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Modelling correlates of adolescent and young people viral suppression pathway in Zambia: a Bayesian multi-state approach

  • University of Ghana
  • University of Zambia
  • African Population and Health Research Center
  • Stellenbosch University

Research output: Contribution to journalArticlepeer-review

Abstract

This study aimed to look into the correlates of viral suppression in HIV-infected adolescents by utilizing Markov renewal multi-state survival models based on the Bayesian Approach, which use a deterministic posterior approximation. We developed a patient-level viral suppression transition framework to quantify the prognostic factors influencing viral suppression. The results showed that HIV-infected adolescents had higher risk of attaining viral suppression relative to remaining engaged, transfer out, being inactive, and dying among patients with VL < 50 compared to those with <100,000 was low. Older adolescents living with HIV were significantly less likely to retain to care and significantly less likely to attain viral suppression relative to those between 10 and 14 years. Those who retained in care, aged between 15–19 and 20–24 years were 9 and 8% more likely to attain viral suppression relative to those who were younger. While adolescents living with HIV attaining viral suppression were 15% more likely to transfer out of care and were 13% less likely to die. The proposed modeling approach identifies the transition rates of moving through each stage in the care cascade. The findings presented have appropriate policy implications and can be helpful in guiding implementation of targeted adolescent specific programs.

Original languageEnglish
Article number2602117
JournalCritical Public Health
Volume36
Issue number1
DOIs
Publication statusPublished - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Bayesian
  • HIV viral suppression
  • adolescent and young people
  • stochastic

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