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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

基本信息

批准号：
9892947
负责人：
Eran Bendavid
金额：
$ 69.14万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9892947
关键词：
Acquired Immunodeficiency Syndrome Adult Advisory Committees Africa South of the Sahara African Algorithms Behavior Therapy Behavioral Big Data Big Data Methods 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 health goals high dimensionality high risk improved insight large scale data men novel novel strategies programs response social sound statistical and machine learning statistical learning testing services therapy development tool treatment services

项目摘要

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.

项目概要/摘要艾滋病毒是撒哈拉以南非洲地区成年人死亡的最大单一原因，造成约占成人死亡人数的三分之一。撒哈拉以南非洲地区遏制艾滋病毒的关键范例之一依赖于识别受感染的个人和群体以提供生物医学和行为学干预措施。然而，到 2015 年底，只有不到一半的艾滋病毒感染者获得了尽管资格范围扩大并且持续努力诊断和启动抗逆转录病毒治疗（ART）治疗。更好地理解社会、行为、环境和经济背景影响艾滋病毒风险可以提高旨在识别和预防艾滋病毒风险的计划的有效性和效率以艾滋病毒感染人群为目标。响应“利用大数据阻止 HIV”（PA-15-273），该提案的总体目标是大规模开发新的分析工具用于预测 HIV 感染风险并生成有关新风险或未被充分认识的风险的假设的数据撒哈拉以南非洲地区的因素。我们计划进行四项主要调查：（1）提取并协调所有子项目撒哈拉非洲国家具有代表性的人口统计和健康调查，包括艾滋病毒状况收集了来自 29 个国家的超过 600,000 名男性和女性，以及成百上千的相关人员暴露变量；（2）开发基于LASSO和XWAS的分析工具来预测HIV感染状态并生成有关社会、行为、环境、经济风险因素的假设； (3) 通过多国大规模数据识别艾滋病毒风险并综合撒哈拉以南地区的研究结果非洲，以及 (4) 制定生物伦理学计划，以确定新干预措施和政策的目标文化和道德上健全的方式。该项目将利用大数据和高通量进行开发为全球艾滋病毒控制服务的分析方法。该项目的成果包括（1）可访问的软件代码，用于有效探索源自艾滋病毒状况的强大相关性最大的高维、和谐、具有全国代表性的代表集合世界各地的家庭调查，(2) 广泛的社会、环境、行为、 29 个亚省超过 60 万人接受了 HIV 检测，预测 HIV 感染的经济因素和经济因素撒哈拉国家，以及 (3) 实现实用、相关和适当翻译的道德框架和交流调查结果。 HIV感染的新模型将有助于识别高危人群制定干预措施以制止撒哈拉以南非洲地区的艾滋病毒流行。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.

DOI：
10.1371/journal.pbio.3001556
发表时间：
2022-03
期刊：
PLoS biology
影响因子：
9.8
作者：
Tierney BT;Tan Y;Yang Z;Shui B;Walker MJ;Kent BM;Kostic AD;Patel CJ
通讯作者：
Patel CJ

Diverse experts' perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study.

DOI：
10.1136/bmjopen-2021-052287
发表时间：
2021-07-28
期刊：
BMJ open
影响因子：
2.9
作者：
Nichol AA;Bendavid E;Mutenherwa F;Patel C;Cho MK
通讯作者：
Cho MK

Temporal exposure and consistency of endocrine disrupting chemicals in a longitudinal study of individuals with impaired fasting glucose.

DOI：
10.1016/j.envres.2021.110901
发表时间：
2021-06
期刊：
ENVIRONMENTAL RESEARCH
影响因子：
8.3
作者：
van der Meer, Thomas P.;Chung, Ming K.;van Faassen, Martijn;Makris, Konstantinos C.;van Beek, Andre P.;Kema, Ido P.;Wolffenbuttel, Bruce H. R.;Van Vliet-Ostaptchouk, Jana V.;Patel, Chirag J.
通讯作者：
Patel, Chirag J.

Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale.