Optimizing outcomes at the intersection of HIV and mental health: Prediction, precision medicine, and population health

优化艾滋病毒与心理健康交叉点的结果：预测、精准医疗和人口健康

基本信息

批准号：
10231211
负责人：
Anthony Todd Fojo
金额：
$ 19.8万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10231211
关键词：
Achievement Acquired Immunodeficiency Syndrome Address Adherence Affect Alcohol consumption Alcohols Amphetamines Anxiety Attention Award Bayesian learning Behavior Big Data Biometry Caring Clinical Clinical Data Clinical Investigator Cocaine Complex Continuity of Patient Care Data Development Diagnosis Emotional Enrollment Epidemic Evaluation Future Geography Goals HIV Health Incidence Individual Internal Medicine Intervention Investigation Machine Learning Marijuana Mental Depression Mental Health Mental Health Services Mental disorders Mentors Methods Modeling Opioid Outcome Pathway interactions Patient Care Patient Outcomes Assessments Patient observation Patient-Focused Outcomes Patients Pattern Persons Pharmaceutical Preparations Pharmacotherapy Physicians Policies Population Probability Psychiatric Diagnosis Psychiatric epidemiology Psychiatric therapeutic procedure Public Health Public Policy Questionnaires Reporting Research Research Personnel Research Training Risk Site Smoking Symptoms System Testing Training Training Programs Universities alcohol misuse alcohol use disorder antiretroviral therapy base career career development clinical phenotype clinically relevant cohort comorbidity complex data computer science depressive symptoms dual diagnosis evidence base improved individualized medicine innovation machine learning method mathematical methods mental health epidemiology metropolitan mortality personalized management personalized medicine personalized predictions population health precision medicine predictive modeling prospective psychiatric symptom racial diversity research and development screening service intervention skills skills training sociodemographics statistical and machine learning statistics substance misuse substance use tool translational scientist transmission process virology

项目摘要

PROJECT SUMMARY Psychiatric disorders and substance misuse are prevalent among people with HIV (PWH) and have negative effects on HIV control. These conditions impose substantial emotional and physical burdens, impede the achievement of virologic suppression, and are associated with behaviors that increase the risk of transmission. While it is common for PWH to have multiple psychiatric diagnoses, relatively little is known about the ways in which combinations of psychiatric symptoms and substance misuse behaviors confer elevated risk for poor HIV control, or how to personalize the management of these commonly co-occurring disorders. Furthermore, estimates of the potential public-health impact of mental health service innovations on the HIV epidemic are generally lacking. Recent innovations in statistics and machine learning make it possible to harness complex data to identify patterns of psychiatric disease in PWH and tailor psychiatric therapy to optimize HIV control in ways that classical statistical approaches cannot. The candidate is a translational and computational investigator and general internal medicine physician at Johns Hopkins University with a background in computer science and biostatistics. During this award period, he will be mentored by a team whose expertise spans HIV care, psychiatric epidemiology, machine learning, population-level HIV modeling, and personalized medicine. The candidate's long-term career goal is to become an independent, translational researcher who applies innovative statistical and machine learning approaches to improve the health of individuals and populations at the intersection of HIV and mental health. The overarching objective for this project is to address psychiatric disorders and substance misuse in the context of the HIV continuum of care by developing models that leverage complex data to inform patient care and public policy. Three aims will be undertaken: (1) to identify how combinations of symptoms of depression and anxiety and misuse of alcohol, cocaine, opioids, marijuana, and amphetamines impact individual-level HIV control; (2) to use repeated patient-reported measures of depression, anxiety, and substance misuse to predict future mental health and HIV control under a potential pharmacotherapies; and (3) to develop population models that project the impact of mental health service interventions on HIV incidence and mortality. The models will leverage data from the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS), a racially diverse, multi-site cohort of over 31,000 PWH. This mentored research will be accompanied by relevant skills training in mental health epidemiology, the care of PWH, and advanced Bayesian and machine-learning methods. Collectively, this research and career development training will provide a clear pathway to an independent career as a clinical investigator focused on optimizing the management of psychiatric disorders and substance misuse in PWH at both the individual and population level.

项目概要精神疾病和药物滥用在艾滋病毒感染者 (PWH) 中普遍存在，并且对艾滋病毒控制产生负面影响。这些情况造成巨大的情感和身体负担，阻碍病毒学抑制的实现，并与增加风险的行为有关传播。虽然感染者患有多种精神病学诊断很常见，但人们对此知之甚少关于精神症状和药物滥用行为的组合如何赋予艾滋病毒控制不良的风险升高，或如何对这些常见的同时发生的问题进行个性化管理失调。此外，对精神卫生服务创新对公共卫生的潜在影响的估计艾滋病毒流行情况普遍缺乏。统计和机器学习领域的最新创新使之成为可能利用复杂的数据来识别 PWH 中的精神疾病模式，并根据需要定制精神治疗以经典统计方法无法做到的方式优化艾滋病毒控制。候选人是一名翻译和计算研究员以及普通内科医生约翰霍普金斯大学拥有计算机科学和生物统计学背景。本次颁奖期间，他将得到一个团队的指导，该团队的专业知识涵盖艾滋病毒护理、精神病流行病学、机器学习、人口水平的艾滋病毒建模和个性化医疗。候选人的长期职业目标是成为一位独立的转化研究人员，应用创新的统计和机器学习方法在艾滋病毒和心理健康的交叉点上改善个人和人群的健康。该项目的总体目标是解决精神疾病和药物滥用问题通过开发利用复杂数据为患者护理提供信息的模型，了解艾滋病毒连续护理的背景和公共政策。我们将实现三个目标：（1）确定抑郁症症状的组合如何焦虑和滥用酒精、可卡因、阿片类药物、大麻和安非他明会影响个体水平的艾滋病毒控制; (2) 使用患者重复报告的抑郁、焦虑和药物滥用指标来预测潜在药物疗法下的未来心理健康和艾滋病毒控制； (3) 发展人口预测心理健康服务干预措施对艾滋病毒发病率和死亡率影响的模型。这模型将利用来自艾滋病研究中心综合临床系统网络的数据 (CNICS)，一个由 31,000 多名 PWH 组成的种族多元化、多地点队列。这项指导研究将伴随心理健康流行病学、感染者护理，以及先进的贝叶斯和机器学习方法。总的来说，这项研究和职业生涯发展培训将为作为一名专注于临床研究的独立职业提供清晰的途径优化 PWH 中精神疾病和药物滥用的管理和人口水平。