Real-time safety surveillance of seasonal influenza vaccines in children, Australia, 2015

Pillsbury, Alexis; Cashman, Patrick; Leeb, Alan; Regan, Annette K.; Westphal, Darren W; Snelling, Tom; Blyth, Christopher C; Crawford, Nigel; Wood, Nicholas; Macartney, Kristine

doi:10.2807/1560-7917.es.2015.20.43.30050

Cited by 33 publications

(52 citation statements)

References 13 publications

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“…The RCA method has been also used based on an alternative data sources other than EHRs. For example, in the UK, H1N1 vaccine was monitored using passive surveillance data,69 and in Australia seasonal influenza vaccines have been monitored since 2015, based on data collected directly from consumers using SMS-messaging and email (AusVaxSafety) 70…”

Section: Discussionmentioning

confidence: 99%

Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review

et al. 2019

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BackgroundConcerns regarding adverse events following vaccination (AEFIs) are a key challenge for public confidence in vaccination. Robust postlicensure vaccine safety monitoring remains critical to detect adverse events, including those not identified in prelicensure studies, and to ensure public safety and public confidence in vaccination. We summarise the literature examined AEFI signal detection using electronic healthcare data, regarding data sources, methodological approach and statistical analysis techniques used.MethodsWe performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Five databases (PubMed/Medline, EMBASE, CINAHL, the Cochrane Library and Web of Science) were searched for studies on AEFIs monitoring published up to 25 September 2017. Studies were appraised for methodological quality, and results were synthesised narratively.ResultWe included 47 articles describing AEFI signal detection using electronic healthcare data. All studies involved linked diagnostic healthcare data, from the emergency department, inpatient and outpatient setting and immunisation records. Statistical analysis methodologies used included non-sequential analysis in 33 studies, group sequential analysis in two studies and 12 studies used continuous sequential analysis. Partially elapsed risk window and data accrual lags were the most cited barriers to monitor AEFIs in near real-time.ConclusionRoutinely collected electronic healthcare data are increasingly used to detect AEFI signals in near real-time. Further research is required to check the utility of non-coded complaints and encounters, such as telephone medical helpline calls, to enhance AEFI signal detection.Trial registration numberCRD42017072741

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Section: Discussionmentioning

confidence: 99%

Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review

et al. 2019

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“…Specific target-group restrictions may also hinder detection of unanticipated signal events. For example, Australian short message service (SMS)-stimulated reactogenicity reporting surveillance systems [24] primarily target the paediatric population and so could not inform on this predominantly adult event. An increasing number of statistical signal detection methodologies have been described, but most studies demonstrate the methodological utility retrospectively and few describe the evolution of a signal detection and investigation in real time as we describe [25].…”

Section: Discussionmentioning

confidence: 99%

Allergic adverse events following 2015 seasonal influenza vaccine, Victoria, Australia

et al. 2017

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Australia was alerted to a possible increase in allergy-related adverse events following immunisation (AEFI) with 2015 seasonal trivalent influenza vaccines (TIV) by the Victorian state vaccine safety service, SAEFVIC. We describe SAEFVIC’s initial investigation and upon conclusion of the 2015 influenza vaccination programme, to define the signal event and implications for vaccine programmes. Allergy-related AEFI were defined as anaphylaxis, angioedema, urticaria or generalised allergic reaction. Investigations compared 2015 TIV AEFI reports to previous years as proportions and reporting risk (RR) per 100,000, stratified by influenza vaccine brand. The initial investigation showed an increased proportion of allergy-related AEFI compared with 2014 (25% vs 12%), predominantly in adults, with insufficient clinical severity to alter the programme risk-benefit. While overall TIV AEFI RR in 2015 was similar to previous years (RR: 1.07, 95% confidence interval (CI): 0.88–1.29), we identified a near-doubling RR for allergy-related AEFI in 2015 (RR: 1.78, 95% CI: 1.14– 2.80) from 2011 to 2014 with no difference by vaccine brand or severity increase identified. This increase in generalised allergy-related AEFI, across all used vaccine brands, supports evidence of variable reactogenicity arising from influenza vaccine strain variations. This investigation underlines the importance of effective seasonal influenza vaccine pharmacovigilance.

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“…FIR CUSUM control charts track the log-likelihood ratios of an observed cumulative event rate at a maximum acceptable level versus an expected level 14. Expected and maximum acceptable rates are set at the start of each influenza vaccination season based on a synthesis of clinical trial data and surveillance data from previous influenza seasons 12 13 15. For children aged 6 months to <5 years the expected MA rate in 2018 was set at 1%, while the expected fever rate was set at 3%.…”

Section: Methodsmentioning

confidence: 99%

“…This occurs on a weekly basis for influenza vaccines, thus facilitating rapid appropriate action in the event of any safety signals 11. AusVaxSafety has verified the safety of seasonal influenza vaccines administered to Australian children 6 months to <5 years of age since 2014, and to individuals of all ages since 2017, within weeks of the roll-out of vaccination programmes 9–13…”

Section: Introductionmentioning

confidence: 99%

Timeliness of signal detection for adverse events following influenza vaccination in young children: a simulation case study

et al. 2020

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ObjectivesTo determine how soon after commencement of the seasonal influenza vaccination programme, the AusVaxSafety active vaccine safety surveillance system, currently in use across Australia, would have detected a safety signal had it been operating in 2010 when there was an unprecedented number of febrile seizures in young children associated with one specific influenza vaccine brand, Fluvax (CSL Biotherapies).DesignSimulation study.SettingWestern Australian vaccine influenza coverage and adverse event surveillance data.Outcome measuresSimulated solicited responses from caregivers who would have received an SMS survey about adverse events experienced following seasonal influenza vaccination of their children aged 6 months to <5 years.ParticipantsNone.ResultsWe estimated a >90% probability of a safety signal being detected by AusVaxSafety based on solicited reports for either fever or medical attendance at or before the week ending 28 March 2010, 3 weeks after the start of vaccine distribution. Suspension of the national paediatric influenza vaccination programme as a result of the passive adverse events surveillance operating at the time did not occur until 23 April 2010.ConclusionsActive vaccine safety surveillance leading to rapid detection of a safety signal would likely have resulted in earlier suspension of Fluvax from the vaccination programme, prevention of many febrile convulsions and maintenance of public confidence in influenza vaccination for young children.

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Real-time safety surveillance of seasonal influenza vaccines in children, Australia, 2015

Cited by 33 publications

References 13 publications

Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review

Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review

Allergic adverse events following 2015 seasonal influenza vaccine, Victoria, Australia

Timeliness of signal detection for adverse events following influenza vaccination in young children: a simulation case study

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