Spread of hospital-acquired infections: A comparison of healthcare networks

PLoS Comput Biol. 2017 Aug 24;13(8):e1005666. doi: 10.1371/journal.pcbi.1005666. eCollection 2017 Aug.

Abstract

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.

MeSH terms

  • Algorithms
  • Cluster Analysis
  • Computational Biology / methods*
  • Cross Infection / epidemiology
  • Cross Infection / prevention & control
  • Cross Infection / transmission*
  • France / epidemiology
  • Hospitals
  • Humans
  • Infection Control
  • Patient Transfer / statistics & numerical data*

Grant support

This work was supported by the Interdisciplinary research program on health crisis and health protection (PRINCEPS) of Sorbonne Paris Cité University, within the program “Investissements d’Avenir” launched by the French State (http://www.ehesp.fr/recherche/domaines-de-recherche/securite-sanitaire/princeps-programme-de-recherche-interdisciplinaire-sur-les-crises-et-la-protection-sanitaire/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.