Closure of medical departments during nosocomial outbreaks: data from a systematic analysis of the literature

J Hosp Infect. 2007 Apr;65(4):348-53. doi: 10.1016/j.jhin.2006.12.018. Epub 2007 Mar 12.


A total closure of an affected medical department is one of the most expensive infection control measures during investigation of a nosocomial outbreak. However, until now there has been no systematic analysis of typical characteristics of outbreaks, for which closure was considered necessary. This article presents data on features of such nosocomial epidemics published during the past 40 years in the medical literature. A search of the Outbreak Database (1561 nosocomial outbreaks in file) revealed a total of 194 outbreaks that ended up with some kind of closure of the unit (median closure time: 14 days). Closure rates (CRs) were calculated and stratified for medical departments, for causative pathogens, for outbreak sources, and for the assumed mode of transmission. Data were then compared to the overall average CR of 12.4% in the entire database. Wards in geriatric patient care were closed significantly more frequently (CR: 30.3%; P<0.001) whereas paediatric wards showed a significantly lower CR (6.1%; P=0.03). Pathogen species with the highest CR were norovirus (44.1%; P<0.001) and influenza/parainfluenza virus (38.5%; P<0.001). If patients were the source of the outbreak, the CR was significantly increased (16.7%; P=0.03). Infections of the central nervous system were most often associated with closure of the ward (24.2%; P=001). A systematic evaluation of nosocomial outbreaks can be a valuable tool for education of staff in the absence of an outbreak, but may be even more helpful for potentially cost-intensive decisions in the acute outbreak setting on the ward.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Cross Infection / classification
  • Cross Infection / epidemiology*
  • Cross Infection / prevention & control
  • Databases, Factual
  • Disease Outbreaks / statistics & numerical data*
  • Health Facility Closure / economics
  • Health Facility Closure / statistics & numerical data*
  • Hospital Units / statistics & numerical data*
  • Humans
  • Infection Control / methods*