Evidence map›Paper›PMID 40580117›Full record

ArticleAnnals of medicine2025

Impact of the COVID-19 pandemic on lung cancer screening utilization and outcome: a health examination center retrospective study.

Chi-Shen Chen, Cai-Sin Yao, Fu-Zong Wu

Abstract read
In one paragraph

Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

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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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

3 authors.

Chi-Shen ChenHealth Management Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
Cai-Sin YaoDepartment of Business Management, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Fu-Zong WuDepartment of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.ORCID 0000-0003-2556-6118

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) pandemic has disrupted healthcare systems, significantly affecting preventive services such as low-dose computed tomography (LDCT) for lung cancer screening. We aimed to evaluate the pandemic's impact on LDCT screening practices at Kaohsiung Veterans General Hospital, focusing on the changes in participation rates, Lung-RADS categories, and lung cancer diagnoses to guide the development of interventions for improving screening programs and early detection during health crises. MATERIALS AND

methodsA retrospective cohort of 56,730 individuals who underwent health examinations between 2017 and 2023 was analyzed. Data on demographics, smoking history, family history of lung cancer, and eligibility for LDCT subsidies in Taiwan were obtained. Screening utilization and outcomes were cross-referenced with cancer registries and imaging databases. A subset of 17,743 individuals who underwent LDCT were examined to determine the pre- and post-COVID19 differences in smoking prevalence, family history, high-risk Lung Imaging Reporting and Data System (Lung-RADS) categories (3 or 4), and lung cancer diagnoses.

resultsFollowing the implementation of the level 3 alert, notable shifts were observed in smoking habits and lung cancer screening eligibility. The prevalence of heavy smokers (≥30 pack-years) declined from 6.9% before the alert to 6.1% after (

conclusionThe pandemic disrupted LDCT screening, reducing access for high-risk smokers while increasing non-smoker participation. High-risk nodules declined but partially recovered post-pandemic. Future policies must prioritize high-risk individuals, optimize resources, and enhance early detection to improve outcomes and crisis preparedness.

Indexed as

COVID-19Early Detection of CancerLung NeoplasmsAgedFemaleHumansMaleMass ScreeningMiddle AgedPandemicsRetrospective StudiesSARS-CoV-2SmokingTaiwanTomography, X-Ray ComputedCOVID-19lung cancer screeningpandemic

Identifiers

PMID40580117
PMCPMC12207767

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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.