Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data

Authors

  • Sharon K. Greene,

    Corresponding author
    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, U.S.A.
    • Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Ave., 6th floor, Boston, MA 02215, U.S.A.
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  • Martin Kulldorff,

    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, U.S.A.
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  • Jie Huang,

    1. Center for Health Policy Studies, Division of Research, Kaiser Permanente Medical Care Program, Oakland, CA, U.S.A.
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  • Richard J. Brand,

    1. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, U.S.A.
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  • Kenneth P. Kleinman,

    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, U.S.A.
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  • John Hsu,

    1. Center for Health Policy Studies, Division of Research, Kaiser Permanente Medical Care Program, Oakland, CA, U.S.A.
    2. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, U.S.A.
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  • Richard Platt

    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, U.S.A.
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Abstract

Timely detection of clusters of localized influenza activity in excess of background seasonal levels could improve situational awareness for public health officials and health systems. However, no single data type may capture influenza activity with optimal sensitivity, specificity, and timeliness, and it is unknown which data types could be most useful for surveillance. We compared the performance of 10 types of electronic clinical data for timely detection of influenza clusters throughout the 2007/08 influenza season in northern California. Kaiser Permanente Northern California generated zip code-specific daily episode counts for: influenza-like illness (ILI) diagnoses in ambulatory care (AC) and emergency departments (ED), both with and without regard to fever; hospital admissions and discharges for pneumonia and influenza; antiviral drugs dispensed (Rx); influenza laboratory tests ordered (Tests); and tests positive for influenza type A (FluA) and type B (FluB). Four credible events of localized excess illness were identified. Prospective surveillance was mimicked within each data stream using a space–time permutation scan statistic, analyzing only data available as of each day, to evaluate the ability and timeliness to detect the credible events. AC without fever and Tests signaled during all four events and, along with Rx, had the most timely signals. FluA had less timely signals. ED, hospitalizations, and FluB did not signal reliably. When fever was included in the ILI definition, signals were either delayed or missed. Although limited to one health plan, location, and year, these results can inform the choice of data streams for public health surveillance of influenza. Copyright © 2010 John Wiley & Sons, Ltd.

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