Nearly 85% of the 1.7 million adolescents with HIV live in sub-Saharan Africa, along with half of the nearly 40 million people in the world living with HIV. Although the government in Uganda provides free antiretroviral treatment (ART), adherence to the regimen by adolescents ages 10-16 is low, increasing the potential for the virus to further spread.
Claire Najjuuko, a doctoral student at Washington University in St. Louis, saw this firsthand while working as a data manager at the International Center for Child Health and Development (ICHAD) in Uganda, founded by Fred M. Ssewamala, the William E. Gordon Distinguished Professor at the WashU Brown School. Now earning a doctorate in WashU’s Division of Computational & Data Sciences, Najjuuko wanted to use artificial intelligence (AI) and data science to help improve adolescent compliance with the treatment in low-resource areas. Her co-advisers are Ssewamala and Chenyang Lu, the Fullgraf Professor of computer science and engineering at the McKelvey School of Engineering. Results of the research were published online Feb. 25 in the journal AIDS.
“The current practice is, adolescents go to the clinic every month or two months for medication refills, and a health-care practitioner checks how many pills the patient has left compared with what is expected, as well as asking the adolescent questions regarding missed doses to establish if the patient is adhering to the therapy,” Najjuuko said. “This project to predict future nonadherence of adolescents can have real impact if implemented in the right way.”
To train the model, Najjuuko used data from a six-year cluster-randomized controlled trial from 39 clinics in southern Uganda, a region most heavily impacted by HIV. The Suubi+Adherence dataset included adolescents between age 10-16 medically diagnosed with HIV; aware of their status; enrolled in ART at one of the clinics; and living within a family. Ultimately, the models analyzed data from 647 patients who had complete data on the outcome at 48 months.