Bayesian exponential random graph modelling of interhospital patient referral networks

• Published in: Statistics in medicine Vol. 36; no. 18; pp. 2902 - 2920
• Main Authors: Caimo, Alberto, Pallotti, Francesca, Lomi, Alessandro
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 15.08.2017
• Subjects: Algorithms; Bayes Theorem; Bayesian inference; Biostatistics; Community Networks - statistics & numerical data; Computer Simulation; exponential random graph models; Hospitals; Humans; interhospital patient referral networks; interorganisational networks; Italy; Markov Chains; Medical referrals; Medical statistics; Models, Statistical; Monte Carlo Method; Monte Carlo methods; Referral and Consultation - statistics & numerical data; Referrals; statistical models for social networks; exponential random graph models; interhospital patient referral networks; Monte Carlo methods; Bayesian inference; interorganisational networks; statistical models for social networks
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.7301

Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well‐known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system of local dependencies that produce – but at the same time are induced by – decentralised collaborative arrangements between hospitals. Copyright © 2017 John Wiley & Sons, Ltd.