Guidance: Infection Prevention and Control Measures for Healthcare Workers in All Healthcare Settings
"Gram-negative bacilli commonly encountered in healthcare settings include species such as Pseudomonas aeruginosa, Acinetobacter spp. and Stenotrophomonas maltophilia, and species belonging to the Enterobacteriaceae family, such as Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae. Recent events indicate an increasing occurrence of antimicrobial resistance in Gram-negative bacteria. The carbapenem group of antimicrobials is a safe and generally effective treatment for severe Gram-negative bacterial infections when resistance to other classes of antimicrobials is present. When resistance to carbapenems occurs, there are often few alternative treatments available. Carbapenem-resistance in Gram-negative bacteria can occur by a number of different mechanisms. Identifying carbapenem resistance and distinguishing between these different mechanisms of resistance can be challenging for clinical microbiology laboratories. Carbapenem resistance develops as a result of the production of carbapenem-hydrolysing enzymes. These enzymes are usually encoded by genes carried on mobile genetic elements such as plasmids which can rapidly spread amongst related bacterial genera. Some notable examples of recently identified carbapenemases are: •Klebsiella pneumoniae carbapenemase (KPC) which is found mostly in K. pneumoniae but also in other Enterobacteriaceae. KPC producing microorganisms have caused major healthcare related outbreaks in Greece, Israel and north eastern USA; •The OXA-type resistance genes found in Acinetobacter spp. Carbapenem-resistant Acinetobacter has been identified worldwide but is currently rarely seen in Canadian hospitals. •Metallo-ß-lactamases which are mostly found in P. aeruginosa and Acinetobacter spp., and rarely in other Enterobacteriaceae; and include the New Delhi metallo beta-lactamase (NDM-1 enzyme) found mostly in Escherichia coli and K. pneumoniae, but also seen in other Enterobacteriaceae. The New Delhi metallo beta-lactamase (NDM-1 enzyme) has recently been identified in India and Pakistan and in patients hospitalized in other countries after receiving health care in India and Pakistan. "
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Additional Info
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| Last Updated | October 20, 2025, 04:01 (UTC) |
| Created | October 20, 2025, 04:01 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Title
Title for the Dataset.
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Guidance: Infection Prevention and Control Measures for Healthcare Workers in All Healthcare Settings |
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Description
A description of the dataset.
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"Gram-negative bacilli commonly encountered in healthcare settings include species such as Pseudomonas aeruginosa, Acinetobacter spp. and Stenotrophomonas maltophilia, and species belonging to the Enterobacteriaceae family, such as Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae. Recent events indicate an increasing occurrence of antimicrobial resistance in Gram-negative bacteria. The carbapenem group of antimicrobials is a safe and generally effective treatment for severe Gram-negative bacterial infections when resistance to other classes of antimicrobials is present. When resistance to carbapenems occurs, there are often few alternative treatments available. Carbapenem-resistance in Gram-negative bacteria can occur by a number of different mechanisms. Identifying carbapenem resistance and distinguishing between these different mechanisms of resistance can be challenging for clinical microbiology laboratories. Carbapenem resistance develops as a result of the production of carbapenem-hydrolysing enzymes. These enzymes are usually encoded by genes carried on mobile genetic elements such as plasmids which can rapidly spread amongst related bacterial genera. Some notable examples of recently identified carbapenemases are: •Klebsiella pneumoniae carbapenemase (KPC) which is found mostly in K. pneumoniae but also in other Enterobacteriaceae. KPC producing microorganisms have caused major healthcare related outbreaks in Greece, Israel and north eastern USA; •The OXA-type resistance genes found in Acinetobacter spp. Carbapenem-resistant Acinetobacter has been identified worldwide but is currently rarely seen in Canadian hospitals. •Metallo-ß-lactamases which are mostly found in P. aeruginosa and Acinetobacter spp., and rarely in other Enterobacteriaceae; and include the New Delhi metallo beta-lactamase (NDM-1 enzyme) found mostly in Escherichia coli and K. pneumoniae, but also seen in other Enterobacteriaceae. The New Delhi metallo beta-lactamase (NDM-1 enzyme) has recently been identified in India and Pakistan and in patients hospitalized in other countries after receiving health care in India and Pakistan. " |
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Tags / Keywords
Keywords/tags categorizing the dataset.
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Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
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Dataset Size
Dataset size in megabytes.
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Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
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Published Date
Published date of the dataset.
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2012-04-03 |
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Time Period Data Span (start date)
Start date of the data in the dataset.
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Time Period Data Span (end date)
End date of time data in the dataset.
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GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
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Access category
Type of access granted for the dataset (open, closed, service, etc).
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License
License used to access the dataset.
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Open Government Licence - Canada |
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Limits on use
Limits on use of data.
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Location
Location of the dataset.
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https://open.canada.ca/data/en/dataset/73f22d5b-4221-4019-8ad8-0ca4f3607593 |
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Data Service
Data service for accessing a dataset.
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Owner
Owner of the dataset.
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Public Health Agency of Canada | Agence de la santé publique du Canada |
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Contact Point
Who to contact regarding access?
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Contact Point Email
The email to contact regarding access?
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Publisher
Publisher of the dataset.
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Publisher Email
Email of the publisher.
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Author
Author of the dataset.
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Author Email
Email of the author.
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Accessed At
Date the data and metadata was accessed.
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Identifier
Unique identifier for the dataset.
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Language
Language(s) of the dataset
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Link to dataset description
A URL to an external document describing the dataset.
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Persistent Identifier
Data is identified by a persistent identifier.
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Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
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Contains data about individuals
Does the data hold data about individuals?
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Contains data about identifiable individuals
Does the data hold identifiable data about individual?
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Portal Type
Platform type of the source portal.
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Version
Version of the datatset
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None |
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Source
Source of the dataset.
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None |
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Version notes
Version notes about the dataset.
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Is version of another dataset
Link to dataset that it is a version of.
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Other versions
Link to datasets that are versions of it.
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Provenance Text
Provenance Text of the data.
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Provenance URL
Provenance URL of the data.
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Temporal resolution
Describes how granular the date/time data in the dataset is.
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GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
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GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
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Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
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Community Permission
Community permission (who gave permission).
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The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
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Number of data rows
If tabular dataset, total number of rows.
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Number of data columns
If tabular dataset, total number of unique columns.
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Number of data cells
If tabular dataset, total number of cells with data.
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Number of data relations
If RDF dataset, total number of triples.
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Number of entities
If RDF dataset, total number of entities.
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Number of data properties
If RDF dataset, total number of unique properties used by the triples.
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Data quality
Describes the quality of the data in the dataset.
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Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.
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