March 2020

IZA DP No. 13046: Infections, Accidents and Nursing Overtime in a Neonatal Intensive Care Unit: A Bayesian Semiparametric Panel Data Logit Model

Marc Beltempo, Georges Bresson, Jean-Michel Etienne, Guy Lacroix

The paper investigates the effects of nursing overtime on nosocomial infections and medical accidents in a neonatal intensive care unit (NICU). The literature lacks clear evidence on this issue and we conjecture that this may be due to empirical and methodological factors. We thus focus on a single NICU, thereby removing much variation in specialty mixes such neonatologists, fellows, residents, nurse practitioners that are observed across units. We model the occurrences of both outcomes using a sample of 3,979 neonates which represents over 84,846 observations (infant/days). We use a semiparametric panel data Logit model with random coefficients. The non-parametric components of the model allow to unearth potentially highly non-linear relationships between the outcomes and various policy-relevant covariates. We use the mean field variational Bayes approximation method to estimate the models. Our results show unequivocally that both health outcomes are affected by nursing overtime. Furthermore, they are both highly sensitive to infant and NICU-related characteristics.