Expanding ART coverage to healthier HIV patients is widely regarded as a potential strategy for addressing the rampant TB epidemic in high HIV-TB burden settings. Estimating the population-level impact of ART expansion on TB disease has proven challenging. We set out to estimate the potential effects of changing HIV treatment policy on TB outcomes in South Africa, comparing the results of three independent TB models. This project was part of a broader effort to shed light on the consequences of HIV policy changes, through model comparison and consensus building, a process pioneered in the HIV modelling field by the HIV Modelling Consortium.
Existing approaches to TB control have been no more than partially successful in areas with high HIV prevalence. In the context of increasingly constrained resources, mathematical modelling can augment understanding and support policy for implementing strategies most likely to bring public health and economic benefits. Recognising the urgency of TB control in high HIV prevalence settings and the potential contributions of modelling, the TB Modelling and Analysis Consortium (TB MAC) convened its first meeting between empirical scientists, policy makers and mathematical modellers in September 2012 in Johannesburg. Here we present a summary of results from these discussions, as well as progress made in South Africa.
In 2011, the Ministry of Health in Swaziland joined forces with the WHO, the Global Fund and SACEMA to do the first in depth health programmes progress evaluation using triangulation from key empirical data sources. The focus was on key questions like: Given increasing coverage of ART, has ART reduced adult and/or infant mortality?; Can TB trends be related to trends in HIV prevalence, ART coverage and combined TB/HIV interventions?; Can trends in infant mortality be related to uptake of PMTCT?
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 system of oscillating labour migration, especially to the gold mines in South Africa, has helped to spread TB throughout southern Africa and it now helps to spread HIV. This article illustrates this link by reporting on a study on the impact of migrant labour in the mines in South Africa on the burden of HIV and TB in Mozambique. Furthermore, modelling studies have shown that even if we maintain the same patterns of sexual behaviour the presence or absence of migration can lead to dramatically different outcomes. Unless a comprehensive and fully coordinated multi-country and multi-sectoral programme is implemented and followed through, we may find that the HIV and TB epidemics are far more resilient than consideration of the epidemics in each country suggests.
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.