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

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，利用生物医学大数据科学研究对健康的多层次影响。为了继续实现这一目标，我提议建立基础设施，为公共卫生研究人员和政策制定者建立一个社区数据存储库HashtagHealth。我是一名训练有素的社会流行病学和定量分析研究人员，特别是大型健康调查。在来犹他州之前，我在美国国立卫生研究院资助的一个项目中担任全职统计程序员/数据分析师，该项目评估了五个城市中低收入家庭的大型社区搬迁政策实验对健康的影响。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。其他现存的研究已经提供了邻里环境与死亡率和发病率之间联系的证据——甚至在调整了个体特征之后。难以获得健康食品、快餐连锁店、缺乏娱乐设施以及较高的犯罪率都与较高的肥胖率有关。尽管如此，缺乏社区数据，特别是缺乏跨地理区域一致的社区质量衡量标准，限制了社区效应的研究。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，从而可以捕获以前隐藏的微观层面的交互。我们将构建数据算法和基础设施，利用相对未开发的、具有成本效益的、无处不在的社交媒体数据来开发社区指标，如食物主题、提及食物的健康程度、提及运动/娱乐的频率、体育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数据挖掘、处理和存储异构、非结构化数据。我们将为犹他州建立一个可测试版本的HashtagHealth，然后将数据资源应用于社区对年轻人肥胖的影响的检查。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训练和以前的研究经验使我能够为大数据领域做出重大贡献，特别是在公共卫生和社会科学的交叉领域。我的具体目标是：1)

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.