Prevention Science

CAPS I&I Town Hall presents: Megha Mehrotra, PhD -- Transportability and Implementation Science for HIV Prevention:

Megha Mehrotra, PhD, completed her doctoral degree in Epidemiology and Biostatistics at UC Berkeley in 2019.  Her dissertation was entitled From Trials to Public Health Impact: Transportability of Causal Effects to Inform Implementation of HIV Pre-exposure Prophylaxis.

This talk will focus on how transportability theory and methods might be useful for thinking about some common issues faced in implementation science research.

CAPS Town Hall presents: Vincent Muturi-Kioi, MBChB, DTM&H, MS -- African Centered Science, Engagement and Testing: The Road to an HIV Vaccine and Other Solutions for Global Health Challenges

Dr. Vincent Muturi-Kioi is Medical Director at International AIDS Vaccine Initiative (IAVI), stationed at the IAVI Africa regional office in Kenya.  In this role, Dr. Muturi-Kioi is responsible for medical monitoring of clinical trials in Africa and involved in the design and implementation of epidemiological studies aimed at providing data to be used for the design of efficacy trials. He also participates in the development and implementation of training activities with clinical partners.

CAPS Town Hall presents: Lindsay Young, PhD -- Social Network Analysis and Machine Learning: Computational Partners in the Study of HIV Prevention and Risk Online

As transmitters of information and progenitors of behavioral norms, social networks are critical mechanisms of HIV prevention and risk in impacted populations like men who have sex with men (MSM), people who inject drugs (PWID), and homeless youth. Today, widespread use of online social networking technologies (e.g., Facebook, Instagram, Twitter) yield unprecedented amounts of relational and communication data far richer than anything previously collected in offline (physical) network settings.

CAPS Methods Core presents: Maya Petersen, MD, PhD, Co-Chair, Graduate Group in Biostatistics, UC Berkeley

Abstract: Targeted Maximum Likelihood Estimation (TMLE) provides an approach for estimating the causal effects of longitudinal interventions with several attractive properties. TMLE uses estimates of both the propensity score (as used in inverse probability weighting) and of a series of outcome regressions (as can be used in parametric G-computation). Machine-learning methods, such as Super Learning (an ensemble approach) can be used to estimate both the propensity score and outcome regressions.

CAPS Community Town Hall presents: Jesse Brooks -- HIV Support with Black Men: "The Brothers Groups"

Jesse Brooks is a longtime activist raised in Oakland, California.  He is currently Co-Chair of the Bay Area State of Emergency Coalition (BASE), Advocacy Coordinator of the AIDS Healthcare Foundation of Northern California, and Co-Chair of UCSF/CAPS Research Community Advisory Board, and a Weekly Columnist for the largest African American paper in the Bay Area, “Post News Group,” reaching over 80,000 readers.