Evidence map›Paper›PMID 39982920›Full record

ArticlePloS one2025

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

Megan S Irgens, Riley M O'Neill, John M Ruiz

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Megan S IrgensJuvenile Justice Behavioral Health Lab, Division of Infant, Child, and Adolescent Psychiatry, Zuckerberg San Francisco General Hospital, University of California San Francisco, San Francisco, California, United States of America.
Riley M O'NeillDepartment of Psychology, University of Arizona, Tucson, Arizona, United States of America.ORCID 0000-0002-1881-2758
John M RuizDepartment of Psychology, University of Arizona, Tucson, Arizona, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Epidemiological MonitoringCenters for Disease Control and Prevention, U.S.Data AccuracyHumansReproducibility of ResultsSARS-CoV-2United States

Identifiers

PMID39982920
PMCPMC11844846

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.