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

走向健康：改变建筑环境如何影响体重和血糖控制

• 批准号: 9754816
• 负责人: David Eric Arterburn
• 金额: $ 49.77万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9754816
• 关键词: Address; Adult; Affect; Age; Area; Arthritis; Asthma; Big Data; Body Weight; Body Weight Changes; Body mass index; Censuses; Characteristics; Child; Clinical; Clinical Data; Code; Comorbidity; Computerized Medical Record; County; Cross-Sectional Studies; Data; Data Linkages; Data Set; Dyslipidemias; Economics; Ethnic Origin; Event; Gender; Geography; Glycosylated hemoglobin A; Grain; Health; Health Food; Health Insurance; Health Planning; Health Policy; Health system; Healthcare Systems; Home environment; Household; Hypertension; Incidence; Income; Individual; Insurance; Intervention; Length; Link; Location; Longitudinal cohort study; Measures; Methodology; Methods; Modeling; Natural experiment; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Personal Satisfaction; Prevalence; Property; Race; Randomized; SMART health; Smoking; Socioeconomic Status; Source; Statistical Methods; Supermarket; System; Taxes; Technology; Testing; Time; Weight; Weight maintenance regimen; base; built environment; cohort; diabetes risk; experimental study; farmers markets; fast food; follow-up; glycemic control; health care delivery; insight; member; novel; prospective; residence; secondary analysis; social; urban planning; walkability

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.

概括 居住地点、建筑环境 (BE) 和社区流行率之间存在多种联系 肥胖和 2 型糖尿病 (T2D)。 GIS 技术的进步使得地理规模发生了变化 从大县和邮政编码到更精细的人口普查区/街区和个人税区。美好的- 粒度数据允许通过公交车客流量、人行道长度等变量更详细地表征BE 和绿化覆盖率。 BE 数据的横截面分析不允许进行因果推断。仅有的 纵向队列研究可以解决一个关键问题：居住BE对身体有因果影响吗？ 长期体重变化和血糖控制？识别与健康最密切相关的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 消费者居住地点的决定。我们提出了一种范式转变，以随机社会为模型 实验，转向机会研究。我们将使用来自 Group Health (GH) 的数据，这是一个大型综合 WA 国家医疗保健系统，服务于广大经济阶层。通过将地理上下文附加到匿名 居住在 King 的 320,000 名成人（其中 30,000 名患有 T2D）和 90,000 名儿童的电子病历 (EMR) 华盛顿州县，我们将研究个体水平 BE 与体重和体重变化之间的纵向关系 血糖控制。该队列中超过 100,000 名成人和 40,000 名儿童在两次迁移之间至少搬过一次家。 2005 年和 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 变量是否可以预测纵向变化 体重和糖化血红蛋白 (HbA1c) 长达 12 年的随访，独立于社会经济的基线测量 状态（SES）、人口统计变量和合并症。目标 2 将确定是否立即、 运动引起的 BE 变化可以预测体重和 HbA1c 变化，与 SES 的变化无关 和其他协变量。目标 3 将确定 BE 的渐进、长期变化是否可以预测体重 不搬家的人的 HbA1c 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交织纵向的复杂性 事件发生时间的结果和空间相关的时变预测因子。该项目将提供新颖的“大 40 多个住宅 BE 特征与详细的纵向临床数据之间的“数据”联系，使我们能够 检查 12 年随访期间的体重轨迹、血糖控制以及肥胖和 T2D 的发生率。 这个自然实验检查了 BE 的突然（对于移动者）和渐进（对于非移动者）的变化。 非常大的队列将为研究地点对健康的影响提供前所未有的见解。我们的发现将有所帮助 城市规划者和政策制定者根据当地和区域的情况，针对不同的BE特征进行干预 现实。消费者和开发商可以利用这些信息做出有关社区的明智决策 可能最有利于健康的特征。