Moving to Health: How changing built environments impact weight and glycemic control

总结 住宅位置，建筑环境（BE）和邻里流行之间存在多重联系。 肥胖和2型糖尿病（T2 D）。地理信息系统技术的进步使地理尺度发生了变化 从大的县和邮政编码到更细的人口普查区/街区和个人税收包裹。好吧- 粒度数据允许更详细地描述BE的变量，如公交车乘客量，人行道长度， 和绿化覆盖率。BE数据的横截面分析不允许进行因果推断。只 纵向队列研究可以解决关键问题：住宅BE对身体有因果影响吗？ 长期的体重变化和血糖控制？识别与健康关系最密切的BE变量 结果将有助于为卫生政策提供信息，鼓励支持健康的城市规划，并支持 消费者的住宅选址决定。我们提出了一个范式转变，仿照随机社会 实验，移动到机会研究。我们将使用来自Group Health（GH）的数据， 国家卫生保健系统，服务于广泛的经济阶层。通过将地理背景附加到匿名 居住在King的320，000名成年人（30，000名T2 D患者）和90，000名儿童的电子医疗记录（EMR） 县，华盛顿州，我们将研究纵向关系之间的个人水平的BE和变化的重量和 血糖控制。该队列中有超过100，000名成年人和40，000名儿童至少在 2005年和2016年，这是一个独特的机会，可以检查商业的重大变化对长期的影响， 体重和T2 D状态。目标1将确定基线BE变量是否可以预测 长达12年随访期间的体重和HbA 1c，与社会经济学基线指标无关 社会经济地位（SES），人口统计学变量和共病条件。目标2将决定是否立即， 运动引起的BE变化可以预测体重和HbA 1c的变化，与SES的变化无关 和其它协变量。目标3将确定BE的更渐进的长期变化是否可以预测体重 和HbA 1c的变化，在不移动居住的人，独立于SES和其他 协变量我们将开发新的空间分析方法，以解决复杂的交织纵向 和时间-事件结果和空间依赖的时变预测因子。该项目将提供新颖的“大 40多个居民BE特征和详细的纵向临床数据之间的“数据”联系，使我们能够 在12年的随访中检查体重轨迹、血糖控制以及肥胖和T2 D的发病率。 这个自然的实验检查了一个人的BE的突然变化（对于移动者）和逐渐变化（对于非移动者）。 非常大的队列将提供前所未有的见解，了解地方对健康的影响。我们的研究结果将有助于 城市规划者和政策制定者针对不同的基础设施特征进行干预， 实消费者和开发商可以利用这些信息对社区做出明智的决定 最可能支持健康的特征。

SUMMARY There are multiple links between residential location, built environment (BE), and neighborhood prevalence of obesity and type 2 diabetes (T2D). Advances in GIS technologies have allowed a shift in geographic scale from large counties and ZIP codes to more granular census tracts/blocks and individual tax parcels. Fine- grained data permits more detailed characterization of BE with variables like bus ridership, sidewalk length, and greenery coverage. Cross-sectional analysis of BE data does not allow causal inferences. Only longitudinal cohort studies can address the critical question: does residential BE have a causal effect on body weight changes and glycemic control in the long term? Identifying BE variables most strongly related to health outcomes would help inform health policies, encourage health-supportive urban planning, and support consumer residential location decisions. We propose a paradigm shift, modeled after the randomized social experiment, the Moving to Opportunity Study. We will use data from Group Health (GH), a large integrated WA State health care system, serving broad economic strata. By attaching a geographic context to anonymized electronic medical records (EMR) of 320,000 adults (30,000 with T2D) and 90,000 children residing in King County, WA, we will examine longitudinal relationships between individual-level BE and changes in weight and glycemic control. More than 100,000 adults and 40,000 children in the cohort moved at least once between 2005 and 2016, allowing a unique opportunity to examine the impact of major changes in BE on long-term weight and T2D status. Aim 1 will determine whether baseline BE variables can predict longitudinal changes in body weight and HbA1c over up to 12 years follow-up, independent of baseline measures of socioeconomic status (SES), demographic variables, and comorbid conditions. Aim 2 will determine whether immediate, moving-induced changes in BE can predict body weight and HbA1c changes, independent of changes in SES and other covariates. Aim 3 will determine whether more gradual, secular changes in BE can predict weight and HbA1c changes, in people who do not move residence, independent of changes in SES and other covariates. We will develop new spatial analysis methods to address the complexity of intertwining longitudinal and time-to-event outcomes and spatially-dependent time-varying predictors. The project will afford novel “big data” linkages between 40+ residential BE characteristics and detailed, longitudinal clinical data, allowing us to examine body weight trajectories, glycemic control, and incidence of obesity and T2D over 12-years' follow-up. This natural experiment examining sudden (for movers) and gradual (for non-movers) changes in the BE for a very large cohort will provide unprecedented insights into the impact of place on health. Our findings will help urban planners and policymakers target different BE features for intervention, based on local and regional realities. Consumers and developers can use this information to make informed decisions about neighborhood features likely to be most supportive of health.