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

> **NIH NIH K08** · JOHNS HOPKINS UNIVERSITY · 2020 · $198,210

## Abstract

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 PW...

## Key facts

- **NIH application ID:** 9995041
- **Project number:** 5K08MH118094-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Anthony Todd Fojo
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $198,210
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995041

## Citation

> US National Institutes of Health, RePORTER application 9995041, Optimizing outcomes at the intersection of HIV and mental health: Prediction, precision medicine, and population health (5K08MH118094-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9995041. Licensed CC0.

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