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.

Exploring Risk Factors of Recall-Associated Foodborne Disease Outbreaks in the United States, 2009–2019

“…By creating multi-pathogen and multi-location food preparation or consumption variables, we might have introduced potential multiplicity when comparing specific pathogen etiology, or location of preparation or consumption to their respective reference groups. Lastly, we paid sufficient attention to missing data and the structure of the missing data [50]. On the surface, we could handle missing data by using imputation; however, due to structural missingness, this could create bias.…”

Data on Humanitarian Crises: Who and What Are We Missing?

“…9 Our analysis of completeness of foodborne outbreak surveillance system revealed that data quality could be improved with targeted efforts, well-defined criteria, and continuous assessments. 10…”

“…9 Our analysis of completeness of foodborne outbreak surveillance system revealed that data quality could be improved with targeted efforts, well-defined criteria, and continuous assessments. 10 Yet, data dashboards on their own are not sufficient. There needs to be greater collaboration with local experts and multidisciplinary teams to identify what might be missing from the data and why, how the missingness biases the results, how it can be avoided, and how the consequences of missingness can be communicated and addressed.…”

Gold Nanoparticle-Based Colorimetric Biosensing for Foodborne Pathogen Detection

“…Surveillance of foodborne outbreaks plays a crucial role in the food industry due to the expanding scope of food distribution. The security and safety of distributed food hinges on the food producers’ capacity to recognize, detect, and track foodborne pathogens [ 1 ]. Pathogens transmitted through food and water can lead to a spectrum of infections, with outcomes spanning from mild fever to fatal consequences [ 2 , 3 , 4 ].…”