Harnessing Bioinformatics for HIV Prevention: Understanding Persistence in Comprehensive HIV Prevention Services

利用生物信息学预防艾滋病毒：了解艾滋病毒综合预防服务的持续性

基本信息

批准号：
10224925
负责人：
Jason Zucker
金额：
$ 19.2万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10224925
关键词：
AIDS prevention Address Adherence Affect African American Area Behavior Behavioral Bioinformatics Caring Clinical Clinical Data Computer Assisted Computerized Medical Record Data Databases Development Enrollment Epidemic Female Future HIV HIV risk Health Health Care Visit Health Personnel Health care facility Housing Individual Institution Insurance Insurance Coverage Intervention Investigation Latino Lead Machine Learning Mental Health Mentors Modeling Motivation New York New York City Patient Self-Report Patients Perception Persons Population Presbyterian Church Prevention Prevention program Provider Questionnaires Research Resources Risk Risk Factors Schedule Services Site Structure Text Messaging Time Training United States Visit base clinical trial analysis cost design follow-up health care disparity health data high risk improved information organization insight machine learning method men who have sex with men modifiable risk pre-exposure prophylaxis predictive modeling prevention service programs prospective skills social substance use transgender

AIDS prevention Address Adherence Affect African American Area Behavior Behavioral Bioinformatics Caring Clinical Clinical Data Computer Assisted Computerized Medical Record Data Databases Development Enrollment Epidemic Female Future HIV HIV risk Health Health Care Visit Health Personnel Health care facility Housing Individual Institution Insurance Insurance Coverage Intervention Investigation Latino Lead Machine Learning Mental Health Mentors Modeling Motivation New York New York City Patient Self-Report Patients Perception Persons Population Presbyterian Church Prevention Prevention program Provider Questionnaires Research Resources Risk Risk Factors Schedule Services Site Structure Text Messaging Time Training United States Visit base clinical trial analysis cost design follow-up health care disparity health data high risk improved information organization insight machine learning method men who have sex with men modifiable risk pre-exposure prophylaxis predictive modeling prevention service programs prospective skills social substance use transgender

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项目摘要

Project Summary/Abstract: Pre-exposure prophylaxis (PrEP) is effective at reducing the acquisition of HIV; however, achieving the full impact of this intervention is contingent on maintaining engagement in care throughout periods of high and low risk. Currently, in the United States, only 270,000 out of 1.2 million individuals with indications for PrEP receive it. Additionally, even for those who start PrEP, adherence, and persistence in comprehensive HIV prevention care is poor, characterized by multiple discontinuations and restarts. Furthermore, HIV risk changes over time and individual-level reasons for engagement, very early disengagement (<2 visits after initiation), early disengagement (2-4 visits after initiation), and late disengagement (>4 visits after initiation) are not well described; social and behavioral determinants of health (SBDH) like insurance, housing status, substance use, and mental health, are increasingly recognized as key factors. As PrEP programs expand to meet the projected need, the cost of providing support services will be substantial and thus identifying patients at highest risk of loss-to-follow-up and selecting optimal services to support persistence in care and PrEP adherence is essential. Furthermore, many individuals receiving HIV prevention services are known to receive highly fragmented care typified by multiple providers and institutions, thereby creating a challenge to accurately characterize persistence in care. Guided by the information-motivation-behavioral skills (IMB) model, we propose to address this critical area of research by focusing on factors that are associated with very early, early, and late disengagement from HIV prevention care. We will identify baseline factors through a comprehensive questionnaire conducted at the time of enrollment into comprehensive HIV prevention care. We will layer onto that longitudinal factors, such as social and behavioral determinants of health (SBDH), which can vary over time, to get a more comprehensive and precise picture of factors affecting persistence in prevention care. To address the issue of fragmented care and better characterize persistence in care, we will utilize information contained in a large Health Information Exchange (HIE), Healthix, to capture all health care visits in New York. We propose to use machine-learning methods to design predictive models of disengagement from HIV prevention care. This comprehensive assessment of persistence in HIV prevention care will inform the development of high quality, scalable models of HIV prevention care, making it possible to target limited resources towards individuals at the highest risk of disengagement.

项目摘要/摘要：暴露前预防（PREP）可有效减少艾滋病毒的获取。但是，实现完整这种干预的影响取决于在高和低时期保持在护理中的参与风险。目前，在美国，在120万人中只有27万人有预告症它。此外，即使对于那些开始进行准备，遵守和持久性的人，预防全面的艾滋病毒护理很差，其特征是多次停产和重新开始。此外，艾滋病毒风险随着时间而变化和个人级别的参与理由，非常早期的脱离接触（启动后<2次访问），早期脱离接触（启动后2-4次访问）和晚期脱离接触（启动后> 4次访问）不好描述；健康的社会和行为决定因素（SBDH），例如保险，住房状况，药物使用，和心理健康，越来越被认为是关键因素。随着准备计划的扩展，以满足预计需求，提供支持服务的成本将是可观的，因此可以确定患者的最高损失到遵守损失的风险和选择最佳服务以支持护理和准备依从性的持久性是基本的。此外，已知许多接受艾滋病毒预防服务的人高度接受以多个提供者和机构为代表的分散护理，从而构成了准确的挑战表征护理的持久性。在信息动机行为技能（IMB）模型的指导下，我们建议通过关注与早期相关的因素来解决这一关键研究领域早期和晚期脱离艾滋病毒预防护理。我们将通过在入学时进行了全面的问卷调查，以进行全面的艾滋病毒预防护理。我们将分层到纵向因素上，例如健康的社会和行为决定因素（SBDH），这些因素（SBDH）会随着时间的流逝而变化，以获取影响持久性因素的更全面和精确的图片预防护理。为了解决零散的护理问题并更好地描述了护理的持久性，我们将利用Healthix的大型健康信息交流（HIE）中包含的信息来捕获所有医疗保健在纽约访问。我们建议使用机器学习方法设计脱离艾滋病毒预防护理。对艾滋病毒预防持续性的全面评估护理将为您的艾滋病毒预防护理的高质量，可扩展模型提供信息，使得将有限的资源定为脱离接触风险最高的个人。