The HIV research and activist communities are counting down to the big AIDS conference in Melbourne (20-25 July 2014). One wonders if the ever greater buzz around ‘cure’, which has attracted so much attention in the last few years, will translate into the hot topic of AIDS 2014. This may be exciting basic science, and offer the (hardly imminent) promise of something better than decades of drug regimens for those infected, but it should not detract attention from the complex immediate situation still faced by much of sub Saharan Africa, and other countries, where access to cure is a very hypothetical lofty goal. This Quarterly discusses some of these immediate challenges.
Kaposi sarcoma (KS) is the most common tumour in HIV-infected individuals in Africa and is preceded by infection with Kaposi sarcoma herpes virus (KSHV). The influence of KS on response to ART is not well defined in resource-limited settings. Additionally, it is unclear if co-infection with oncogenic viruses such as KSHV places untreated HIV-infected patients at increased risk even without clinically apparent illness. The analysis presented here aimed to determine the effect of clinical disease due to KS and also to estimate the impact of co-infection with KSHV among HIV-1 infected adults receiving ART.
In recent years, scientific innovations in HIV control have expanded the range of available interventions – male circumcision, topical microbicides, oral pre-exposure prophylaxis (PrEP) and treatment as prevention (TasP) have all sparked significant interest due to their potential effectiveness and versatility. While all these options are potentially available, resources remain limited and choosing which interventions to implement at scale is a difficult task, given the complex nature of disease transmission, the impact of behaviour in epidemic dynamics, and the different costs of these programs. Here we analyse the effects of scaling up PrEP and ART for HIV prevention in South Africa, to help decision makers understand how these interventions would work if considered independently or in combination.
T. b. rhodesiense is the acute form of African human trypanosomiasis or sleeping sickness which is common in East and Southern Africa. Trypanosomiasis is caused by the parasite Trypanosoma brucei and transmitted by tsetse flies (genus Glossina spp). Treatment of livestock in sub-Saharan Africa with trypanocidal drugs has been hindered by drug resistance and proves to be too expensive for many farmers. Tsetse control methods include aerial and ground spraying, sterile insect technique, and bait technology, including the use of insecticide-treated cattle (ITC). We compared two techniques of application of insecticides on cattle using a mathematical model: whole-body (WB), where insecticides are applied on the entire animals body and restricted application (RAP), where insecticides are applied on the legs, belly and ears of the animal.
In most of sub-Saharan Africa, estimates of the burden of disease due to malaria are unreliable as many people with fever do not reach public health facilities, and there are also imperfect health reporting systems in many of the countries with the largest burden. However, many general population studies exist recording the proportion of people with detectable malaria parasites. Researchers at the Malaria Atlas Project (MAP) have collated these datasets and fitted geo-spatial models to them, providing an estimate of parasite prevalence at any location along with the uncertainty in that estimate.
Community assault (CA) is widespread in the township of Khayelitsha, Cape Town, South Africa. Anecdotal evidence suggests that victims of CA are worse off than other assault cases, but scientific data on the rate and severity of CA cases are lacking for SA. We therefore conducted a case count study to estimate the rate of CA among adults in Khayelitsha and comparing the injury severity and survival probability between cases of CA and other assault (non-CA) cases.
According to the President’s Emergency Plan for AIDS Relief (PEPFAR), an AIDS-free generation entails that first, no one will be born with the virus; second, that as people get older, they will be at a far lower risk of becoming infected than they are today; and third, that if they do acquire HIV, they will get treatment that keeps them healthy and prevents them from transmitting the virus to others. We argue that an AIDS-free generation is possible in Southern Africa, but not unless the high rates of incident infections in key populations are reduced.
Participants of this one-week summer school 2014 at Ghent University Hospital, Belgium, will be introduced to the field of statistical analysis of network data, with an emphasis of model applications in health research. After a brief review of traditional compartmental (SIR) models and the methodology for classical descriptive network analysis, (static) Exponential family Random Graph Models (ERGMs) and dynamic temporal ERGMS will be introduced. Stochastic Actor Oriented Models (SAOMs) offer an alternative approach to model the evolution of a network, and the changes in actor attributes.
Over the past eight years SACEMA has collaborated with the African Institute for Mathematical Sciences (AIMS) and North American scientists in running workshops now held annually at AIMS in Muizenberg, and in the United States at the University of Florida. This editorial particularly celebrates the inputs of five North American scientists who have given freely of their time and efforts in making this valuable training effort possible.
While traditional regression methods have been proven as a powerful research tool, no technique is suitable for all circumstances. Two common situations in which regression techniques alone are likely to produce biased results is when the outcome is rare and the number of measured confounders is large; and/or when important confounders are neglected.
Analysing briefly an example in which both circumstances are present simultaneously, this article shows how propensity score matching associated with Monte Carlo sensitivity analysis can be considered an interesting complement to traditional multivariate modelling.