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.
Psychiatric comorbidities and substance misuse present major obstacles for many persons with HIV in achieving virologic suppression, with negative consequences for both individual and population health. I propose to address psychiatric disorders and substance misuse in the context of the HIV continuum of care by developing innovative models that leverage complex patient data (1) to guide the care of individual persons with HIV and mental illness and (2) to inform policy decisions about systems-level interventions in mental health. As part of a mentored research training program, this award will allow me to become a leading independent researcher with the expertise to leverage complex individual- and population-level data to optimize the health of individuals and populations with HIV who suffer from mental illness.
