Factors Influencing Treatment Outcomes Among Multi Drug Resistant Tuberculosis/Hiv Co-Infected Patients In Dar Es Salaam Region And Kibong'oto Infectious Disease Hospital In Kilimanjaro Region, Tanzania

Abstract

Background: Multidrug resistant Tuberculosis is a public health problem that is increasing yearly. Sub-Saharan Africa is home to about 72% of the estimated burden of multidrug resistant tuberculosis among HIV co-infected patients. In Tanzania, 534 multidrug resistant tuberculosis cases were notified in the year 2019 and 28% of them were HIV positive. However, HIV co-infection escalates and make multi drug resistant tuberculosis (MDTR) worse. Current responses include the World Health Organization commitment to reduce unfavorable outcome and especially among multidrug resistant tuberculosis/HIV co-infected patients who are the most affected. At the same time, limited information is available to make real time plans and strategies to achieve the goal of 90% favorable outcome by 2030. Our study aimed at determining treatment outcomes and factors influencing unfavorable treatment outcome among MDRTB/HIV. Objectives: To determine treatment outcomes and influencing factors among HIV co-infected multidrug resistant tuberculosis patients in Dar es Salaam region and Kibong’oto Infectious Disease Hospital in Kilimanjaro region from 2009 to 2017 and each patient evaluated for treatment outcome from 2011 to 2019. Methods: We conducted a retrospective cohort study involving MDRTB/HIV co-infected patients from the MDRTB database aged ≥ 15 years from Dar es Salaam region and Kibong’oto Infectious Disease Hospital in Kilimanjaro region. The study included analysis of patients registered in the MDRTB/HIV co-infected register between 2009 and 2017 and who were censored from 2011 to 2019 to determine the treatment outcome. The outcome was favorable if the patient was declared cured or completed treatment. The outcome was unfavorable if the outcome was died, lost to follow-up or the treatment failed. Independent variables included individual social-demographic characteristics, clinical as well as health system characteristics. Quantitative variables were expressed in mean and standard deviation; qualitative variables were expressed in frequency and percentage using STATA. Cox proportion hazard model was used to determine the risk of unfavorable outcome and factors associated with increased risk.