A classification system for hospital-based infection outbreaks

Area of Science:

• * Epidemiology and Public Health
• * Health Informatics and Modeling
• * Infection Control and Hospital Management

Background:

• * Hospital-acquired infections (HAIs) and community-acquired infections pose significant management challenges in semi-closed environments like hospitals.
• * Common pathogens include Clostridium difficile (C.Diff), Methicillin-resistant Staphylococcus aureus (MRSA), and Norwalk-like viruses (NLVs).
• * Effective management requires robust methods for analyzing and comparing outbreak data.

Purpose of the Study:

• * To develop a novel classification system for quantifying the organizational impact of infectious outbreaks.
• * To provide a framework for comparing computer simulation models with historical outbreak data.
• * To facilitate the comparison of different infection control and containment strategies.

Main Methods:

• * Development of a seven-element classification system to describe outbreak impact.
• * Analysis and classification of historical Norwalk-like virus (NLV) outbreak data from Hull and East Yorkshire Hospitals NHS Trust.
• * Implementation of a goodness-of-fit method for comparing new classifications to existing ones and evaluating simulation realism.

Main Results:

• * A comprehensive classification system for assessing infectious outbreaks in healthcare settings was established.
• * The system was applied to analyze NLV outbreaks, considering both staff and patient infections.
• * A method for evaluating the accuracy of outbreak simulations against real-world data was demonstrated.

Conclusions:

• * The proposed classification system offers a standardized approach to understanding and comparing infectious outbreaks.
• * This system is valuable for validating computer models used in outbreak simulation and management.
• * It supports evidence-based decision-making for infection control strategies and resource allocation in hospitals.