Big Data Analysis of HIV Risk and Epidemiology in Sub-Saharan Africa

撒哈拉以南非洲地区艾滋病毒风险和流行病学的大数据分析

• 批准号: 9349034
• 负责人: Eran Bendavid
• 金额: $ 69.19万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9349034
• 关键词: Acquired Immunodeficiency Syndrome; Adult; Advisory Committees; Africa South of the Sahara; African; Algorithms; Behavior Therapy; Behavioral; Big Data; Bioethics; Bioethics Consultants; Caring; Catalogs; Cause of Death; Cessation of life; Code; Collection; Communication; Computer software; Consult; Country; Data; Data Analyses; Data Set; Data Sources; Decision Making; Demographic Analyses; Demographic and Health Surveys; Development; Diagnosis; Dimensions; Economic Factors; Economics; Effectiveness; Eligibility Determination; Environmental Risk Factor; Epidemic; Epidemiology; Ethics; Generations; Goals; HIV; HIV Infections; HIV Seropositivity; HIV risk; Health Services Accessibility; Household; Human immunodeficiency virus test; Individual; Intervention; Investigation; Machine Learning; Methodology; Methods; Modeling; Modernization; NIH Program Announcements; Output; Persons; Policies; Policy Maker; Population; Populations at Risk; Predictive Factor; Privacy; Process; Program Efficiency; Public Health; Research; Research Personnel; Risk; Risk Factors; Services; Social Behavior; Surveys; Testing; Time; Training; Translations; Variant; Woman; analytical tool; antiretroviral therapy; base; behavioral economics; cohort; high dimensionality; high risk; improved; insight; men; novel; novel strategies; programs; response; social; sound; therapy development; tool

Project Summary/Abstract HIV is the largest single cause of death among adults in Sub-Saharan Africa, responsible for about a third of all deaths among adults. One of the key paradigms to halting HIV in Sub-Saharan Africa relies on identification of infected individuals and populations for delivery of biomedical and behavioral interventions. However, by the end of 2015 less that half of HIV-infected individuals accessed antiretroviral therapy (ART) despite expansion of eligibility and ongoing efforts to diagnose and initiate treatment. A better understanding of the social, behavioral, environmental, and economic contexts that influence HIV risk could improve the effectiveness and efficiency of programs that aim to identify and target HIV-infected populations. In response to the program announcement for “Harnessing Big Data to Halt HIV” (PA-15-273), the overall goal of this proposal is to develop new analytic tools in large-scale data to predict risk of HIV infection and to generate hypotheses about new or under-recognized risk factors in Sub-Saharan Africa. We plan four primary investigations: (1) Extract and harmonize all Sub- Saharan African nationally representative Demographic and Health Surveys that include the HIV status of over 600,000 men and women collected in 29 countries, and hundreds to thousands of associated exposure variables; (2) Develop analytic tools based on LASSO and XWAS to predict HIV infection status and generate hypotheses about social, behaviorial, environmental, an economic risk factors; (3) Identify HIV risk in multi-country, large-scale data and synthesize findings across in Sub-Saharan Africa, and (4) develop a bioethics program to identify targets for new interventions and policies in a culturally and ethically sound manner. The project will develop leverage big data and high-throughput analytic methodology in the service of global HIV control. The outputs of this project include (1) accessible software code for efficient exploration of robust correlates of HIV status derived from the biggest collection of high-dimensional, harmonized, and nationally representative representative household surveys in the world, (2) an extensive landscape of the social, environmental, behavioral, and economic factors predictive of HIV infection among over 600K people tested for HIV in 29 Sub- Saharan countries, and (3) a ethical framework to enable practical, relevant, and appropriate translation and communication of findings. New models of HIV infection will facilitate identification of at-risk groups and the development of interventions to halt the HIV epidemic in Sub-Saharan Africa.

项目摘要/摘要 艾滋病毒是撒哈拉以南非洲成年人中最大的唯一死亡原因，负责大约 成年人中所有死亡的三分之一。在撒哈拉以南非洲停止艾滋病毒的关键范例之一依赖 关于识别感染的个体和人群的生物医学和行为的人群 干预措施。但是，到2015年底，较少的艾滋病毒感染者访问了一半 抗逆转录病毒疗法（ART）的任务扩大资格和持续诊断和启动的努力 治疗。更好地了解社会，行为，环境和经济环境 影响艾滋病毒的风险可以提高旨在识别和的计划的有效性和效率 目标HIV感染人群。响应“将大数据利用的计划公告 HALT HIV”（PA-15-273），该提案的总体目标是开发大规模的新分析工具 数据以预测艾滋病毒感染的风险，并产生有关新风险或不足的假设 撒哈拉以南非洲的因素。我们计划四个主要研究：（1）提取和协调所有子 - 撒哈拉非洲国家代表包括艾滋病毒状况的人口和健康调查 在29个国家 /地区收集的60万以上男女中，数百至数千个相关 暴露变量； （2）开发基于Lasso和XWA的分析工具以预测HIV感染 地位并产生有关社会，行为，环境，经济风险因素的假设； （3） 在撒哈拉以南的多国，大规模数据中确定艾滋病毒风险 非洲和（4）制定生物伦理学计划，以确定针对新干预措施和政策的目标 在文化和道德上有道理的方式。该项目将开发利用大数据和高通量 为全球艾滋病毒控制服务的分析方法。该项目的输出包括（1） 可访问的软件代码，以有效探索从 高维，统一和全国代表的最大收藏 世界上的家庭调查，（2）社会，环境，行为的广泛景观 和经济因素可预测超过60万人在29个以下测试HIV的人中感染艾滋病毒感染的人 撒哈拉国家和（3）一个道德框架，可实现实用，相关和适当的翻译 和发现的沟通。艾滋病毒感染的新模型将有助于识别高危人群 以及制定干预措施以阻止撒哈拉以南非洲的艾滋病毒流行。