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Area of Science:

  • Infectious Disease Epidemiology
  • Healthcare Network Analysis
  • Hospital Infection Prevention

Background:

  • Clostridium difficile infection (CDI) remains a significant healthcare-associated infection.
  • Understanding transmission dynamics within hospitals is crucial for effective prevention strategies.
  • Patient mobility between units is a potential driver of CDI spread.

Purpose of the Study:

  • To investigate the link between unit-level CDI susceptibility and inpatient mobility patterns.
  • To develop and validate a novel predictive measure for CDI transmission, termed contagion centrality (CC).

Main Methods:

  • A retrospective cohort study analyzed 2 years of electronic health record data from a large hospital (72,636 admissions).
  • A mobility network was built to represent patient transfers between hospital units.
  • Network centrality measures and daily unit-wide CDI susceptibility scores were calculated and compared.

Main Results:

  • Closeness centrality, reflecting incoming patient mobility, was significantly associated with unit CDI susceptibility (P < .05).
  • Contagion centrality (CC), incorporating transfer rates, unit susceptibility, and current infections, significantly predicted hospital-onset CDI (P < .05).
  • CC effectively captures transmission risks associated with patient movement.

Conclusions:

  • Inpatient mobility, particularly incoming transfers, plays a key role in unit-level CDI susceptibility.
  • Contagion centrality offers a valuable tool for real-time risk assessment and prediction of hospital-onset CDI.
  • Visualizing mobility and infection data can proactively identify at-risk units, enabling targeted prevention efforts.