.

描述（由申请人提供）：虽然体重有多种决定因素，从生物到环境，但过去几十年肥胖流行病的发展涉及环境条件的变化高于其他因素。一个受到相当关注的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和他或她的BMI之间的关联的证据仍然是不确定的。以前的研究受到横截面设计的限制，不完全或不好的确定接近食品企业，或狭隘地关注住宅食品环境，而不是更全面的评估，说明在家庭和工作中的暴露。使用一个样本，结合受试者从心脏研究（FHS）后代和全方位的队列，我们将建立在以前的工作1）检查近40年的纵向数据的BMI和暴露于食品企业; 2）使用多个来源的数据的食品企业; 3）将住宅和工作场所社区作为暴露区域，并提供纵向数据，以说明住宅和工作场所的流动性; 4）随时间精确测量住宅和工作地点地址与食品机构之间的距离（说明随时间的机构的开放和关闭）;以及5）识别沿着上下班的共同路线的食品机构。有关食品企业的数据将来自多个来源，包括当地卫生局、历史黄页和白色页以及商业数据库。对于主要分析，样本将包括居住在4个城镇、地理集中区域的受试者，来自3，160名FHS后代队列受试者的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BMI与1971年至2008年6种类型的食品机构（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）的接近程度的关联。我们还将确定BMI随时间变化的比例，这些变化是由居住或工作场所社区（在这种情况下，人口普查区）的聚类引起的，对于这个地理集中的样本以及结合整个FHS后代和全方位队列的样本。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，重复观察的个人，其群体成员（在我们的情况下，住宅和工作场所的社区）可能会随着时间的推移而变化。该软件将容纳一系列交叉分类的多水平随机效应模型，使用贝叶斯方法进行模型估计。总的来说，这项资助的工作将为有关食品环境与BMI之间关系的问题提供重要的答案。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。我们将传播用于类似复杂数据研究的方法和软件。 公共卫生相关性：在心脏病研究后代和全方位队列的受试者中，我们建议研究1971年至2008年期间住宅和工作场所与食品机构的接近程度与体重指数的关系。我们将开发有关食品企业和雇主的新数据，并将开发新的统计软件，以方便对复杂的纵向和地理数据进行分析。这项研究将提供重要信息，帮助决策者确定创造健康的食品环境是否可以改善健康。