Logo image
Back
Prioritizing healthcare worker vaccinations on the basis of social network analysis
Journal article   Peer reviewed

Prioritizing healthcare worker vaccinations on the basis of social network analysis

Philip M Polgreen, Troy Leo Tassier, Sriram Venkata Pemmaraju and Alberto Maria Segre
Infection control and hospital epidemiology, Vol.31(9), pp.893-900
09/2010
DOI: 10.1086/655466
PMCID: PMC3024853
PMID: 20649412
url
https://www.ncbi.nlm.nih.gov/pmc/articles/3024853View
Open Access

Abstract

To use social network analysis to design more effective strategies for vaccinating healthcare workers against influenza. An agent-based simulation. A simulation based on a 700-bed hospital. We first observed human contacts (defined as approach within approximately 0.9 m) performed by 15 categories of healthcare workers (eg, floor nurses, intensive care unit nurses, staff physicians, phlebotomists, and respiratory therapists). We then constructed a series of contact graphs to represent the social network of the hospital and used these graphs to run agent-based simulations to model the spread of influenza. A targeted vaccination strategy that preferentially vaccinated more "connected" healthcare workers was compared with other vaccination strategies during simulations with various base vaccination rates, vaccine effectiveness, probability of transmission, duration of infection, and patient length of stay. We recorded 6,654 contacts by 148 workers during 606 hours of observations from January through December 2006. Unit clerks, X-ray technicians, residents and fellows, transporters, and physical and occupational therapists had the most contacts. When repeated contacts with the same individual were excluded, transporters, unit clerks, X-ray technicians, physical and occupational therapists, and social workers had the most contacts. Preferentially vaccinating healthcare workers in more connected job categories yielded a substantially lower attack rate and fewer infections than a random vaccination strategy for all simulation parameters. Social network models can be used to derive more effective vaccination policies, which are crucial during vaccine shortages or in facilities with low vaccination rates. Local vaccination priorities can be determined in any healthcare facility with only a modest investment in collection of observational data on different types of healthcare workers. Our findings and methods (ie, social network analysis and computational simulation) have implications for the design of effective interventions to control a broad range of healthcare-associated infections.
 Models, Theoretical Risk Assessment - methods Iowa Health Personnel Humans Disease Outbreaks - prevention & control Hospital Bed Capacity, 500 and over Infection Control - methods Influenza, Human - prevention & control Mathematical Computing

Details

Metrics

12 Record Views
39 Times Cited - Web of Science
Logo image