Multi-level quality assessment of United States COVID-19 epidemiological surveillance

Although the coronavirus disease 2019 (COVID-19) tasked United States (U.S.) health departments with tracking and informing disease mitigation efforts, there has been no national assessment of public COVID-19 data quality. The current study aimed to illustrate U.S. COVID-19 reporting quality by examining epidemiological surveillance from U.S. health departments in 50 states and the District of Columbia between July and August 2020 along five central quality dimensions: timeliness, reliability, completeness, accuracy, and accessibility. A follow-up assessment was conducted 19 months later for a subset of states. Results broadly demonstrated that the quality of COVID-19 data reporting was significantly heterogenous. States varied in timeliness of initial data reports (median: 3/2/2020, minimum: 9/29/2019, maximum: 4/20/2020). Most states (56.8%) did not indicate sources of data for their official published reports. In assessing for 10 standard variables from the Centers for Disease Control and Prevention (CDC), states widely differed in granularity of reported variables and no state health department reported data for all CDC surveillance variables (e.g., race, ethnicity, sex). Most same-day comparisons of state-specific counts reported by the CDC differed from respective state health departments (cases: 84.3% differed; deaths: 68.6% differed). Follow-up examination indicated improvements in reliability, accuracy, and accessibility, but issues in data source verification and completeness persisted. Authors conclude with recommendations to improve disease mitigation efficiency and improve public trust in health protection efforts: establish formal reporting guidelines; standardize operational definitions for key disease variables; and, require health departments to publicly identify sources of reported data.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-02-21
DOI
https://doi.org/10.1371/journal.pone.0319263
Citations
1
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
1.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-level quality assessment of United States COVID-19 epidemiological surveillance

Megan Suzanne Irgens, John M. Ruiz, Riley M. O’Neill
1 citations
PLoS ONE
Data-Driven Disease Surveillance
1.35
article

Multi-level quality assessment of United States COVID-19 epidemiological surveillance

Megan Suzanne Irgens, John M. Ruiz, Riley M. O’Neill
article en
1 citations

Abstract

Although the coronavirus disease 2019 (COVID-19) tasked United States (U.S.) health departments with tracking and informing disease mitigation efforts, there has been no national assessment of public COVID-19 data quality. The current study aimed to illustrate U.S. COVID-19 reporting quality by examining epidemiological surveillance from U.S. health departments in 50 states and the District of Columbia between July and August 2020 along five central quality dimensions: timeliness, reliability, completeness, accuracy, and accessibility. A follow-up assessment was conducted 19 months later for a subset of states. Results broadly demonstrated that the quality of COVID-19 data reporting was significantly heterogenous. States varied in timeliness of initial data reports (median: 3/2/2020, minimum: 9/29/2019, maximum: 4/20/2020). Most states (56.8%) did not indicate sources of data for their official published reports. In assessing for 10 standard variables from the Centers for Disease Control and Prevention (CDC), states widely differed in granularity of reported variables and no state health department reported data for all CDC surveillance variables (e.g., race, ethnicity, sex). Most same-day comparisons of state-specific counts reported by the CDC differed from respective state health departments (cases: 84.3% differed; deaths: 68.6% differed). Follow-up examination indicated improvements in reliability, accuracy, and accessibility, but issues in data source verification and completeness persisted. Authors conclude with recommendations to improve disease mitigation efficiency and improve public trust in health protection efforts: establish formal reporting guidelines; standardize operational definitions for key disease variables; and, require health departments to publicly identify sources of reported data.

PLoS ONEVol. 20(2)
San Francisco General Hospital (US), University of Arizona (US), University of California, San Francisco (US)
Good health and well-being
Openalex Percentile: Top 23%
Data-Driven Disease Surveillance
1.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.