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

• 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
• 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

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.

产品说明： 我寻求导师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调查员，利用生物医学大数据科学调查对健康的多层次影响。为了继续实现这一目标，我建议建立基础设施，为公共卫生研究人员和政策制定者建立一个社区数据库HashtagHealth。我是一名训练有素的社会流行病学和定量分析研究员，特别是大型健康调查。在来犹他州之前，我在一个由NIH资助的项目中担任全职统计程序员/数据分析师，该项目旨在评估一项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，从高贫困社区搬到低贫困社区与肥胖和糖尿病的减少以及心理健康的改善有关。其他现存的研究提供了证据，证明邻里环境与死亡率和发病率之间的联系，即使在调整了个人特征之后。获得健康食品的机会少，快餐连锁店少，娱乐设施缺乏，犯罪率高，这些都与肥胖率高有关。尽管如此，缺乏邻里数据，特别是邻里质量的措施，是一致的地理区域，限制了邻里效应的研究。此外，社区不仅由其资源定义，还由居住在那里的人的社会互动和活动定义。互联网的广泛使用和许多交易的公开记录导致了大量数据的可用性，这些数据允许捕获先前隐藏的微观层面的交互。我们将建立数据算法和基础设施，利用相对未开发的，具有成本效益的和普遍的社交媒体数据来开发社区指标，如食物主题，食物提及的健康程度，运动/娱乐提及的频率，身体活动的代谢强度和幸福水平。HashtagHealth的创建需要使用和改进大数据方法来执行异构非结构化数据的数据挖掘、处理和存储。我们将为犹他州建立一个可测试的HashtagHealth版本，然后将数据资源应用于检查社区对年轻成年人肥胖的影响。我在健康的社会决定因素，因果推理和数据分析方面的严格培训和以前的研究经验使我为大数据领域做出了重大贡献，特别是在公共卫生和社会科学的交叉领域。 具体目标：1） 为公共卫生研究人员开发社区数据资源HashtagHealth，2）开发大数据技术以产生新的社区质量指标（例如，食物提及的健康程度、锻炼/娱乐的频率和类型以及幸福水平），以及3）利用来自犹他州人口数据库的HashtagHealth和个人水平数据来调查邻居对年轻人肥胖的影响。我的导师团队包括生物医学研究（Ken Smith博士，Jim VanDerslice），计算机科学（Feifei Li博士）和统计学（Ming Wen博士）的专家。我的团队拥有广泛的专业知识，帮助我获得关键的多学科技能，并成功地实现我的研究目标。除了我的研究目标，我的具体职业发展目标包括以下内容：1）发展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）进一步获得地理信息系统（GIS）的知识，4）发展研究设计和邻域效应分析方面的专业知识，及5）发展撰写拨款及研究管理技巧，以领导未来的项目。从这项提案中获得的知识和经验将使我能够成功地竞争R 01资金，以创建一个国家邻里数据库，并调查邻里对肥胖的影响的国家模式。该提案对该领域做出了重大的相关贡献，因为1）邻里环境越来越多地与重要的健康结果联系在一起，2）该项目通过提供新的，具有成本效益的数据资源和表征邻里的方法来解决由于缺乏邻里数据而导致的研究限制。