Unlocking the transformative potential of data science in improving maternal, newborn and child health in Africa: A scoping review protocol

Akuze Joseph Waiswa, Bancy Ngatia, Samson Yahannes Amare, Phillip Wanduru, Grieven P Otieno, Rornald Muhumuza Kananura, Fati Kirakoya-Samadoulougou, Agbessi Amouzou, Abiy Seifu Estifanos, Eric O Ohuma
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Abstract

ABSTRACT 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. Keywords: Data Science, Maternal Health, Newborn and Perinatal Health, Child Health, Africa
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释放数据科学在改善非洲孕产妇、新生儿和儿童健康方面的变革潜力:范围界定审查协议
摘要 导言:数据科学在整个非洲孕产妇、新生儿和儿童健康(MNCH)领域的应用情况不一,文献资料有限。尽管为降低可预防的孕产妇、新生儿和儿童健康(MNCH)发病率和死亡率做出了努力,但进展依然缓慢。准确的数据对于追究各国的责任、跟踪实现可持续发展目标 3 中有关母婴健康目标的进展情况以及指导干预措施至关重要。数据科学可以改善数据的可用性、质量、医疗保健服务的提供以及母婴保健计划的决策。我们的目标是绘制和综合数据科学在非洲地区母婴保健中的应用案例:我们将制定一个概念框架,其中包括七个领域:方法与分析:我们将制定包含七个领域的概念框架:基础设施与系统挑战、数据获取、数据质量、治理、监管动态与政策、技术创新与数字健康、能力发展、人力资本、合作与战略框架、数据分析、可视化、传播以及实施与推广建议。将采用范围审查方法,包括在七个数据库中进行文献检索、灰色文献来源以及从数字健康倡议数据库中提取数据。三名审稿人将筛选文章并提取数据。我们将以叙述的方式综合和呈现数据,并使用表格、数字和地图。我们将在学术数据库和灰色文献资源中采用结构化搜索策略,查找有关非洲母婴保健数据科学的相关研究。伦理与传播:本范围界定综述不需要正式的伦理道德,因为我们不会收集原始数据。研究结果将展示差距、机遇、进步、创新、实施情况、需要进一步研究的领域,并提出将数据科学纳入非洲母婴保健计划的下一步措施。研究结果的影响将与在社区、临床环境、监测和评估等非母婴保健环境中加强数据科学的可能方法有关。这项研究将阐明数据科学在解决 MNCH 问题中的应用,并提供一个全面的视角,说明在哪些领域存在差距,在哪些领域有机会利用和挖掘已有的数据。这项工作将与 MNCH 领域的利益相关者、政策制定者和研究人员息息相关,为规划提供信息。研究结果将通过非洲的同行评审期刊、会议、政策简报、博客和社交媒体平台进行传播:数据科学、产妇保健、新生儿和围产期保健、儿童保健、非洲
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