Essential health care service disruption due to COVID-19: lessons for sustainability in Nigeria

The pandemic revealed a strained Nigerian health system, forced to transfer already limited resources to combatting COVID-19, whilst coping with disruptions to health supplies and increased demand for health services. These supply and demand side factors resulted in disruption across child and maternal health services delivery, as well as to the prevention, testing, and treatment of HIV, tuberculosis (TB), and malaria, amongst other EHS. Innovative service and goods delivery strategies, such as mobile immunisation services and multi-month drug dispensing, were implemented to mitigate the impact of disruptions. Evidence suggests that embedding these practices into regular EHS delivery, alongside increased investment in health infrastructure and health workforces, could help build EHS resilience in future. The brief concludes that sustaining the continuity of EHS requires policies that ensure a whole-society and systems strengthening approach. This involves increased health care investment, community engagement, disease control regulations, and multisector approaches to improve resilience, EHS quality, and equity

Prospects for Genomic Selection in Cassava Breeding

Article purchased; Published online: 28 Sept 2017Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden