A simulation-based methodological analysis of caesarean-section patterns in women of reproductive age in Saudi Arabia

Caesarean section (CS) rates have increased substantially in both developed and developing nations. Although CS is a life-saving procedure when medically indicated, clinical guidelines generally recommend vaginal birth for uncomplicated pregnancies. In the Kingdom of Saudi Arabia (KSA), reported national statistics indicate that caesarean sections account for approximately 34.6% of all births as of 2023, with considerable regional variation. However, limited individual-level data are available to empirically examine factors associated with CS in this high-income setting. The goal of this study was to reproduce publicly reported national caesarean section distributions and to illustrate statistical associations between maternal, neonatal, and regional characteristics using a simulation-based analytical framework. Publicly available aggregate statistics from the Women Health and Reproductive Care Survey 2023 were used to construct a synthetic dataset for methodological illustration. A synthetic dataset representing 28,741 women was generated for the analysis. Chi-square methods and multivariate logistic regression were used to determine the associations between maternal age, region, nationality, anaemia status, delivery assistance type, newborn weight, and delivery type (normal vs. CS). In the simulated dataset, maternal age, region, nationality, anaemia status, and newborn weight exhibited statistically detectable associations with caesarean delivery; these relationships reflect imposed population distributions rather than empirical effects. In the simulation, women aged 45–49 showed inflated odds ratios (OR = 15.64, 95% CI: 9.77–25.44) due to sparse category structure and imposed marginal distributions, illustrating a common instability in regression modelling of synthetic data. There were regional variations, with Al-Baha showing the highest CS rate (46.5%), followed by Tabuk (42.3%) and Northern Border Region (41.3%). Low birth weight (< 2.5 kg) was a strong predictor of CS (OR = 34.37, 95% CI: 30.84–38.36). Saudi nationality corresponded to lower estimated odds of CS (OR = 0.68, 95% CI: 0.62–0.76) compared to non-Saudi residents, reflecting imposed nationality distributions rather than empirical effects. The model demonstrated high internal discrimination within the simulated dataset, which reflects internal consistency rather than real-world predictive performance. Maternal age and regional characteristics exhibited the strongest model-based associations with C-sections in Saudi Arabia. These results support creating region-specific health strategies targeted at different age groups to optimize delivery practices and improve maternal and neonatal outcomes. Future studies using comprehensive individual-level data are required to empirically evaluate these patterns and investigate additional factors influencing C-section trends.