Evidence map›Paper›PMID 33292800›Full record

ArticleDiagnostic and prognostic research2020

Lung cancer screening by low-dose computed tomography: a cost-effectiveness analysis of alternative programmes in the UK using a newly developed natural history-based economic model.

Edward Griffin, Chris Hyde, Linda Long, Jo Varley-Campbell, Helen Coelho, Sophie Robinson, Tristan Snowsill

Abstract read
In one paragraph

Article in Diagnostic and prognostic research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 5 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

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

16 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Review
  7. Article
  8. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  9. Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025
    Article
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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.

Edward GriffinPeninsula Technology Assessment Group (PenTAG), College of Medicine and Health, University of Exeter, St Luke's campus, Heavitree Road, Exeter, EX1 2LU, UK.ORCID http://orcid.org/0000-0002-3587-8417
Chris HydeExeter Test Group, College of Medicine and Health, University of Exeter, St Luke's campus, Heavitree Road, Exeter, EX1 2LU, UK.
Linda LongPeninsula Technology Assessment Group (PenTAG), College of Medicine and Health, University of Exeter, St Luke's campus, Heavitree Road, Exeter, EX1 2LU, UK.
Jo Varley-CampbellPeninsula Technology Assessment Group (PenTAG), College of Medicine and Health, University of Exeter, St Luke's campus, Heavitree Road, Exeter, EX1 2LU, UK.
Helen CoelhoPeninsula Technology Assessment Group (PenTAG), College of Medicine and Health, University of Exeter, St Luke's campus, Heavitree Road, Exeter, EX1 2LU, UK.
Sophie RobinsonPeninsula Technology Assessment Group (PenTAG), College of Medicine and Health, University of Exeter, St Luke's campus, Heavitree Road, Exeter, EX1 2LU, UK.
Tristan SnowsillPeninsula Technology Assessment Group (PenTAG), College of Medicine and Health, University of Exeter, St Luke's campus, Heavitree Road, Exeter, EX1 2LU, UK. TristanSnowsill.t.m.snowsill@exeter.ac.uk.

Funding

Health Technology Assessment Programme hta22690
6 · The paper itself

Abstract

backgroundA systematic review of economic evaluations for lung cancer identified no economic models of the UK setting based on disease natural history. We first sought to develop a new model of natural history for population screening, then sought to explore the cost-effectiveness of multiple alternative potential programmes.

methodsAn individual patient model (ENaBL) was constructed in MS Excel® and calibrated against data from the US National Lung Screening Trial. Costs were taken from the UK Lung Cancer Screening Trial and took the perspective of the NHS and PSS. Simulants were current or former smokers aged between 55 and 80 years and so at a higher risk of lung cancer relative to the general population. Subgroups were defined by further restricting age and risk of lung cancer as predicted by patient self-questionnaire. Programme designs were single, triple, annual and biennial arrangements of LDCT screens, thereby examining number and interval length. Forty-eight distinct screening strategies were compared to the current practice of no screening. The primary outcome was incremental cost-effectiveness of strategies (additional cost per QALY gained).

resultsLDCT screening is predicted to bring forward the stage distribution at diagnosis and reduce lung cancer mortality, with decreases versus no screening ranging from 4.2 to 7.7% depending on screen frequency. Overall healthcare costs are predicted to increase; treatment cost savings from earlier detection are outweighed by the costs of over-diagnosis. Single-screen programmes for people 55-75 or 60-75 years with ≥ 3% predicted lung cancer risk may be cost-effective at the £30,000 per QALY threshold (respective ICERs of £28,784 and £28,169 per QALY gained). Annual and biennial screening programmes were not predicted to be cost-effective at any cost-effectiveness threshold. LIMITATIONS: LDCT performance was unaffected by lung cancer type, stage or location and the impact of a national screening programme of smoking behaviour was not included.

conclusionLung cancer screening may not be cost-effective at the threshold of £20,000 per QALY commonly used in the UK but may be cost-effective at the higher threshold of £30,000 per QALY.

Indexed as

Cost-effectivenessDiagnosisLow-dose computed tomographyLung cancerNatural history modelQALYScreening

Identifiers

PMID33292800
PMCPMC7709236

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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.