Evidence map›Paper›PMID 33929519›Full record

Observational studyJAMA network open2021

Patterns and Factors Associated With Adherence to Lung Cancer Screening in Diverse Practice Settings.

Lori C Sakoda, M Patricia Rivera, Jie Zhang, Pasangi Perera, Cecile A Laurent, Danielle Durham, Roger Huamani Velasquez, Lindsay Lane, Adam Schwartz, Charles P Quesenberry and 2 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 2 pooled it
8.8field-weighted citation impact, top 2% 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

54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 87 citations in OpenAlex.

  1. Knowledge translation strategies to enhance lung cancer screening programme implementation: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2025
    Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Improving lung cancer screening diagnostic efficiency.Current opinion in pulmonary medicine · 2026
    Review
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  12. Health Beliefs and Adherence to Lung Cancer Screening in a Diverse Safety-Net Population.Journal of the National Comprehensive Cancer Network : JNCCN · 2026
    Article
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  15. Journal of health communication · 2026
    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

12 authors at 3 institutions in 1 country.

Lori C SakodaDivision of Research, Kaiser Permanente Northern California, Oakland.
M Patricia RiveraDivision of Pulmonary and Critical Care Medicine, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill.
Jie ZhangDivision of Research, Kaiser Permanente Northern California, Oakland.
Pasangi PereraDepartment of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill.
Cecile A LaurentDivision of Research, Kaiser Permanente Northern California, Oakland.
Danielle DurhamDepartment of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill.
Roger Huamani VelasquezDepartment of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill.
Lindsay LaneDepartment of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill.
Adam SchwartzDivision of Pulmonary and Critical Care Medicine, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill.
Charles P QuesenberryDivision of Research, Kaiser Permanente Northern California, Oakland.
George MinowadaDepartment of Pulmonary Medicine, Kaiser Permanente Northern California, Vallejo.
Louise M HendersonLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill.
University of North Carolina at Chapel Hill · USKaiser Permanente · USKaiser Permanente Vallejo Medical Center · US

Funding

Evaluating Lung Cancer Screening Patterns and Outcomes through a North Carolina RegistryR01CA212014 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Louise Henderson · 2017 to 2026
$5.4M
Comorbidity and Functional Status in a Population Undergoing Lung Cancer ScreeningR01CA251686 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HENDERSON, LOUISE, RIVERA, MARIA PATRICIA · 2020 to 2023
$1.5M
Evaluating a Risk Prediction Model for Lung CancerK07CA188142 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI SAKODA, LORI · 2015 to 2019
$920k
NCI NIH HHS K07 CA188142NCI NIH HHS R01 CA212014NCI NIH HHS R01 CA251686
6 · The paper itself

Abstract

Importance: For lung cancer screening to confer mortality benefit, adherence to annual screening with low-dose computed tomography scans is essential. Although the National Lung Screening Trial had an adherence rate of 95%, current data are limited on screening adherence across diverse practice settings in the United States. Objective: To evaluate patterns and factors associated with adherence to annual screening for lung cancer after negative results of a baseline examination, particularly in centralized vs decentralized screening programs. Design, Setting, and Participants: This observational cohort study was conducted at 5 academic and community-based sites in North Carolina and California among 2283 individuals screened for lung cancer between July 1, 2014, and March 31, 2018, who met US Preventive Services Task Force eligibility criteria, had negative results of a baseline screening examination (American College of Radiology Lung Imaging Reporting and Data System category 1 or 2), and were eligible to return for a screening examination in 12 months. Exposures: To identify factors associated with adherence, the association of adherence with selected baseline demographic and clinical characteristics, including type of screening program, was estimated using multivariable logistic regression. Screening program type was classified as centralized if individuals were referred through a lung cancer screening clinic or program and as decentralized if individuals had a direct clinician referral for the baseline low-dose computed tomography scan. Main Outcomes and Measures: Adherence to annual lung cancer screening, defined as a second low-dose computed tomography scan within 11 to 15 months after baseline screening. Results: Among the 2283 eligible individuals (1294 men [56.7%]; mean [SD] age, 64.9 [5.8] years; 1160 [50.8%] aged ≥65 years) who had negative screening results at baseline, overall adherence was 40.2% (n = 917), with higher adherence among those who underwent screening through centralized (46.0% [478 of 1039]) vs decentralized (35.3% [439 of 1244]) programs. The independent factor most strongly associated with adherence was type of screening program, with a 2.8-fold increased likelihood of adherence associated with centralized screening (adjusted odds ratio [aOR], 2.78; 95% CI, 1.99-3.88). Another associated factor was age (65-69 vs 55-59 years: aOR, 1.38; 95% CI, 1.07-1.77; 70-74 vs 55-59 years: aOR, 1.47; 95% CI, 1.10-1.96). Conclusions and Relevance: After negative results of a baseline examination, adherence to annual lung cancer screening was suboptimal, although adherence was higher among individuals who were screened through a centralized program. These results support the value of centralized screening programs and the need to further implement strategies that improve adherence to annual screening for lung cancer.

Indexed as

Academic Medical CentersAgedAge FactorsCohort StudiesDecision Making, SharedDelivery of Health CareEarly Detection of CancerFemaleHumansLogistic ModelsLung NeoplasmsMaleMiddle AgedMultivariate AnalysisPatient CompliancePrimary Health Care

Identifiers

PMID33929519
PMCPMC8087957
OpenAlexW3158539237

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.