Identification of Spatial Hot spots Clustering and Geographically Weighted Regression Analysis to Assess Predictors of Cesarean Section Delivery in Northeastern States, India.

Background: It is difficult to achieve health related Sustainable Development Goals when a higher proportion of birth delivery occurs through cesarean section (CS) than vaginal delivery without considerable medical benefits. This study aims to identify the spatial hot spot clustering and determinants of cesarean section in northeastern states, India.

Methods: The study utilized data from the fifth round of the National Family Health Survey (NFHS-5, 2019-2021), which included responses from 34,222 mothers who delivered live births in the five years preceding the survey. The study investigated spatial hot spot clustering of CS prevalence using Getis-Ord Gi* statistics and applied multiscale geographically weighted regression (MGWR) to identify spatial clusters in the relationships between predictor variables and CS delivery.

Results: The study identified spatial hot spot clustering of CS rates in districts of Sikkim, western and southern Tripura, eastern and western Assam, and central Manipur. MGWR results indicated that significant determinants of CS include maternal age (30-49 years), first birth order, highest educational level, high body mass index, and highest wealth quintile, with regression coefficients varying significantly by district in this region.

Conclusion: The study found that CS rates vary by clusters in the districts of northeastern states of India. It suggests that piloting educational interventions for pregnant women and regularly monitoring CS facilities could be initial strategies to better understand and address the higher CS trends in these regions.