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J Hosp Infect. 2018 Oct 12. pii: S0195-6701(18)30540-1. doi: 10.1016/j.jhin.2018.10.005. [Epub ahead of print]

Automated detection of outbreaks of antimicrobial-resistant bacteria in Japan.

Author information

1
Antimicrobial Resistance Research Center, National Institute of Infectious Diseases, Tokyo, Japan. Electronic address: atsutsui@nih.go.jp.
2
Antimicrobial Resistance Research Center, National Institute of Infectious Diseases, Tokyo, Japan.
3
WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Brigham and Women's Hospital, Boston, MA, USA.
4
Teikyo University Graduate School of Public Health, Tokyo, Japan.
5
Division of Infection Control and Prevention, Tottori University Hospital, Tottori, Japan.
6
Department of Infectious Diseases, Nagoya University Hospital, Aichi, Japan.
7
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health, Care Institute, Boston, MA, USA; Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
8
WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

Abstract

BACKGROUND:

Hospital outbreaks of antimicrobial-resistant (AMR) bacteria should be detected and controlled as early as possible.

AIM:

To develop a framework for automatic detection of AMR outbreaks in hospitals.

METHODS:

Japan Nosocomial Infections Surveillance (JANIS) is one of the largest national AMR surveillance systems in the world. For this study, all bacterial data in the JANIS database were extracted between 2011 and 2016. WHONET, a free software for the management of microbiology data, and SaTScan, a free cluster detection tool embedded in WHONET, were used to analyse 2015-2016 data of eligible hospitals. Manual evaluation and validation of 10 representative hospitals around Japan were then performed using 2011-2016 data.

FINDINGS:

Data from 1031 hospitals were studied; mid-sized (200-499 beds) hospitals accounted for 60%, followed by large hospitals (≥500 beds; 24%) and small hospitals (<200 beds; 16%). More clusters were detected in large hospitals. Most of the clusters included five or fewer patients. From the in-depth analysis of 10 hospitals, ∼80% of the detected clusters were unrecognized by infection control staff because the bacterial species involved were not included in the priority pathogen list for routine surveillance. In two hospitals, clusters of more susceptible isolates were detected before outbreaks of more resistant pathogens.

CONCLUSION:

WHONET-SaTScan can automatically detect clusters of epidemiologically related patients based on isolate resistance profiles beyond lists of high-priority AMR pathogens. If clusters of more susceptible isolates can be detected, it may allow early intervention in infection control practices before outbreaks of more resistant pathogens occur.

KEYWORDS:

Antimicrobial resistance; Automated outbreak detection; JANIS; Outbreak; WHONET-SaTScan

PMID:
30321629
DOI:
10.1016/j.jhin.2018.10.005

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