Litcius/Paper detail

COVID-19 Case Surveillance: Trends in Person-Level Case Data Completeness, United States, April 5–September 30, 2020

Jeremy A.W. Gold, Jennifer DeCuir, Jayme P. Coyle, Lindsey M. Duca, Jennifer Adjemian, Kayla N. Anderson, Brittney Baack, Achuyt Bhattarai, Deborah L. Dee, Tonji Durant, Raimi Ewetola, Teresa Finlayson, Sandra W. Roush, Shaoman Yin, Brendan R. Jackson, Kathleen E. Fullerton

2021Public Health Reports23 citationsDOIOpen Access PDF

Abstract

OBJECTIVES: To obtain timely and detailed data on COVID-19 cases in the United States, the Centers for Disease Control and Prevention (CDC) uses 2 data sources: (1) aggregate counts for daily situational awareness and (2) person-level data for each case (case surveillance). The objective of this study was to describe the sensitivity of case ascertainment and the completeness of person-level data received by CDC through national COVID-19 case surveillance. METHODS: We compared case and death counts from case surveillance data with aggregate counts received by CDC during April 5-September 30, 2020. We analyzed case surveillance data to describe geographic and temporal trends in data completeness for selected variables, including demographic characteristics, underlying medical conditions, and outcomes. RESULTS: As of November 18, 2020, national COVID-19 case surveillance data received by CDC during April 5-September 30, 2020, included 4 990 629 cases and 141 935 deaths, representing 72.7% of the volume of cases (n = 6 863 251) and 71.8% of the volume of deaths (n = 197 756) in aggregate counts. Nationally, completeness in case surveillance records was highest for age (99.9%) and sex (98.8%). Data on race/ethnicity were complete for 56.9% of cases; completeness varied by region. Data completeness for each underlying medical condition assessed was <25% and generally declined during the study period. About half of case records had complete data on hospitalization and death status. CONCLUSIONS: Incompleteness in national COVID-19 case surveillance data might limit their usefulness. Streamlining and automating surveillance processes would decrease reporting burdens on jurisdictions and likely improve completeness of national COVID-19 case surveillance data.

Topics & Concepts

Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicinePublic health surveillanceCompleteness (order theory)PandemicDisease surveillanceOutbreakEnvironmental healthPublic healthGeographyVirologyInfectious disease (medical specialty)DiseasePathologyMathematical analysisMathematicsCOVID-19 epidemiological studiesData-Driven Disease SurveillanceCensus and Population Estimation