Evidence map›Paper›PMID 39674825›Full record

ReviewBritish journal of cancer2025

The long-lasting impacts of the COVID-19 pandemic on population-based cancer survival: what are the implications for data analysis?

Rachael Stannard, Paul C Lambert, Georgios Lyratzopoulos, Therese M-L Andersson, Sam Khan, Mark J Rutherford

Abstract readReview
In one paragraph

Review in British journal of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Naïve CD4Nature communications · 2026
    Article
  5. Article
  6. COVID-19 and Lung Cancer Interactions: A Literature Review.Medical sciences (Basel, Switzerland) · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
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

6 authors.

Rachael StannardBiostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK. res53@leicester.ac.uk.ORCID http://orcid.org/0000-0002-1919-5034
Paul C LambertCancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
Georgios LyratzopoulosEpidemiology of Cancer Healthcare & Outcomes (ECHO), Dept. of Behavioural Science and Health, Institute of Epidemiology & Health Care (IEHC) University College London (UCL), London, UK.ORCID http://orcid.org/0000-0002-2873-7421
Therese M-L AnderssonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0001-8644-9041
Sam KhanLeicester Cancer Research Centre, University of Leicester, Leicester, UK.
Mark J RutherfordBiostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK.

Funding

DH | National Institute for Health Research (NIHR) NIHR206132DH | National Institute for Health Research (NIHR) NIHR303007
6 · The paper itself

Abstract

Monitoring trends of cancer incidence, mortality and survival is vital for the planning and delivery of health services, and the evaluation of diagnostics and treatment at the population level. Furthermore, comparisons are often made between population subgroups to explore inequalities in outcomes. During the COVID-19 pandemic routine delivery of health services were severely disrupted. Resources were redeployed to COVID-19 services and patient risk of COVID-19 infection required serious consideration. Cancer screening services were paused, the availability of healthcare providers was reduced and, in some cases, patients faced difficulty in accessing optimal treatment in a timely manner. Given these major disruptions, much care should be taken when interpreting changes in cancer survival estimates during this period. The impact on cancer incidence and mortality statistics that have already been reported in some jurisdictions should drive further thought on the corresponding impact on cancer survival, and whether any differences observed are real, artificial or a combination of the two. We discuss the likely impact on key cancer metrics, the likely implications for the analysis of cancer registration data impacted by the pandemic and the implications for comparative analyses between population groups and other risk factor groups when using data spanning the pandemic period.

Indexed as

COVID-19NeoplasmsData AnalysisHumansIncidencePandemicsSARS-CoV-2

Identifiers

PMID39674825
PMCPMC11997115

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.