Accelerated Failure Time Model for Determination of Effectiveness of Antiretroviral Therapy at General Hospital Adikpo, Benue State, Nigeria
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Page Numbers:
1922-1936
Digital Object Identifier:
10.58578/ajstea.v3i6.7710
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Authors:
Mom T. T.1*, Ikughur J. A.2, Udoumoh E. F.3 
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Main Article Content
Abstract
The introduction of antiretroviral therapy (ART) has markedly improved the clinical outcomes of individuals living with HIV, transforming the infection from a fatal illness to a manageable chronic condition. Central to HIV management is the CD4 count, a critical biomarker that reflects immune system status and informs clinical decisions regarding ART initiation and the management of opportunistic infections. Reaching a CD4 threshold of ≥500 cells/mm³ is widely considered a key indicator of immune reconstitution and long-term treatment success. However, there remains limited guidance for patients and clinicians on the expected timeline or probability of reaching this milestone, highlighting the need for robust statistical models to support evidence-based treatment planning. This study employed a retrospective cohort design using data from 400 HIV-positive patients who initiated ART at General Hospital Adikpo, Benue State, Nigeria, between 2012 and 2022. Inclusion criteria required that patients begin treatment with a CD4 count <500 cells/mm³. Over the study period, 54% of patients (216) achieved a CD4 count ≥500 cells/mm³, while 46% (184) did not. The Anderson-Darling (test statistic: 0.3660) and Chi-square (test statistic: 12.73) tests confirmed that the lognormal accelerated failure time (AFT) model was an appropriate fit for estimating the time to immune recovery. The analysis revealed that the rate of CD4 improvement was highest in the initial years following ART initiation, with diminishing returns over time. The median time to achieve the CD4 threshold was five years. Key predictors of successful immune reconstitution included baseline CD4 count, patient age, and tuberculosis (TB) status (p < 0.05). These findings reinforce the critical importance of early HIV detection, timely initiation of ART, and integrated TB management in improving long-term immunological outcomes.
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References
Aalen, O., Borgan, O. and Gjessing, H. (2008). Survival and Event History Analysis: A Process Point of View. http://dx.doi.org/10.1007/978-0-387-68560-1
Abhaya, I. and Chandra, T. (2021). Survival Analysis: Where, Why, What and How? Indian Pediatrics. 59(1):74-79.
Alon, V. (2020). Buying Time: An Ecological Survival Analysis of COVID-19 Spread Based on the Gravity Model. medRxiv. http://dx.doi.org/10.1101/2020.05.01.20087569
Be-in-the-know, 2024. https://www.beintheknow.org/understanding-hiv-epidemic/data/glance-hiv-nigeria
Belay, D. B. and Derebe, K. (2022). Determinant Factors Associated with Time to Death of HIV/AIDS Patients: An Application of Parametric Shared Frailty Models. Scientific African. 15:1-9.
Ben-Aharon, O., Magnezi, R., Leshno, M. and Goldstein, D.A. (2019), Median Survival or Mean Survival: Which Measure Is the Most Appropriate for Patients, Physicians, and Policymakers? The Oncologist, 24:1469-1478.
Chakraborty S. (2018): A Step-wise Guide to Performing Survival Analysis. Cancer Research Statistics and Treatment, 1:41-45.
Cohen, M. S., Chen, Y. Q., McCauley, M., Gamble, T., Hosseinipour, M. C., Kumarasamy, N. and HPTN 052 Study Team. (2011). Prevention of HIV-1 Infection with Early Antiretroviral Therapy. New England Journal of Medicine, 365(6):493-505.
Deeks, S. G., Lewin, S. R. and Havlir, D. V. (2013). The End of AIDS: HIV Infection as a Chronic Disease. The Lancet, 382(9903):1525-1533.
Gupta, R. K., Gregson, J., Parkin, N., Haile-Selassie, H., Tanuri, A., Andrade Forero, L. and Böttger, E. C. (2020). HIV-1 Drug Resistance before Initiation or Re-initiation of First-line Antiretroviral Therapy in Low-income and Middle-income Countries: A Systematic Review and Meta-regression Analysis. The Lancet Infectious Diseases, 20(6):779-787.
Ho, L. L. and Silva, A. F. (2006). Unbiased Estimators for Mean Time to Failure and Percentiles in a Weibull Regression Model. International Journal of Quality and Reliability, 23(3):323-339.
Jaya, C., Narendra, K. T., Shashi, R. P., Saurabh, S., Anurag, T., Rabindra, N. M. and Shyam, S. (2014). Determinants of Survival in Adult HIV Patients on Antiretroviral Therapy in Eastern Uttar Pradesh: A Prospective Study. Indian Journal of Medical Research, 140: 491-500.
Jie, Y. (2022). Prediction of Survival Time for Breast Cancer Patients. Bachelor’s Degree Thesis at Dalhousie University Halifax, Nova Scotia https://www.mathstat.dal.ca/~tsusko/honours-theses/jie-yu.pdf.
Lifson, A. R., Grund, B., Gardner, E. M., Kaplan, R., Denning, E.and Engen, N. W. (2010). Improved Quality of Life with Immediate Versus Deferred Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. AIDS, 24(6):965-971.
NACA (2012). Nigeria Prevalence Rate. https://naca.gov.ng
Nasejje, J. (2013). Application of survival analysis methods to study under-five child mortality in Uganda. A Thesis Submitted in the School of Mathematics, Statistics and Computer Science, University of KwaZulu–Natal. https://www.researchgate.net/publication/281240839
Neamvonk, J. and Phuenaree, B. (2023). New Modified Anderson Darling Goodness of Fit Test for Lognormal and Gamma distributions. Asian Journal of Applied Sciences, 10(6):490-497.
Nigussie, F., Alamer, A., Mengistu, Z. and Tachbele, E. (2020). Survival and Predictors of Mortality Among Adult HIV/AIDS Patients Initiating Highly Active Antiretroviral Therapy in Debre-Berhan Referral Hospital, Amhara, Ethiopia: A Retrospective Study. HIV/AIDS - Research and Palliative Care. 12:757-768.
Obeagu, E. I. and Obeagu, G. U. (2022). An Update on Survival of People Living with HIV in Nigeria. Journal of Public Health Nutrition, 5(6):129-36.
Simoni, J. M., Frick, P. A., Lockhart, D. and Liebovitz, D. (2006). Mediators of Social Support and Antiretroviral Adherence among an Indigent Population in New York City. AIDS Patient Care and STDs, 20(6):411-423.
UNAIDS (2024). Global HIV and AIDS Statistics. https://www.unaids.org/en/resources/fact-sheet
Zhang, W., Ota, T., Shridhar, V., Chien, J., Wu, B. and Kuang, R. (2013). Network-based Survival Analysis Reveals Sub Network Signatures for Predicting Outcomes of Ovarian Cancer Treatment. PLoS Computational Biol ogy, 9(3):1-16.