dc.contributor.author | Saidi, James K | |
dc.date.accessioned | 2021-01-26T11:56:38Z | |
dc.date.available | 2021-01-26T11:56:38Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | http://erepository.uonbi.ac.ke/handle/11295/154202 | |
dc.description.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 | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Nairobi | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.title | Modelling Tuberculosis Treatment Outcomes Using a Discrete Time Markov Chain Model | en_US |
dc.type | Thesis | en_US |