Abstract
Survival modelling is a technique which exploits repeated measures of continuous covariates to predict explanatory variable’s effects on the response factor. The survival modelling helps design interventions in the health sector, which has seen one of its applications in the management of Human Immune Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS). However, despite improvement in Anti-Retroviral Therapy (ART) interventions over the years, the observed disease effects (morbidity, progression and mortality) remain high and varies across geographical borders. This study utilizes survival models to determine the predictors of survival among adult HIV/AIDS patients on ART in Moi Teaching and Referral Hospital (MTRH) Kenya. This is achieved by fitting a Cox proportional hazard regression model to adult HIV/AIDS patients data and determine predictors of survival amongst the study subjects. A retrospective study design was adopted where a target population of 10,038 patients who were on ART and were enrolled between January 2005 and January 2007 were investigated for a ten years follow-up period. The Cox proportional hazard regression model (CPHRM) was fitted to the data using log partial likelihood function. The log rank test and 95% confidence Interval (C.I) were used to analyze the significance of the hazard ratios of each variable. The results showed that HIV severity with unadjusted Hazard Ratio [UHR=0.729, p=0.032], level of education [lower UHR=0.952, p=0.019], and perfect adherence of antiretroviral drugs (ARV) [UHR=0.668, p=0.004] positively influenced patient survival time. Patient’s gender [male UHR=1.633, p< 0.001] showed negative effect on patient survival time. The adjusted hazard ratios for multivariate Cox model were, HIV severity [AHR1.18, p=0.735] age category between 30-40 in reference to age less than 30 [AHR=0.459, p=0.178] and age category above 40 years [AHR=0.644, p=0.447], Body Mass Index (BMI) less than 18.5kg/m2 in reference to between 18.5-<25kg/m2 [AHR=1.65, p=0.847] and BMI above 25 kg/m2 [AHR=0.861, p=0.847], level of education [lower AHR=0.931, p=0.209], patients’ gender [male AHR=1.884, p=0.19] and ARV adherence [perfect AHR=1.393, p=0.498]. In conclusion, HIV severity, level of education, ARV adherence and patients' gender were significant predictors of survival time. In addition, none of the patient's characteristics predicted survival time in the multivariate Cox model. Therefore, this study recommends to the government of Kenya to spearhead the development of policy framework for the provision of regular screening services for the male population to avoid late diagnosis and interventions of HIV/AIDS disease.
Highlights
Survival models are essential in research because of their unique attributes
In order to study the relationship between survival time and predictor variables, a regression modeling approach to survival data using Cox proportional hazard regression model was used with the aim of estimating the regression coefficients, performing statistical test, construction of confidence intervals and drawing inference based on the hazard function
The Unadjusted Hazard Ratios From the above Cox model, the unadjusted hazard ratio, confidence interval and p-value for each explanatory variable considered in this study were discussed as follows: Results of the unadjusted Cox model showed that patients who were HIV/AIDS severe had less risk of death or associated with improved survival since the UHR was less than 1.00 i.e. [UHR=0.729, 95% C.I=0.547, 0.972 pvalue=0.032]
Summary
Survival models are essential in research because of their unique attributes. The factors of interest comprise of survival time, defined by either death or censoring, and the presence of explanatory variables. Binary outcomes are common in medical research, where "success "may indicate that the patient is alive after treatment while "failure" implies the death of a patient. Survival analysis is interested in the statistical study of such occurrence (time until event) in a Mengich Kibichii Robert et al.: Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral. The follow-up period of these individual patients occurs within a definite period with attention on the time in which the outcome occurs, known as failure time, survival time, or event time. The event time can be measured in hours, days, weeks, months, quarters, semiannually, annually, or years. Examples include death occurrence or re-occurrence of the disease, marriage, and divorce
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