Abstract

Joint modeling of longitudinal and time-to-event outcomes has been well-established but underutilized in resource-limited settings like Malawi. Separate analyses can lead to biased conclusions by ignoring shared random effects. This study aimed to use joint modeling to analyze HIV viral load suppression and ART outcomes, providing com prehensive insights into treatment dynamics in Malawi. Data from two ART facilities in Lilongwe were used for a retrospective cohort analysis. Separate longitudinal and survival models were developed to understand the influence of various covariates on viral load suppression and time-to-event outcomes. A Bayesian joint model was then used to understand the association between longitudinal viral load suppression trajec tories and survival time to a composite event. Model comparisons determined the best fit and examined the strength of the association between outcomes. The longitudinal model highlighted the significance of ART regimen choice and treatment duration on viral load suppression, with INSTI-based regimens associated with improved outcomes (OR 1.25, 95% CI: 1.06-1.47, p = 0.007). The survival model identified advanced HIV stages and longer ART durations as risk factors for adverse outcomes (HR 0.41, 95% CI: 0.18-0.90, p = 0.010 for advanced stages and HR 1.43, 95% CI: 1.20-1.69, p < 0.001 for longer durations). The joint model analysis revealed a critical association between viral load suppression and survival outcomes, showing that sustained viral suppression significantly reduced the risk of defaulting, stopping treatment, or death (HR 0.19, 95% CI: 0.14-0.23, p < 0.001). This study demonstrates the utility of joint modeling in understanding the relationships between viral load suppression and ART outcomes. By identifying key factors influenc ing these outcomes and quantifying their effects, the findings supports the personalized treatment strategies and enhanced monitoring for patients, particularly those with ad vanced HIV stages or longer treatment duration’s.

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School	: School of Natural and Applied Sciences
Issued Date	: 2024

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