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Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach

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  • M. Cannas
  • C. Conversano
  • F. Mola
  • E. Sironi

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.

Suggested Citation

  • M. Cannas & C. Conversano & F. Mola & E. Sironi, 2017. "Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2095-2107, September.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2095-2107
    DOI: 10.1080/02664763.2016.1247785
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    Cited by:

    1. Claudio Conversano & Massimo Cannas & Francesco Mola & Emiliano Sironi, 2019. "Random effects clustering in multilevel modeling: choosing a proper partition," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 279-301, March.

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