Samson Yohannes Amare

Background: In 2019, Ethiopia’s neonatal mortality rate was 33 deaths per 1000 live births, far from the Sustainable Development Goal 3.2 of 12 or fewer deaths per 1000 live births by 2030. With funding from the Global Financing Facility, the Ministry of Health designed the Saving Little Lives (SLL) program in collaboration with local universities to address the gap. The SLL program aimed to decrease 28-day neonatal mortality by 35% by implementing evidence-based Minimum Care Package (MCP) interventions in 290 hospitals in Ethiopia, prioritizing preterm and low-birth-weight newborns. Methods: From June 2021 to May 2024, we conducted a pragmatic before-and-after evaluation within the planned non-randomized stepped-wedge implementation study, including 290 hospitals in four regions of Ethiopia. We selected 36 primary hospitals as evaluation sites to compare neonatal mortality rate (NMR) before and after the implementation of the SLL program. The implementation occurred in three phases, with Regional Health Bureaus (RHBs) purposively selecting SLL-supported hospitals. The SLL MCP interventions consisted of strengthening care at the labor and delivery (LD) unit, including preparation for birth and essential newborn care components, including resuscitation for asphyxiated newborns, CPAP initiation at birth when indicated, and safe referral of small and sick neonates. At the Neonatal Intensive Care Unit (NICU), interventions included CPAP for respiratory distress syndrome, sepsis management, thermal care, and feeding management. At the Kangaroo Mother Care (KMC) Unit, interventions included skin-to-skin care and exclusive breastfeeding for preterm or LBW newborns. We strengthened care through hands-on training, mentoring, provision of essential equipment and devices, and infrastructure improvements. After addressing missing values using multiple imputations, we applied descriptive statistics to estimate the neonatal mortality rate (NMR), defined as the 28-day mortality among newborns born in or admitted to participating primary hospitals per 1000 live births, before and after the SLL MCP implementation, and a mixed-effects logistic regression model to estimate the pooled mean predicted probability of mortality before and after the program’s implementation. Results: We recorded 63,624 live births across the 36 evaluation hospitals; 59,086 (92.9%) were inborn (born in the same hospital), and 4538 (7.1%) were outborn (born elsewhere and admitted to the evaluation hospitals). We excluded 31,839 live births (50.0%) (all live births from 16 of the 36 hospitals) from the final analysis because they lacked observations in the pre- or post-intervention period. In nine hospitals, the Ministry of Health (MoH) and RHBs initiated the interventions before baseline measurements were taken. In three hospitals in Tigray, the war in the region suspended implementation and evaluation activities, leading to missing baseline evaluation data. Post-intervention data could not be collected at two hospitals in Amhara due to armed conflict, and at two hospitals in the Southern Nations, Nationalities, and Peoples (SNNP) region because research staff left the team. The predicted neonatal mortality rate using data from 20 primary hospitals with 31,785 mother-baby dyads was significantly reduced from 42.9 (95% CI: 34.2–51.6) per 1000 live births during the pre-intervention period to 30.2 (95% CI: 24.2–36.5) per 1000 live births during the post-intervention period (adjusted relative risk of 0.71; 95% CI: 0.58–0.84), showing a 29% reduction in neonatal mortality. Conclusions: The SLL program implemented a large-scale and complex package of interventions in a resource-limited setting and was associated with a significant reduction in neonatal mortality.

