| Title: |
Evaluation of factors related to longitudinal CD4 count and the risk of death among HIV-infected patients using Bayesian joint models. |
| Authors: |
Pilangorgi, Sahar Souri1 (AUTHOR), Khodakarim, Soheila1 (AUTHOR) khodakarimsoheila@gmail.com, Shayan, Zahra1 (AUTHOR), Nejat, Mehdi2 (AUTHOR) |
| Source: |
BMC Public Health. 3/12/2025, Vol. 25 Issue 1, p1-9. 9p. |
| Subject Terms: |
*CD4 lymphocyte count, *STATISTICAL significance, *PUBLIC health, *MEDICAL sciences, *HEPATITIS B |
| Abstract: |
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 to 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 (67.8%) with a median age of 34 years. During the follow-up, 212 patients (28.0%) died. The age-standardized mortality and incidence rates from 2011 to 2016 were 0.496 and 2.49 per 100,000 person-years respectively. The 1-year and 5-year survival rates are 91% (95%CI: 89%, 93%) and 79% (95%CI: 77%, 82%) respectively. There is a significant association in this model between the CD4 cell count and the risk of death. Age, addiction, and men were all significantly linked to CD4 cell count. Age was positively correlated with the risk of death. Men, those with hepatitis B and history of addiction had a higher risk of death. Conclusion: This study uses the power of Bayesian joint models to explore the complex relationship between changes in CD4 counts over time and the risk of death in patients with HIV. Our findings highlight a strong and statistically significant connection between CD4 cell count and mortality risk. By modeling CD4 counts alongside survival data, we offer a deeper understanding of the factors influencing patient outcomes over time, significantly enhancing traditional separate modeling methods. This comprehensive approach leads to more accurate predictions, ultimately aiding in better-informed clinical decisions for HIV care. [ABSTRACT FROM AUTHOR] |
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