HashtagHealth: A Social Media Big Data Resource for Neighborhood Effects Research

HashtagHealth：用于邻里效应研究的社交媒体大数据资源

基本信息

批准号：
8828979
负责人：
QUYNH NGUYEN
金额：
$ 15.57万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-29 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8828979
关键词：
Address Adolescence Advocacy Affect Age Alcohol consumption Algorithms Area Behavior Big Data Biomedical Research Birth Birth Weight Characteristics Chronic Disease Cities Communications Media Crime Data Data Analyses Data Sources Databases Development Diabetes Mellitus Environment Epidemic Epidemiology Exercise Family Food Food Access Food Chain Frequencies Funding Future Geographic Information Systems Geographic Locations Geography Goals Grant Happiness Health Health Food Health Promotion Health Sciences Health Services Accessibility Health Surveys Health behavior Health care facility Healthcare Hypertension Individual Informal Social Control Internet Investigation K-Series Research Career Programs Knowledge Lead Life Link Longevity Low income Machine Learning Maintenance Maps Marital Status Measures Medical Records Mental Health Mentors Mentorship Metabolic Methodology Methods Modeling Morbidity - disease rate Natural Language Processing Neighborhoods Obesity Outcome Pattern Physical activity Policies Policy Maker Population Database Poverty Principal Investigator Process Public Health Quality Indicator Recreation Research Research Design Research Infrastructure Research Personnel Resources Science Semantics Siblings Signal Transduction Smoking Social Environment Social Interaction Social Network Social Sciences System Techniques Time Training United States National Institutes of Health Update Utah Work Writing career career development computer science contextual factors cost cost efficient data integration data management data mining design experience fast food food consumption improved mortality multidisciplinary news novel obesity risk physical conditioning population health public health relevance research study screening sex skills social social health determinants statistics tool user-friendly young adult

项目摘要

DESCRIPTION: My goal in seeking a Mentored Research Career Development Award is to acquire the necessary training, practical experience, and knowledge to become a leading independent investigator who harnesses biomedical Big Data Science for the investigation of multilevel influences on health. To continue my progress towards this goal, I am proposing to build the infrastructure to establish a neighborhood data repository, HashtagHealth, for public health researchers and policy makers. I am a highly trained researcher in social epidemiology and quantitative analyses, particularly large health surveys. Before coming to Utah, I worked as a full-time statistical programmer/data analyst on a NIH-funded project to evaluate the health effects of a large neighborhood relocation policy experiment on low-income families in five cities. Our study results suggested that moving from high- to lower poverty neighborhood is related to reductions in obesity and diabetes and improved mental health. Other extant research has provided evidence on associations between the neighborhood environment and mortality and morbidity-even after adjusting for individual characteristics. Poor access to healthy food, fast food chains, the lack of recreational facilities, and higher crime rates all correlate with hiher obesity rates. Nonetheless, the dearth of neighborhood data, especially measures of neighborhood quality that are consistent across geographic areas, limits neighborhood effects research. Moreover, neighborhoods are not only defined by their resources, but also by the social interactions and activities of people who live there. The widespread usage of the internet and open recording of many transactions has led to the availability of massive amounts of data that permits capture of previously hidden micro-level interactions. We will build the data algorithms and infrastructure to harness relatively untapped, cost efficient, and pervasive social media data to develop neighborhood indicators such as food themes, healthiness of food mentions, frequency of exercise/recreation mentions, metabolic intensity of physical activities, and happiness levels. The creation of HashtagHealth requires the use and refinement of Big Data methods to perform data mining, processing and storing of heterogeneous, unstructured data. We will build a testable version of HashtagHealth for the state of Utah and then apply the data resource to the examination of neighborhood effects on young adult obesity. My rigorous training and previous research experiences in social determinants of health, causal inference, and data analyses uniquely prepare me to make significant contributions to the field of Big Data, particularly at the intersection of public health and social sciences. My Specific Aims are: 1) to develop a neighborhood data resource, HashtagHealth, for public health researchers, 2) to develop Big Data techniques to produce novel neighborhood quality indicators (e.g., healthiness of food mentions, frequency and type of exercise/recreation and happiness levels), and 3) to utilize HashtagHealth and individual-level data from the Utah Population Database to investigate neighborhood influences on obesity among young adults. My mentorship team includes experts in biomedical research (Drs. Ken Smith, Jim VanDerslice), computer science (Dr. Feifei Li), and statistics (Dr. Ming Wen). My team has the breadth of expertise to help me obtain critical multidisciplinary skills and successfully implement my research aims. In addition to my research aims, my Specific Career Development Aims include the following: 1) to develop expertise in data mining and database systems, 2) to acquire training in natural language processing and machine learning, 3) to further gain knowledge of geographic information systems (GIS), 4) to develop expertise in study design and analysis of neighborhood effects, and 5) and to develop grant writing and research management skills to lead future projects. The knowledge and experience gained from this proposal will allow me to successfully compete for R01 funding to create a national neighborhood data repository and to investigate national patterns of neighborhood effects on obesity. This proposal makes significant, relevant contributions to the field because 1) neighborhood environments are increasingly linked to important health outcomes, and 2) this project addresses the limits to research resulting from the lack of neighborhood data by providing new, cost-efficient data resources and methods for characterizing neighborhoods.

