ObjectivesKangaroo Mother Care (KMC), prolonged skin-to-skin care of the low birth weight baby with the mother plus exclusive breastfeeding reduces neonatal mortality. Global KMC coverage is low. This study was conducted to develop and evaluate context-adapted implementation models to achieve improved coverage.DesignThis study used mixed-methods applying implementation science to develop an adaptable strategy to improve implementation. Formative research informed the initial model which was refined in three iterative cycles. The models included three components: (1) maximising access to KMC-implementing facilities, (2) ensuring KMC initiation and maintenance in facilities and (3) supporting continuation at home postdischarge.Participants3804 infants of birth weight under 2000 g who survived the first 3 days, were available in the study area and whose mother resided in the study area.Main outcome measuresThe primary outcomes were coverage of KMC during the 24 hours prior to discharge and at 7 days postdischarge.ResultsKey barriers and solutions were identified for scaling up KMC. The resulting implementation model achieved high population-based coverage. KMC initiation reached 68%–86% of infants in Ethiopian sites and 87% in Indian sites. At discharge, KMC was provided to 68% of infants in Ethiopia and 55% in India. At 7 days postdischarge, KMC was provided to 53%–65% of infants in all sites, except Oromia (38%) and Karnataka (36%).ConclusionsThis study shows how high coverage of KMC can be achieved using context-adapted models based on implementation science. They were supported by government leadership, health workers’ conviction that KMC is the standard of care, women’s and families’ acceptance of KMC, and changes in infrastructure, policy, skills and practice.Trial registration numbersISRCTN12286667; CTRI/2017/07/008988; NCT03098069; NCT03419416; NCT03506698.

The limited volume of COVID‐19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS‐CoV‐2 mutations. The Virus Outbreak Data Network (VODAN)‐Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID‐19, producing these as human‐ and machine‐readable data objects in a distributed architecture of locally governed, linked, human‐ and machine‐readable data. This architecture supports analytics at the point of care and—through data visiting, across facilities—for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia.

The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally. This has become particularly clear with the recent emergence of new variants of concern. The Virus Outbreak Data Network (VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care, which renders data production largely meaningless to those producing it. This modus operandi leads to disfranchisement over the control of health data, which is extracted to be processed elsewhere. In response to this problem, VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process would 2 have a greater chance of being adopted. The design team based their work on the legal requirements of the European Union's General Data Protection Regulation (GDPR); the FAIR Guidelines on curating data as Findable, Accessible (under well-defined conditions), Interoperable and Reusable (FAIR); and national regulations applying in the context where the data is produced. The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data. A condition of such innovation is that the innovation team is intradisciplinary, involving stakeholders and experts from all of the places where the innovation is designed, and employs a methodology of co-creation and capacity-building.

This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’ (under well-defined conditions), ‘Interoperable’ and ‘Reusable’ (FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their ‘FAIR Equivalency’. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and (re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.

Background Kangaroo mother care (KMC) is an evidence-based approach to reducing morbidity and mortality in low-birth-weight and preterm newborns. Barriers for KMC and its effective practice at a larger scale are highly affected by contextual factors. The purpose of this study is to explore barriers and enablers in the community and health facilities for implementation and continuation of KMC. Methods This formative study employed a qualitative exploratory approach using focus group discussions and in-depth interviews in five zones of Tigray region, Northern Ethiopia. A total of 16 focus group discussions and 46 in-depth interviews were conducted with health workers and community members. The whole process of data collection took an iterative approach. An inductive thematic analysis was done by going through the transcribed data using ATLAS.ti software. Results The current study found that problems of infrastructure and equipment for KMC practice, shortage of staff, and absence of trained health workers as the most frequently mentioned barriers by health workers. Low level of awareness, lack of support, mother being responsible for the rest of the family, holding babies in the front being traditionally unacceptable, and preference of incubators for better care of small babies were among the barriers identified in the community. Presence of community health workers and the positive attitude of the community towards them, as well as antenatal and postnatal care were among the favorable conditions for the implementation of KMC at health facilities and continuation of KMC at home. Conclusion Empowering health workers through training to identify preterm and low-birth-weight babies, to do follow-ups after discharge, and creating awareness in the community to change the perception of kangaroo mother care are necessary.