Do Differences in Community Viral Load Explain Disparities in HIV Incidence
Moupali Das-Douglas, Public Health Foundation Enterprises, Inc., San Francisco
Social and Behavioral Sciences
Thematic Priority Area: Contextual, Cultural, and Structural Issues in HIV Prevention and Care
Innovative, Developmental, Exploratory Award (IDEA)
Why do some communities continue to have disproportionately high rates of new HIV infections? Community viral load (cVL), the average number of HIV virus particles circulating in a defined community or group, may be one such community-level factor that explains disparities in HIV incidence. By tracking cVL, we will be able to pinpoint the communities at highest risk, allowing us to target treatment and prevention efforts. This novel approach departs from the usual practice of tracking HIV prevalence which, at best, tells us where the epidemic was several years ago. We will use San Francisco’s unique HIV/AIDS surveillance system to evaluate cVL and its relationship to new HIV infections. Our highly accurate and validated system collects viral load data for all cases, geolocating information, health status information, HIV medication history, risk behavior and demographics. Viral load is a routine blood test that doctors use to measure the amount of viral particles in an HIV-positive individual’s bloodstream. The quantity of virus particles in the blood is a marker for the health of that individual and how infectious that person may be. What is new and different about this proposal is looking at the community viral load (cVL), or the average viral load for a specific population or group. Because cVL is a sensitive early marker of new HIV infections, seeing an increase in cVL in a particular community could allow us to intervene and stop new HIV infections before they happen. We will examine the relationship between cVL and the number of new HIV infections using statistical methods. We plan to measure the cVL of various groups within the county of San Francisco to see whether cVL levels correspond with HIV infection rates within these groups. We want to know if differences in cVL might account for differences in HIV infection rates in San Francisco by race/ethnicity, neighborhood, transmission risk category, and other factors. We will also look at changes in cVL over time to see whether trends in cVL can predict future HIV infection rates.
We will use cutting-edge mapping software to visualize the geographic distribution of cVL over time in San Francisco. This will help find any HIV “hot spots,” show which neighborhoods have the greatest viral burden, and evaluate the role of neighborhood environments in contributing to the cVL. These maps may also be useful educational tools to share data about HIV-related disparities with our community partners and agencies. If we successfully demonstrate the relationship between cVL and HIV incidence disparities, we will contribute a novel epidemic surveillance tool that is a sensitive biomarker of infectiousness temporally upstream from HIV incidence. Longitudinal measuring of cVL could allow us to evaluate the effectiveness of treatment and prevention interventions at the community level. We plan to propose a multi-site community-based structural intervention to reduce cVL and improve HIV treatment and decrease transmission on a population level. What gets measured gets managed. Raising awareness about cVL, both among treatment and prevention advocates, politicians, and policymakers, could facilitate efforts to reduce and suppress their community’s respective viral burden. Using cVL as a public health tool to determine how well we are doing in our prevention and treatment efforts could revolutionize how we monitor and assess what is “working” at the community level.