Identifying high‐risk medications and error types in Danish patient safety database using disproportionality analysis
Background Medication error (ME) surveillance in Danish healthcare relies on the mandatory national incident reporting system, the Danish Patient Safety Database (DPSD). Individual case reviews and descriptive statistics with frequency counts are the most often used approaches when analyzing MEs in...
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| Veröffentlicht in: | Pharmacoepidemiology and drug safety 2024-02, Vol.33 (2), p.e5735-n/a |
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| container_title | Pharmacoepidemiology and drug safety |
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| creator | Tchijevitch, Olga Birkeland, Søren F. Bogh, Søren B. Hallas, Jesper |
| description | Background
Medication error (ME) surveillance in Danish healthcare relies on the mandatory national incident reporting system, the Danish Patient Safety Database (DPSD). Individual case reviews and descriptive statistics with frequency counts are the most often used approaches when analyzing MEs in incident reporting systems, including the DPSD. However, incident reporting systems often generate a large number of reports and may suffer from underreporting; consequently, additional approaches are needed to overcome these challenges. Disproportionality analysis (DPA) is a statistical tool used for signal detection of adverse drug reactions in pharmacovigilance reports, but the evidence for using DPA on ME analysis in safety reporting systems is limited.
Objectives
We aimed to test the feasibility of DPA by analysing harmful MEs reported to DPSD 2014–2018.
Methods
We utilized proportional reporting ratios (PRR) to identify signals of diproportionality.
Results
We identified well‐known high‐risk medicines, including anticoagulants, opioids, insulins, antiepileptic, and antipsychotic drugs, and their association with several ME types and stages in a medication process.
Conclusion
DPA might be suggested as an additional tool for screening MEs and identifying priority areas for further investigation in safety reporting systems. |
| doi_str_mv | 10.1002/pds.5735 |
| format | Article |
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Medication error (ME) surveillance in Danish healthcare relies on the mandatory national incident reporting system, the Danish Patient Safety Database (DPSD). Individual case reviews and descriptive statistics with frequency counts are the most often used approaches when analyzing MEs in incident reporting systems, including the DPSD. However, incident reporting systems often generate a large number of reports and may suffer from underreporting; consequently, additional approaches are needed to overcome these challenges. Disproportionality analysis (DPA) is a statistical tool used for signal detection of adverse drug reactions in pharmacovigilance reports, but the evidence for using DPA on ME analysis in safety reporting systems is limited.
Objectives
We aimed to test the feasibility of DPA by analysing harmful MEs reported to DPSD 2014–2018.
Methods
We utilized proportional reporting ratios (PRR) to identify signals of diproportionality.
Results
We identified well‐known high‐risk medicines, including anticoagulants, opioids, insulins, antiepileptic, and antipsychotic drugs, and their association with several ME types and stages in a medication process.
Conclusion
DPA might be suggested as an additional tool for screening MEs and identifying priority areas for further investigation in safety reporting systems.</description><identifier>ISSN: 1053-8569</identifier><identifier>ISSN: 1099-1557</identifier><identifier>EISSN: 1099-1557</identifier><identifier>DOI: 10.1002/pds.5735</identifier><identifier>PMID: 38357842</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Inc</publisher><subject>Adverse Drug Reaction Reporting Systems ; Antiepileptic agents ; Antipsychotics ; Denmark - epidemiology ; Drug-Related Side Effects and Adverse Reactions - epidemiology ; Humans ; incident reporting ; medication error ; Medication Errors ; medication safety ; Patient Safety ; Pharmacovigilance ; Safety ; signal detection ; Statistical analysis</subject><ispartof>Pharmacoepidemiology and drug safety, 2024-02, Vol.33 (2), p.e5735-n/a</ispartof><rights>2023 John Wiley & Sons Ltd.</rights><rights>2024 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3105-3c00ac40f2c4bdb43cf5820f1b1bfe2594ad26e24369f831ff3fa388ebb84ff43</cites><orcidid>0000-0002-2356-8366 ; 0000-0002-8097-8708</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fpds.5735$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fpds.5735$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38357842$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tchijevitch, Olga</creatorcontrib><creatorcontrib>Birkeland, Søren F.</creatorcontrib><creatorcontrib>Bogh, Søren B.</creatorcontrib><creatorcontrib>Hallas, Jesper</creatorcontrib><title>Identifying high‐risk medications and error types in Danish patient safety database using disproportionality analysis</title><title>Pharmacoepidemiology and drug safety</title><addtitle>Pharmacoepidemiol Drug Saf</addtitle><description>Background
Medication error (ME) surveillance in Danish healthcare relies on the mandatory national incident reporting system, the Danish Patient Safety Database (DPSD). Individual case reviews and descriptive statistics with frequency counts are the most often used approaches when analyzing MEs in incident reporting systems, including the DPSD. However, incident reporting systems often generate a large number of reports and may suffer from underreporting; consequently, additional approaches are needed to overcome these challenges. Disproportionality analysis (DPA) is a statistical tool used for signal detection of adverse drug reactions in pharmacovigilance reports, but the evidence for using DPA on ME analysis in safety reporting systems is limited.
Objectives
We aimed to test the feasibility of DPA by analysing harmful MEs reported to DPSD 2014–2018.
Methods
We utilized proportional reporting ratios (PRR) to identify signals of diproportionality.
Results
We identified well‐known high‐risk medicines, including anticoagulants, opioids, insulins, antiepileptic, and antipsychotic drugs, and their association with several ME types and stages in a medication process.
