Adolescent Health in an Urban Environment

城市环境中的青少年健康

• 批准号: 9311866
• 负责人: Catherine A Calder
• 金额: $ 30.96万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-14 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311866
• 关键词: Adolescent; Adolescent Development; Affect; Algorithms; Area; Behavior; Budgets; Cellular Phone; Computer software; Consequentialism; County; Cues; Data; Data Collection; Data Sources; Dependence; Development; Disease; Ecological momentary assessment; Environment; Exposure to; Geographic Locations; Geography; Health; Health Sciences; Home environment; Homelessness; Human; Individual; Link; Location; Measures; Methodology; Methods; Modeling; Moods; Names; Nature; Ohio; Outcome; Participant; Pattern; Physiological; Population; Process; Prostitution; Research; Research Personnel; Risk Behaviors; Sampling; Schools; Series; Social Network; Specific qualifier value; Statistical Data Interpretation; Statistical Methods; Statistical Models; Stress; Structure; Survey Methodology; Surveys; Time; Tweens; Violence; Work; Youth; adolescent health; base; cost; data mining; disease transmission; drinking; experience; flexibility; health science research; interest; man; novel; residence; social; social media; urban setting

Project Summary Co-location networks – two-mode networks that capture connections between individuals and locations in geographic space – have broad relevance in the health sciences in areas ranging from the study of infectious disease transmission to understanding the influence of social processes on health outcomes and behaviors. Despite their broad relevance, however, statistical methods for understanding co-location networks are limited. This methodologically oriented proposal focuses on the development of a statistical framework for the study of co-location networks using a bilinear mixed-effects model with interacting latent activity pattern motifs and profiles. Through latent interacting random effects, our model captures the dependence between individuals based on their shared use of space and between locations based on the individuals who frequent them. Our flexible modeling framework uses a mixed-membership structure to relax the assumption that activity profiles are static and takes advantage of a data augmentation strategy to allow versions of the model with either direct or indirect specification of the dependence between actor-location ties. Our novel statistical models will be used in analyses of activity pattern data collected as part of the Adolescent Health and Development in Context (AHDC) Study, an ongoing data collection effort in Franklin County, Ohio. Through GPS-based smartphone tracking and space-time budget software, the AHDC Study provides rich detail on the co-location networks of adolescents in the study area. In addition, a wealth of survey data, smartphone-administered Ecological Momentary Assessments (capturing real-time measures of location, social network partner presence, activities, risk behaviors, and mood), and biomeasure data on the study participants are available. Recognizing that our proposed statistical model may not be able to capture the structure the co-location network structure of AHDC adolescents based entirely on their observed activity patterns, we also propose to embed relevant information derived from social media into our analyses through informative prior distributions on model parameters. To do so, we propose novel data mining algorithms to retrieve potential activity pattern motifs and coincident profiles from Twitter posts and network structure. In particular, we extend named entity identification methods to the spatial setting to automatically retrieve information relevant to activity patterns and develop novel methods for prioritizing activity pattern information based on its relevance to particular subpopulations (here, adolescents) using scalable sentiment analysis. Using our new statistical and data mining methodology, we will perform detailed statistical analyses to explore the relationship between spatial and socio-spatial exposures derived from an inferred co-location network and physiological stress in adolescents.

项目摘要 协同定位网络-双模式网络，捕捉个人和位置之间的联系， 地理空间-在健康科学领域具有广泛的相关性， 疾病的传播，以了解社会进程对健康结果和行为的影响。 然而，尽管它们具有广泛的相关性，但用于理解协同定位网络的统计方法是有限的。 这一注重方法论的建议侧重于制定一个统计框架， 使用具有相互作用的潜在活动模式基元的双线性混合效应模型的协同定位网络， 数据区.通过潜在的相互作用的随机效应，我们的模型捕捉个体之间的依赖性 基于他们共享空间的使用，以及基于经常光顾的个人的位置之间的位置。我们 灵活的建模框架使用混合成员结构来放松活动概要的假设 是静态的，并利用数据扩充策略，允许模型的版本直接 或者间接指定参与者-位置关系之间的依赖性。我们新颖的统计模型将 用于分析作为青少年健康和发展的一部分收集的活动模式数据， 上下文（AHDC）研究，俄亥俄州富兰克林县正在进行的数据收集工作。通过GPS 智能手机跟踪和空间时间预算软件，AHDC研究提供了关于共址的丰富细节 研究区域的青少年网络。此外，还有大量智能手机管理的调查数据 生态瞬时评估（捕获位置、社交网络合作伙伴的实时测量 存在、活动、危险行为和情绪），以及关于研究参与者的生物测量数据是可用的。 认识到我们提出的统计模型可能无法捕捉结构的共址 AHDC青少年的网络结构完全基于他们观察到的活动模式，我们还建议， 通过信息先验分布将来自社交媒体的相关信息嵌入到我们的分析中 模型参数。为此，我们提出了新的数据挖掘算法来检索潜在的活动模式 来自Twitter帖子和网络结构的主题和重合的个人资料。特别地，我们扩展了命名实体， 识别方法，以自动检索与活动模式相关的信息， 开发新的方法，根据活动模式信息与特定 亚群（这里是青少年）使用可扩展的情感分析。利用我们新的统计数据 挖掘方法，我们将执行详细的统计分析，以探索空间 以及从推断的协同定位网络和生理压力中得出的社会空间暴露 青少年。