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Bad apples or spoiled barrels? Multilevel modelling analysis of variation in high-risk prescribing in Scotland between general practitioners and between the practices they work in

ObjectivesPrimary care high-risk prescribing causes significant harm, but it is unclear if it is largely driven by individuals (a ‘bad apple’ problem) or by practices having higher or lower risk prescribing cultures (a ‘spoiled barrel’ problem). The study aimed to examine the extent of variation in...

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Published in:BMJ open 2015-11, Vol.5 (11), p.e008270
Main Authors: Guthrie, Bruce, Donnan, Peter T, Murphy, Douglas J, Makubate, Boikanyo, Dreischulte, Tobias
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Murphy, Douglas J
Makubate, Boikanyo
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description ObjectivesPrimary care high-risk prescribing causes significant harm, but it is unclear if it is largely driven by individuals (a ‘bad apple’ problem) or by practices having higher or lower risk prescribing cultures (a ‘spoiled barrel’ problem). The study aimed to examine the extent of variation in high-risk prescribing between individual prescribers and between the practices they work in.Design, setting and participantsMultilevel logistic regression modelling of routine cross-sectional data from 38 Scottish general practices for 181 010 encounters between 398 general practitioners (GPs) and 26 539 patients particularly vulnerable to adverse drug events (ADEs) of non-steroidal anti-inflammatory drugs (NSAIDs) due to age, comorbidity or co-prescribing.Outcome measureInitiation of a new NSAID prescription in an encounter between GPs and eligible patients.ResultsA new high-risk NSAID was initiated in 1953 encounters (1.1% of encounters, 7.4% of patients). Older patients, those with more vulnerabilities to NSAID ADEs and those with polypharmacy were less likely to have a high-risk NSAID initiated, consistent with GPs generally recognising the risk of NSAIDs in eligible patients. Male GPs were more likely to initiate a high-risk NSAID than female GPs (OR 1.73, 95% CI 1.39 to 2.16). After accounting for patient characteristics, 4.2% (95% CI 2.1 to 8.3) of the variation in high-risk NSAID prescribing was attributable to variation between practices, and 14.2% (95% CI 11.4 to 17.3) to variation between GPs. Three practices had statistically higher than average high-risk prescribing, but only 15.7% of GPs with higher than average high-risk prescribing and 18.5% of patients receiving such a prescription were in these practices.ConclusionsThere was much more variation in high-risk prescribing between GPs than between practices, and only targeting practices with higher than average rates will miss most high-risk NSAID prescribing. Primary care prescribing safety improvement should ideally target all practices, but encourage practices to consider and act on variation between prescribers in the practice.
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Multilevel modelling analysis of variation in high-risk prescribing in Scotland between general practitioners and between the practices they work in</title><source>BMJ Open Access Journals</source><source>Open Access: PubMed Central</source><source>BMJ journals single titles</source><source>Publicly Available Content Database</source><creator>Guthrie, Bruce ; Donnan, Peter T ; Murphy, Douglas J ; Makubate, Boikanyo ; Dreischulte, Tobias</creator><creatorcontrib>Guthrie, Bruce ; Donnan, Peter T ; Murphy, Douglas J ; Makubate, Boikanyo ; Dreischulte, Tobias</creatorcontrib><description>ObjectivesPrimary care high-risk prescribing causes significant harm, but it is unclear if it is largely driven by individuals (a ‘bad apple’ problem) or by practices having higher or lower risk prescribing cultures (a ‘spoiled barrel’ problem). The study aimed to examine the extent of variation in high-risk prescribing between individual prescribers and between the practices they work in.Design, setting and participantsMultilevel logistic regression modelling of routine cross-sectional data from 38 Scottish general practices for 181 010 encounters between 398 general practitioners (GPs) and 26 539 patients particularly vulnerable to adverse drug events (ADEs) of non-steroidal anti-inflammatory drugs (NSAIDs) due to age, comorbidity or co-prescribing.Outcome measureInitiation of a new NSAID prescription in an encounter between GPs and eligible patients.ResultsA new high-risk NSAID was initiated in 1953 encounters (1.1% of encounters, 7.4% of patients). Older patients, those with more vulnerabilities to NSAID ADEs and those with