Conclusion
DPA might be suggested as an additional tool for screening MEs and identifying priority areas for further investigation in safety reporting systems.</description><subject>Adverse Drug Reaction Reporting Systems</subject><subject>Antiepileptic agents</subject><subject>Antipsychotics</subject><subject>Denmark - epidemiology</subject><subject>Drug-Related Side Effects and Adverse Reactions - epidemiology</subject><subject>Humans</subject><subject>incident reporting</subject><subject>medication error</subject><subject>Medication Errors</subject><subject>medication safety</subject><subject>Patient Safety</subject><subject>Pharmacovigilance</subject><subject>Safety</subject><subject>signal detection</subject><subject>Statistical analysis</subject><issn>1053-8569</issn><issn>1099-1557</issn><issn>1099-1557</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kV1LwzAUhoMobk7BXyABb7zpTPOxNpcyvwYDBfW6pG2yZXZtzWmR3vkT_I3-ElM3RQSvTiDPeXg5L0LHIRmHhNDzOoexiJjYQcOQSBmEQkS7_VuwIBYTOUAHACtC_J_k-2jAYiaimNMhep3lumys6Wy5wEu7WH68vTsLz3itc5upxlYlYFXmWDtXOdx0tQZsS3ypSgtLXHvC72NQRjcdzlWjUgUat9D7cgu1q-rK9RpVWE8oPzuwcIj2jCpAH23nCD1dXz1Ob4P53c1sejEPMubTBywjRGWcGJrxNE85y4yIKTFhGqZGUyG5yulEU84m0sQsNIYZxeJYp2nMjeFshM42Xh_kpdXQJGsLmS4KVeqqhYRKGlEiCY88evoHXVWt83k3FIkYIb-EmasAnDZJ7exauS4JSdKXkfgykr4Mj55shW3qz_kDfl_fA8EGeLWF7v4VJfeXD1_CTwn2lpU</recordid><startdate>202402</startdate><enddate>202402</enddate><creator>Tchijevitch, Olga</creator><creator>Birkeland, Søren F.</creator><creator>Bogh, Søren B.</creator><creator>Hallas, Jesper</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TK</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-2356-8366</orcidid><orcidid>https://orcid.org/0000-0002-8097-8708</orcidid></search><sort><creationdate>202402</creationdate><title>Identifying high‐risk medications and error types in Danish patient safety database using disproportionality analysis</title><author>Tchijevitch, Olga ; Birkeland, Søren F. ; Bogh, Søren B. ; Hallas, Jesper</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3105-3c00ac40f2c4bdb43cf5820f1b1bfe2594ad26e24369f831ff3fa388ebb84ff43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adverse Drug Reaction Reporting Systems</topic><topic>Antiepileptic agents</topic><topic>Antipsychotics</topic><topic>Denmark - epidemiology</topic><topic>Drug-Related Side Effects and Adverse Reactions - epidemiology</topic><topic>Humans</topic><topic>incident reporting</topic><topic>medication error</topic><topic>Medication Errors</topic><topic>medication safety</topic><topic>Patient Safety</topic><topic>Pharmacovigilance</topic><topic>Safety</topic><topic>signal detection</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tchijevitch, Olga</creatorcontrib><creatorcontrib>Birkeland, Søren F.</creatorcontrib><creatorcontrib>Bogh, Søren B.</creatorcontrib><creatorcontrib>Hallas, Jesper</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Pharmacoepidemiology and drug safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tchijevitch, Olga</au><au>Birkeland, Søren F.</au><au>Bogh, Søren B.</au><au>Hallas, Jesper</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying high‐risk medications and error types in Danish patient safety database using disproportionality analysis</atitle><jtitle>Pharmacoepidemiology and drug safety</jtitle><addtitle>Pharmacoepidemiol Drug Saf</addtitle><date>2024-02</date><risdate>2024</risdate><volume>33</volume><issue>2</issue><spage>e5735</spage><epage>n/a</epage><pages>e5735-n/a</pages><issn>1053-8569</issn><issn>1099-1557</issn><eissn>1099-1557</eissn><abstract>Background
Medication error (ME) surveillance in Danish healthcare relies on the mandatory national incident reporting system, the Danish Patient Safety Database (DPSD). Individual case reviews and descriptive statistics with frequency counts are the most often used approaches when analyzing MEs in incident reporting systems, including the DPSD. However, incident reporting systems often generate a large number of reports and may suffer from underreporting; consequently, additional approaches are needed to overcome these challenges. Disproportionality analysis (DPA) is a statistical tool used for signal detection of adverse drug reactions in pharmacovigilance reports, but the evidence for using DPA on ME analysis in safety reporting systems is limited.
Objectives
We aimed to test the feasibility of DPA by analysing harmful MEs reported to DPSD 2014–2018.
Methods
We utilized proportional reporting ratios (PRR) to identify signals of diproportionality.
Results
We identified well‐known high‐risk medicines, including anticoagulants, opioids, insulins, antiepileptic, and antipsychotic drugs, and their association with several ME types and stages in a medication process.
Conclusion
DPA might be suggested as an additional tool for screening MEs and identifying priority areas for further investigation in safety reporting systems.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Inc</pub><pmid>38357842</pmid><doi>10.1002/pds.5735</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-2356-8366</orcidid><orcidid>https://orcid.org/0000-0002-8097-8708</orcidid></addata></record> |
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| source | MEDLINE; Access via Wiley Online Library |
| subjects | Adverse Drug Reaction Reporting Systems Antiepileptic agents Antipsychotics Denmark - epidemiology Drug-Related Side Effects and Adverse Reactions - epidemiology Humans incident reporting medication error Medication Errors medication safety Patient Safety Pharmacovigilance Safety signal detection Statistical analysis |
| title | Identifying high‐risk medications and error types in Danish patient safety database using disproportionality analysis |
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