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Change Point Analysis for Detecting Vaccine Safety Signals

It is important to detect signals of abrupt changes in adverse event reporting in order to notice public safety concerns and take prompt action, especially for vaccines under national immunization programs. In this study, we assessed the applicability of change point analysis (CPA) for signal detect...

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Autores principales: You, Seung-Hun, Jang, Eun Jin, Kim, Myo-Song, Lee, Min-Taek, Kang, Ye-Jin, Lee, Jae-Eun, Eom, Joo-Hyeon, Jung, Sun-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001699/
https://www.ncbi.nlm.nih.gov/pubmed/33801188
http://dx.doi.org/10.3390/vaccines9030206
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author You, Seung-Hun
Jang, Eun Jin
Kim, Myo-Song
Lee, Min-Taek
Kang, Ye-Jin
Lee, Jae-Eun
Eom, Joo-Hyeon
Jung, Sun-Young
author_facet You, Seung-Hun
Jang, Eun Jin
Kim, Myo-Song
Lee, Min-Taek
Kang, Ye-Jin
Lee, Jae-Eun
Eom, Joo-Hyeon
Jung, Sun-Young
author_sort You, Seung-Hun
collection PubMed
description It is important to detect signals of abrupt changes in adverse event reporting in order to notice public safety concerns and take prompt action, especially for vaccines under national immunization programs. In this study, we assessed the applicability of change point analysis (CPA) for signal detection in vaccine safety surveillance. The performances of three CPA methods, namely Bayesian change point analysis, Taylor’s change point analysis (Taylor-CPA), and environmental time series change point detection (EnvCpt), were assessed via simulated data with assumptions for the baseline number of events and degrees of change. The analysis was validated using the Korea Adverse Event Reporting System (KAERS) database. In the simulation study, the Taylor-CPA method exhibited better results for the detection of a change point (accuracy of 96% to 100%, sensitivity of 7% to 100%, specificity of 98% to 100%, positive predictive value of 25% to 85%, negative predictive value of 96% to 100%, and balanced accuracy of 53% to 100%) than the other two CPA methods. When the CPA methods were applied to reports of syncope or dizziness following human papillomavirus (HPV) immunization in the KAERS database, Taylor-CPA and EnvCpt detected a change point (Q2/2013), which was consistent with actual public safety concerns. CPA can be applied as an efficient tool for the early detection of vaccine safety signals.
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spelling pubmed-80016992021-03-28 Change Point Analysis for Detecting Vaccine Safety Signals You, Seung-Hun Jang, Eun Jin Kim, Myo-Song Lee, Min-Taek Kang, Ye-Jin Lee, Jae-Eun Eom, Joo-Hyeon Jung, Sun-Young Vaccines (Basel) Article It is important to detect signals of abrupt changes in adverse event reporting in order to notice public safety concerns and take prompt action, especially for vaccines under national immunization programs. In this study, we assessed the applicability of change point analysis (CPA) for signal detection in vaccine safety surveillance. The performances of three CPA methods, namely Bayesian change point analysis, Taylor’s change point analysis (Taylor-CPA), and environmental time series change point detection (EnvCpt), were assessed via simulated data with assumptions for the baseline number of events and degrees of change. The analysis was validated using the Korea Adverse Event Reporting System (KAERS) database. In the simulation study, the Taylor-CPA method exhibited better results for the detection of a change point (accuracy of 96% to 100%, sensitivity of 7% to 100%, specificity of 98% to 100%, positive predictive value of 25% to 85%, negative predictive value of 96% to 100%, and balanced accuracy of 53% to 100%) than the other two CPA methods. When the CPA methods were applied to reports of syncope or dizziness following human papillomavirus (HPV) immunization in the KAERS database, Taylor-CPA and EnvCpt detected a change point (Q2/2013), which was consistent with actual public safety concerns. CPA can be applied as an efficient tool for the early detection of vaccine safety signals. MDPI 2021-03-02 /pmc/articles/PMC8001699/ /pubmed/33801188 http://dx.doi.org/10.3390/vaccines9030206 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
You, Seung-Hun
Jang, Eun Jin
Kim, Myo-Song
Lee, Min-Taek
Kang, Ye-Jin
Lee, Jae-Eun
Eom, Joo-Hyeon
Jung, Sun-Young
Change Point Analysis for Detecting Vaccine Safety Signals
title Change Point Analysis for Detecting Vaccine Safety Signals
title_full Change Point Analysis for Detecting Vaccine Safety Signals
title_fullStr Change Point Analysis for Detecting Vaccine Safety Signals
title_full_unstemmed Change Point Analysis for Detecting Vaccine Safety Signals
title_short Change Point Analysis for Detecting Vaccine Safety Signals
title_sort change point analysis for detecting vaccine safety signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001699/
https://www.ncbi.nlm.nih.gov/pubmed/33801188
http://dx.doi.org/10.3390/vaccines9030206
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