Advertisement

Canadian Journal of Public Health

, Volume 104, Issue 4, pp e340–e347 | Cite as

Assessing the Relative Timeliness of Ontario’s Syndromic Surveillance Systems for Early Detection of the 2009 Influenza H1N1 Pandemic Waves

  • Anna ChuEmail author
  • Rachel Savage
  • Michael Whelan
  • Laura C. Rosella
  • Natasha S. Crowcroft
  • Don Willison
  • Anne-Luise Winter
  • Richard Davies
  • Ian Gemmill
  • Pia K. Mucchal
  • Ian Johnson
Quantitative Research
  • 1 Downloads

Abstract

OBJECTIVES: Building on previous research noting variations in the operation and perceived utility of syndromic surveillance systems in Ontario, the timeliness of these different syndromic systems for detecting the onset of both 2009 H1N1 pandemic (A(H1N1)pdm09) waves relative to laboratory testing data was assessed using a standardized analytic algorithm.

METHODS: Syndromic data, specifically local emergency department (ED) visit and school absenteeism data, as well as provincial Telehealth (telephone helpline) and antiviral prescription data, were analyzed retrospectively for the period April 1, 2009 to January 31, 2010. The C2-MEDIUM aberration detection method from the US Centers for Disease Control and Prevention’s EARS software was used to detect increases above expected in syndromic data, and compared to laboratory alerts, defined as notice of confirmed A(H1N1)pdm09 cases over two consecutive days, to assess relative timeliness.

RESULTS: In Wave 1, provincial-level alerts were detected for antiviral prescriptions and Telehealth respiratory calls before the laboratory alert. In Wave 2, Telehealth respiratory calls similarly alerted in advance of the laboratory, while local alerts from ED visit, antiviral prescription and school absenteeism data varied in timing relative to the laboratory alerts. Alerts from syndromic data were also observed to coincide with external factors such as media releases.

CONCLUSIONS: Alerts from syndromic surveillance systems may be influenced by external factors and variation in system operations. Further understanding of both the impact of external factors on surveillance data and standardizing protocols for defining alerts is needed before the use of syndromic surveillance systems can be optimized.

Key Words

Public health surveillance algorithms influenza A virus H1N1 subtype outbreaks 

Résumé

OBJECTIFS: À partir des résultats de recherches antérieures sur les écarts dans le fonctionnement et l’utilité perçue des systèmes de surveillance syndromique en Ontario, nous avons évalué, à l’aide d’un algorithme analytique standardisé, la rapidité avec laquelle les différents systèmes syndromiques ont détecté l’apparition des deux vagues de la pandémie de grippe H1N1 de 2009 (A(H1N1)pdm09) par rapport aux données des épreuves de laboratoire.

MÉTHODE: Les données syndromiques, plus précisément les visites aux services d’urgence locaux et l’absentéisme dans les écoles, ainsi que les appels à Télésanté (une ligne d’assistance téléphonique provinciale) et les ordonnances d’antiviraux, ont été analysées rétrospectivement pour la période du 1er avril 2009 au 31 janvier 2010. Nous avons utilisé la méthode de détection des aberrations C2-MEDIUM du logiciel EARS des Centers for Disease Control and Prevention des États-Unis pour déceler les hausses supérieures aux prévisions dans les données syndromiques, et nous les avons comparées aux alertes des laboratoires, définies comme étant les avis de cas de grippe A(H1N1)pdm09 confirmés au cours de deux journées consécutives, pour évaluer la rapidité relative des systèmes de surveillance syndromique.

RÉSULTATS: Durant la 1e vague, des alertes de niveau provincial ont été détectées, dans les ordonnances d’antiviraux et les appels pour problèmes respiratoires à Télésanté, avant les alertes des laboratoires. Durant la 2e vague, les appels pour problèmes respiratoires à Télésanté ont aussi précédé les alertes des laboratoires, mais les alertes locales liées aux visites aux urgences, aux ordonnances d’antiviraux et aux taux d’absentéisme dans les écoles ont varié dans le temps par rapport aux alertes des laboratoires. Il a aussi été observé que les alertes déclenchées par les données syndromiques coïncidaient avec des facteurs externes, comme les communiqués.

