Rapid Tests for Recent Infection (RTRI) for Precision Public Health in Sub-Saharan Africa: Next-Generation Strategies Amid Changing HIV Epidemiology

撒哈拉以南非洲地区近期感染快速检测 (RTRI) 实现精准公共卫生：艾滋病毒流行病学变化中的下一代策略

• 批准号: 10620014
• 负责人: Anna Bershteyn
• 金额: $ 80.93万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-04 至 2027-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10620014
• 关键词: AIDS prevention; Acceleration; Adopted; Adult; Africa South of the Sahara; Age; Antibody Avidity; Authorization documentation; Avidity; Big Data; Big Data Methods; Bioethics; Botswana; Budgets; Cause of Death; Client; Cost Measures; Country; Data; Data Element; Decision Making; Detection; Diagnosis; Disease Outbreaks; Epidemic; Epidemiology; Exposure to; Fingers; Goals; Guidelines; HIV; HIV Antibodies; HIV Genome; HIV Infections; HIV Seropositivity; Health; Health Benefit; Health Care Costs; Health care facility; Home; Human immunodeficiency virus test; Incidence; Individual; Infection; Infectious Diseases Research; International; Intervention; Intuition; Investments; Lead; Lesotho; Life; Machine Learning; Maps; Measures; Modeling; Mozambique; Namibia; Network-based; Newly Diagnosed; Paper; Pattern; Persons; Phylogenetic Analysis; Plasma; Policies; Policy Making; Population; Populations at Risk; Prevalence; Public Health; Radial; Reaction Time; Resources; Role; Running; Sensitivity and Specificity; Sepsis; Serum; Services; Social Network; South Africa; Specificity; Stigmatization; Sustainable Development; Testing; Time; Training; Venous; Work; Zambia; antibody detection; authority; cohort; cost; cost effectiveness; cost estimate; design; epidemic response; health economics; heuristics; improved; incremental cost; infectious disease model; machine learning model; mathematical model; microcosting; network models; next generation; novel; outbreak response; point of care; point of care testing; prevention service; programs; rapid test; response; standard of care; test strip; testing services; tool; transmission process; AIDS prevention; Acceleration; Adopted; Adult; Africa South of the Sahara; Age; Antibody Avidity; Authorization documentation; Avidity; Big Data; Big Data Methods; Bioethics; Botswana; Budgets; Cause of Death; Client; Cost Measures; Country; Data; Data Element; Decision Making; Detection; Diagnosis; Disease Outbreaks; Epidemic; Epidemiology; Exposure to; Fingers; Goals; Guidelines; HIV; HIV Antibodies; HIV Genome; HIV Infections; HIV Seropositivity; Health; Health Benefit; Health Care Costs; Health care facility; Home; Human immunodeficiency virus test; Incidence; Individual; Infection; Infectious Diseases Research; International; Intervention; Intuition; Investments; Lead; Lesotho; Life; Machine Learning; Maps; Measures; Modeling; Mozambique; Namibia; Network-based; Newly Diagnosed; Paper; Pattern; Persons; Phylogenetic Analysis; Plasma; Policies; Policy Making; Population; Populations at Risk; Prevalence; Public Health; Radial; Reaction Time; Resources; Role; Running; Sensitivity and Specificity; Sepsis; Serum; Services; Social Network; South Africa; Specificity; Stigmatization; Sustainable Development; Testing; Time; Training; Venous; Work; Zambia; antibody detection; authority; cohort; cost; cost effectiveness; cost estimate; design; epidemic response; health economics; heuristics; improved; incremental cost; infectious disease model; machine learning model; mathematical model; microcosting; network models; next generation; novel; outbreak response; point of care; point of care testing; prevention service; programs; rapid test; response; standard of care; test strip; testing services; tool; transmission process

