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Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older

OBJECTIVE: Many jurisdictions lack comprehensive population-based antibiotic use data and rely on third party companies, most commonly IQVIA. Our objective was to validate the accuracy of the IQVIA Xponent antibiotic database in identifying high prescribing physicians compared to the reference stand...

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Autores principales: Schwartz, Kevin L., Chen, Cynthia, Langford, Bradley J., Brown, Kevin A., Daneman, Nick, Johnstone, Jennie, Wu, Julie HC, Leung, Valerie, Garber, Gary
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6762161/
https://www.ncbi.nlm.nih.gov/pubmed/31557249
http://dx.doi.org/10.1371/journal.pone.0223097
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author Schwartz, Kevin L.
Chen, Cynthia
Langford, Bradley J.
Brown, Kevin A.
Daneman, Nick
Johnstone, Jennie
Wu, Julie HC
Leung, Valerie
Garber, Gary
author_facet Schwartz, Kevin L.
Chen, Cynthia
Langford, Bradley J.
Brown, Kevin A.
Daneman, Nick
Johnstone, Jennie
Wu, Julie HC
Leung, Valerie
Garber, Gary
author_sort Schwartz, Kevin L.
collection PubMed
description OBJECTIVE: Many jurisdictions lack comprehensive population-based antibiotic use data and rely on third party companies, most commonly IQVIA. Our objective was to validate the accuracy of the IQVIA Xponent antibiotic database in identifying high prescribing physicians compared to the reference standard of a highly accurate population-wide database of outpatient antimicrobial dispensing for patients ≥65 years. METHODS: We conducted this study between 1 March 2016 and 28 February 2017 in Ontario, Canada. We evaluated the agreement and correlation between the databases using kappa statistics and Bland-Altman plots. We also assessed performance characteristics for Xponent to accurately identify high prescribing physicians with sensitivity, specificity, positive predictive value (PPV), and negative predictive value. RESULTS: We included 9,272 physicians. The Xponent database has a specificity of 92.4% (95%CI 92.0%-92.8%) and PPV of 77.2% (95%CI 76.0%-78.4%) for correctly identifying the top 25(th) percentile of physicians by antibiotic volume. In the sensitivity analysis, 94% of the top 25(th) percentile physicians in Xponent were within the top 40(th) percentile in the reference database. The mean number of antibiotic prescriptions per physician were similar with a relative difference of -0.4% and 2.7% for female and male patients, respectively. The error was greater in rural areas with a relative difference of -8.4% and -5.6% per physician for female and male patients, respectively. The weighted kappa for quartile agreement was 0.68 (95%CI 0.67–0.69). CONCLUSION: We validated the IQVIA Xponent antibiotic database to identify high prescribing physicians for patients ≥65 years, and identified some important limitations. Collecting accurate population-based antibiotic use data will remain vital to global antimicrobial stewardship efforts.
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spelling pubmed-67621612019-10-13 Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older Schwartz, Kevin L. Chen, Cynthia Langford, Bradley J. Brown, Kevin A. Daneman, Nick Johnstone, Jennie Wu, Julie HC Leung, Valerie Garber, Gary PLoS One Research Article OBJECTIVE: Many jurisdictions lack comprehensive population-based antibiotic use data and rely on third party companies, most commonly IQVIA. Our objective was to validate the accuracy of the IQVIA Xponent antibiotic database in identifying high prescribing physicians compared to the reference standard of a highly accurate population-wide database of outpatient antimicrobial dispensing for patients ≥65 years. METHODS: We conducted this study between 1 March 2016 and 28 February 2017 in Ontario, Canada. We evaluated the agreement and correlation between the databases using kappa statistics and Bland-Altman plots. We also assessed performance characteristics for Xponent to accurately identify high prescribing physicians with sensitivity, specificity, positive predictive value (PPV), and negative predictive value. RESULTS: We included 9,272 physicians. The Xponent database has a specificity of 92.4% (95%CI 92.0%-92.8%) and PPV of 77.2% (95%CI 76.0%-78.4%) for correctly identifying the top 25(th) percentile of physicians by antibiotic volume. In the sensitivity analysis, 94% of the top 25(th) percentile physicians in Xponent were within the top 40(th) percentile in the reference database. The mean number of antibiotic prescriptions per physician were similar with a relative difference of -0.4% and 2.7% for female and male patients, respectively. The error was greater in rural areas with a relative difference of -8.4% and -5.6% per physician for female and male patients, respectively. The weighted kappa for quartile agreement was 0.68 (95%CI 0.67–0.69). CONCLUSION: We validated the IQVIA Xponent antibiotic database to identify high prescribing physicians for patients ≥65 years, and identified some important limitations. Collecting accurate population-based antibiotic use data will remain vital to global antimicrobial stewardship efforts. Public Library of Science 2019-09-26 /pmc/articles/PMC6762161/ /pubmed/31557249 http://dx.doi.org/10.1371/journal.pone.0223097 Text en © 2019 Schwartz et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Schwartz, Kevin L.
Chen, Cynthia
Langford, Bradley J.
Brown, Kevin A.
Daneman, Nick
Johnstone, Jennie
Wu, Julie HC
Leung, Valerie
Garber, Gary
Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older
title Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older
title_full Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older
title_fullStr Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older
title_full_unstemmed Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older
title_short Validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older
title_sort validating a popular outpatient antibiotic database to reliably identify high prescribing physicians for patients 65 years of age and older
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6762161/
https://www.ncbi.nlm.nih.gov/pubmed/31557249
http://dx.doi.org/10.1371/journal.pone.0223097
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