Evidence map›Paper›PMID 36055880›Full record

Observational studyAmerican journal of preventive medicine2022

Cancer Evaluations During the COVID-19 Pandemic: An Observational Study Using National Veterans Affairs Data.

Ashley N D Meyer, Hardeep Singh, Andrew J Zimolzak, Li Wei, Debra T Choi, Abigail D Marinez, Daniel R Murphy

Abstract readObservational Study
In one paragraph

Observational study in American journal of preventive medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

7 authors.

Ashley N D MeyerCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas. Electronic address: ameyer@bcm.edu.
Hardeep SinghCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.
Andrew J ZimolzakCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.
Li WeiCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.
Debra T ChoiCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.
Abigail D MarinezCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.
Daniel R MurphyCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.

Funding

Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety EventsR01HS027363 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2019 to 2021
$1.5M
AHRQ HHS R01 HS027363
6 · The paper itself

Abstract

introductionFewer cancer diagnoses have been made during the COVID-19 pandemic. Pandemic-related delays in cancer diagnosis could occur from limited access to care or patient evaluation delays (e.g., delayed testing after abnormal results). Follow-up of abnormal test results warranting evaluation for cancer was examined before and during the pandemic.

methodsElectronic trigger algorithms were applied to the Department of Veterans Affairs electronic health record data to assess follow-up of abnormal test results before (March 10, 2019-March 7, 2020) and during (March 8, 2020-March 6, 2021) the pandemic.

resultsElectronic triggers were applied to 8,021,406 veterans' electronic health records to identify follow-up delays for abnormal results warranting evaluation for 5 cancers: bladder (urinalysis with high-grade hematuria), breast (abnormal mammograms), colorectal (positive fecal occult blood tests/fecal immunochemical tests or results consistent with iron deficiency anemia), liver (elevated alpha-fetoprotein), and lung (chest imaging suggestive of malignancy) cancers. Between prepandemic and pandemic periods, test quantities decreased by 12.6%-27.8%, and proportions of abnormal results lacking follow-up decreased for urinalyses (-0.8%), increased for fecal occult blood tests/fecal immunochemical test (+2.3%) and chest imaging (+1.8%), and remained constant for others. Follow-up times decreased for most tests; however, control charts suggested increased delays at 2 stages: early (pandemic beginning) for urinalyses, mammograms, fecal occult blood tests/fecal immunochemical test, iron deficiency anemia, and chest imaging and late (30-45 weeks into pandemic) for mammograms, fecal occult blood tests/fecal immunochemical test, and iron deficiency anemia.

conclusionsAlthough early pandemic delays in follow-up may have led to reduced cancer rates, the significant decrease in tests performed is likely a large driver of these reductions. Future emergency preparedness efforts should bolster essential follow-up and testing procedures to facilitate timely cancer diagnosis.

Indexed as

AnemiaCOVID-19NeoplasmsVeteransHumansPandemicsUnited States

Identifiers

PMID36055880
PMCPMC9359503

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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.