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Factors associated with weighing a child at birth: Evidence from 16 sub-Saharan African countries

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  • Alex Bawuah
  • Samuel Ampaw
  • Edward Nketiah-Amponsah

Abstract

Background: Several children from sub-Saharan Africa (SSA) are not weighed at birth. The lack of birthweight data is a significant challenge in monitoring the global prevalence of extreme birthweight, either low or high, and newborn health. This data guides resource allocation and the design of targeted health policies to address neonatal complications and mortalities. This paper explores the demand-side predictors of newborn weighing. Methods: Data were obtained from the Demographic and Health Surveys (DHS) of 16 countries in SSA, conducted from 2014 to 2021. Multivariate logistic regression was used to achieve the study’s objectives. Results: Approximately 59% of the study population were weighed at birth. This prevalence rate varied widely across the 16 countries, ranging from 23% in Chad to 94% in Gabon. The study documents a positive association between higher socioeconomic status and the probability of being weighed at birth. Specifically, older women and women with higher education and wealth were more likely to weigh their newborns at birth. Also, women who delivered at healthcare facilities and those who used antenatal care had a higher likelihood of weighing their children at birth. Urban residents were more likely to weigh their children at birth. On the contrary, the likelihood of weighing a child at birth decreases with parity. Conclusion: The study highlights the need to target pregnant women of lower socioeconomic status for interventions aimed at averting severe morbidity and mortality occasioned by conditions of low birthweight.

Suggested Citation

  • Alex Bawuah & Samuel Ampaw & Edward Nketiah-Amponsah, 2025. "Factors associated with weighing a child at birth: Evidence from 16 sub-Saharan African countries," PLOS ONE, Public Library of Science, vol. 20(7), pages 1-12, July.
    • Handle: RePEc:plo:pone00:0328463
    DOI: 10.1371/journal.pone.0328463
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    1. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LLC, edition 2, number long2, July.
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