A considerable share of South Africa’s tuberculosis burden affects those people who have previously been treated for tuberculosis – many of them successfully. In a retrospective cohort study that was conducted using tuberculosis treatment register data from two communities in suburban Cape Town, it was found that the hazard rate of re-treatment for smear-positive tuberculosis was between 3- and 5.26-times higher in tuberculosis cases who had defaulted from treatment compared to successfully treated cases. But although the rate of re-treatment was substantially higher among defaulters, cases after treatment success account for the vast majority of smear-positive re-treatment cases due to the fact that far more tuberculosis cases were successfully treated than had defaulted.
The Mycobacterium tuberculosis (TB) bacilli’s potency to cause persistent latent infection that is unresponsive to the current cocktail of TB drugs is strongly associated with its ability to adapt to changing intracellular environments, and tolerating, evading and subverting host defence mechanisms. We applied a combination of bioinformatics and mathematical modelling methods to enhance the understanding Read More
The introduction and scale-up of new tools for the diagnosis of tuberculosis (TB) has the potential to make a huge difference to the lives of millions of people. To realise these benefits and make the best decisions, policy makers need answers to many difficult questions about which new tools to implement and where in the diagnostic algorithm to apply them cost effectively. Here we explore virtual implementation as a tool to predict the health system, patient, and community impacts of alternative diagnostics and diagnostic algorithms for TB, in order to facilitate context specific decisions on scale-up. Virtual implementation is an approach that can model the impacts of implementation of a new diagnostics by taking data from the context being considered alongside data from contexts where the new technology has been implemented (probably as a trial).
A new case of TB is the outcome of a recent infection event (primary TB) or is the result of the reactivation of a latent infection acquired some years previously. In a community where TB is endemic it is important to know the extent to which primary cases contribute to the overall burden as this can inform strategies to deal with the epidemic. This article discusses the methods for estimating the proportion of cases due to recent transmission by using cluster analysis. Sputum specimens from cases reporting to clinics are cultured and the TB strains are identified, commonly using molecular techniques of DNA ‘fingerprinting’. By comparing these fingerprints from various patients it becomes possible to classify them as unique or clustered. The proportion of clustered individuals can then be used as an indicator of the proportion of on-going or recent transmission.
Although antibiotics have saved millions of lives, their use and misuse has often led to the development of resistance in the agents that they are intended to fight. SACEMA's Brian Williams recently published a commentary on an article in published in Science Translational Medicine in which Sergeev et al. used dynamical models to investigate the Read More
There are few students in epidemiological modeling and analysis who can resist the temptation to fit a theoretical disease model to real epidemic data. A recent DNA fingerprinting project from Masiphumelele, a township near Cape Town, offered such a temptation. The result is a short journey into the world of statistically rare events, in this case brought about by the relatively small size of Masiphumele and by the slow reactivation rates of TB.
Transmission of tuberculosis (TB) involves random processes operating at two very different levels. On the one hand, an infectious person must be in reasonably close proximity to a susceptible person for a minimum period of time (macro-level). On the other hand, minute droplets containing bacteria entities that are exhaled by the infectious person are inhaled by the recipient (micro-level). At both these levels the chance events can be described by a statistical formula, the Poisson distribution. An analysis of these two processes in this way reveals a surprising phenomenon that manifests in communities experiencing high incidences of TB disease.
The central theme of this SACEMA Quarterly is the HIV/TB co-epidemic. From 1-4 June 2010 the 2nd TB conference was held in Durban, in which most of the presentations discussed TB in relation to HIV. A SACEMA affiliate presented a study concluding that intensified HIV testing and early initiation of antiretroviral therapy (ART) for women and men aged 25-40 would optimise the cost-effectiveness of applying the test-and-treat strategy in South Africa, as this would have the largest impact on TB as well. One of the main articles in this Quarterly discusses whether ART will help, and to what extend, in lowering TB incidence. Other articles focus on meaningful statistical modelling in public health, and the Zibambele programme (job creation and poverty alleviation) and it’s role in the battle against HIV/AIDS and TB. Finally, the new director of SACEMA is announced: Dr. Alex Welte.
The article from Tony Davies in this issue of the Quarterly, gives a historical overview on occupational lung disease in South Africa. Recently a report was published by the Health Systems Trust, which highlights the actuality of this topic (2). The aim of the research presented in the report was to investigate health systems surveillance Read More
South Africa is faced with a public health catastrophe due, in part at least, to the mining activity, which laid the foundation of our economy. In the mines millions of men have worked in dangerous and dusty conditions. There have been three very high risk exposures in South African workplaces: silica, asbestos and tuberculosis, all resulting in serious lung problems. This article gives an historical perspective on the causes of occupational lung disease and what should have been done to lower the risks.