Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account?
The objective of this study was to assess potential biases that may influence the validity of contemporary antimicrobial-resistant (AMR) pathogen surveillance systems. Although surveillance data have been widely published and used by researchers and decision makers, little attention has been devoted...
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2011-01-01
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Series: | Canadian Journal of Infectious Diseases and Medical Microbiology |
Online Access: | http://dx.doi.org/10.1155/2011/276017 |
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doaj-5977d21bd2824487ac3aba5a6596a6622021-07-02T03:19:35ZengHindawi LimitedCanadian Journal of Infectious Diseases and Medical Microbiology1712-95322011-01-01224e24e2810.1155/2011/276017Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account?Olivia Rempel0Johann DD. Pitout1Kevin B. Laupland2O’Brien Centre for the Bachelor of Health Sciences Program, Health Sciences Centre, Faculty of Medicine, University of Calgary, Calgary, Alberta, CanadaDepartments of Pathology and Laboratory Medicine and Microbiology and Infectious Diseases, University of Calgary and Alberta Health Services, Calgary, Alberta, CanadaDepartments of Medicine, Critical Care Medicine, Pathology and Laboratory Medicine, and Community Health Sciences, and Centre for Antimicrobial Resistance, University of Calgary and Alberta Health Services, Calgary, Alberta, CanadaThe objective of this study was to assess potential biases that may influence the validity of contemporary antimicrobial-resistant (AMR) pathogen surveillance systems. Although surveillance data have been widely published and used by researchers and decision makers, little attention has been devoted to the assessment of their validity. A Medline search was used to identify reports, in 2008, of laboratory-based AMR surveillance systems. Identified surveillance systems were appraised for six different types of bias. Scores were assigned as ‘2’ (good), ‘1’ (fair) and ‘0’ (poor) for each bias. The results of this assessment indicate that there are several potential biases that can influence the validity of AMR surveillance information and, therefore, the potential for bias should be considered in the interpretation and use of AMR surveillance data.http://dx.doi.org/10.1155/2011/276017 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Olivia Rempel Johann DD. Pitout Kevin B. Laupland |
spellingShingle |
Olivia Rempel Johann DD. Pitout Kevin B. Laupland Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account? Canadian Journal of Infectious Diseases and Medical Microbiology |
author_facet |
Olivia Rempel Johann DD. Pitout Kevin B. Laupland |
author_sort |
Olivia Rempel |
title |
Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account? |
title_short |
Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account? |
title_full |
Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account? |
title_fullStr |
Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account? |
title_full_unstemmed |
Antimicrobial Resistance Surveillance Systems: Are Potential Biases Taken into Account? |
title_sort |
antimicrobial resistance surveillance systems: are potential biases taken into account? |
publisher |
Hindawi Limited |
series |
Canadian Journal of Infectious Diseases and Medical Microbiology |
issn |
1712-9532 |
publishDate |
2011-01-01 |
description |
The objective of this study was to assess potential biases that may influence the validity of contemporary antimicrobial-resistant (AMR) pathogen surveillance systems. Although surveillance data have been widely published and used by researchers and decision makers, little attention has been devoted to the assessment of their validity. A Medline search was used to identify reports, in 2008, of laboratory-based AMR surveillance systems. Identified surveillance systems were appraised for six different types of bias. Scores were assigned as ‘2’ (good), ‘1’ (fair) and ‘0’ (poor) for each bias. The results of this assessment indicate that there are several potential biases that can influence the validity of AMR surveillance information and, therefore, the potential for bias should be considered in the interpretation and use of AMR surveillance data. |
url |
http://dx.doi.org/10.1155/2011/276017 |
work_keys_str_mv |
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1721341826487549952 |