Early signal detection of adverse events following influenza vaccination using proportional reporting ratio, Victoria, Australia

English version

Autoři: Hazel J. Clothier ^aff001; Jock Lawrie ^aff001; Melissa A. Russell ^aff003; Heath Kelly ^aff004; Jim P. Buttery ^aff001
Působiště autorů: Monash Centre for Health Research Implementation, Monash University, Clayton, Australia ^aff001; SAEFVIC, Murdoch Children’s Research Institute, Parkville, Victoria, Australia ^aff002; School of Population & Global Health, Melbourne University, Parkville, Victoria, Australia ^aff003; School of Population Health, Australian National University, Canberra, Australia ^aff004; Ritchie Centre, Hudson Institute, Monash Health, Clayton, Victoria, Australia ^aff005; Monash Immunisation, Monash Health, Clayton, Victoria, Australia ^aff006
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0224702

Souhrn

Introduction

Timely adverse event following immunisation (AEFI) signal event detection is essential to minimise further vaccinees receiving unsafe vaccines. We explored the proportional reporting ratio (PRR) ability to detect two known signal events with influenza vaccines with the aim of providing a model for prospective routine signal detection and improving vaccine safety surveillance in Australia.

Methods

Passive AEFI surveillance reports from 2008–2017 relating to influenza vaccines were accessed from the Australian SAEFVIC (Victoria) database. Proportional reporting ratios were calculated for two vaccine-event categories; fever and allergic AEFI. Signal detection sensitivity for two known signal events were determined using weekly data; cumulative data by individual year and; cumulative for all previous years. Signal event thresholds of PRR ≥2 and Chi-square ≥4 were applied.

Results

PRR provided sensitive signal detection when calculated cumulatively by individual year or by all previous years. Known signal events were detected 15 and 11 days earlier than traditional methods used at the time of the actual events.

Conclusion

Utilising a single jurisdiction’s data, PRR improved vaccine pharmacovigilance and showed the potential to detect important safety signals much earlier than previously. It has potential to maximise immunisation safety in Australia. This study progresses the necessary work to establish national cohesion for passive surveillance signal detection and strengthen routine Australian vaccine pharmacovigilance.

Klíčová slova:

Vaccination and immunization – Vaccines – Adverse events – Fevers – Influenza – Immune receptor signaling – Infectious disease surveillance – Statistical signal processing

Zdroje

1. Stokes B. Ministerial Review into the Public Health Response into the Adverse Events to the Seasonal Influenza Vaccine. In: Government of Western Australia Department of Health. Perth 2010. Available from: www.health.wa.gov.au/publications/documents/Stokes_Report.pdf [Accessed 20 July 2018].

2. Clothier HJ, Crawford NW, Russell M, Kelly H, Buttery JP. Evaluation of 'SAEFVIC', A Pharmacovigilance Surveillance Scheme for the Spontaneous Reporting of Adverse Events Following Immunisation in Victoria, Australia. Drug safety. 2017;40(6):483–95. doi: 10.1007/s40264-017-0520-7 28342074

3. Gold MS, Effler P, Kelly H, Richmond PC, Buttery JP. Febrile convulsions after 2010 seasonal trivalent influenza vaccine: implications for vaccine safety surveillance in Australia. The Medical journal of Australia. 2010;193(9):492–3. 21034379

4. Australian Governement Department of Health and Ageing Therapeutic Goods Administration. Overview of Vaccine Regulation and Safety Monitoring and Investigation into Adverse events Following 2010 Seasonal Influenza Vaccination in Young Children Canberra: 8 October 2010. [cited 20 July 2018] Available from: www.tga.gov.au/sites/default/files/alerts-medicine-seasonal-flu-101008.pdf

5. Moulton Emily. Parents of Saba Button who was victim of flu debacle received payout from WA Government. PerthNow 2014. [cited 12 November 2018] Available from: www.perthnow.com.au/news/wa/parents-of-saba-button-who-was-victim-of-flu-vaccine-debacle-receive-payout-from-wa-government-ng-bcbf1a145ec457cc170ef5692f3b691d

6. Clothier HJ, Crawford N, Russell MA, Buttery JP. Allergic adverse events following 2015 seasonal influenza vaccine, Victoria, Australia. Euro surveillance. 2017;22(20). doi: 10.2807/1560-7917.ES.2017.22.20.30535 28552101

7. Crawford NW, Clothier H, Hodgson K, Selvaraj G, Easton ML, Buttery JP. Active surveillance for adverse events following immunization. Expert review of vaccines. 2014;13(2):265–76. doi: 10.1586/14760584.2014.866895 24350637

8. Waldman EA, Luhm KR, Monteiro SA, Freitas FR. Surveillance of adverse effects following vaccination and safety of immunization programs. Revista de saude publica. 2011;45(1):173–84. doi: 10.1590/s0034-89102011000100020 21181055

9. Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clinical pharmacology and therapeutics. 2013;93(6):539–46. doi: 10.1038/clpt.2013.24 23571771

10. Collet JP, MacDonald N, Cashman N, Pless R. Monitoring signals for vaccine safety: the assessment of individual adverse event reports by an expert advisory committee. Advisory Committee on Causality Assessment. Bull World Health Organ. 2000;78(2):178–85. 10743282

11. Almenoff J, Tonning JM, Gould AL, Szarfman A, Hauben M, Ouellet-Hellstrom R, et al. Perspectives on the use of data mining in pharmaco-vigilance. Drug safety. 2005;28(11):981–1007. doi: 10.2165/00002018-200528110-00002 16231953

12. Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and drug safety. 2001;10(6):483–6. Epub 2002/02/07. doi: 10.1002/pds.677 11828828

13. Australian Government Department of Health and Ageing. Investigation into febrile reactions in young children following 2010 seasonal trivalent influenza vaccination. Canberra: Therapeutic Goods Administration, 2010. [cited 18 November 2018] Available from: www.tga.gov.au/safety/alerts-medicine-seasonal-flu-100702.htm#fullreport

14. Armstrong PK, Dowse GK, Effler PV, Carcione D, Blyth CC, Richmond PC, et al. Epidemiological study of severe febrile reactions in young children in Western Australia caused by a 2010 trivalent inactivated influenza vaccine. BMJ open. 2011;1(1):e000016. doi: 10.1136/bmjopen-2010-000016 22021725

15. Wisniewski AF, Bate A, Bousquet C, Brueckner A, Candore G, Juhlin K, et al. Good Signal Detection Practices: Evidence from IMI PROTECT. Drug safety. 2016;39(6):469–90. doi: 10.1007/s40264-016-0405-1 26951233

16. Szarfman A, Machado SG, O'Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database. Drug safety.2002;25(6):381–92. doi: 10.2165/00002018-200225060-00001 12071774

17. Hauben M. A brief primer on automated signal detection. The Annals of pharmacotherapy. 2003;37(7–8):1117–23. doi: 10.1345/aph.1C515 12841826

18. McClure DL, Xu S, Weintraub E, Glanz JM. An efficient statistical algorithm for a temporal scan statistic applied to vaccine safety analyses. Vaccine. 2012;30(27):3986–91. doi: 10.1016/j.vaccine.2012.04.040 22531555

19. Martin D, Menschik D, Bryant-Genevier M, Ball R. Data mining for prospective early detection of safety signals in the Vaccine Adverse Event Reporting System (VAERS): a case study of febrile seizures after a 2010–2011 seasonal influenza virus vaccine. Drug safety.2013;36(7):547–56. doi: 10.1007/s40264-013-0051-9 23657824

20. Musonda P, Hocine MN, Andrews NJ, Tubert-Bitter P, Farrington CP. Monitoring vaccine safety using case series cumulative sum charts. Vaccine. 2008;26(42):5358–67. doi: 10.1016/j.vaccine.2008.08.010 18723063

21. Nelson JC, Yu O, Dominguez-Islas CP, Cook AJ, Peterson D, Greene SK, et al. Adapting group sequential methods to observational postlicensure vaccine safety surveillance: results of a pentavalent combination DTaP-IPV-Hib vaccine safety study. Am J Epidemiol. 2013;177(2):131–41. doi: 10.1093/aje/kws317 23292957

22. Sakaeda T, Tamon A, Kadoyama K, Okuno Y. Data mining of the public version of the FDA Adverse Event Reporting System. International journal of medical sciences. 2013;10(7):796–803. doi: 10.7150/ijms.6048 23794943

23. Shibata A, Hauben M. Pharmacovigilance, Signal Detection and Signal Intelligence Overview. 14th International Conference on Information Fusion; July 5–8; Chicago, Illinois, USA2011. Available from: http://isif.org/fusion/proceedings/Fusion_2011/data/papers/200.pdf

24. Hauben M. Early postmarketing drug safety surveillance: data mining points to consider. The Annals of pharmacotherapy. 2004;38(10):1625–30. doi: 10.1345/aph.1E023 15304626

25. Banks D, Woo EJ, Burwen DR, Perucci P, Braun MM, Ball R. Comparing data mining methods on the VAERS database. Pharmacoepidemiology and drug safety. 2005;14(9):601–9. doi: 10.1002/pds.1107 15954077

26. Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clinical pharmacology and therapeutics. 2012;91(6):1010–21. doi: 10.1038/clpt.2012.50 22549283