polypharmacy were less likely to have a high-risk NSAID initiated, consistent with GPs generally recognising the risk of NSAIDs in eligible patients. Male GPs were more likely to initiate a high-risk NSAID than female GPs (OR 1.73, 95% CI 1.39 to 2.16). After accounting for patient characteristics, 4.2% (95% CI 2.1 to 8.3) of the variation in high-risk NSAID prescribing was attributable to variation between practices, and 14.2% (95% CI 11.4 to 17.3) to variation between GPs. Three practices had statistically higher than average high-risk prescribing, but only 15.7% of GPs with higher than average high-risk prescribing and 18.5% of patients receiving such a prescription were in these practices.ConclusionsThere was much more variation in high-risk prescribing between GPs than between practices, and only targeting practices with higher than average rates will miss most high-risk NSAID prescribing. Primary care prescribing safety improvement should ideally target all practices, but encourage practices to consider and act on variation between prescribers in the practice.</description><identifier>ISSN: 2044-6055</identifier><identifier>EISSN: 2044-6055</identifier><identifier>DOI: 10.1136/bmjopen-2015-008270</identifier><identifier>PMID: 26546137</identifier><language>eng</language><publisher>England: BMJ Publishing Group LTD</publisher><subject>Aged ; Aged, 80 and over ; Ambulatory care ; Anti-Inflammatory Agents, Non-Steroidal - adverse effects ; Computerized physician order entry ; Cross-Sectional Studies ; Datasets ; Decision making ; Drug-Related Side Effects and Adverse Reactions - epidemiology ; Female ; General Practice ; General Practitioners ; Hospitals ; Humans ; Inappropriate Prescribing - statistics &amp; numerical data ; Informatics ; Logistic Models ; Male ; Medical records ; Morbidity ; Multilevel Analysis ; Outcome Assessment, Health Care ; Patient admissions ; Patient safety ; Pharmacology and Therapeutics ; Polypharmacy ; Practice Patterns, Physicians' - standards ; Prescription drugs ; Primary care ; Primary Health Care ; Quality ; Scotland ; Studies</subject><ispartof>BMJ open, 2015-11, Vol.5 (11), p.e008270</ispartof><rights>Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing</rights><rights>Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ 2015 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b472t-5aa1547b838ac7b31fb5c43c77b22aea94b24b726662b087728ccf9fde1028db3</citedby><cites>FETCH-LOGICAL-b472t-5aa1547b838ac7b31fb5c43c77b22aea94b24b726662b087728ccf9fde1028db3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1861652533/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1861652533?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>112,113,230,314,727,780,784,885,3194,25753,27549,27550,27924,27925,37012,44590,53791,53793,75126,77594,77595,77601,77632</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26546137$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Guthrie, Bruce</creatorcontrib><creatorcontrib>Donnan, Peter T</creatorcontrib><creatorcontrib>Murphy, Douglas J</creatorcontrib><creatorcontrib>Makubate, Boikanyo</creatorcontrib><creatorcontrib>Dreischulte, Tobias</creatorcontrib><title>Bad apples or spoiled barrels? Multilevel modelling analysis of variation in high-risk prescribing in Scotland between general practitioners and between the practices they work in</title><title>BMJ open</title><addtitle>BMJ Open</addtitle><description>ObjectivesPrimary care high-risk prescribing causes significant harm, but it is unclear if it is largely driven by individuals (a ‘bad apple’ problem) or by practices having higher or lower risk prescribing cultures (a ‘spoiled barrel’ problem). The study aimed to examine the extent of variation in high-risk prescribing between individual prescribers and between the practices they work in.Design, setting and participantsMultilevel logistic regression modelling of routine cross-sectional data from 38 Scottish general practices for 181 010 encounters between 398 general practitioners (GPs) and 26 539 patients particularly vulnerable to adverse drug events (ADEs) of non-steroidal anti-inflammatory drugs (NSAIDs) due to age, comorbidity or co-prescribing.Outcome measureInitiation of a new NSAID prescription in an encounter between GPs and eligible patients.ResultsA new high-risk NSAID was initiated in 1953 encounters (1.1% of encounters, 7.4% of patients). Older patients, those with more vulnerabilities to NSAID ADEs and those with polypharmacy were less likely to have a high-risk NSAID initiated, consistent with GPs generally recognising the risk of NSAIDs in eligible patients. Male GPs were more likely to initiate a high-risk NSAID than female GPs (OR 1.73, 