Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach. Issue 12 (10th September 2017)

• Record Type: Journal Article
• Authors: Cannas, M.; Conversano, C.; Mola, F.; Sironi, E.
• Is Part Of: Journal of applied statistics. Volume 44:Issue 12(2017)
• Page Start: 2095
• Page End: 2107
• Publication Date: 2017-09-10
• Subjects: Caesarean sections -- random effect models -- Dirichlet process -- random partition -- label-switching problem; 6207 -- 62G08; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1247785
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: This article presents a Bayesian semi-parametric approach for modeling the occurrence of cesarean sections using a sample of women delivering in 20 hospitals of Sardinia (Italy). A multilevel logistic regression has been fitted on the data using a Dirichlet process prior for modeling the random-effects distribution of the unobserved factors at the hospital level. Using the estimated random effects at the hospital level, a partition of the hospitals in terms of similar medical practice has been obtained that identifies different profiles of hospitals in terms of caesarean section risks. The limited number of clusters may be useful for suggesting policy implications that help to reduce the heterogeneity of caesarean delivery risks.