Evidence map›Paper›PMID 33399825›Full record

ArticleJournal of the National Cancer Institute2021

Using Prediction Models to Reduce Persistent Racial and Ethnic Disparities in the Draft 2020 USPSTF Lung Cancer Screening Guidelines.

Rebecca Landy, Corey D Young, Martin Skarzynski, Li C Cheung, Christine D Berg, M Patricia Rivera, Hilary A Robbins, Anil K Chaturvedi, Hormuzd A Katki

Registry-linked trialAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07073898 (Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial), which is not on this map. Cited by 67 papers.

0numbers the graph read from it
0cells of the map it votes in
67citing 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.

NCT07073898 nacompletednot on this mapstarted 2025, after this paper: background citation

Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial

TypeinterventionalSponsorUniversity of UtahRan2025 to 2026Enrolled65ConditionsLung CancerArmsRepeated Text Messages (TM+), Conversational Agent (CA), Educational Video, Proactive Patient Navigation (PPN), Reactive Patient Navigation (RPN)
3 · Its place in the literature

Who cites it

67 citing papers in PubMed.

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  8. Addressing algorithmic bias in lung cancer screening eligibility.Journal of the National Cancer Institute · 2026
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7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Rebecca LandyDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.ORCID 0000-0003-4042-4820
Corey D YoungDepartment of Microbiology, Biochemistry and Immunology, Morehouse School of Medicine, Atlanta, GA, USA.
Martin SkarzynskiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
Li C CheungDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.ORCID 0000-0003-1625-4331
Christine D BergDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
M Patricia RiveraDivision of Pulmonary and Critical Care Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Hilary A RobbinsInternational Agency for Research on Cancer, Lyon, France.ORCID 0000-0001-6041-6866
Anil K ChaturvediDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
Hormuzd A KatkiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.

Funding

Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Christopher I. Amos, Rayjean J. Hung · 2017 to 2026
$23.7M
Evaluating risk prediction models for use in lung cancer screening in diverse populations around the worldR03CA245979 · NCI · INTERNATIONAL AGENCY FOR RES ON CANCER · PI ROBBINS, HILARY A. · 2020 to 2020
$112k
NCI NIH HHS R03 CA245979NCI NIH HHS U19 CA203654World Health Organization 001
6 · The paper itself

Abstract

We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung cancer screening recommendations "partially ameliorate racial disparities in screening eligibility" compared with the 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%-33.4% = 15.0% to 64.5%-48.5% = 16.0%; Asian Americans: 48.3%-35.6% = 12.7% to 64.5%-45.2% = 19.3%; Hispanic Americans: 48.3%-24.8% = 23.5% to 64.5%-37.0% = 27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%-75.5% = 1.2%) and improved screening efficiency for Asian and Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). The draft USPSTF-2020 guidelines increased the number of eligible minorities vs USPSTF-2013 but may inadvertently increase racial and ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit regardless of race and ethnicity.

Indexed as

EthnicityLung NeoplasmsBlack or African AmericanEarly Detection of CancerHealthcare DisparitiesHumansUnited StatesWhite People

Identifiers

PMID33399825
PMCPMC8562965

What OpenQuestion holds

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Registered trials

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.