Meta-Analysis of Predictive Modelling Approaches and Systematic Reviews for Maternal Healthcare Outcomes
DOI:
https://doi.org/10.70112/ajsat-2024.13.1.4110Keywords:
Maternal Health, Predictive Modeling, Healthcare Outcomes, Systematic Review, PregnancyAbstract
Enhancing parental care is of utmost importance in ensuring the well-being of pregnant women throughout their
pregnancy and childbirth journey. Although there have been significant advancements in this area, persistent challenges such as infections, hemorrhage, hypertension, unsafe abortions, and other concerns remain. Prioritizing maternal health could greatly reduce mortality rates and promote safer pregnancies. This meta-analysis assesses research methodologies in maternal healthcare outcomes, evaluating their strengths and weaknesses. We also explore the prevalence of systematic reviews in maternal health to enhance healthcare outcomes. We examined five major databases—Google Scholar, PubMed, Elsevier, PLOS, and BMC—encompassing descriptive and computational research on maternal outcomes between 2000 and 2021. Our search terms included predicting, modeling, maternal, outcome, healthcare forecasting, demonstrating, consequence, diagnosis, machine learning, mathematical, and statistical. Forty-four papers related to maternal outcomes were reviewed. Google Scholar yielded 50 articles (46.30%), PubMed 33 articles (31.48%), Elsevier 12 articles (11.11%), BMC nine articles (9.26%), and PLOS two articles (1.85%). Our findings highlight a high awareness of maternal outcome prevalence. Multiple factors contribute to maternal risk, including maternal education, economic circumstances, financial constraints, and access to antenatal
care. Therefore, this work advocates for the adoption of additional methods and mathematical models to predict
maternal outcomes, ultimately improving maternal healthcare.
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