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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MDPI AG
2021-03-01
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Series: | Vaccines |
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Online Access: | https://www.mdpi.com/2076-393X/9/3/206 |
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author | Seung-Hun You Eun Jin Jang Myo-Song Kim Min-Taek Lee Ye-Jin Kang Jae-Eun Lee Joo-Hyeon Eom Sun-Young Jung |
author_facet | Seung-Hun You Eun Jin Jang Myo-Song Kim Min-Taek Lee Ye-Jin Kang Jae-Eun Lee Joo-Hyeon Eom Sun-Young Jung |
author_sort | Seung-Hun You |
collection | DOAJ |
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. |
first_indexed | 2024-03-09T06:01:01Z |
format | Article |
id | doaj.art-2fb8a1e36af74d288373f9a9c529c8c0 |
institution | Directory Open Access Journal |
issn | 2076-393X |
language | English |
last_indexed | 2024-03-09T06:01:01Z |
publishDate | 2021-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Vaccines |
spelling | doaj.art-2fb8a1e36af74d288373f9a9c529c8c02023-12-03T12:08:35ZengMDPI AGVaccines2076-393X2021-03-019320610.3390/vaccines9030206Change Point Analysis for Detecting Vaccine Safety SignalsSeung-Hun You0Eun Jin Jang1Myo-Song Kim2Min-Taek Lee3Ye-Jin Kang4Jae-Eun Lee5Joo-Hyeon Eom6Sun-Young Jung7College of Pharmacy, Chung-Ang University, Seoul 06974, KoreaDepartment of Information Statistics, Andong National University, Andong 36729, KoreaCollege of Pharmacy, Chung-Ang University, Seoul 06974, KoreaCollege of Pharmacy, Chung-Ang University, Seoul 06974, KoreaCollege of Pharmacy, Chung-Ang University, Seoul 06974, KoreaCollege of Pharmacy, Chung-Ang University, Seoul 06974, KoreaCollege of Pharmacy, Chung-Ang University, Seoul 06974, KoreaCollege of Pharmacy, Chung-Ang University, Seoul 06974, KoreaIt 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.https://www.mdpi.com/2076-393X/9/3/206change point analysisvaccinesdata miningpharmacovigilanceadverse eventssignal detection |
spellingShingle | Seung-Hun You Eun Jin Jang Myo-Song Kim Min-Taek Lee Ye-Jin Kang Jae-Eun Lee Joo-Hyeon Eom Sun-Young Jung Change Point Analysis for Detecting Vaccine Safety Signals Vaccines change point analysis vaccines data mining pharmacovigilance adverse events signal detection |
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 | change point analysis vaccines data mining pharmacovigilance adverse events signal detection |
url | https://www.mdpi.com/2076-393X/9/3/206 |
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