You are what you eat: Longitudnal associations between the neighborhood food envi

描述（由申请人提供）：物理食物环境被假设在影响饮食模式、肥胖和糖尿病方面发挥关键作用；然而，关于这一主题的研究在很大程度上是横向的。因此，人们对远离家的食物（餐馆和快餐店）、远离家的饮食模式和健康结果之间的长期联系知之甚少。目的：本研究旨在考察物理食物环境的纵向变化，并确定其对饮食和健康结果的影响。具体来说，我们希望(1)确定建筑环境中与个人居住社区中离家在外的食物场所数量相关的因素；(2)确定个人居住小区外出就餐场所数量与外出就餐频次的关系；(3)确定外出就餐频率与13年体重和血糖状态变化之间的纵向关联。方法：收集的数据来自年轻人冠状动脉风险发展（CARDIA）研究，5115名年轻人的纵向队列，将用于这些分析。个人一级的健康和饮食数据已按居住地点与地理信息系统（GIS）导出的数据库联系起来，其中包括食品环境措施（即餐馆位置）、环境特征（即道路网）和人口普查措施（即街区组平均家庭收入），从而形成一个包含个人和环境因素的复杂数据库。我们将使用多元、层次线性回归（[HLM]一种多层次建模形式）和纵向固定效应模型的组合。HLM允许我们检查个人和环境因素对离家吃饭频率的独立贡献。固定效应模型用于检查个体间行为和健康结果随时间的变化，它还具有对内生性（个体内部未观察到的、时不变的异质性）提供内在控制的额外好处。启示：仅关注个人因素的体重减轻和体重增加预防工作取得了有限的成功。了解社区食物环境对饮食选择和健康相关结果的影响对于促进健康的生活方式可能是必要的。这项研究的结果将确定一些因素，这些因素可以作为设计和开发社区环境的目标，改善个人的饮食和支持健康的行为。

DESCRIPTION (provided by applicant): The physical food environment is hypothesized to play a critical role in influencing dietary patterns, obesity, and diabetes; however research on this topic has been largely cross-sectional. Consequently, little is known about the long-term associations between access to away-from-home food (restaurants and fast food places), patterns of away-from-home eating, and health outcomes. Objectives: This study seeks to examine longitudinal changes in the physical food environment and determine their effect on diet and health outcomes. Specifically, we hope to (1) determine factors in the built environment that are associated with the number of away-from-home food places in an individual's residential neighborhood; (2) determine the relationship between the number of away-from-home eating places in an individual's residential neighborhood and frequency of away-from-home food consumption; and (3) determine the longitudinal associations between the frequency of away-from-home eating with 13-year changes in weight and glucose status. Methods: Data collected from the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a longitudinal cohort of 5,115 young adults, will be used in these analyses. Individual-level health and diet data have been linked by residential location to a Geographic Information Systems (GIS)-derived database which includes measures of the food environment (i.e. location of restaurants), environmental features (i.e. road networks), and census measures (i.e. block-group average household income), resulting in a complex database of both individual and environmental factors. We will use a combination of multivariate, hierarchical linear regression ([HLM] a form of multilevel modeling), and longitudinal fixed-effect models. HLM allows us to examine the independent contribution of both individual and environmental factors to the frequency of away-from-home eating. Fixed effect models, used to examine inter-individual changes in behaviors and health outcomes over time, have the additional benefit of providing inherent control for endogeneity (unobserved, time-invariant heterogeneity within individuals). Implications: Weight loss and weight gain prevention efforts focused solely on individual-level factors have been met with limited success. Understanding influences of the community food environment on dietary choice and health-related outcomes may be necessary for promoting healthy lifestlyes. Results from this study will identify factors that could be targeted for the design and development of community environments that improve individuals' diets and support healthy behaviors.