Evaluation of factors related to longitudinal CD4 count and the risk of death among HIV-infected patients using Bayesian joint models

Background In many epidemiological HIV studies, patients are frequently monitored over time to predict their survival by examining their CD4 levels repeatedly. This study aims to evaluate factors related to longitudinal CD4 count and the risk of death among HIV-infected patients using Bayesian joint models. Methods The information of patients who were infected with HIV in Fars Province, from 2011–2016 and followed up until 2022 was used in this study. A joint model of count longitudinal outcome and time to death is used to model information of HIV patients. Results The majority of patients were male (64.8%) with a median age of 35 years. During the follow-up, 123 patients (19%) died. The age-standardized mortality and incidence rates from 2011 to 2016 were 0.496 and 2.49 per 100000 person-years respectively. The 1-year and 5-year survival rates are 98% (95%CI: 97%, 99%) and 88% (95%CI: 85%, 91%) respectively. There is a significant association in this model between the CD4 cell count and the risk of death. Age, addiction, and unemployment were all significantly linked to a lower CD4 cell count. Age was positively correlated with the risk of death. Men, unemployed individuals, and those with hepatitis B had a higher risk of death. Conclusion In this study, we used the Bayesian joint model to investigate the association between the risk of death and the change in CD4 biomarkers that is repeatedly measured over time to determine the factors associated with the survival of HIV-infected persons. The joint model finds a strong association between the CD4 cell count and the risk of death. The joint model allows for a more comprehensive understanding of the factors influencing the CD4 cell count and survival time, compared to using separate models.