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.

抽象的 尽管在艾滋病毒预防方面取得了许多显着进展，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：计划”中规定的目标太慢 为了美国”（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。 流行病，特别是艾滋病毒聚集性和爆发，表现为数量大量或增加 某些关键人群或地理位置（“热点”）中的新诊断和/或晚期诊断。 我们当前的艾滋病毒监测和有针对性的预防工作仍然存在重大差距。 及时预测快速公共卫生反应所需的艾滋病毒风险，依赖于有限的数据 热点地区的来源和新感染群的预测因素识别以及利用方面缺乏努力 各种数据源为州和地方有针对性的预防和控制的选择和实施提供信息 为了制定更有效、及时和有针对性的艾滋病毒预防方法，至关重要的是制定一个 数据驱动的新诊断和晚期诊断监测和预测系统，以便地方卫生部门 医疗保健系统可以快速识别艾滋病毒传播的重点人群和地理位置，或者 我们的预防工作是建立在综合的多层次数据结构和强大的基础上的。 学术与政府的伙伴关系，我们提出当前的研究，以加强理解和 HIV感染聚类可视化，实现疫情爆发预测，有助于优化 与南卡罗来纳州部门密切合作，制定基于证据的预防策略。 健康与环境控制 (SC DHEC) 和其他利益相关者，我们将整合多级数据 来源，包括联邦电子健康记录数据、县级背景数据、地理空间数据和 社交媒体数据，预测 SC 中新的和晚期的 HIV 诊断，并开发一个交互式门户网站 可视化 SC 跨地理位置和晚期 HIV 诊断的时空模式和趋势 随着时间的推移，特别是在 COVID-19 和其他未来因素导致艾滋病毒服务意外中断的情况下 利用大数据方法和多级数据源的整合，提出了应对公共卫生危机的建议。 研究将提供对动态时空模式的更好理解和可视化以及新的 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快速识别、描述和预测新的艾滋病毒集群和 及时部署有针对性的艾滋病防治工作。