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大多是横截面的。因此，人们对外出就餐(餐馆和快餐店)、外出就餐模式和健康结果之间的长期联系知之甚少。目的：这项研究旨在检查物理食物环境的纵向变化，并确定它们对饮食和健康结果的影响。具体地说，我们希望(1)确定建筑环境中与个人居住社区的外出就餐场所数量相关的因素；(2)确定个人居住社区的外出就餐场所数量与外出就餐频率之间的关系；以及(3)确定外出就餐频率与13年体重和血糖状况变化之间的纵向关联。方法：从青年冠状动脉风险发展(CARDIA)研究中收集的数据将用于这些分析，该研究对5115名年轻人进行纵向队列研究。个人一级的健康和饮食数据通过居住地点与地理信息系统(GIS)衍生的数据库相联系，该数据库包括对食物环境(即餐馆的位置)、环境特征(即道路网络)和人口普查措施(即街区组平均家庭收入)的衡量，从而形成了一个关于个人和环境因素的复杂数据库。我们将结合使用多变量、分层线性回归([HLM]一种多水平建模形式)和纵向固定效应模型。HLM允许我们检查个人和环境因素对外出就餐频率的独立贡献。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，它还有一个额外的好处，即提供了对内生性(个体内未观察到的、不变的异质性)的内在控制。影响：仅专注于个人水平因素的减肥和体重增加预防努力取得的成功有限。了解社区食物环境对饮食选择和与健康相关的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可以有针对性地设计和发展社区环境的因素，以改善个人的饮食并支持健康行为。

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.