Evidence map›Paper›PMID 42175471›Full record

Observational studyMedicine2026

The impact of the COVID-19 pandemic in 2020 on the diagnosis, treatment, and cancer-specific survival of major urological cancers: An observational study.

Mingsong Wang, Kang Jia, Yu Wang, Dan Yuan, Long Huang, Kang Li, Xiao Zhong

Abstract readObservational Study
In one paragraph

Observational study in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mingsong WangDepartment of Urology, The Affiliated Chengdu 363 Hospital of Southwest Medical University, Chengdu, China.ORCID 0009-0006-0198-711
Kang Jia
Yu Wang
Dan Yuan
Long Huang
Kang Li
Xiao Zhong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The impact of the COVID-19 pandemic in 2020 on the diagnosis, treatment, and outcomes of major urological cancers remains poorly understood. Patients diagnosed with prostate cancer, bladder cancer, and kidney cancer were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Ordered logistic regression was used to evaluate the effect of the COVID-19 pandemic in 2020 on cancer stage at diagnosis. Logistic regression was employed to determine the impact of the pandemic on the diagnosis of different patient subgroups. Kaplan-Meier curves were used to evaluate cancer-specific survival (CSS). In the SEER-17 and SEER-22 datasets, the number of newly diagnosed cases of prostate cancer, bladder cancer, and kidney cancer decreased in 2020. Patients diagnosed with prostate cancer (OR = 1.04, 95% CI, 1.01-1.07, P = .016), bladder cancer (OR = 1.07, 95% CI, 1.01-1.14, P = .019), and kidney cancer (OR = 1.08, 95% CI, 1.02-1.14, P < .001) in 2020 were at a higher risk of advanced stages. Among the 3 cancers, patients with a median household income > 75,000 USD were more likely to be diagnosed in 2020, while urban and female patients (bladder and kidney cancers) tended to be diagnosed in 2018/2019. For prostate cancer (HR = 1.06, 95% CI, 0.97-1.15, P = .207), bladder cancer (HR = 1.04, 95% CI, 0.98-1.11, P = .188), and kidney cancer (HR = 1.03, 95% CI, 0.94-1.12, P = .496), no significant differences in CSS were observed between patients diagnosed in 2020 and those diagnosed in 2018/2019. The COVID-19 pandemic in 2020 led to a decrease in the number of diagnoses for prostate cancer, bladder cancer, and kidney cancer, with varying impacts on different patient subgroups. Patients diagnosed during this period were more likely to have later-stage disease. Additionally, no significant differences in CSS were observed between patients diagnosed during the COVID-19 pandemic in 2020 and those diagnosed in 2018/2019; however, this finding should be interpreted cautiously given the relatively short follow-up period and the potential influence of competing risks during the COVID-19 pandemic.

Indexed as

COVID-19Kidney NeoplasmsProstatic NeoplasmsUrinary Bladder NeoplasmsUrologic NeoplasmsAgedFemaleHumansKaplan-Meier EstimateMaleMiddle AgedNeoplasm StagingPandemicsSARS-CoV-2SEER ProgramUnited StatesCancer-specific survivalCOVID-19DiagnosisTreatmentUrological cancers

Identifiers

PMID42175471
PMCPMC13200996

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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.