The Automatic Context Measurement Tool: bringing environmental data to non-specialists

项目总结/摘要 最近的研究重申了美国的地理差异，表明这些差异并没有得到充分的解释 社会经济差异，表明背景或环境强烈影响健康。然而，研究 环境对健康的影响，特别是行为干预的环境影响改变， 一直受到计算环境背景的特定主题措施的成本的限制。我们建议 使非专业研究人员和健康促进专家能够采取环境措施， 建立和验证自动上下文测量工具（ACMT）。ACMT是一个软件工具， 研究人员和从业人员可以使用有效地编译，归因于个人，并分析环境 从免费和全国可用的数据集（如美国人口普查和全国土地覆盖）中提取的指标 数据库在建立ACMT之后，我们将采取五个步骤来验证和推广它。 这些国家可用的措施捕捉了研究参与者环境的健康相关方面， 比较国家现有的预测身体活动的环境措施与当地现有的 环境措施预测的身体活动的队列在国王县，华盛顿州。二是 通过对比环境指标，展示ACMT如何应用于多中心试验， 金郡的体育活动与犹他州盐湖和俄勒冈州波特兰的体育活动预测。 第三，我们将使用ACMT与来自Kaiser Permanente的大型电子健康记录（EHR）数据集 华盛顿（原集团健康）的患者和华盛顿大学医学的患者，以探讨哪些环境 测量最能预测健康成年人的BMI轨迹。第四，我们将确定ACMT可用于 通过比较环境预测因子，确定健康干预有效性的环境修饰因子， 接受减肥手术的成人与未接受手术的肥胖成人的BMI轨迹比较。 最后，我们将确保ACMT在网络上提供一个非专业人士可用的界面， 从体重管理项目和其他患者护理项目中招募项目协调员， 测试ACMT，并提供反馈，以便我们改进它。一旦正式可用，ACMT将 为没有接受过地理空间培训的研究人员和从业人员开放环境措施的使用 以前被收集和分析所需的大量专业知识（和相关费用）所阻碍 对健康的潜在环境影响。我们将采取的验证ACMT的步骤还将提供 对身体活动和肥胖的环境影响的进一步了解。最后，互补 培训计划包括课程，结构化的指导，和体验式学习将让我发展 技能，以启动我的职业生涯作为一个独立的科学家在信息学和 流行病学

PROJECT SUMMARY/ABSTRACT Recent studies reaffirming geographic disparities in the United States showed that they are not fully explained by socioeconomic differences, suggesting context or environment strongly impacts health. Yet research on environmental influences on health, particularly environmental effect modification of behavioral interventions, has been limited by the cost of computing subject-specific measures of environmental context. We propose to put environmental measures within the reach of non-specialist researchers and health promotion experts by building and validating the Automatic Context Measurement Tool (ACMT). ACMT is a software tool that researchers and practitioners can use to efficiently compile, attribute to individuals, and analyze environmental measures drawn from free and nationally available datasets such as US Census and the National Land Cover Database. After building ACMT, we will take five steps to validate and promote it. First, we will quantify how well these nationally available measures capture health-relevant aspects of study participants' environments by comparing nationally available environment measures predictive of physical activity to locally-available environment measures predictive of physical activity for a cohort based in King County, WA. Second, we will demonstrate how ACMT might apply to multi-site trials by contrasting environment measures predictive of physical activity in King County with those predictive of physical activity in Salt Lake City, UT and Portland, OR. Third, we will use ACMT with large electronic health record (EHR) datasets from Kaiser Permanente Washington (formerly Group Health) patients and UW Medicine patients to explore which environmental measures best predict BMI trajectories in healthy adults. Fourth, we will establish that ACMT can be used to identify environmental modifiers of health intervention effectiveness by comparing environmental predictors of BMI trajectories among adults receiving bariatric surgery compared with obese adults not receiving surgery. Finally, we will ensure ACMT is available on the web with an interface that is usable by non-specialists, recruiting project coordinators from weight management programs and other patient care projects to usability test ACMT and provide feedback allowing us to improve it. Once made publically available, the ACMT will unlock the use of environment measures for researchers and practitioners without geospatial training who had previously been hindered by the considerable expertise (and related expense) required to collect and analyze potential environmental influences on health. The steps we will take to validate ACMT will also provide additional insight into environmental influences on physical activity and obesity. Finally, the complementary training plan comprising coursework, structured mentoring, and experiential learning will let me develop the skills to launch my career as an independent scientist working at the intersection of informatics and epidemiology.