Combinations of interventions to achieve a national HIV incidence reduction goal: insights from an agent-based model

Authored by Chaitra Gopalappa, Paul G Farnham, Yao-Hsuan Chen, Stephanie L Sansom

Date Published: 2017

DOI: 10.1097/qad.0000000000001653

Sponsors: United States National Institutes of Health (NIH)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

Objective: Analyzing HIV care service targets for achieving a national goal of a 25\% reduction in annual HIV incidence and evaluating the use of annual HIV diagnoses to measure progress in incidence reduction. Design: Because there are considerable interactions among HIV care services, we model the dynamics of `combinations' of increases in HIV care continuum targets to identify those that would achieve 25\% reductions in annual incidence and diagnoses. Methods: We used Progression and Transmission of HIV/AIDS 2.0, an agent-based dynamic stochastic simulation of HIV in the United States. Results: A 25\% reduction in annual incidence could be achieved by multiple alternative combinations of percentages of persons with diagnosed infection and persons with viral suppression including 85 and 68\%, respectively, and 90 and 59\%, respectively. The first combination corresponded to an 18\% reduction in annual diagnoses, and infections being diagnosed at a median CD4+ cell count of 372 cells/mu l or approximately 3.8 years from time of infection. The corresponding values on the second combination are 4\%, 462 cells/mu l, and 2.0 years, respectively. Conclusion: Our analysis provides policy makers with specific targets and alternative choices to achieve the goal of a 25\% reduction in HIV incidence. Reducing annual diagnoses does not equate to reducing annual incidence. Instead, progress toward reducing incidence can be measured by monitoring HIV surveillance data trends in CD4+ cell count at diagnosis along with the proportion who have achieved viral suppression to determine where to focus local programmatic efforts. Copyright (C) 2017 Wolters Kluwer Health, Inc. All rights reserved.

Tags

Agent-based simulation model transmission United-states Us Hiv intervention combinations Hiv simulation model National hiv/aids strategy Hiv/aids strategy