2511. Estimating Numbers of Influenza Excess Deaths and Hospitalizations at the State-Level
Session: Poster Abstract Session: Virology Potpourri
Saturday, October 6, 2018
Room: S Poster Hall
Posters
  • FBE_IDweek_poster_091718.pdf (213.5 kB)
  • Background: Influenza surveillance activities inform the state public health response to influenza but may under-detect influenza events. We applied modeling methods to estimate influenza excess pneumonia and influenza (P&I) and respiratory and circulatory (R&C) deaths and hospitalizations to Colorado data from July 1, 2007 through June 30, 2016.

    Methods: Data included P&I and R&C deaths and hospitalizations (events) listed as underlying or primary diagnoses on death certificates and hospital discharge records, respectively, and local sentinel lab surveillance for influenza A and B. We evaluated four negative binomial models for each event type. Model 1 estimated a seasonal baseline of events from a weekly time series of diagnoses and included coefficients for excess events due to influenza A and B. In Model 2, we created influenza A subtype coefficients by applying subtype proportions from national surveillance to the percent of local specimens positive for influenza A. Models 3 and 4 were similar to Models 1 and 2, except influenza-specific diagnoses were removed from the baseline model and added to final estimates. We calculated 95% confidence intervals (CI) using bootstrap methods. Statewide laboratory-based surveillance was a reference.

    Results: Model 2 better captured seasonal variability than Model 1. Models 3 and 4 inconsistently predicted events. According to Model 2 (Figure), during 9 influenza seasons there were 701 P&I deaths (median 37 A(H1) and 52 A(H3) per year) , 2,368 R&C deaths (median 73 A(H1) and 203 A(H3) per year), 18,950 P&I hospitalizations (median 1,068 A(H1), 1,021 A(H3), and 272 B per year), and 27,844 R&C hospitalizations (median 1,156 A(H1), 1,112 A(H3), and 1,037 B per year) due to influenza. While A(H3) was most frequently associated with death, A(H1) was more often associated with hospitalization. Compared to laboratory-based surveillance, we estimated 1.2 and 4.0 times as many P&I/R&C deaths and 1.3 and 1.9 times as many P&I/R&C hospitalizations.

    Conclusion: Robust statistical models applied to state-level data better estimate local influenza burden, and augment those of laboratory-based surveillance. Such models may be useful for prevention planning and more accurate public information about the burden of influenza.

     

    Figure.

    Christopher Czaja, MD MPH1,2, Lisa Miller, MD, MSPH3, John Hughes, PhD4, Nisha Alden, MPH2 and Eric Simoes, MD, DCH5, (1)Epidemiology, Colorado School of Public Health, Aurora, CO, (2)Colorado Department of Public Health and Environment, Denver, CO, (3)Preventive Medicine Residency Program, University of Colorado School of Public Health, Aurora, CO, (4)No Affilliation, Denver, CO, (5)The Children's Hospital, Aurora, CO

    Disclosures:

    C. Czaja, None

    L. Miller, None

    J. Hughes, None

    N. Alden, None

    E. Simoes, None

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