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

点击翻译按钮获取中文摘要

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.