CONCLUSIONS: Les alertes des systèmes de surveillance syndromique peuvent être influencées par des facteurs externes et des variations dans le fonctionnement des systèmes. Il faudrait pousser la recherche sur deux plans: l’impact exercé par les facteurs externes sur les données de surveillance et la normalisation des protocoles de déclenchement des alertes, avant de pouvoir optimiser l’utilisation des systèmes de surveillance syndromique.

Mots Clés

surveillance sanitaire algorithme virus A de la grippe soustype H1N1 flambées épidémiques 

References

  1. 1.
    Buckeridge DL: Outbreak detection through automated surveillance: A review of the determinants of detection. J Biomed Inform 2007;40:370–79.CrossRefGoogle Scholar
  2. 2.
    Gault G, Larrieu S, Durand C, Josseran L, Jouves B, Filleul L. Performance of a syndromic system for influenza based on the activity of general practitioners, France. J Public Health 2009;31:286–92.CrossRefGoogle Scholar
  3. 3.
    Griffin B, Jain A, Davies-Cole J, Glymph C, Lum G, Washington S, et al. Early detection of influenza outbreaks using the DC Department of Health’s syndromic surveillance system. BMC Public Health 2009;9:483.Google Scholar
  4. 4.
    Smith GE, Cooper DL, Loveridge P, Chinemana F, Gerard E, Verlander N. A national syndromic surveillance system for England and Wales using calls to a telephone helpline. Euro Surveill 2006;11:220–24.CrossRefGoogle Scholar
  5. 5.
    van den Wijngaard CC, van Pelt W, Nagelkerke NJ, Kretzschmar M, Koopmans, MP. Evaluation of syndromic surveillance in the Netherlands: Its added value and recommendations for implementation. Euro Surveill 2011;16:19806.Google Scholar
  6. 6.
    Lynn H. Improving population health by syndromic surveillance. Public Health Ontario Portal, Syndromic Surveillance Ontario, Discussion Forum, July 4, 2007.Google Scholar
  7. 7.
    Sider D. Syndromic surveillance: That giant sucking sound of wasted, scarce public health resources. Public Health Ontario Portal, Syndromic Surveillance Ontario, Discussion Forum, July 4, 2007.Google Scholar
  8. 8.
    Savage R, Chu A, Rosella LC, Crowcroft NS, Varia M, Policarpio ME, et al. Perceived usefulness of syndromic surveillance in Ontario during the H1N1 pandemic. J Public Health 2012;34:195–202.CrossRefGoogle Scholar
  9. 9.
    Chu A, Savage R, Willison D, Crowcroft NS, Rosella LC, Sider D, et al. The use of syndromic surveillance for decision-making during the H1N1 pandemic: A qualitative study. BMC Public Health 2012;12:929.Google Scholar
  10. 10.
    Uscher-Pines L, Farrell CL, Cattani J, Hsieh YH, Moskal MD, Babin SM, et al. A survey of usage protocols of syndromic surveillance systems by state public health departments in the United States. J Public Health Manag Pract 2009;15:432–38.CrossRefGoogle Scholar
  11. 11.
    Ontario Ministry of Health and Long-Term Care. Initial Report on Public Health. 2009.Google Scholar
  12. 12.
    Ontario Ministry of Health and Long-Term Care, Chief Medical Officer of Health. The H1N1 pandemic - How Ontario fared: A report by Ontario’s Chief Medical Officer of Health. Toronto, ON: Queen’s Printer for Ontario, 2010.Google Scholar
  13. 13.
    Epidemiological summary of pandemic influenza A (H1N1) 2009 virus — Ontario, Canada, June 2009. Wkly Epidemiol Rec 2009;84:485–91.Google Scholar
  14. 14.
    Duncan C, Guthrie JL, Tijet N, Elgngihy N, Turenne C, Seah C, et al. Analytical and clinical validation of novel real-time reverse transcriptasepolymerase chain reaction assays for the clinical detection of swine-origin H1N1 influenza viruses. Diagn Microbiol Infect Dis 2011;69:167–71.CrossRefGoogle Scholar
  15. 15.
    U.S.Centers for Disease Control and Prevention. Early Aberration Reporting System (EARS) V5.0, 2010. Available at: http://www.bt.cdc.gov/surveillance/ ears/ (Accessed March 15, 2012).Google Scholar
  16. 16.
    Fricker RD, Jr., Hegler BL, Dunfee, DA. Comparing syndromic surveillance detection methods: EARS’ versus a CUSUM-based methodology. Stat Med 2008;27:3407–29.CrossRefGoogle Scholar
  17. 17.
    Uscher-Pines L, Farrell CL, Babin SM, Cattani J, Gaydos CA, Hsieh YH, et al. Framework for the development of response protocols for public health syndromic surveillance systems: Case studies of 8 US states. Disaster Med Public Health Prep 2009;3:S29-S36.CrossRefGoogle Scholar
  18. 18.
    Bellazzini MA, Minor, KD. ED syndromic surveillance for novel H1N1 spring 2009. Am J Emerg Med 2011;29:70–74.CrossRefGoogle Scholar
  19. 19.
    Malik MT, Gumel A, Thompson LH, Strome T, Mahmud, SM. “Google flu trends” and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba. Can J Public Health 2011;102:294–97.PubMedGoogle Scholar
  20. 20.
    Zheng W, Aitken R, Muscatello DJ, Churches T. Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments. BMC Public Health 2007;7:250.CrossRefGoogle Scholar
  21. 21.
    Kara EO, Elliot AJ, Bagnall H, Foord DG, Pnaiser R, Osman H, et al. Absenteeism in schools during the 2009 influenza A(H1N1) pandemic: A useful tool for early detection of influenza activity in the community? Epidemiol Infect 2012;140:1328–36.CrossRefGoogle Scholar
  22. 22.
    Kom Mogto CA, De Serres G, Douville Fradet M, Lebel G, Toutant S, Gilca R, et al. School absenteeism as an adjunct surveillance indicator: Experience during the Second Wave of the 2009 H1N1 pandemic in Quebec, Canada. PLoS ONE 2012;7:e34084.CrossRefGoogle Scholar
  23. 23.
    Bravata DM, McDonald KM, Smith WM, Rydzak C, Szeto H, Buckeridge DL, et, al. Systematic review: Surveillance systems for early detection of bioterrorism- related diseases. Ann Intern Med 2004;140:910–22.CrossRefGoogle Scholar
  24. 24.
    Savage R, Whelan M, Johnson I, Rea E, LaFreniere M, Rosella LC, et al. Assessing secondary attack rates among household contacts at the beginning of the influenza A (H1N1) pandemic in Ontario, Canada, April-June 2009: A prospective, observational study. BMC Public Health 2011;11:234.Google Scholar
  25. 25.
    Ontario Ministry of Health and Long-Term Care. Information for healthcare professionals: Update June 11, 2009. Important Health Notice 2009;6(14):1–2.Google Scholar
  26. 26.
    Ontario Ministry of Health and Long-Term Care. Information for healthcare professionals: Update June 4, 2009. Important Health Notice 2009;6(13):1–2.Google Scholar
  27. 27.
    Ontario Ministry of Health and Long-Term Care. Pandemic (H1N1) 2009: A review of Ontario’s response. Toronto: Queen’s Printer for Ontario, 2010.Google Scholar

