Akuze Joseph, Ngatia Bancy, Amare Samson Yohannes, Wanduru Phillip, Otieno Grieven P, Kananura Rornald M, Fati Kirakoya-Samadoulougou, Amouzou Agbessi, Estifanos Abiy Seifu, Ohuma Eric
Centre of Excellence for Maternal Newborn and Child Health Research, Makerere University School of Public Health, Kampala, Uganda
Centre for Maternal, Adolescent, Reproductive, and Child Health, London School of Hygiene & Tropical Medicine, London, UK.
BMJ Open. 2024 Dec 10;14(12):e091883. doi: 10.1136/bmjopen-2024-091883.
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.
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.
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.
数据科学在非洲孕产妇、新生儿和儿童健康（MNCH）领域的应用情况参差不齐，且相关记录有限。尽管各方努力降低可预防的孕产妇、新生儿和儿童健康发病率及死亡率，但进展依然缓慢。准确的数据对于促使各国为实现可持续发展目标3中关于孕产妇、新生儿和儿童健康的具体目标负责追踪进展情况至关重要。数据科学能够提高数据的可得性、质量、医疗保健服务水平以及孕产妇、新生儿和儿童健康项目的决策能力。我们旨在梳理并综合非洲孕产妇、新生儿和儿童健康领域的数据科学用例。
我们将构建一个涵盖七个领域的概念框架：（1）基础设施与系统性挑战；（2）数据质量；（3）数据治理、监管动态与政策；（4）技术创新与数字健康；（5）能力发展、人力资本与机遇；（6）协作与战略框架；（7）实施与推广建议。我们将采用一种范围综述方法，包括在七个数据库中进行文献检索、灰色文献来源检索以及从数字健康地图集中提取数据。三位评审人员将筛选文章并提取数据。我们将以叙述方式综合并呈现数据，并使用表格、图表和地图。我们在学术数据库和灰色文献来源中采用的结构化搜索策略将找到关于非洲孕产妇、新生儿和儿童健康领域数据科学的相关研究。
本范围综述无需正式的伦理审查和批准，因为它不涉及收集原始数据。研究结果将展示差距、机遇、进展、创新、实施情况以及需要进一步研究的领域。它们还将为将数据科学整合到非洲孕产妇、新生儿和儿童健康项目中提出下一步建议。我们将结合可能用于加强孕产妇、新生儿和儿童健康领域数据科学的方法，如社区和临床环境、监测与评估，来审视研究结果的影响。本研究将阐明数据科学在解决孕产妇、新生儿和儿童健康问题方面的应用，并全面呈现存在差距的领域以及有机会利用和挖掘现有资源的领域。这项工作将对孕产妇、新生儿和儿童健康领域的利益相关者、政策制定者和研究人员进行规划具有参考价值。研究结果将通过同行评审期刊、会议、政策简报、博客和社交媒体平台进行传播。