Background: Neonatal mortality remains a significant public health challenge in Ethiopia. Despite efforts to implement key evidence-based interventions, their coverage and utilization remain low. The Saving Little Lives (SLL) program aims to scale-up a Minimum Care Package (MCP) of synergistic, life-saving interventions for all liveborn neonates, with a focus on preterm and low birth weight (LBW) infants, across 290 hospitals in Ethiopia (206 primary, 69 general, and 15 referral hospitals), representing 82% of all hospitals in the country at the time of the study, and evaluate the impact on neonatal mortality. Methods: A non-randomized stepped-wedge trial will be conducted to evaluate the impact of implementing the SLL MCP interventions. Quantitative evaluation data will be collected from 36 primary hospitals, selected from 206 primary hospitals across four regions, receiving the interventions. An independent evaluation research assistant will be deployed in each of the hospitals to collect data using Open Data Kit (ODK) through interviewing mothers before discharge, on the 29th day of life if discharged, and reviewing medical records. A mixed-method, cross-sectional formative assessment will be conducted prior to implementation, employing quantitative facility assessment and qualitative interviews with mothers, healthcare providers, and facility managers. This will be followed by continuous program learning assessment once implementation begins. Descriptive data will be presented using numbers, percentages, tables, and graphs. Regression modeling and generalized estimating equations (GEEs) will be used to estimate the impact of the SLL MCP interventions. Qualitative data will be gathered through in-depth interviews, digitally recorded, transcribed, and thematically analyzed using ATLAS.ti Version 7.5 software to assess facility readiness, barriers, and enablers of implementing the SLL MCP interventions. Expected Outcome: We hypothesize that achieving 80% coverage of the SLL MCP interventions among eligible neonates will result in a 35% reduction in neonatal mortality at implementation facilities.

Introduction Application of data science in maternal, newborn, and child health (MNCH) across Africa is variable with limited documentation. Despite efforts to reduce preventable MNCH morbidity and mortality, progress remains slow. Accurate data are crucial for holding countries accountable for tracking progress towards achieving the Sustainable Development Goal 3 targets on MNCH. Data science can improve data availability, quality, healthcare provision and decision-making for MNCH programmes. We aim to map and synthesise data science use cases in MNCH across Africa. Methods and analysis We will develop a conceptual framework encompassing seven domains: (1) infrastructure and systemic challenges, (2) data quality, (3) data governance, regulatory dynamics and policy, (4) technological innovations and digital health, (5) capacity development, human capital and opportunity, (6) collaborative and strategic frameworks and (7) recommendations for implementation and scaling. We will use a scoping review methodology involving literature searches in seven databases, grey literature sources and data extraction from the Digital Health Atlas. Three reviewers will screen articles and extract data. We will synthesise and present data narratively and use tables, figures and maps. Our structured search strategy across academic databases and grey literature sources will find relevant studies on data science in MNCH in Africa. Ethics and dissemination This scoping review does not require formal ethical review and approval because it will not involve collecting primary data. The findings will showcase gaps, opportunities, advances, innovations, implementation and areas needing additional research. They will also propose next steps for integrating data science in MNCH programmes in Africa. The implications of our findings will be examined in relation to possible methods for enhancing data science in MNCH, such as community and clinical settings, monitoring and evaluation. This study will illuminate data science applications in addressing MNCH issues and provide a holistic view of areas where gaps exist and where there are opportunities to leverage and tap into what already exists. The work will be relevant for stakeholders, policymakers and researchers in the MNCH field to inform planning. Findings will be disseminated through peer-reviewed journals, conferences, policy briefs, blogs and social media platforms.