27. Horvath J.C. Review of the management of adverse effects associated with Panvax and Fluvax. 2017. Australian Government Department of health and Ageing. Available from: www.health.gov.au/resources/publications/review-of-the-management-of-adverse-events-associated-with-panvax-and-fluvax-0

28. Australian Government Therpeutic Goods Administration. Response to the West Australian (Stokes) Review into the Handling of AEFIs following 2010 Seasonal Flu Vaccination. Office of Product Review; 2010. Available from: www.tga.gov.au/pdf/alerts-medicine-seasonal-flu-101018.pdf

29. Moore N, Thiessard F, Begaud B. The history of disproportionality measures (reporting odds ratio, proportional reporting rates) in spontaneous reporting of adverse drug reactions. Pharmacoepidemiology and drug safety. 2005;14(4):285–6. doi: 10.1002/pds.1058 15782397

30. Rothman KJ, Lanes S, Sacks ST. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiology and drug safety. 2004;13(8):519–23. doi: 10.1002/pds.1001 15317031

31. Council for International Organizations of Medical Sciences. Practical aspects of signal detection in pharmacovigilance: Report of CIOMS Working Group VIII. Geneva: CIOMS; 2010.

32. Hauben M, Madigan D, Gerrits CM, Walsh L, Van Puijenbroek EP. The role of data mining in pharmacovigilance. Expert opinion on drug safety. 2005;4(5):929–48. doi: 10.1517/14740338.4.5.929 16111454

33. Working Party of Experts. Report on the response to selected recommendations from the ‘Review of the management of adverse events associated with Panvax and Fluvax’ [Unpublished report]. In press 2013.

34. Australian Government Department of Health & Ageing. Review of the management of adverse events associated with Panvax and Fluvax (Horvath Review): Outcome of recommendations [cited 02 August 2018]. Available from: http://www.immunise.health.gov.au/internet/immunise/publishing.nsf/Content/AF9FE9D551D0879BCA257C0600029C4F/$File/Horvath-review-outcomes.pdf.

35. Clothier HJ, Crawford NW, Kempe A, Buttery JP. Surveillance of adverse events following immunisation: the model of SAEFVIC, Victoria. Communicable diseases intelligence quarterly report. 2011;35(4):294–8. 22624490

36. Australian Government Department of Health. 2015 seasonal influenza vaccines 2015. [cited 25 October 2018]. Available from: https://www.tga.gov.au/media-release/2015-seasonal-influenza-vaccines.

37. Australian Techinical Advisory Group on Immunisation (ATAGI). Australian Immunisation Handbook. Canberra: Australian Government Department of Health; 2018. Available from: https://immunisationhandbook.health.gov.au/

38. European Medicines Agency. Screening for adverse reactions in EudraVigilance. London, United Kingdom: European Medicines Agency, 2016. [cited 12 October 2017] Available from: http://www.ema.europa.eu/docs/en_GB/document_library/Other/2016/12/WC500218606.pdf

39. Slattery J, Alvarez Y, Hidalgo A. Choosing thresholds for statistical signal detection with the proportional reporting ratio. Drug safety. 2013;36(8):687–92. doi: 10.1007/s40264-013-0075-1 23754759

40. McNeil MM, DeStefano F. Vaccine-associated hypersensitivity. The Journal of allergy and clinical immunology. 2018;141(2):463–72. doi: 10.1016/j.jaci.2017.12.971 29413255

41. Australian Government Department of Health—Pharmaceutical Benefits Scheme. July 2019 PBAC Meeting—Positive recommendations Canberra: Australian Government Department of Health; 2019 [cited 28 August 2019]. Available from: http://www.pbs.gov.au/industry/listing/elements/pbac-meetings/pbac-outcomes/2019-07/positive-recommendations-07-2019.pdf.

42. Beard FH, Hendry AJ, Macartney K. Early success with room for improvement: influenza vaccination of young Australian children. The Medical journal of Australia. 2019;210(11):484–6 e1. Epub 2019/05/08. doi: 10.5694/mja2.50141 31063595

43. Hochberg AM, Hauben M, Pearson RK, O'Hara DJ, Reisinger SJ, Goldsmith DI, et al. An evaluation of three signal-detection algorithms using a highly inclusive reference event database. Drug safety. 2009;32(6):509–25. Epub 2009/05/23. doi: 10.2165/00002018-200932060-00007 19459718

44. Dey A, Wang H, Quinn H, Cook J, Macartney K. Surveillance of adverse events following immunisation in Australia, 2015. Communicable diseases intelligence quarterly report. 2017;41(3):E264–E78.

45. Arnaud M, Begaud B, Thurin N, Moore N, Pariente A, Salvo F. Methods for safety signal detection in healthcare databases: a literature review. Expert opinion on drug safety. 2017;16(6):721–32. Epub 2017/05/12. doi: 10.1080/14740338.2017.1325463 28490262