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

摘要 在居住位置、建成环境(BE)和社区疾病流行之间存在多种联系 肥胖与2型糖尿病(T2D)地理信息系统技术的进步使地理尺度发生了变化 从大县和邮政编码到更细粒度的人口普查区域/区块和个人纳税地块。好的- 粒度数据允许使用诸如公交车载客量、人行道长度、 和绿化覆盖面。对BE数据的横断面分析不允许进行因果推断。仅限 纵向队列研究可以解决一个关键问题：居住对身体有因果关系吗？ 长期的体重变化和血糖控制？确定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 消费者住宅选址决策。我们提出了一种范式转变，模仿随机的社会 实验，移动到机会的研究。我们将使用来自大型综合WA Group Health(GH)的数据 国家医疗保健系统，服务于广泛的经济阶层。通过将地理环境附加到匿名 居住在King的320,000名成年人(30,000名T2D)和90,000名儿童的电子病历(EMR) 我们将研究个体水平的BE与体重和体重变化之间的纵向关系 血糖控制。队列中超过10万名成年人和4万名儿童至少在 2005和2016年，提供了一个独特的机会来研究BE的重大变化对长期的影响 体重和T2D状态。目标1将确定基线BE变量是否可以预测 体重和糖化血红蛋白在长达12年的随访中，独立于社会经济基线指标 状态(SES)、人口统计变量和共病情况。目标2将决定是否立即、 运动诱导的BE变化可以预测体重和HbA1c的变化，而不依赖于SES的变化 和其他协变量。目标3将确定BE更渐进、更长期的变化是否可以预测体重 在不搬家的人中，HbA1c发生变化，与SES和其他因素的变化无关 协变量。我们将开发新的空间分析方法来解决纵向交织的复杂性 以及时间到事件的结果和空间相关的时变预测因子。该项目将提供新奇的“大” 数据：40多个住宅BE特征与详细的纵向临床数据之间的联系，使我们能够 在12年的随访中检查体重轨迹、血糖控制、肥胖和T2D的发生率。 这个自然的实验考察了BE的突然变化(对于移动的人)和渐进的变化(对于不移动的人) 非常大的队列将对地点对健康的影响提供前所未有的见解。我们的发现将对我们有所帮助 城市规划者和政策制定者基于地方和区域，针对不同的BE特征进行干预 现实。消费者和开发商可以使用这些信息来做出有关社区的明智决定 可能是最支持健康的特征。

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.