Survival Analysis of Data on Human Immunodeficiency Virus for Infected Children from The Ages of One to Ten Years

Abstract

This research utilised survival analysis to study and model data on human immunodeficiency virus (HIV) infection, among children aged one to ten years old. The study adopted the Gompertz parametric and Cox proportional semi parametric survival model to fit the HIV data and estimated the survival functions using the Kaplan-Meier estimator. The predictor variables of interest included age, weight, height, sex, mother-to-child transmission, residence, viral load, HIV and educational status of the parents. Purposive sampling technique was used to collect the required data from January, 2007 to December, 2021 at General Hospital Minna, Niger State. The analysis revealed that only viral load with a p-value of 0.000 was statistically significant predictor of the event, indicating it is significant at 0.05 level. This indicated lower risk of death and higher chance of surviving. The Gompertz model gave the best fit with Akaike Information Criterion of 569.17 and Bayesian Information Criterion of 609.19. Early initiation and consistent adherence to Antiretroviral Therapy (ART) can effectively suppress viral load. Children born to HIV-positive mothers, particularly those with unsuppressed viral loads, require regular monitoring due to their increased risk of death from mother-to-child transmission.