Optimal Strategies for HIV Treatment and Prevention in Sub-Saharan Africa

撒哈拉以南非洲艾滋病毒治疗和预防的最佳策略

• 批准号: 9206127
• 负责人: Sally Margaret Blower
• 金额: $ 59.85万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9206127
• 关键词: AIDS prevention; Acquired Immunodeficiency Syndrome; Address; Africa South of the Sahara; African; Area; Botswana; Cessation of life; Communities; Country; Data; Decision Making; Demographic and Health Surveys; Effectiveness; Effectiveness of Interventions; Epidemic; Geographic Locations; Geography; Goals; Grant; HIV; HIV Infections; Health; Healthcare; Incidence; Individual; Infection prevention; Interruption; Intervention; Investments; Lesotho; Link; Male Circumcision; Maps; Medical; Methods; Modeling; Prevalence; Prevention; Prevention program; Probability; Process; Program Effectiveness; Research; Research Infrastructure; Resource Allocation; Resources; Rest; Risk; Risk Factors; Rural; Specific qualifier value; Statistical Data Interpretation; Study models; Techniques; Testing; Time; Work; base; design; evidence base; geographic difference; male; mathematical model; novel strategies; prevent; programs; public health relevance; therapy design; tool; transmission process; treatment program

 DESCRIPTION (provided by applicant): The tools are now available to effectively treat HIV; to implement interventions that could prevent it; and even, according to modeling studies, to potentially eliminate it. However, in the Sub-Saharan African (SSA) countries with generalized HIV epidemics where treatment and intervention is most needed, a lack of resources could limit the impact of such programs. The three sequential studies proposed in this application (Specific Aims 1-3, respectively) will combine modeling and statistical analyses to determine how limited resources can be deployed with maximum efficiency, and effectiveness, to achieve clearly defined goals for treatment and prevention goals. We propose, and will test, the Geographic Optimization (GeO) Hypothesis: the efficiency (and thus effectiveness) of HIV treatment programs and interventions in SSA countries with generalized epidemics can be increased by disproportionately allocating resources to areas where incidence is higher than the national average, i.e., by using geographic targeting. We will focus on two different "types" of generalized HIV epidemics in SSA: predominantly urban (the "type" found in Botswana) and predominantly rural (the "type" found in Lesotho). Both epidemics are among the most severe worldwide. We will maximize the significance of our work by collaborating with two NGOs (ACHAP and PIH) that are responsible for designing and implementing HIV treatment and prevention programs in Botswana and Lesotho, respectively. The project links two approaches new to HIV modeling: (1) Geographic analysis By estimating the number, and geographic location, of infected and at-risk individuals (which we will accomplish by analyzing detailed georeferenced HIV data available from both countries), we can target treatment programs and interventions to the communities in geographic regions where they are most needed; (2) Optimization We will use the georeferenced data from both countries to develop mathematical models, which we will analyze using optimization techniques to determine how limited resources can be utilized most effectively. The optimization approaches that we employ will be based on the GeO Hypothesis and tailored to each aim: in Aim 1 to minimize the probability of interruptions in the treatment supply chain; in Aim 2 to maximize (given resource constraints) the impact of two types of interventions (Voluntary Male Medical Circumcision and Treatment as Prevention (TasP)) on preventing HIV infections; in Aim 3 to minimize the resources needed to use TasP-based interventions to eliminate HIV. An interdisciplinary collaborative team will conduct the research, including experts from the countries that are its focus. As we will use data from Botswana and Lesotho to parameterize our models, our results will be particularly informative for their policymakers, enabling evidence-based decision-making. Notably, the methods and models we will develop will be sufficiently general to be useful for the 23 other SSA countries that have generalized epidemics and georeferenced HIV data from Demographic Health Surveys. The questions we address (e.g., how to minimize stock-outs) are relevant for all 25 countries.

 描述（由申请人提供）：现在可以有效地治疗艾滋病毒的工具;实施可以预防艾滋病毒的干预措施;甚至，根据模拟研究，可能消除艾滋病毒。然而，在撒哈拉以南非洲（SSA）国家，艾滋病毒普遍流行，最需要治疗和干预，缺乏资源可能会限制此类计划的影响。本申请中提出的三项连续研究（分别为特定目标1-3）将结合联合收割机建模和统计分析，以确定如何以最大效率和有效性部署有限的资源，以实现明确定义的治疗和预防目标。我们提出并将测试地理优化（GeO）假设：通过将资源不成比例地分配给发病率高于全国平均水平的地区，可以提高艾滋病毒治疗计划和干预措施在具有普遍流行病的撒哈拉以南非洲国家的效率（从而有效性），即，通过地理定位。我们将集中在两个不同的“类型”的普遍艾滋病毒流行病在撒南非洲：主要是城市（“类型”在博茨瓦纳发现）和主要是农村（“类型”在莱索托发现）。这两种流行病都是世界上最严重的流行病之一。我们将通过与两个分别负责在博茨瓦纳和莱索托设计和实施艾滋病毒治疗和预防方案的非政府组织（非洲艾滋病咨询委员会和国际卫生组织）合作，最大限度地发挥我们工作的重要性。该项目将两种新的方法与艾滋病毒建模联系起来：（1）地理分析通过估计感染者和高危人群的数量和地理位置（我们将通过分析两国提供的详细的地理参考艾滋病毒数据来实现），我们可以将治疗计划和干预措施针对最需要的地理区域的社区;（2）优化我们将利用两国的地理参考数据开发数学模型，并使用优化技术进行分析，以确定如何最有效地利用有限的资源。我们采用的优化方法将基于地理位置假设，并针对每个目标进行定制：在目标1中，最小化治疗供应链中断的概率;在目标2中，最大化（在资源有限的情况下）两类干预措施的影响（自愿男性包皮环切和治疗预防）预防艾滋病毒感染;在目标3中，最大限度地减少使用基于TasP的干预措施消除艾滋病毒所需的资源。一个跨学科合作小组将进行研究，其中包括来自其重点国家的专家。由于我们将使用博茨瓦纳和莱索托的数据来参数化我们的模型，我们的结果将为他们的政策制定者提供特别有用的信息，从而实现基于证据的决策。值得注意的是，我们将开发的方法和模型将具有足够的通用性，可用于其他23个已从人口健康调查中推广流行病和地理参考艾滋病毒数据的撒哈拉以南非洲国家。我们提出的问题（例如，如何尽量减少缺货）对所有25个国家都有意义。