Visualizing and predicting new and late HIV diagnosis in South Carolina: A Big Data approach

可视化和预测南卡罗来纳州新的和晚期的艾滋病毒诊断：大数据方法

• 批准号: 10815140
• 负责人: Xiaoming Li
• 金额: $ 69.91万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10815140
• 关键词: AIDS prevention; Acquired Immunodeficiency Syndrome; Affect; Age; Age Years; Behavioral; Big Data; Big Data Methods; COVID-19; COVID-19 pandemic; Calibration; Caring; Censuses; Collaborations; Communities; County; Data; Data Set; Data Sources; Decision Making; Detection; Diagnosis; Disease Outbreaks; Electronic Health Record; Epidemic; Ethnic Origin; Future; Gender; Goals; Government; HIV; HIV Infections; HIV diagnosis; HIV risk; Health; Health Professional; Health care facility; Healthcare Systems; Hispanic; Income; Infection; Interruption; Intervention; Knowledge; Latino; Link; Location; Machine Learning; Maps; Modeling; Monitor; Newly Diagnosed; Patients; Pattern; Persons; Population; Prevention; Prevention strategy; Public Health; Race; Research; Risk Factors; Rural; Services; Source; South Carolina; Subgroup; System; Time; Translating; United States National Institutes of Health; Update; Viral; Visualization; cohort; contextual factors; data structure; economic indicator; evidence base; experience; hospital care; improved; machine learning algorithm; men who have sex with men; multidisciplinary; multilevel analysis; multiple data sources; outbreak prediction; population based; pre-exposure prophylaxis; predictive modeling; residential segregation; response; service organization; social; social media; socioeconomics; spatiotemporal; success; surveillance data; targeted delivery; transmission process; treatment as prevention; treatment strategy; trend; web portal

Abstract Although many notable strides have been made in HIV prevention, the current pace of progress in the US, especially in the Southern states, is too slow to achieve the goals laid out in “Ending the HIV Epidemic: A Plan for America” (EtHE). The success of effective HIV interventions demands more precise and timely surveillance of the epidemic, especially for the HIV clusters and outbreaks as indicated by a large or an increase in number of new and/or late diagnoses among certain key populations or geolocations (“hotspots”). However, some critical gaps remain in our current HIV surveillance and targeted prevention efforts. These gaps include a lack of timely prediction of HIV risk that is needed for a rapid public health response, reliance on data from limited sources in identifying the hotspots and predictors of new infection clusters, and a lack of efforts in utilizing various data sources to inform the selection and delivery of targeted prevention and control at state and local levels. To develop more effective, timely, and targeted approaches for HIV prevention, it is critical to develop a data-driven surveillance and prediction system of new and late diagnoses so that the local health departments and healthcare systems can rapidly identify priority populations and geolocations where HIV is spreading, or our prevention efforts are lagging behind. Building on an integrative multi-level data structure and a strong academic-government partnership, we propose the current study to strengthen the understanding and visualization of HIV infection clustering, enable the prediction of outbreaks, and contribute to the optimization of strategies to deliver evidence-based prevention. In close collaboration with the South Carolina Department of Health and Environmental Control (SC DHEC) and other stakeholders, we will integrate multi-level data sources, including statewide electronic health records data, county-level contextual data, geospatial data, and social media data, to predict new and late HIV diagnoses in SC and develop an interactive web portal to visualize the spatiotemporal patterns and trends of new and late HIV diagnoses in SC across geolocations and over time, particularly in the context of unanticipated HIV service disruptions by COVID-19 and other future public health crises. Using a Big Data approach and the integration of multi-level data sources, the proposed research will provide a better understanding and visualization of the dynamic spatiotemporal patterns and new case predictions. The prediction models and the interactive web portal will assist SC DHEC, AIDS service organizations, and other healthcare systems to rapidly identify, characterize, and predict new HIV clusters and to deploy targeted HIV prevention and control efforts in a timely fashion.

摘要 尽管在艾滋病预防方面取得了许多显著的进步，但美国目前的进展速度， 特别是在南部各州，实现“结束艾滋病毒流行：一项计划”中规定的目标的速度太慢 美国”（Ethe）。有效的艾滋病毒干预措施的成功需要更精确和及时的监测 特别是对于艾滋病毒聚集和爆发， 在某些关键人群或地理位置（“热点”）中的新诊断和/或晚期诊断。但也有 在我们目前的艾滋病毒监测和有针对性的预防工作中，仍然存在重大差距。这些差距包括缺乏 及时预测艾滋病毒风险是快速公共卫生反应所必需的，依赖于有限的数据， 在确定新感染集群的热点和预测因素方面， 各种数据来源，为州和地方选择和提供有针对性的预防和控制提供信息 程度.为了制定更有效、更及时和更有针对性的艾滋病毒预防方法， 数据驱动的监测和预测系统的新的和后期诊断，使当地卫生部门 医疗保健系统可以快速识别艾滋病毒传播的优先人群和地理位置， 我们的预防努力落后了。基于集成的多级数据结构和强大的 学术与政府的伙伴关系，我们建议目前的研究，以加强了解， 可视化的艾滋病毒感染聚类，使预测的爆发，并有助于优化 提供循证预防的战略。与南卡罗来纳州的部门密切合作， 健康和环境控制（SC DHEC）和其他利益相关者，我们将整合多层次的数据 来源，包括全州范围的电子健康记录数据，县级背景数据，地理空间数据，以及 社交媒体数据，以预测新的和晚期的艾滋病毒诊断在SC和开发一个互动的门户网站， 可视化跨地理位置SC中新的和晚期HIV诊断的时空模式和趋势， 随着时间的推移，特别是在2019冠状病毒病和其他未来的艾滋病毒服务意外中断的情况下， 公共卫生危机。使用大数据方法和多级数据源的集成， 研究将提供更好的理解和可视化的动态时空模式和新的 案例预测预测模型和交互式门户网站将协助SC DHEC，艾滋病服务 组织和其他医疗保健系统，以快速识别，表征和预测新的艾滋病毒集群， 及时部署有针对性的艾滋病防控工作。