Characterizing health impacts of built environment features using complex data

使用复杂数据表征建筑环境特征对健康的影响

• 批准号: 9883037
• 负责人: Brisa N Sanchez
• 金额: $ 72.79万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9883037
• 关键词: Address; Adoption; Affect; Behavior; Big Data; Biological; Blood Pressure; Cardiovascular Diseases; Characteristics; Chronic Disease; Clinical; Communities; Complex; Computer software; Data; Databases; Diabetes Mellitus; Diet; Disease Outcome; Electronic Health Record; Elements; Environment; Environmental Exposure; Environmental Impact; Environmental Risk Factor; Exposure to; Future; Geography; Health; Health Food; Health Promotion; Health Status; Health behavior; Healthcare; Healthy Eating; Hypertension; Individual; Intervention; Joints; Link; Location; Longitudinal Studies; Longitudinal observational study; Maintenance; Measurement; Measures; Medical Records; Methodology; Methods; Multi-Ethnic Study of Atherosclerosis; Obesity; Observational Study; Outcome; Physical activity; Policies; Policy Developments; Predisposition; Randomized; Research; Research Design; Resources; Risk Factors; Science; Scientist; Selection Bias; Source; Specific qualifier value; Supermarket; Time; Translations; Uncertainty; analytical tool; base; built environment; care outcomes; clinical practice; cohort; complex data; design; disorder prevention; fasting glucose; good diet; health data; improved; innovation; large-scale database; new technology; next generation; novel; novel strategies; population health; precision medicine; public health relevance; standardize measure; uptake; walkability

PROJECT SUMMARY Background and significance: Features of the built environment and the availability of specific community resources constrain individuals’ choices and thus may contribute to the adoption and maintenance of health- promoting behaviors, such as eating healthy diets and engaging in physical activity, that in turn may affect downstream biological (e.g., BMI) and clinical outcomes (e.g., cardiovascular disease). New technologies have rapidly increased the ability to link databases with geo-referenced information on environmental features to individual-level health data, thereby exponentially propelling research that examines associations between the built environment and health. This linked information is highly relevant to clinical practice, making it feasible to tailor interventions and treatments; and to novel study designs that use large databases, such as electronic health records, to investigate the joint effects of built environment factors and individual susceptibility on a range of health and health care outcomes. These linked data are also important for building evidence to support emerging urban design strategies that seek to create environments that promote health. However, methodological challenges, including exposure assessment and selection biases, make it difficult to identify the true impact of the built environment on individual behaviors, and the consequent ability to design place-based interventions to improve health. Objectives, innovation: This project uses data from a state of the art longitudinal study and methods that address residential self-selection, and develops and applies innovative approaches to address measurement challenges. The project will: (1a) estimate the geographic and temporal scales that are relevant for the effects of time-varying availability of community resources and built environment features on repeated measures of health behaviors, biological and clinical outcomes, and (1b) quantify differences in geographic and temporal scales across individuals and specific mechanisms; (2) develop and apply novel methods to ascertain and quantify exposures to multiple, complex environmental features, and assess their simultaneous impact on health indicators; (3) quantify the impact of measurement error in existing large scale built environment databases on estimated associations. The proposed analytical tools will be made freely available through R software packages, and results will be disseminated to multiple audiences, including urban planners. Impact: Results from this project will (a) increase precision in the translation, interpretation and evidence synthesis of past, current, and future studies of built environment health effects; (b) provide scientists with specific guidance on how to standardize measures of the built environment, and incorporate them into large scale individual-level health databases (e.g., electronic health records); and (c) inform best practices for the next generation of research on the impact of the built environment on population health. The project will result in approaches to more accurately and comprehensively measure the impact of multiple environmental factors on health.

项目概要 背景和意义：建成环境的特征和特定社区的可用性 资源限制了个人的选择，因此可能有助于采用和维持健康 促进行为，例如健康饮食和参加体育活动，这反过来可能会影响 下游生物学（例如体重指数）和临床结果（例如心血管疾病）。 迅速提高了将数据库与环境特征地理参考信息联系起来的能力 个人层面的健康数据，从而以指数方式推动检查个体之间关联的研究 这些链接的信息与临床实践高度相关，使其成为可能。 定制干预措施和治疗；以及使用大型数据库（例如电子数据库）的新颖研究设计 健康记录，调查建筑环境因素和个人易感性对健康的联合影响 这些关联数据对于建立证据也很重要。 支持旨在创造促进健康的环境的新兴城市设计战略。 方法上的挑战，包括暴露评估和选择偏差，使得很难确定 建筑环境对个人行为的真正影响，以及随之而来的基于场所的设计能力 改善健康的干预措施 目标、创新：该项目使用最先进的数据。 解决住宅自我选择问题的纵向研究和方法，并开发和应用创新 解决测量挑战的方法 该项目将： (1a) 估计地理和时间。 与随时间变化的社区资源可用性的影响相关的尺度，并建立 重复测量健康行为、生物学和临床结果的环境特征，以及 (1b) 量化个体和特定机制之间地理和时间尺度的差异（2）； 开发并应用新方法来确定和量化多种复杂环境的暴露 (3)量化测量的影响 现有大型构建环境数据库中估计关联的错误。 工具将通过 R 软件包免费提供，结果将传播给多个 受众，包括城市规划者 影响：该项目的结果将 (a) 提高精度。 过去、现在和未来建筑环境研究的翻译、解释和证据综合 健康影响；(b) 为科学家提供关于如何标准化建筑测量的具体指导。 环境，并将其纳入大规模个人级健康数据库（例如电子健康 记录）；以及（c）为下一代研究建筑影响提供最佳实践 该项目将带来更准确和更准确的方法。 综合衡量多种环境因素对健康的影响。