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.
Loss to follow-up (LTFU) is a serious problem in most sub-Saharan African ART programmes. If ART is interrupted or reduced, HIV again progresses, and this increases the risk of opportunistic infections and, ultimately, AIDS and death. In addition, the viral load of a patient who interrupts ART will rebound, and the probability of onward transmission increases. If tracing programmes can accelerate the return of lost patients, these patients may be less likely to transmit the virus, since the period of lapsed treatment is shortened. In this study, we created a mathematical model to determine whether tracing patients LTFU from ART programs would lower the rate of HIV transmission.
The currently used mathematical models for medical treatment at the individual or population level are largely phenomenological and have limited quantitative predictive power. In 2013 Prof J. Snoep took up the SACEMA SARCHI Chair in mechanistic modelling of health and epidemiology. The task of the chair is to provide a mechanistic modelling approach with more predictive strength to pharmaceutical drug and intervention steps for individual and public health compared to current models. In this contribution an overview of the project is given and some of the work performed in the first year is highlighted.
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?
SACEMA has been very active in refining the use of laboratory tests for identifying recent infection (in particular of HIV) for the purpose of estimating disease incidence. We have just published a conceptual analysis of the notion of ‘test optimisation’ in a surveillance context, which should help clarify some persistent confusion that has hindered discourse in this area for years.
Young women in relationships with older men are typically at an elevated risk for acquisition of HIV in sub-Saharan Africa. Most qualitative studies have tended to focus on why women are motivated to participate in these relationships, offering little insight into perceived risks of these relationships. Therefore we conducted a qualitative study in three urban communities in Cape Town using thematic content analysis to explore women’s perceived risks of (non-)age-disparate relationships, the benefits of dating older men, and risk perceptions that influence decisions around these relationships.