Evidence map›Paper›PMID 29415013›Full record

ArticlePLoS medicine2018

Population impact of lung cancer screening in the United States: Projections from a microsimulation model.

Steven D Criss, Deirdre F Sheehan, Lauren Palazzo, Chung Yin Kong

Open access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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

18 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025
    Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. [Research Progress on Lung Cancer Screening].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020
    Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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

4 authors at 2 institutions in 1 country.

Steven D CrissInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Deirdre F SheehanInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.ORCID 0000-0002-0097-1582
Lauren PalazzoInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Chung Yin KongInstitute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.ORCID 0000-0001-6431-7830
Massachusetts General Hospital · USHarvard University · US

Funding

Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
NCI NIH HHS U01 CA199284
6 · The paper itself

Abstract

backgroundPrevious simulation studies estimating the impacts of lung cancer screening have ignored the changes in smoking prevalence over time in the United States. Our primary rationale was to perform, to our knowledge, the first simulation study that estimates the health outcomes of lung cancer screening with explicit modeling of smoking trends for the whole US population. METHODS/

findingsUtilizing a well-validated microsimulation model, we estimated the benefits and harms of an annual low-dose computed tomography screening scenario with a realistic screening adherence rate versus a no-screening scenario for the US population from 2016-2030. The Centers for Medicare and Medicaid Services (CMS) eligibility criteria were applied: age 55-77 years at time of screening, history of at least 30 pack-years of smoking, and current smoker or former smoker with fewer than 15 years since quitting. In the screened population, cumulative mortality reduction was projected to reach 16.98% (95% CI 16.90%-17.07%). Cumulative mortality reduction was estimated to be 3.52% (95% CI 3.50%-3.53%) for the overall study population, with annual mortality reduction peaking at 4.38% (95% CI 4.36%-4.41%) in 2021 and falling to 3.53% (95% CI 3.50%-3.56%) by 2030. Lung cancer screening would save a projected 148,484 life-years (95% CI 147,429-149,540) across the total population through 2030. There were estimated to be 9,054 (95% CI 9,011-9,098) overdiagnosed cases among the 252,429 (95% CI 251,208-253,649) screen-detected lung cancer diagnoses, yielding an overdiagnosis rate of 3.59%. The limitations of our study are that we do not explicitly model race or socioeconomic status and our model was calibrated to data from studies performed in academic centers, both of which may impact the generalizability of our results. We also exclusively model the effects of the CMS guidelines for lung cancer screening and not any other screening strategies.

conclusionsThe mortality reduction and life-years gained estimated by this study are lower than those of single birth cohort studies. Single cohort studies neglect the changing dynamics of smoking behavior across generations, whereas this study reflects the trend of decreasing smoking prevalence since the 1960s. Maximum benefit could be derived from lung cancer screening through 2021; in later years, mortality reduction due to screening will decline. If a comprehensive screening program is not implemented in the near future, the opportunity to achieve these benefits will have passed.

Indexed as

Computer SimulationAgedAged, 80 and overClinical Trials as TopicCohort StudiesEarly Detection of CancerFemaleHumansLung NeoplasmsMaleMass ScreeningMortalitySmokersSurvival AnalysisUnited States

Identifiers

PMID29415013
PMCPMC5802442
OpenAlexW2786913899

What OpenQuestion holds

Textmetadata
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.