Massimo Cannas

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

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.

Hospital differences in rates of cesarean deliveries in the Sardinian region: An observational study

Background: The rates of cesarean deliveries have been increasing steadily in several European countries in recent decades, with Italy having the second-highest rate (38% in 2010), causing concern and debate about the appropriateness of many interventions. Moreover, some recent studies suggest that rates of common obstetric interventions are not homogeneous across hospitals, maybe not only because of patient case mix but also possibly because of different hospital practices and cultures. Thus, it is important to investigate whether the variation in rates of cesarean sections can be traced back to patient characteristics or whether it depends upon context variables at the hospital level. Objective and method: Using official hospital abstracts on deliveries that occurred in Sardinia over a two-year period, we implement multilevel logistic regression models in order to assess whether the observed differences in cesarean rates across hospitals can be justified by case-mix differences across hospitals. Results: The between-hospital variation in rates of cesarean delivery is estimated to be 0.388 in the model with only the intercept and 0.382 in the model controlling for the mother’s clinical and sociodemographic characteristics. Conclusions: The results show that taking into account the individual characteristics of delivered mothers is not enough to justify the observed variation across hospital rates, suggesting the important role of unobserved variables at the hospital level in determining cesarean section rates.

Estimating the Effect of Prenatal Care on Birth Outcomes

Using data from official hospital abstracts on deliveries occurred in Sardinia during the years 2010 and 2011, we implemented an Augmented Inverse Probability Weighted (AIPW) model in order to study the effect of increased prenatal care during pregnancy on birth outcomes. Results showed that moderate levels of prenatal care, as measured by the number of sonograms, increase the Apgar score of the infant, while a higher number of sonograms does not have any additional marginal effect on the outcome.

A Note on the Use of Recursive Partitioning in Causal Inference

A tree-based approach for identification of a balanced group of observations in causal inference studies is presented. The method uses an algorithm based on a multidimensional balance measure criterion applied to the values of the covariates to recursively split the data. Starting from an ad-hoc resampling scheme, observations are finally partitioned in subsets characterized by different degrees of homogeneity, and causal inference is carried out on the most homogeneous subgroups.