95% CI 1.39 to 2.16). After accounting for patient characteristics, 4.2% (95% CI 2.1 to 8.3) of the variation in high-risk NSAID prescribing was attributable to variation between practices, and 14.2% (95% CI 11.4 to 17.3) to variation between GPs. Three practices had statistically higher than average high-risk prescribing, but only 15.7% of GPs with higher than average high-risk prescribing and 18.5% of patients receiving such a prescription were in these practices.ConclusionsThere was much more variation in high-risk prescribing between GPs than between practices, and only targeting practices with higher than average rates will miss most high-risk NSAID prescribing. Primary care prescribing safety improvement should ideally target all practices, but encourage practices to consider and act on variation between prescribers in the practice.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Ambulatory care</subject><subject>Anti-Inflammatory Agents, Non-Steroidal - adverse effects</subject><subject>Computerized physician order entry</subject><subject>Cross-Sectional Studies</subject><subject>Datasets</subject><subject>Decision making</subject><subject>Drug-Related Side Effects and Adverse Reactions - epidemiology</subject><subject>Female</subject><subject>General Practice</subject><subject>General Practitioners</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Inappropriate Prescribing - statistics &amp; numerical data</subject><subject>Informatics</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Medical records</subject><subject>Morbidity</subject><subject>Multilevel Analysis</subject><subject>Outcome Assessment, Health Care</subject><subject>Patient admissions</subject><subject>Patient safety</subject><subject>Pharmacology and Therapeutics</subject><subject>Polypharmacy</subject><subject>Practice Patterns, Physicians' - standards</subject><subject>Prescription drugs</subject><subject>Primary care</subject><subject>Primary Health Care</subject><subject>Quality</subject><subject>Scotland</subject><subject>Studies</subject><issn>2044-6055</issn><issn>2044-6055</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>9YT</sourceid><sourceid>PIMPY</sourceid><recordid>eNqNkc1qGzEUhYfSkoQ0TxAogq4n0b_Gm5Y2pGkhoYu2ayFp7thyZGkqjR38XH3BytgN7i5CoJ_7naOLTtNcEnxFCJPXdrVMI8SWYiJajDuq8KvmjGLOW4mFeH20P20uSlniOriYCUFPmlMqBZeEqbPmz2fTIzOOAQpKGZUx-QA9siZnCOUjeliHqd5sIKBV6iEEH-fIRBO2xVfFgDYmezP5FJGPaOHnizb78ojGDMVlb3d4LfxwaQomVmOYngAimkOEbELljJv8Tg-5oGNiWsCh6mpv9bRFTyk_Vre3zZvBhAIXh_W8-fXl9ufN1_b--923m0_3reWKTq0whgiubMc645RlZLDCceaUspQaMDNuKbeKSimpxZ1StHNumA09EEy73rLz5sPed1zbFfQO4lRb1mP2K5O3Ohmv_69Ev9DztNFcMllnNXh_MMjp9xrKpJdpnevnFU06SaSggrFKsT3lciolw_D8AsF6F7Y-hK13Yet92FX17ri5Z82_aCtwtQeq-kWOfwFm8rt9</recordid><startdate>20151106</startdate><enddate>20151106</enddate><creator>Guthrie, Bruce</creator><creator>Donnan, Peter T</creator><creator>Murphy, Douglas J</creator><creator>Makubate, Boikanyo</creator><creator>Dreischulte, Tobias</creator><general>BMJ Publishing Group LTD</general><general>BMJ Publishing Group</general><scope>9YT</scope><scope>ACMMV</scope><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>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BTHHO</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>5PM</scope></search><sort><creationdate>20151106</creationdate><title>Bad apples or spoiled barrels? Multilevel modelling analysis of variation in high-risk prescribing in Scotland between general practitioners and between the practices they work in</title><author>Guthrie, Bruce ; Donnan, Peter T ; Murphy, Douglas J ; Makubate, Boikanyo ; Dreischulte, Tobias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b472t-5aa1547b838ac7b31fb5c43c77b22aea94b24b726662b087728ccf9fde1028db3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Ambulatory care</topic><topic>Anti-Inflammatory Agents, Non-Steroidal - adverse effects</topic><topic>Computerized physician order entry</topic><topic>Cross-Sectional Studies</topic><topic>Datasets</topic><topic>Decision making</topic><topic>Drug-Related Side Effects and Adverse Reactions - epidemiology</topic><topic>Female</topic><topic>General Practice</topic><topic>General Practitioners</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Inappropriate Prescribing - statistics &amp; numerical data</topic><topic>Informatics</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Medical records</topic><topic>Morbidity</topic><topic>Multilevel Analysis</topic><topic>Outcome Assessment, Health Care</topic><topic>Patient admissions</topic><topic>Patient safety</topic><topic>Pharmacology and Therapeutics</topic><topic>Polypharmacy</topic><topic>Practice Patterns, Physicians' - standards</topic><topic>Prescription