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Vaccine. 2017 Oct 19. pii: S0264-410X(17)31239-2. doi: 10.1016/j.vaccine.2017.09.022. [Epub ahead of print]
Implementing near real-time vaccine safety surveillance using the Clinical Practice Research Datalink (CPRD).
Leite A1, Thomas SL2, Andrews NJ3.
Author information
Abstract
INTRODUCTION:
Near real-time vaccine safety surveillance (NRTVSS) using electronic health records is increasingly used to rapidly detect vaccine safety signals. NRTVSS has not been fully implemented in the UK. We assessed the feasibility of implementing this surveillance using the UK Clinical Practice Research Datalink (CPRD).
METHODS:
We selected seasonal influenza vaccine/Guillain-Barr? Syndrome (GBS) as an example of a rare outcome and measles-mumps-rubella (MMR) vaccine/febrile seizures as a positive control. For influenza/GBS we implemented a system for the 2013/2014 and 2014/2015 influenza seasons; for MMR/seizures the surveillance period was July 2014-June 2015. We used the continuous Poisson-based maximized sequential probability ratio test (PMaxSPRT), comparing observed-to-expected events, for both pairs. We calculated an age-sex-adjusted rate using 5years of historic data and used this rate to calculate the expected number of events in pre-specified post-vaccination risk-window (GBS: 0-42days, seizures: 6-21days). For MMR/seizures we also implemented the system using the Binominal-based maximized sequential probability ratio test (BMaxSPRT). For this, we compared seizures in the risk-window (6-21days) to a control window (0-5 and 22-32days). Delays in recording outcomes influence the data available, so we adjusted the expected number of events using a historical distribution of delays in recording GBS/febrile seizures. Analyses were run using data up to each CPRD monthly release. We also performed power calculations for detecting increases in relative risk (RR) from 1.5 to 10.
RESULTS:
For influenza/GBS we implemented a system in both seasons with no signal. Power to detect a signal was >80% for RR≥4. For MMR/seizures we were able to identify a signal with PMaxSPRT but not with BMaxSPRT. Power≥80% for RR≥2.5 for both tests.
CONCLUSION:
CPRD is a potential data source to implement NRTVSS to exclude large increases in the risk of rare outcomes after seasonal influenza and lower increases in risk for more frequent outcomes.
Copyright ? 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
KEYWORDS:
Electronic health records; Safety; Surveillance; Timeliness; Vaccines
PMID: 29056424 DOI: 10.1016/j.vaccine.2017.09.022
Implementing near real-time vaccine safety surveillance using the Clinical Practice Research Datalink (CPRD).
Leite A1, Thomas SL2, Andrews NJ3.
Author information
Abstract
INTRODUCTION:
Near real-time vaccine safety surveillance (NRTVSS) using electronic health records is increasingly used to rapidly detect vaccine safety signals. NRTVSS has not been fully implemented in the UK. We assessed the feasibility of implementing this surveillance using the UK Clinical Practice Research Datalink (CPRD).
METHODS:
We selected seasonal influenza vaccine/Guillain-Barr? Syndrome (GBS) as an example of a rare outcome and measles-mumps-rubella (MMR) vaccine/febrile seizures as a positive control. For influenza/GBS we implemented a system for the 2013/2014 and 2014/2015 influenza seasons; for MMR/seizures the surveillance period was July 2014-June 2015. We used the continuous Poisson-based maximized sequential probability ratio test (PMaxSPRT), comparing observed-to-expected events, for both pairs. We calculated an age-sex-adjusted rate using 5years of historic data and used this rate to calculate the expected number of events in pre-specified post-vaccination risk-window (GBS: 0-42days, seizures: 6-21days). For MMR/seizures we also implemented the system using the Binominal-based maximized sequential probability ratio test (BMaxSPRT). For this, we compared seizures in the risk-window (6-21days) to a control window (0-5 and 22-32days). Delays in recording outcomes influence the data available, so we adjusted the expected number of events using a historical distribution of delays in recording GBS/febrile seizures. Analyses were run using data up to each CPRD monthly release. We also performed power calculations for detecting increases in relative risk (RR) from 1.5 to 10.
RESULTS:
For influenza/GBS we implemented a system in both seasons with no signal. Power to detect a signal was >80% for RR≥4. For MMR/seizures we were able to identify a signal with PMaxSPRT but not with BMaxSPRT. Power≥80% for RR≥2.5 for both tests.
CONCLUSION:
CPRD is a potential data source to implement NRTVSS to exclude large increases in the risk of rare outcomes after seasonal influenza and lower increases in risk for more frequent outcomes.
Copyright ? 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
KEYWORDS:
Electronic health records; Safety; Surveillance; Timeliness; Vaccines
PMID: 29056424 DOI: 10.1016/j.vaccine.2017.09.022