JMIR Publications

ABSTRACT

Background:

HIV incidence estimates are published for each year for all Ending the HIV Epidemic counties, but are not stratified by the demographic variables highly associated with risk of infection. Regularly updated robust estimates of HIV incidence -- disaggregated by key variables and available at local levels -- are required to monitor the epidemic in the United States (U.S.) over time, and could contribute to background incidence rate estimates for alternative clinical trial designs for new HIV prevention products. These alternative trial designs will enable more HIV prevention options to enter the market and fill gaps in prevention products that meet the needs of the population of people at risk for infection.

Objective:

We describe methods using existing, robust data sources within Ending the HIV Epidemic (EHE) areas in the U.S. to reliably estimate longitudinal HIV incidence stratified by race and age categories among men who have sex with other men (MSM) eligible for PrEP.

Methods:

We reviewed past methods used to estimate incidence and explored opportunities to improve these estimates. We will use existing surveillance data sources and population sizes of HIV pre-exposure prophylaxis (PrEP)-eligible MSM estimated from population-based data sources (e.g., U.S. Census data, pharmaceutical prescription databases) to develop metropolitan statistical area (MSA)-level estimates of new HIV infections among PrEP-eligible MSM. Key input parameters needed are number of new diagnoses among MSM, estimates of MSM with an indication for PrEP, and prevalent PrEP use including median duration of use; these parameters will be stratified by EHE jurisdiction and age group or race/ethnicity.

Results:

Data to parameterize HIV diagnoses among PrEP-eligible MSM are available, but with varying levels of public availability and timeliness. Outdated modeled estimates will need to be updated and used most recent estimates for other data, including commercial pharmacy claims data. The rate of new HIV diagnoses among MSM can be estimated from the number of new diagnoses within each demographic group (numerator) and the total person-time at risk of diagnosis for each group (denominator) by MSA and year. To estimate time at risk, the person-time of individuals on PrEP or person-time after incident HIV infection but before diagnosis should be removed from stratified population size estimates of the total number of person-years with indications for PrEP.

Conclusions:

These methods will provide reliable, serial, cross-sectional estimates of HIV incidence rates for MSM with higher risk of HIV infection. These estimates could serve as benchmark community incidence estimates to support both public health epidemic monitoring and alternative clinical trial designs.