Introduction Application of data science in Maternal, Newborn, and Child Health (MNCH) across Africa is variable with limited documentation. Despite efforts to reduce preventable MNCH morbidity and mortality, progress remains slow. Accurate data is crucial for holding countries accountable, tracking progress towards realisation of SDG3 targets on MNCH, and guiding interventions. Data science can improve data availability, quality, healthcare provision, and decision-making for MNCH programs. We aim to map and synthesise use cases of data science in MNCH across Africa. Methods and Analysis We will develop a conceptual framework encompassing seven domains: Infrastructure and Systemic Challenges, Data Acquisition, Data Quality, Governance, Regulatory Dynamics and Policy, Technological Innovations and Digital Health, Capacity Development, Human Capital, Collaborative and Strategic Frameworks, data analysis, visualization, dissemination and Recommendations for Implementation and Scaling. A scoping review methodology will be used including literature searches in seven databases, grey literature sources and data extraction from the Digital Health Initiatives database. Three reviewers will screen articles and extract data. We will synthesise and present data narratively, and use tables, figures, and maps. Our structured search strategy across academic databases and grey literature sources will find relevant studies on data science in MNCH in Africa. Ethics and dissemination This scoping review require no formal ethics, because no primary data will be collected. Findings will showcase gaps, opportunities, advances, innovations, implementation, areas needing additional research and propose next steps for integration of data science in MNCH programs in Africa. The findings’ implications will be examined in relation to possible methods for enhancing data science in MNCH settings, such as community, and clinical settings, monitoring and evaluation. This study will illuminate data science applications in addressing MNCH issues and provide a holistic view of areas where gaps exist and where there are opportunities to leverage and tap into what already exists. The work will be relevant for stakeholders, policymakers, and researchers in the MNCH field to inform planning. Findings will be disseminated through peer-reviewed journals, conferences, policy briefs, blogs, and social media platforms in Africa. Strengths and limitations of this study ➣ This scoping review is the first to examine the role and potential of data science applications in maternal, newborn and child health (MNCH) in Africa, with assessments on healthcare infrastructure, data quality improvement, innovative data collection and analyses, policy formulation, data-driven interventions, technologies for healthcare delivery, and capacity building. ➣ We will conduct systematic searches across multiple databases (PubMed, Scopus, Web of Science, Google Scholar, CINAHL, EMBASE, and Ovid) and grey literature. ➣ Focusing on studies that have used data science we will synthesise our findings with detailed explanations, informative charts, graphs, and tables. ➣ The study will deliver actionable recommendations for stakeholders engaged in MNCH policy formulation, strategic planning, academia, funders and donors, and clinicians aimed at improving MNCH outcomes in Africa. ➣ Our scoping review will primarily rely on published literature in English, therefore, will omit valuable insights that may have been published for non-anglophone and francophone regions of Africa.

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.

Background: Kangaroo Mother Care (KMC), defined as prolonged skin-to-skin contact of the low birth weight baby with the mother and exclusive breastfeeding, is highly effective in reducing neonatal mortality. Yet the coverage of KMC globally is <5%. We conducted this study to find ways to achieve sustainable improvements in coverage. Methods: This multi-site, implementation research study used a mixed-methods design in four regions of Ethiopia and three states of India, covering 8 million population. Our innovative approach did not have a fixed implementation strategy. It used principles of implementation science to develop a strategy that helped programme staff to find ways to improve implementation. Formative research (1-3 months) informed the initial implementation model, followed by rapid iterative cycles of implementation and review for model refinement based on qualitative and quantitative data on KMC (15-18 months). Implementation intended to cover the entire study population. The implementation models included three components: (1) maximising access of babies to KMC-implementing facilities, (2) ensuring KMC was initiated and maintained in KMC-implementing facilities, and (3) supporting continuation of KMC after discharge. Evaluation of the optimized implementation model was conducted in the entire study population (9-12 months). Primary outcomes were coverage of effective KMC ( ≥ 8 hours of skin to skin contact per day and exclusive breastfeeding) before discharge, and 7 days after discharge, among infants with birth weight <2000g born in study districts. Findings: At baseline, KMC was not implemented systematically in any of the study sites’ hospitals, thus baseline coverage was assumed to be <5%. An optimized implementation model was developed and applied to the entire study population. No other programmes for KMC coverage were implemented in the population during the study period. In the evaluation period, with this final model implemented, KMC was initiated for 68-86% of eligible infants in Ethiopian sites and 87-88% in Indian sites. KMC was initiated at a mean age of 1 ·2-9·7 days. Effective KMC was provided to 68% infants in Ethiopia and 55% in India at discharge. At a home visit 7 days after discharge, effective KMC was provided to 53%-66% of infants in all sites, except Oromia (38%) and Karnataka (36%). Interpretation: Our study is the first to show how KMC implementation achieving high coverage is possible at scale in LMICs. Success factors include strong government leadership, health workers’ conviction of the importance of KMC, and changes in infrastructure, policy and practice to achieve zero separation between mothers and newborns. Funding Statement: This study was coordinated by the World Health Organization with financial support from the Bill & Melinda Gates Foundation (OPP1136804). Declaration of Interests: All authors declare no competing interests. Ethics Approval Statement: Ethics approvals were received from the ethics committees of the World Health Organization and the participating research institutions.