A Dynamic Latent Space Model for Healthcare Mobility Networks: the Italian National Health Service case

2026-05-29

AI Summary

This study addresses the inequities in healthcare resource allocation and fiscal imbalances arising from cross-regional patient mobility among Italy’s 109 Local Health Authorities (LHAs), particularly the persistent structural asymmetry between northern and southern regions. To this end, the authors develop a Bayesian dynamic latent space model coupled with an obstacle-augmented negative binomial likelihood to analyze patient flows for hip replacement surgeries from 2018 to 2024. The model innovatively integrates directional heterogeneity, population-size adjustments, and time-evolving geometric structures to effectively handle zero inflation, overdispersion, and network dependencies. The analysis provides the first quantitative assessment of the COVID-19 pandemic’s impact on patient mobility and uncovers enduring asymmetric patterns in LHAs’ outflow propensities and attractiveness, thereby offering a precise characterization of the dynamic spatial architecture of Italy’s National Health Service.

Abstract

Healthcare mobility -- patients seeking treatment outside their territory of residence -- represents a major source of inequality and financial imbalance in decentralised health systems. In Italy, persistent north-south asymmetries in patient flows among Local Health Authorities (ASLs) have reinforced existing disparities within the National Health Service; yet the structural organisation and temporal dynamics of these flows remain poorly understood at the sub-regional level. We propose a Bayesian dynamic latent space model for directed weighted networks with a hurdle negative binomial likelihood, and apply it to administrative discharge records on mobility for hip replacement procedures among 109 Italian ASLs over 2018-2024. The model jointly addresses excess zeros, overdispersion and network dependence, while capturing directional heterogeneity through multiplicative sender and receiver effects and controlling for differences in territorial size via an appropriate exposure term. Applied to Italian mobility data, the model reveals the evolving geometry of the healthcare system, quantifies the disruption induced by the COVID-19 pandemic, and uncovers structural asymmetries in outward propensity and ASLs attractiveness. The framework provides a flexible tool for the statistical analysis of dynamic healthcare mobility networks with direct relevance to the monitoring and evaluation of territorial healthcare provision.

Problem

• healthcare mobility
• inequality
• decentralised health systems
• patient flows
• territorial disparities

Innovation

• dynamic latent space model
• healthcare mobility networks
• hurdle negative binomial
• Bayesian network analysis
• territorial healthcare disparities