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Cited by 8Lung Cancer Diagnosis and TreatmentGlobal Cancer Incidence and ScreeningHealth Policy Implementation Science

The DOI matches its Crossref record; 78 of the 79 references it cites were found in a registry or an index, and 1 are not checked yet.

Facilitators and Barriers to Lung Cancer Screening during Long COVID: A Global Systematic Review and Meta-Study Synthesis of Qualitative Research

Open access
Gebremeskel, Teferi Gebru, Romeo, Frank, Shama, Adisu Tafari, Bonevski, Billie, Trigg, Joshua·2024·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph21050534PMC PMC11121223
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Abstract

Background: Participation in targeted screening reduces lung cancer mortality by 30–60%, but screening is not universally available. Therefore, the study aimed to synthesize the evidence and identify facilitators and barriers to lung cancer screening participation globally. Methods: Two reviewers screened primary studies using qualitative methods published up to February 2023. We used two-phase synthesis consistent with a meta-study methodology to create an interpretation of lung cancer screening decisions grounded in primary studies, carried out a thematic analysis of group themes as specific facilitators and barriers, systematically compared investigations for similarities and differences, and performed meta-synthesis to generate an expanded theory of lung cancer screening participation. We used the Social Ecological Model to organize and interpret the themes: individual, interpersonal, social/cultural, and organizational/structural levels. Results: Fifty-two articles met the final inclusion criteria. Themes identified as facilitating lung cancer screening included prioritizing patient education, quality of communication, and quality of provider-initiated encounter/coordination of care (individual patient and provider level), quality of the patient–provider relationship (interpersonal group), perception of a life’s value and purpose (cultural status), quality of tools designed, and care coordination (and organizational level). Themes coded as barriers included low awareness, fear of cancer diagnosis, low perceived benefit, high perceived risk of low-dose computerized tomography, concern about cancer itself, practical obstacle, futility, stigma, lack of family support, COVID-19 fear, disruptions in cancer care due to COVID-19, inadequate knowledge of care providers, shared decision, and inadequate time (individual level), patient misunderstanding, poor rapport, provider recommendation, lack of established relationship, and confusing decision aid tools (interpersonal group), distrust in the service, fatalistic beliefs, and perception of aging (cultural level), and lack of institutional policy, lack of care coordinators, inadequate infrastructure, absence of insurance coverage, and costs (and organizational status). Conclusions: This study identified critical barriers, facilitators, and implications to lung cancer screening participation. Therefore, we employed strategies for a new digital medicine (artificial intelligence) screening method to balance the cost–benefit, “workdays” lost in case of disease, and family hardship, which is essential to improve lung cancer screening uptake.

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Crossref · 10.3390/ijerph21050534

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  • Emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies.

    “Similarly, emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies”

    From A Scoping Review of Implemented Innovations in Cancer Care: Implications for Pan-Canadian Scaling (2026) · cites this paper as [46]

    Section 4.2 (page unavailable) proposes AI-based lung cancer screening to identify higher-risk people and says digital screening methods can reduce costs and assist physicians’ decision-making. It also recommends policies, infrastructure, and provider training. The paper does not support the broader claims about AI improving scheduling or treatment planning, and it notes a lack of high-quality empirical evidence for a specific screening strategy during COVID-19. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. Rolfo, 2021, Lung Cancer and SARS-CoV-2 infection: Identifying important knowledge gaps for investigation, J. Thorac. Oncol. Off. Publ. Int. Assoc. Study Lung Cancer, 17, 214

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Metadata

Authors
Gebremeskel, T. G., Romeo, F., Shama, A. T., Bonevski, B., & Trigg, J.
Affiliations
  • Flinders University (AU)
  • Wollega University (ET)
Corresponding author
Gebremeskel, Teferi Gebru
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2024-04-25
Volume
21
Issue
5
Pages
534
ISSN
1660-4601 (ISSN-L)
DOI
10.3390/ijerph21050534
PMC
PMC11121223
OpenAlex
W4395675169
Cited by
6
References
78
Citations by year
2026: 3 · 2025: 3
FWCI
0.44
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • TUScholarShare (Temple University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
Keywords
lung cancer screening, long COVID, screening uptake, barriers and facilitators, low-dose CT, patient education, patient-provider relationship, care coordination, fear of cancer diagnosis, stigma, shared decision-making, socio-ecological model, digital medicine, artificial intelligence screening, health insurance coverage
MeSH terms
COVID-19, SARS-CoV-2, Humans, Lung Neoplasms, Qualitative Research, Early Detection of Cancer

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