BERD Seminar: Modeling HIV seroconversion and mortality trends in South Africa using a discrete survival model that accounts for interval-censored covariates

A fundamental problem in HIV research is how to handle the interval-censoring of HIV status. As researchers, we may have information about an individual's HIV status from one or more HIV tests, but we rarely know when that individual seroconverted, and for individuals whose last test was negative, we do not know their current status. To handle this problem, we developed a flexible class of discrete-time parametric survival models that handle the censoring problem through simultaneous modeling of the seroconversion process, the outcome (e.g., mortality), and the censoring mechanism. We apply this model to the research question that motivated the methodology, estimating the effect of HIV status on all-cause mortality in a prospective cohort study in South Africa. Our model has applicability for many open questions, including estimating the impact of policy decisions on population level HIV-related outcomes and determining causes of morbidity and mortality for which the HIV positive population may be at increased risk. Examples include determining how the large-scale transition from efavirenz-based to dolutegravir-based first-line ART impacted mortality for people living with HIV and determining whether HIV status is associated with increased risk of stroke, diabetes, hypertension, and other non-communicable diseases. Zoom link: https://duke.zoom.us/j/99193151349?pwd=a0RaQzdJWEtpcmhZTGQrdmdubWlBUT09 This event is being cross-promoted by the NC BERD Consortium, a collaboration of the CTSA-funded BERD cores at UNC-Chapel Hill, Wake Forest University School of Medicine, and Duke University School of Medicine.