Exploration of factors contributing to the tuberculosis default rate in the East London area of Buffalo City Metropolitan Municipality

Abstract

Tuberculosis (TB) kills approximately three million people per year worldwide. Of the estimated two billion people infected with tuberculosis, eight million develop the disease annually. The present global tuberculosis situation indicates that both the disease and default burdens are still very high, especially in developing countries. Defaulting from treatment has been one of the major obstacles to treatment management and an important challenge for tuberculosis control. The main purpose of this study was to explore factors that contribute to the high default rate of tuberculosis in the East London area of the Buffalo City Metropolitan. In this study the researcher used the qualitative approach whereby the participants were given an opportunity to express their lived experiences on the factors contributing to defaulting on tuberculosis treatment. The population consisted of all TB clients who were defaulting treatment from January to December 2012 in Zanempilo, Gonubie and Chris Hani clinics in the East London Primary Health care facilities under the Buffalo City Metropolitan Municipality (BCMM). The researcher used the non-probability purposive sampling method. TB registers from January to December 2012 were used as a source of information to get the names and addresses of TB defaulters. The study was conducted in the East London area of Buffalo City Metropolitan Municipality. East London is a city on the south east coast of Buffalo City Metropolitan Municipality in the Eastern Cape Province. The East London health care system is operating under the Buffalo City Metropolitan Municipality (health district). It is composed of 35 fixed clinics, six mobile clinics, two community health centres, one specialized hospital, one regional hospital and three private hospitals. In-depth unstructured face-to-face interviews were used to collect data from 14 participants. In this study the researcher identified factors such as patient behaviour, sickness, feeling better, nurses’ attitudes, work issues and temporary change of residence as reasons why patients stopped taking their treatment. It is imperative to understand predictive factors for treatment default so that programs can implement specific measures to target the population at risk, since other strategies such simpler treatment regimens and Directly Observed Treatment Short course (DOTS) have not given enough satisfactory results.