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

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 02, 2022
Source ID
10.3390/ijerph19052898

Entities

People

  • Elena N. Naumova
  • Emily Sanchez
  • Kyle Monahan
  • Lauren Sallade
  • Ryan Simpson
  • Yutong Zhang

Organizations

  • National Institute of Food and Agriculture
  • National Science Foundation
  • Office of the Director of National Intelligence
  • Tufts University
  • United States Department of Defense

Tags

Readers

  • Computational Modeling and Simulation
  • Microbial Pathology
  • Personnel Management and Statistics in the Military and Department of Defense

Technology Areas

  • Microelectronics