The development in high income

The development in high-income countries shows the possibilities for progress in twin health and survival: Danish twins born between 1870 and 1900 had higher early-life mortality rates than twins born in the late 20th century and contemporary sub-Saharan twins, with about 40% under-6 mortality (double the risk for singletons at that time). Today, this risk is cut to about 2%. Furthermore, there is some evidence that twin cohorts with high early-life mortality can also have long-term health disadvantages compared with singletons. Best documented is the cognitive disadvantage of twins being born in the first part of the 20th century—a disadvantage that has vanished in more recent twin cohorts. Therefore, a continued progress in twin early-life survival in sub-Saharan Africa is likely to also have a positive impact in the future with better later-life health for twins, making increased attention to mothers of twins and the health care of their infants in sub-Saharan Africa even more pertinent. In this context, WHO should consider including twins specifically in the recommendations for postnatal care, which is currently not the case.
Although the global burden of malaria has been greatly reduced through, for example, vector control, the disease is still a major concern and there is renewed interest in mass drug administration (MDA) as an additional control intervention. A Cochrane review of MDA for malaria, which included 32 studies, concluded that MDA substantially reduces the risk of infection leading to malaria. However, this study also raised the concern that these reductions are often not sustained. Another review from 2015 suggested that, although MDA is successful in controlling and eliminating disease, there remain substantial knowledge gaps and further studies are essential, particularly on optimal size of the target population, methods to improve coverage, and drug safety. Oliver Brady and colleagues address some of these research gaps on MDA for malaria in (July 2017 issue). Given the relatively small number of studies of the Deferoxamine mesylate impact of MDA, computational models that can simulate the underlying biological processes of malaria in human and mosquito populations and also be fitted to the available data represent attractive “what-if?” tools. However, several approaches to modelling malaria epidemiology exist, and Brady and colleagues present a model comparison analysis done by the Bill & Melinda Gates Foundation-funded Malaria Modelling Consortium (MMC) on the effectiveness of MDA in different settings, identifying the most important determinants of MDA effectiveness. They compare a specific model outcome (the Parasite Rate following 2 years of MDA) generated by four established models, which represent different, yet reasonable, ways of simulating malaria transmission and MDA. The study provides much needed support to researchers responsible for quantitative analysis in the service of malaria control programmes, who are often faced with a plethora of mathematical models, run under non-standardised sets of interventions, and reporting different outcomes. Until recently, these analysts would typically depend on the work of a single modelling group. This approach was clearly less than optimal, since multinucleate often failed to take into account the complementary work of several other research groups around the world. Thanks to the leadership of organisations such as The Bill & Melinda Gates Foundation, there has been a shift during the past decade towards modelling consortia (eg, the MMC), in which representatives from several modelling groups meet frequently and evaluate scientific or operational questions collectively. Brady and colleagues, as members of the MMC, compare the quantitative predictions of a set of four malaria computational models. There is no simple recipe for performing such a comparison. Various modelling consortia have had to determine the best way to combine outcomes into consensus reports for diseases such as HIV, tuberculosis, and neglected tropical diseases. In the non-communicable disease domain, the US National Cancer Institute\'s Cancer Intervention and Surveillance Modeling Network (CISNET) is charged with investigating various questions in a comparative fashion, such as those relating to the potential medical and cost-benefit of cancer screening programmes.