Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections

Abstract

Globally, there are many people living with Human Immune Deficiency Virus (HIV), and the rate increases every day. Research has shown that Nigeria is the second largest country with HIV epidemic, as many are living with advanced HIV. People with advanced stage of HIV infection are vulnerable to secondary infections and malignancies, generally termed Opportunistic Infections (OIs). This is because, these infections take advantage of the opportunity offered by a weakened immune system, thereby causing complications in HIV infected persons and causing harm to individuals. The aim of this work is to investigate and model the survival, by stages of immune suppression and opportunistic infections on patients undergoing Antiretroviral Therapy (ART), in a population in South-South Nigeria. 221 Human Immune Deficiency Virus (HIV) patients data obtained from St. Luke’s Hospital, Anua, for the period of 2008 to 2017 were used. Four different parametric models, the extreme, lognormal, logistics, log-logistics distributions and nonparametric Kaplan-Meier method were considered in order to carry out modeling of survival, and survival of patients respectively. The models were subjected to life application using lifetime datasets and a test of goodness of fit was made using Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) criteria. From the results obtained, extremedistribution had the lowest AIC and BIC value, indicating that it is the best parametric model for modeling survival of HIV patients in the hospital. Also, the Kaplan-Meier method indicates that the survival experience of female patients were favorable than male patients.