Copyright information

© The Canadian Public Health Association 2013

Authors and Affiliations

  • Anna Chu
    • 1
    Email author
  • Rachel Savage
    • 1
  • Michael Whelan
    • 1
  • Laura C. Rosella
    • 1
    • 2
  • Natasha S. Crowcroft
    • 1
    • 2
    • 3
  • Don Willison
    • 1
    • 2
    • 4
  • Anne-Luise Winter
    • 1
  • Richard Davies
    • 5
  • Ian Gemmill
    • 6
  • Pia K. Mucchal
    • 7
  • Ian Johnson
    • 1
    • 2
  1. 1.Public Health OntarioTorontoCanada
  2. 2.Dalla Lana School of Public HealthUniversity of TorontoTorontoCanada
  3. 3.Laboratory Medicine and PathobiologyUniversity of TorontoTorontoCanada
  4. 4.Department of Clinical Epidemiology and BiostatisticsMcMaster UniversityHamiltonCanada
  5. 5.University of Ottawa Heart InstituteOttawaCanada
  6. 6.Kingston, Frontenac and Lennox & Addington Public HealthKingstonCanada
  7. 7.Centre for Foodborne, Environmental, and Zoonotic Infectious DiseasesPublic Health Agency of CanadaOttawaCanada

Personalised recommendations