Maternal and Neonatal Delivery Outcomes in the Gambia: A Hospital-Based Analysis of Socioeconomic Status and Clinical Determinants
Charity Nneka John-Emaimo, Alice John Emaimo, Ikemesit Udeme Peter
Asian Journal of Pregnancy and Childbirth · pp. 150–173 · Published 2 May 2025
10.9734/ajpcb/2025/v8i1155Abstract
Maternal and neonatal health outcomes in low-resource settings like The Gambia remain a critical public health concern, with high mortality rates driven by socioeconomic and clinical factors. This hospital-based study at Edward Francis Small Teaching Hospital (EFSTH) examined the interplay of these determinants among 200 Gambian women. Data from the cross-sectional study were collected via structured questionnaires and medical records, analyzing socioeconomic status (SES), clinical variables, and delivery outcomes using SPSS. Results revealed no significant association between Socioeconomic Status (SES) indicators (income, education) and adverse outcomes (p > 0.05), likely due to population homogeneity and universal healthcare access. However, clinical factors were pivotal: antenatal care (ANC) participation significantly improved outcomes (p = 0.02), though visit frequency was non-significant (p = 0.17). Comorbidities like preeclampsia (4%) and neonatal indicators (birth weight, APGAR scores) were strongly linked to complications (p < 0.05). Most deliveries were vaginal (82.5%), with 3% stillbirths and 10% low birth weight neonates. The study underscores the dominance of clinical over socioeconomic factors in this cohort, emphasizing the need for quality antenatal care (ANC), early comorbidity management, and community-based education. However, its cross-sectional design limits causal inferences, and convenience sampling at a single hospital reduces generalizability. Additionally, unmeasured confounders (e.g., nutrition, environment) and self-reported socioeconomic status (SES) data may introduce bias. Policymakers should prioritize ANC quality and equitable healthcare access to mitigate risks. Further research should employ longitudinal designs to explore causal relationships and include diverse populations particularly rural communities to enhance generalizability.
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