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Systematic Assessment of Multiple Routine and Near-Real Time Indicators to Classify the Severity of Influenza Seasons and Pandemics in the United States, 2003–04 Through 2015–2016

Supporting Files
File Language:
English


Details

  • Alternative Title:
    Am J Epidemiol
  • Personal Author:
  • Description:
    Assessments of influenza season severity can guide public health action. We used the moving epidemic method to develop intensity thresholds (ITs) for 3 US surveillance indicators from the 2003-2004 through 2014-2015 influenza seasons (excluding the 2009 pandemic). The indicators were: 1) outpatient visits for influenza-like illness; 2) influenza-related hospitalizations; and 3) influenza- and pneumonia-related deaths. ITs were developed for the population overall and separately for children, adults, and older adults, and they were set at the upper limit of the 50% (IT50), 90% (IT90), and 98% (IT98) 1-sided confidence intervals of the geometric mean of each season's 3 highest values. Severity was classified as low if ≥2 systems peaked below IT50, moderate if ≥2 peaked between IT50 and IT90, high if ≥2 peaked between IT90 and IT98, and very high if ≥2 peaked above IT98. We pilot-tested this method with the 2015-2016 season and the 2009 pandemic. Overall, 4 seasons were classified as low severity, 7 as moderate, 2 as high, and none as very high. Among the age groups, older adults had the most seasons (n = 3) classified as high, and children were the only group to have seasons (n = 2) classified as very high. We will apply this method to classify the severity of future seasons and inform pandemic response.
  • Subjects:
  • Source:
    Am J Epidemiol. 187(5):1040-1050
  • Pubmed ID:
    29053783
  • Pubmed Central ID:
    PMC5908755
  • Document Type:
  • Funding:
  • Volume:
    187
  • Issue:
    5
  • Collection(s):
  • Main Document Checksum:
    urn:sha256:a190e21f08a845f70b219269cde988907362af0322bebb043ba3b32de6fce719
  • Download URL:
  • File Type:
    Filetype[PDF - 769.98 KB ]
File Language:
English
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