ABSTRACT/SUMMARY HIV is a leading cause of death in sub-Saharan Africa (SSA), but rates of new infections in declining. As a result, some HIV services are becoming less efficient: HIV prevention is averting fewer infections per client served, and HIV testing yield (i.e., the fraction of tests leading to new diagnoses) is declining. Given resource constraints, HIV prevention and testing programs will need to increase efficiency in order to maintain momentum toward epidemic control. Specifically, a precision public health approach could be used to focus services where their health benefits would be greatest. Precision public health has been challenging to implement in SSA’s generalized HIV epidemics because most transmission is not confined to identifiable key populations, eluding precise responses. Until recently, the only tool for describing HIV transmission has been phylogenetic analysis of HIV genome sequences, which is too slow and costly for real-time response at scale. Rapid tests for recent HIV infection (RTRI) recently became available in SSA, offering a novel opportunity to identify active transmission clusters and respond with precision. RTRI detects high-avidity antibodies that appear approximately one year after HIV infection. If only low-avidity antibodies are detected, this infection likely occurred in the past year. Zambia is an early adopter of RTRI and followed-up two-thirds of positive HIV tests with RTRI in 2021. Despite the large scale of Zambia’s RTRI program, it has not been evaluated in terms of impact or cost-effectiveness, and outbreak response thresholds have not been optimized. However, the wealth of data now collected by the RTRI program could guide higher-performing, next-generation outbreak response strategies. Our team has expertise in Zambian HIV policy, program implementation, health economics, bioethics, geospatial analysis, machine learning, and infectious disease modeling. We helped to develop the only HIV social network model rigorously validated to predict HIV incidence, prevalence, and transmission patterns in SSA, which has been used for international and country-level HIV policy decision-making for over a decade. We propose to partner with the Center for Infectious Disease Research in Zambia (CIDRZ), a Zambian-run NGO that has been supporting MoH for over a decade in HIV policy-making and implementation (including RTRI), to (Aim 1) determine whether the RTRI program can help Zambia achieve its epidemic control goals, (Aim 2) measure the cost and estimate the cost-effectiveness of the RTRI program, and (Aim 3) use “big data” methods to design next-generation outbreak responses, taking into account trade-offs of sensitivity vs. specificity, precision vs. ease-of-use, and precision vs. avoidance of collecting potentially stigmatizing data elements. While pursuing scientific Aims and hypotheses aligned with the NOSI “Harnessing Big Data to Halt HIV”, we will also have direct policy impact by providing real-time analytics, HIV outbreak mapping, and input for RTRI policy decision-making.

摘要/摘要 艾滋病毒是撒哈拉以南非洲（SSA）死亡的主要原因，但新的感染率下降。因此， 一些艾滋病毒服务的效率降低了：预防艾滋病毒是避免每位服务对象的感染更少，并且 艾滋病毒测试产量（即导致新诊断的测试部分）正在下降。给定资源限制， 预防艾滋病毒和测试计划将需要提高效率，以保持势头 流行病控制。特别是，可以使用精确的公共卫生方法来集中服务 健康益处将是最大的。精密公共卫生已受到挑战，要在SSA的 广义HIV发作，因为大多数传播不仅限于可识别的关键人群，因此 精确的回应。直到最近，描述HIV传播的唯一工具是系统发育分析 HIV基因组序列的序列太慢，对于实时响应而言太慢且昂贵。最近的快速测试 艾滋病毒感染（RTRI）最近在SSA上使用，提供了一个新的机会来识别主动传播 集群并精确地做出回应。 RTRI检测到大约一年出现的高狂欢抗体 艾滋病毒感染后。如果仅检测到低持续抗体，则可能在过去一年中发生这种感染。 赞比亚是RTRI的早期采用者，在2021年对RTRI进行阳性HIV测试的三分之二。 赞比亚的RTRI计划的大规模尚未根据影响或成本效益进行评估， 爆发响应阈值尚未优化。但是，现在收集的大量数据 RTRI计划可以指导表现更高的下一代暴发响应策略。我们的团队有 赞比亚艾滋病毒政策，计划实施，卫生经济学，生物伦理学，地理空间分析的专业知识， 机器学习和传染病建模。我们帮助开发了唯一的HIV社交网络模型 经过严格验证以预测SSA中的HIV事件，患病率和传播模式， 十多年来用于国际和国家级艾滋病毒政策决策。我们建议合作 赞比亚（Cidrz）的传染病研究中心，这是一个赞比亚经营的非政府组织 在HIV政策制定和实施（包括RTRI）中支持MOH十多年来（AIM 1） 确定RTRI计划是否可以帮助赞比亚实现其流行控制目标，（目标2）衡量 成本并估算RTRI计划的成本效益，（目标3）使用“大数据”方法设计 下一代爆发的反应，考虑到敏感性与特异性的权衡，精确与精确性。 易用性和精度与避免收集潜在的污名化数据元素。在追求时 科学目的和假设与NOSI“利用大数据避免HIV”一致，我们还将直接 通过提供实时分析，艾滋病毒爆发制图和RTRI政策决策的意见来影响政策。