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

• 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
• 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

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.

项目摘要 精神障碍和药物滥用在艾滋病毒感染者（PWH）中很普遍， 对艾滋病毒控制的负面影响。这些情况造成了巨大的情感和身体负担，阻碍了 实现病毒学抑制，并且与增加以下风险的行为相关： 传输虽然威尔斯亲王有多种精神病诊断是很常见的，但相对而言， 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增加，或者如何个性化管理这些常见的并发症， 紊乱此外，估计心理健康服务创新对公共卫生的潜在影响， 艾滋病毒流行普遍缺乏。统计学和机器学习的最新创新使之成为可能 利用复杂的数据，以确定威尔斯亲王医院的精神病模式， 以经典统计方法无法实现的方式优化艾滋病毒控制。 候选人是一个翻译和计算研究员和普通内科医生在 约翰霍普金斯大学计算机科学和生物统计学背景。在本次颁奖期间， 他将接受一个团队的指导，该团队的专业知识涵盖艾滋病护理，精神病学，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 一个独立的，翻译研究人员谁适用于创新的统计和机器学习方法， 改善处于艾滋病毒和心理健康交叉点的个人和人群的健康。 该项目的总体目标是解决儿童中的精神障碍和药物滥用问题， 通过开发利用复杂数据为患者护理提供信息的模型， 和公共政策。三个目标将进行：（1）确定如何组合的症状，抑郁症 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人水平的艾滋病毒 控制;（2）使用重复的患者报告的抑郁，焦虑和物质滥用措施来预测 未来的心理健康和艾滋病毒控制下的一个潜在的药物疗法;和（3）发展人口 预测心理健康服务干预对艾滋病毒发病率和死亡率影响的模型。的 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），这是一个种族多样的多中心队列，共有31，000多名PWH。 这项指导性研究将伴随着心理健康流行病学的相关技能培训， PWH的护理，以及先进的贝叶斯和机器学习方法。总的来说，这项研究和事业 发展培训将为作为临床研究者的独立职业生涯提供明确的途径 优化威尔斯亲王医院对精神疾病和药物滥用的管理， 人口水平。