drugs</topic><topic>Primary care</topic><topic>Primary Health Care</topic><topic>Quality</topic><topic>Scotland</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guthrie, Bruce</creatorcontrib><creatorcontrib>Donnan, Peter T</creatorcontrib><creatorcontrib>Murphy, Douglas J</creatorcontrib><creatorcontrib>Makubate, Boikanyo</creatorcontrib><creatorcontrib>Dreischulte, Tobias</creatorcontrib><collection>BMJ Open Access Journals</collection><collection>BMJ Journals:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>BMJ Journals</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Consumer Health Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMJ open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guthrie, Bruce</au><au>Donnan, Peter T</au><au>Murphy, Douglas J</au><au>Makubate, Boikanyo</au><au>Dreischulte, Tobias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bad apples or spoiled barrels? Multilevel modelling analysis of variation in high-risk prescribing in Scotland between general practitioners and between the practices they work in</atitle><jtitle>BMJ open</jtitle><addtitle>BMJ Open</addtitle><date>2015-11-06</date><risdate>2015</risdate><volume>5</volume><issue>11</issue><spage>e008270</spage><pages>e008270-</pages><issn>2044-6055</issn><eissn>2044-6055</eissn><abstract>ObjectivesPrimary care high-risk prescribing causes significant harm, but it is unclear if it is largely driven by individuals (a ‘bad apple’ problem) or by practices having higher or lower risk prescribing cultures (a ‘spoiled barrel’ problem). The study aimed to examine the extent of variation in high-risk prescribing between individual prescribers and between the practices they work in.Design, setting and participantsMultilevel logistic regression modelling of routine cross-sectional data from 38 Scottish general practices for 181 010 encounters between 398 general practitioners (GPs) and 26 539 patients particularly vulnerable to adverse drug events (ADEs) of non-steroidal anti-inflammatory drugs (NSAIDs) due to age, comorbidity or co-prescribing.Outcome measureInitiation of a new NSAID prescription in an encounter between GPs and eligible patients.ResultsA new high-risk NSAID was initiated in 1953 encounters (1.1% of encounters, 7.4% of patients). Older patients, those with more vulnerabilities to NSAID ADEs and those with polypharmacy were less likely to have a high-risk NSAID initiated, consistent with GPs generally recognising the risk of NSAIDs in eligible patients. Male GPs were more likely to initiate a high-risk NSAID than female GPs (OR 1.73, 95% CI 1.39 to 2.16). After accounting for patient characteristics, 4.2% (95% CI 2.1 to 8.3) of the variation in high-risk NSAID prescribing was attributable to variation between practices, and 14.2% (95% CI 11.4 to 17.3) to variation between GPs. Three practices had statistically higher than average high-risk prescribing, but only 15.7% of GPs with higher than average high-risk prescribing and 18.5% of patients receiving such a prescription were in these practices.ConclusionsThere was much more variation in high-risk prescribing between GPs than between practices, and only targeting practices with higher than average rates will miss most high-risk NSAID prescribing. Primary care prescribing safety improvement should ideally target all practices, but encourage practices to consider and act on variation between prescribers in the practice.</abstract><cop>England</cop><pub>BMJ Publishing Group LTD</pub><pmid>26546137</pmid><doi>10.1136/bmjopen-2015-008270</doi><oa>free_for_read</oa></addata></record>
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source BMJ Open Access Journals; Open Access: PubMed Central; BMJ journals single titles; Publicly Available Content Database
subjects Aged
Aged, 80 and over
Ambulatory care
Anti-Inflammatory Agents, Non-Steroidal - adverse effects
Computerized physician order entry
Cross-Sectional Studies
Datasets
Decision making
Drug-Related Side Effects and Adverse Reactions - epidemiology
Female
General Practice
General Practitioners
Hospitals
Humans
Inappropriate Prescribing - statistics & numerical data
Informatics
Logistic Models
Male
Medical records
Morbidity
Multilevel Analysis
Outcome Assessment, Health Care
Patient admissions
Patient safety
Pharmacology and Therapeutics
Polypharmacy
Practice Patterns, Physicians' - standards
Prescription drugs
Primary care
Primary Health Care
Quality
Scotland
Studies
title Bad apples or spoiled barrels? Multilevel modelling analysis of variation in high-risk prescribing in Scotland between general practitioners and between the practices they work in
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