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Evaluating Completeness of Foodborne Outbreak Reporting in the United States, 1998–2019

Author

Listed:
  • Yutong Zhang

    (Division of Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, 150 Harrison Avenue, Boston, MA 02111, USA)

  • Ryan B. Simpson

    (Division of Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, 150 Harrison Avenue, Boston, MA 02111, USA)

  • Lauren E. Sallade

    (Division of Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, 150 Harrison Avenue, Boston, MA 02111, USA)

  • Emily Sanchez

    (Division of Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, 150 Harrison Avenue, Boston, MA 02111, USA)

  • Kyle M. Monahan

    (Gordon Institute, Tufts University School of Engineering, 200 Boston Avenue, Medford, MA 02155, USA)

  • Elena N. Naumova

    (Division of Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, 150 Harrison Avenue, Boston, MA 02111, USA)

Abstract

Public health agencies routinely collect time-referenced records to describe and compare foodborne outbreak characteristics. Few studies provide comprehensive metadata to inform researchers of data limitations prior to conducting statistical modeling. We described the completeness of 103 variables for 22,792 outbreaks publicly reported by the United States Centers for Disease Control and Prevention’s (US CDC’s) electronic Foodborne Outbreak Reporting System (eFORS) and National Outbreak Reporting System (NORS). We compared monthly trends of completeness during eFORS (1998–2008) and NORS (2009–2019) reporting periods using segmented time series analyses adjusted for seasonality. We quantified the overall, annual, and monthly completeness as the percentage of outbreaks with blank records per our study period, calendar year, and study month, respectively. We found that outbreaks of unknown genus ( n = 7401), Norovirus ( n = 6414), Salmonella ( n = 2872), Clostridium ( n = 944), and multiple genera ( n = 779) accounted for 80.77% of all outbreaks. However, crude completeness ranged from 46.06% to 60.19% across the 103 variables assessed. Variables with the lowest crude completeness (ranging 3.32–6.98%) included pathogen, specimen etiological testing, and secondary transmission traceback information. Variables with low (<35%) average monthly completeness during eFORS increased by 0.33–0.40%/month after transitioning to NORS, most likely due to the expansion of surveillance capacity and coverage within the new reporting system. Examining completeness metrics in outbreak surveillance systems provides essential information on the availability of data for public reuse. These metadata offer important insights for public health statisticians and modelers to precisely monitor and track the geographic spread, event duration, and illness intensity of foodborne outbreaks.

Suggested Citation

  • Yutong Zhang & Ryan B. Simpson & Lauren E. Sallade & Emily Sanchez & Kyle M. Monahan & Elena N. Naumova, 2022. "Evaluating Completeness of Foodborne Outbreak Reporting in the United States, 1998–2019," IJERPH, MDPI, vol. 19(5), pages 1-19, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:5:p:2898-:d:762268
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    References listed on IDEAS

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    1. Siobhan M Mor & Alfred DeMaria Jr. & Elena N Naumova, 2014. "Hospitalization Records as a Tool for Evaluating Performance of Food- and Water-Borne Disease Surveillance Systems: A Massachusetts Case Study," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    2. Chui, K.K.H. & Jagai, J.S. & Griffiths, J.K. & Naumova, E.N., 2011. "Hospitalization of the elderly in the United States for nonspecific gastrointestinal diseases: A search for etiological clues," American Journal of Public Health, American Public Health Association, vol. 101(11), pages 2082-2086.
    3. Tania M. Alarcon Falconi & Bertha Estrella & Fernando Sempértegui & Elena N. Naumova, 2020. "Effects of Data Aggregation on Time Series Analysis of Seasonal Infections," IJERPH, MDPI, vol. 17(16), pages 1-21, August.
    4. Julia B Wenger & Elena N Naumova, 2010. "Seasonal Synchronization of Influenza in the United States Older Adult Population," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-11, April.
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