Towards data-driven models for diverging emerging technologies for maternal, neonatal and child health services in Sub-Saharan Africa: a systematic review

Batani, John; Maharaj, Manoj

doi:10.1016/j.glohj.2022.11.003

Cited by 14 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Robust research can provide insights into the economic impact of investing in maternal and child health, making a compelling case for increased funding and resource allocation. Data-driven policies enable policymakers to identify cultural and societal barriers, allowing for the design of culturally sensitive interventions that respect local contexts (Bachmann, et al, 2022, Batani & Maharaj, 2022.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Maternal and child health policy: A global review of current practices and future directions

Evangel Chinyere Anyanwu,

Chinedu Paschal Maduka,

Oluwatoyin Ayo-Farai

et al. 2024

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

Maternal and child health remains a paramount concern worldwide, with policy frameworks playing a pivotal role in shaping outcomes for mothers and children. This paper provides a concise overview of the global landscape of maternal and child health policies, encompassing current practices and outlining key directions for the future. Current practices in maternal and child health policies vary significantly across regions and nations. Successful policies often integrate evidence-based interventions that span the continuum of care, from preconception to postpartum and early childhood. Maternal health policies typically focus on ensuring access to quality antenatal care, skilled birth attendance, and postnatal support. Child health policies emphasize immunization, nutrition, and early childhood development programs. However, disparities persist, reflecting the need for targeted interventions to address specific regional challenges. Future directions in maternal and child health policies emphasize a holistic and integrated approach. The global community recognizes the importance of addressing social determinants, promoting gender equity, and strengthening health systems to ensure comprehensive care for mothers and children. Innovations in digital health, community engagement, and cross-sectoral collaborations are identified as crucial components for improving policy effectiveness. Additionally, a life-course perspective, extending beyond childbirth to adolescence and beyond, is gaining prominence to address the evolving health needs of women and children. This paper provides a snapshot of the intricate landscape of maternal and child health policies globally. By understanding current practices and identifying future directions, policymakers, healthcare professionals, and stakeholders can contribute to advancing maternal and child health outcomes on a global scale.

show abstract

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Maternal and child health policy: A global review of current practices and future directions

Evangel Chinyere Anyanwu,

Chinedu Paschal Maduka,

Oluwatoyin Ayo-Farai

et al. 2024

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…Several emerging technologies for maternal, neonatal, and child health services have already been implemented in Africa (e.g., JamboMama, Mum&Baby). Those technologies lie in Blockchain, Artificial Intelligence, Big Data Analytics, IoT, Virtual Clinics, and Telemedicine (Batani & Maharaj, 2022).…”

Section: Introductionmentioning

confidence: 99%

KIA-CHAT: A QnA Chatbot for Postnatal and Newborn Care

Vinarti,

Sani,

Amalia

et al. 2024

JPK

View full text Add to dashboard Cite

Background: Postnatal care information is relatively less provided than information about prenatal or pregnancy. Several causes of this is the mothers already delivered the baby safely, and soon after the baby's birth, mothers will be busy taking care of the newborns. They frequently miss their postnatal meeting with doctors because of these reasons. Aims: Therefore, this article aims to develop a chatbot in which the knowledge is taken from Buku KIA and focus group discussion. Method: The targeted users of the KIA chatbot are postpartum mothers with live newborns. Rapid Application Development method is used to develop the KIA chatbot. The KIA chatbot is constructed using Google Dialog Flow with Telegram-based messenger. Results: The chatbot evaluation follows the Chatbot Usability Questionnaire with an overall score is 84.23 out of 100. Sixty-nine respondents confess that the KIA chatbot is easy to use and the knowledge is easy to comprehend. But, since the chatbot provides the answer options, the users feel some limitations. One of the limitations is they are unable to type their questions to the chatbot; only type the numerical order of the answer options. Conclusion: However, this limitation also brings another advantage to the mothers who have no time to type because of busy taking care of the newborns.

show abstract

“…Data science applications have also enabled tracking and progress of several SDG targets with improvements in interoperability and improved routine information systems (27,28). In MNCH, especially in Africa, various applications have been documented, for example predicting infant mortality in Rwanda using ML (29), Prediction of poliovirus, vaccine coverage and immunisation programs in Africa (30,31) and using data science approaches for decision-making and utilisation of maternal healthcare services in rural Ethiopia (32,33). With data science applications, there is potential to integrate African MNCH data with other planetary datasets like temperatures, seasonality, rainfall, air pollution, flooding, and NASA's space data (24).…”

Section: Introductionmentioning

confidence: 99%

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

Joseph,

Ngatia,

Amare

et al. 2024

Preprint

View full text Add to dashboard Cite

IntroductionApplication 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 AnalysisWe 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 disseminationThis 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.ARTICLE SUMMARYStrengths 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.

show abstract

Towards data-driven models for diverging emerging technologies for maternal, neonatal and child health services in Sub-Saharan Africa: a systematic review

Cited by 14 publications

References 42 publications

Maternal and child health policy: A global review of current practices and future directions

Maternal and child health policy: A global review of current practices and future directions

KIA-CHAT: A QnA Chatbot for Postnatal and Newborn Care

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

Contact Info

Product

Resources

About