Predicting Treatment Interruption Among People Living With HIV in Nigeria: Machine Learning Approach.

Background: Antiretroviral therapy (ART) has transformed HIV from a fatal illness to a chronic disease. Given the high rate of treatment interruptions, HIV programs use a range of approaches to support individuals in adhering to ART and in re-engaging those who interrupt treatment. These interventions can often be time-consuming and costly, and thus providing for all may not be sustainable.

Accelerating access and scale-up of optimized antiretroviral therapy in low-income and middle-income countries: results of a coordinated end-to-end approach.

The global HIV community invested in multiple, high-profile partnerships and shepherded unprecedented political support to expedite the transition to dolutegravir (DTG)-based regimens. The goal? To accelerate access to simpler, safer, more robust, and more affordable HIV treatment by harnessing the collective power of scientists, regulators, drug companies, donors, implementers, advocates, and people with HIV (PWH). The inspiration? End-to-end approaches to introducing new products that mitigate risk and encourage early planning and resource allocation for all aspects of product introduction and preparation for scale-up.