Time to Treatment Outcome and Associated Factors Among Tuberculosis Patients: an Application of Cox Regression Model

Abstract

Background: Tuberculosis is among the leading causes of morbidity in the world. According to the World Health Organization (WHO), TB is ranked ninth leading cause of death worldwide and the leading cause of a single infectious agent, ranking above HIV/AIDS. Over 25% of TB deaths occur in the African Region. Kenya is one of the 30 high burden countries that together account for more than 80% of the world’s TB cases. This study aims to find out whether treatment outcome of TB patients at Isiolo TB Center is dependent/associated on/with the study variables Objective: This study was conducted to investigate risk factors associated with: treatment outcome and time to recovery/death of Tuberculosis (TB) patients, using the Cox regression model. The study variables were socio-demographic factors (age and gender), treatment regimen type and HIV status of the patients. Methodology: The study population was the cohort of TB patients enrolled at the Isiolo TB center for the period 2018-2019. The research data were gotten from the record (registers) of patients at the Isiolo County Referral Hospital in Isiolo County, with a sample size of two hundred and twenty eight (228), obtained using a systematic sampling technique. The data collected was cleaned in Excel and imported to R where analysis was done. Cox Regression Model was fitted to the data to determine the effects of socio-demographic factors on the treatment outcome of the TB patients. The analysis was also used to evaluate the relationship between treatment regimen type and the recovery time and time to death and also to assess the effects of HIV status on the time to recovery or death. Results: A total of 228 patients were included in the study. Male (138) and female (90). The Cox Regression analysis was done in two parts: when the event of interest=”Cured” and when the event of interest=”Dead”. The covariates age and regiment type were associated with the treatment outcome time though partially. HIV status had influence on the time to recovery and time to death of the TB patients. While gender of the patients had no influence on treatment outcome time. Conclusion: Socio-demographic factor age influences TB treatment outcome according to this study. For example, being young increased patient’s chance of cure from TB. Treatment regimen type affects the treatment outcome of the patients as those who were on 1st line treatment regimen type had more chance of cure as compared to those on the special line treatment. HIV positive patients had poor survivorship as compared to those who were negative.