ArticleBMC public health2024
Exploring Cesarean Section Delivery Patterns in South India: A Bayesian Multilevel and Geospatial analysis of Population-Based Cross-Sectional Data.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assessment of prevalence, perceptions, and factors associated with reported history of caesarean section among women in the United Arab Emirates: a cross-sectional study.BMC pregnancy and childbirth · 2026Article
- Exploring spatial clusters of caesarean sections across India - Insights from National Family Health Survey data.PLOS global public health · 2026Article
- Epidemiology and determinants of stillbirth disparities in India: a geospatial hotspot clustering study using integrated population-level data.The Lancet regional health. Southeast Asia · 2025Article
- Perceptions of community on the preference of cesarean section deliveries: A qualitative study in the state of Andhra Pradesh, India.Journal of education and health promotion · 2025Article
- Correlation of transcutaneous and serum bilirubin levels in late preterm and term neonates at a tertiary care center in south India.F1000Research · 2025Article
- Community health providers' perceptions on the preference of caesarean section deliveries: A qualitative study in the state of Andhra Pradesh, India.Journal of education and health promotion · 2025Article
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Authors and funding
3 authors.
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Abstract
backgroundThis paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data.
methodsNational Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods.
resultsOur analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns.
conclusionIn conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.
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