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Seminar repost-EG-PS

#SSACAB | #Scienceforafricafoundation | #Wellcometrust | #ResearchSeminar | #GlobalHealth | #PublicHealthResearch | #ClinicalEpidemiology | #Biostatistics | #HIVResearch | #MalariaResearch | #ChildHealth

*********************** Repost *****************************

Reminder not to miss this intriguing seminar

Date: 28 April 2026
Time: 14:00–15:00 (SAST)
Join the seminar: https://bit.ly/45ir990

🔹Talk 1
Cardiovascular disease risk among people living with HIV, with a focus on arterial stiffness measured using pulse wave velocity in longitudinal analyses.

Presenter: Patane Shilabye (SSACAB fellow)

Key focus areas:

    ➡️HIV and arterial stiffness
    ➡️Effects of antiretroviral therapy (ART)
    ➡️Treatment-related factors (especially ART adherence)
    ➡️Psychosocial factors (sleep and stress)
    ➡️Longitudinal analysis of these relationships over time

🔍Why it matters:
As people living with HIV are living longer due to effective antiretroviral therapy, cardiovascular disease has become an important emerging health concern. Understanding how HIV, treatment, and psychosocial factors like stress and sleep influence arterial stiffness can help identify early risk, guide prevention strategies, and improve long-term cardiovascular health outcomes.

🔹Talk 2
Modelling time to eat unaided in Southeast Asian children with severe malaria: a comparison of time-to-event survival models.

Presenter: Emmanuel Guzani (SSACAB fellow)

Key focus areas:

    ➡️Functional recovery in children with severe malaria (time to eat unaided as the main outcome)
    ➡️Comparison of survival analysis models (Kaplan–Meier, Cox, AFT, and competing risks)
    ➡️Identification of prognostic factors and optimal model selection

🔍Why it matters:
Severe malaria remains a leading cause of child morbidity and mortality in many regions. Accurately modelling recovery—using meaningful clinical milestones like time to eat unaided—can improve how clinicians monitor progress, identify high-risk patients earlier, and choose the most effective treatment approaches. Comparing survival models also ensures that the most reliable methods are used to inform clinical and public health decisions.