- Early Detection of Nosocomial Outbreaks Caused by Rare Pathogens: A Case Study Employing Score Prediction Interval
-
Hiroshi Nishiura
-
Osong Public Health Res Perspect. 2012;3(3):121-127. Published online June 30, 2012
-
DOI: https://doi.org/10.1016/j.phrp.2012.07.010
-
-
1,842
View
-
14
Download
-
8
Citations
-
 Abstract
 PDF
- Objectives
Nosocomial outbreaks involve only a small number of cases and limited baseline data. The present study proposes a method to detect the nosocomial outbreaks caused by rare pathogens, exploiting score prediction interval of a Poisson distribution.
Methods
The proposed method was applied to three empirical datasets of nosocomial outbreaks in Japan: outbreaks of (1) multidrug-resistant Acinetobacter baumannii (n = 46) from 2009 to 2010, (2) multidrug-resistant Pseudomonas aerginosa (n = 18) from 2009 to 2010, and (3) Serratia marcescens (n = 226) from 1999 to 2000.
Results
The proposed method successfully detected all three outbreaks during the first 2 months. Both the model-based and empirically derived threshold values indicated that the nosocomial outbreak of rare infectious disease may be declared upon diagnosis of index case(s), although the sensitivity and specificity were highly variable.
Conclusion
The findings support the practical notion that, upon diagnosis of index patient(s), one should immediately start the outbreak investigation of nosocomial outbreak caused by a rare pathogen. The proposed score prediction interval can permit easy computation of outbreak threshold in hospital settings among healthcare experts.
-
Citations
Citations to this article as recorded by  - Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals
Christin Schröder, Luis Alberto Peña Diaz, Anna Maria Rohde, Brar Piening, Seven Johannes Sam Aghdassi, Georg Pilarski, Norbert Thoma, Petra Gastmeier, Rasmus Leistner, Michael Behnke, Surbhi Leekha
PLOS ONE.2020; 15(1): e0227955. CrossRef - Time series non-Gaussian Bayesian bivariate model applied to data on HMPV and RSV: a case of Dadaab in Kenya
Raymond Nyoka, Thomas N. O. Achia, Jimmy Omony, Samuel M. Musili, Anthony Gichangi, Henry Mwambi
BMC Public Health.2019;[Epub] CrossRef - Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications
H. M. Dipu Kabir, Abbas Khosravi, Mohammad Anwar Hosen, Saeid Nahavandi
IEEE Access.2018; 6: 36218. CrossRef - Automated detection of hospital outbreaks: A systematic review of methods
Brice Leclère, David L. Buckeridge, Pierre-Yves Boëlle, Pascal Astagneau, Didier Lepelletier, Andre Scherag
PLOS ONE.2017; 12(4): e0176438. CrossRef - Journal Publishing: Never Ending Saga
Hae-Wol Cho, Chaeshin Chu
Osong Public Health and Research Perspectives.2014; 5(1): 1. CrossRef - Roll the Dice
Hae-Wol Cho, Chaeshin Chu
Osong Public Health and Research Perspectives.2014; 5(5): 243. CrossRef - Summing Up Again
Hae-Wol Cho, Chaeshin Chu
Osong Public Health and Research Perspectives.2014; 5(4): 177. CrossRef - Sample Size Considerations for One-to-One Animal Transmission Studies of the Influenza A Viruses
Hiroshi Nishiura, Hui-Ling Yen, Benjamin J. Cowling, Maciej F. Boni
PLoS ONE.2013; 8(1): e55358. CrossRef
|
