Application of a joint multivariate probit model for mixed outcomes of CD4 cell count and tuberculosis using a Bayesian latent variable approach in KwaZulu-Natal

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This analysis jointly models CD4 cell count and TB diagnosis in HIV patients using Bayesian methods, highlighting significant associations.

The analysis reveals a moderate negative correlation between CD4 cell count and TB risk, indicating immune suppression increases TB likelihood.
Bayesian multivariate latent variable model estimated outcomes, showing that antiretroviral therapy significantly improves immune status and reduces TB risk.
Data from 7,776 HIV-positive individuals demonstrated that factors like male sex and higher viral load are linked to increased TB susceptibility.
Findings underscore the clinical importance of integrated HIV-TB programming for targeted screening and resource prioritization.

Chireshe et al. (2025) studied this question.

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View Full Paper

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Authors

This analysis jointly models CD4 cell count and TB diagnosis in HIV patients using Bayesian methods, highlighting significant associations.

The analysis reveals a moderate negative correlation between CD4 cell count and TB risk, indicating immune suppression increases TB likelihood.
Bayesian multivariate latent variable model estimated outcomes, showing that antiretroviral therapy significantly improves immune status and reduces TB risk.
Data from 7,776 HIV-positive individuals demonstrated that factors like male sex and higher viral load are linked to increased TB susceptibility.
Findings underscore the clinical importance of integrated HIV-TB programming for targeted screening and resource prioritization.

Chireshe et al. (2025) studied this question.

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