title: Assessing the relative timeliness of Ontario's syndromic surveillance systems for early detection of the 2009 influenza H1N1 pandemic waves.
abstract: OBJECTIVE
  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.
authors.affiliation: Public Health Ontario. rachel.savage@oahpp.ca.
authors.forename: Anna
authors.initials: A
authors.lastname: Chu
authors.forename: Rachel
authors.initials: R
authors.lastname: Savage
authors.forename: Michael
authors.initials: M
authors.lastname: Whelan
authors.forename: Laura C
authors.initials: LC
authors.lastname: Rosella
authors.forename: Natasha S
authors.initials: NS
authors.lastname: Crowcroft
authors.forename: Don
authors.initials: D
authors.lastname: Willison
authors.forename: Anne-Luise
authors.initials: AL
authors.lastname: Winter
authors.forename: Richard
authors.initials: R
authors.lastname: Davies
authors.forename: Ian
authors.initials: I
authors.lastname: Gemmill
authors.forename: Pia K
authors.initials: PK
authors.lastname: Mucchal
authors.forename: Ian
authors.initials: I
authors.lastname: Johnson
mesh_terms: D000465:Algorithms; D000998:Antiviral Agents / Q000627:therapeutic use; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000302:isolation & purification; D007251:Influenza, Human / Q000175:diagnosis / Q000453:epidemiology*; D007753:Laboratories / Q000706:statistics & numerical data; D009864:Ontario / Q000453:epidemiology; D058873:Pandemics; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D017216:Telemedicine / Q000706:statistics & numerical data; D013997:Time Factors
chemical_list: D000998:Antiviral Agents
keywords: H1N1 subtype; Public health surveillance; algorithms; influenza A virus; outbreaks
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