Modelling Tuberculosis Treatment Outcomes Using a Discrete Time Markov Chain Model

Abstract

Tuberculosis (TB) is a disease a_ecting mostly the Lungs and can be fatal when not followed and appropriate measures taken to manage its severity and advancement in a population. Despite TB being preventable and curable, approximately 10 million people worldwide get it every year. This study investigated TB management outcome dynamics, the transition probabilities of TB treatment outcomes and predicted future treatment outcomes using Discrete Time Markov Chain Model. The results showed that there was a gradual increase in transition probabilities from the non-absorbing states to cured/dead states, although the proportion of persons transiting to cure were higher than those transiting to death. Further, transition from the non-absorbing states to again non-absorbing states steadily decline from 80.62% in the 1st year to 0 for most of the transition in the 10th year. In the 13th year, the patients were either in cured or dead state. Those lost to follow up (6.11%) were more than those Transferred out (2.47%) and more patients with Extra-Pulmonary TB (10.94%) were dying despite none having a treatment failure and all completing treatment in comparison to those with Pulmonary TB (7.04%). Future research could investigate why the proportion of Extra-pulmonary TB patients who die is higher than those with Pulmonary TB and why more patients are lost to follow-up. Increasing the patients’ follow up period beyond one year would also shade more light on the transiting probabilities of TB treatment outcomes. Master Thesis