描述：我寻求指导研究职业发展奖的目标是获得必要的培训，实践经验和知识，成为一名领先的独立研究者，他们利用生物医学大数据科学来调查多层次的影响对健康的影响。为了继续朝着这一目标朝着这个目标迈进，我建议建立基础架构，以建立一个邻里数据存储库Hashtaghealth，以供公共卫生研究人员和政策制定者。我是社会流行病学和定量分析的训练有素的研究员，尤其是大型健康调查。来到犹他州之前，我曾在NIH资助的项目中担任专职统计程序员/数据分析师，以评估大型社区搬迁政策实验对五个城市低收入家庭的健康影响。我们的研究结果表明，从高贫困社区转移到肥胖和糖尿病的降低以及改善心理健康有关。其他现有的研究为邻里环境与死亡率与发病率之间的关联提供了证据，即使在调整个人特征后也是如此。获得健康食品，快餐连锁店，缺乏休闲设施和较高犯罪率的不良获取都与Hiher肥胖率有关。尽管如此，邻里数据的缺乏，尤其是在地理区域之间一致的邻里质量衡量标准，这限制了邻里影响研究。此外，社区不仅取决于他们的资源，而且还取决于居住在那里的人们的社会互动和活动。互联网的广泛使用和许多交易的开放记录导致了大量数据的可用性，该数据允许捕获先前隐藏的微观级别的交互。我们将构建数据算法和基础设施，以利用相对未开发，具有成本效益和普遍的社交媒体数据来开发邻里指标，例如食品主题，食物的健康性提及，运动/娱乐性的频率，体育锻炼的代谢强度和幸福水平。 Hashtaghealth的创建需要使用和完善大数据方法来执行异质，非结构化数据的数据挖掘，处理和存储。我们将为犹他州构建可测试版本的Hashtaghealth版本，然后将数据资源应用于对邻里对年轻成人肥胖的影响的检查。我在健康，因果推断和数据分析的社会决定因素方面的严格培训和以前的研究经验，为我提供了为大数据领域做出重大贡献的唯一准备，尤其是在公共卫生和社会科学的交汇处。我的具体目的是：1）开发邻里数据资源，Hashtaghealth，供公共卫生研究人员，2）开发大数据技术，以生成新型的邻里质量指标（例如，食物的健康性，锻炼/锻炼/娱乐和幸福水平的健康状况，频率和类型）以及3）利用Hashtaghealth和来自犹他州数据库的个人级别数据对OBES的影响。我的指导团队包括生物医学研究专家（Ken Smith博士，Jim Vanderslice），计算机科学（Feifei Li博士）和统计学（Ming Wen博士）。我的团队具有广泛的专业知识，可以帮助我获得重要的多学科技能，并成功地实施了我的研究目标。除了我的研究目的外，我的特定职业发展目的还包括以下目的：1）开发数据挖掘和数据库系统方面的专业知识，2）获得自然语言处理和机器学习方面的培训，3），3）进一步获得地理信息系统（GIS）的知识，4）在邻里效果方面发展方面的专业知识，以及发展邻里效果的专业知识，以及开发赠款和研究培训和研究的领导者，以实现未来的未来项目。从这项提案中获得的知识和经验将使我能够成功竞争R01资金，以创建国家邻里数据存储库，并调查国家对肥胖症影响的国家影响。该提案对该领域做出了重要的，相关的贡献，因为1）邻里环境越来越多地与重要的健康成果联系在一起，2）该项目通过提供新的，具有成本效益的数据资源和表征社区的方法来解决由于缺乏社区数据而导致的研究的局限性。