Geospatial patterns and multilevel determinants of cesarean section rates in Iran: a Bayesian spatial analysis using...

Cesarean section (CS) rates in Iran remain among the highest globally, far exceeding the WHO's recommended 10-15%. While individual determinants are well-studied, provincial spatial heterogeneity and contextual influences are underexplored. This study quantifies multilevel predictors and examines spatial patterns of CS using Bayesian spatial modeling.

Cross-sectional analysis of 24,982 deliveries from the 2010 Iran Demographic and Health Survey (IrMIDHS).

mode of delivery (cesarean vs. vaginal).

maternal age, education, economic status, contraceptive use, smoking. Multilevel logistic regression with provincial random intercepts was fitted first, followed by a Bayesian spatial logistic model using INLA with BYM2 structure. Model fit compared via DIC/WAIC; spatial autocorrelation via Moran's I. Survey weights were not applied due to the focus on spatial patterns rather than population prevalence.

National CS rate: 52.1%. Advanced age (OR = 1.036/year, 95% CrI: 1.028-1.045), university education (OR = 1.584, 95% CrI: 1.422-1.762), and high income (OR = 1.142, 95% CrI: 1.045-1.247) increased odds; contraceptive use was protective (OR = 0.458, 95% CrI: 0.418-0.501). Moderate clustering (ICC = 0.18). Spatial model showed superior fit (ΔDIC = 670; ΔWAIC = 670) and significant autocorrelation (Moran's I = 0.42, p < 0.001). Northern/central provinces exhibited excess adjusted risk; southeastern regions showed reduced risk.

CS in Iran is associated with individual socioeconomic factors and substantial spatial heterogeneity. Bayesian spatial modeling with INLA/BYM2 identifies high-risk clusters, supporting geographically targeted interventions to reduce unnecessary CS and promote equitable obstetric care nationwide.