Abstract PO-262: Interventions designed to increase the uptake of lung cancer screening and implications for populations experiencing the greatest burden of lung cancer disparities: A scoping study
Bibliographic record
Abstract
Abstract Background: Lung cancer screening (LCS) with low-dose CT can reduce mortality due to lung cancer by detecting early-stage tumours that are amenable to treatment. Participation in LCS programs however has not been equally distributed among at-risk groups, such that populations with the highest burden of lung cancer risk (through the social patterning of smoking behaviour) and lowest levels of healthcare utilization (through care which is structurally inaccessible) can experience a widening in healthcare disparities as a result of LCS interventions. Approach: We sought to inform equitable access to LCS by illuminating knowledge and implementation gaps in current interventions designed to increase the uptake of LCS. To do this, we conducted a scoping study using the Arksey and O'Malley methodological framework. We conducted comprehensive searches for lung cancer screening promotion interventions (Ovid Medline, Embase, the Cochrane Library, CINAHL and Scopus) and included published English language peer-reviewed and grey literature published between January 2000 and 2020 that describe an intervention designed to increase the uptake of LDCT lung cancer screening in the Organization for Economic Cooperation and Development (OECD) countries. We extracted data onto a chart modified from the Template for Intervention Description and Republication (TIDieR) checklist and the Consolidated Standards of Reporting Trials. We used the Health Equity Impact Assessment (HEIA) tool to analyse the intended/unintended and positive/negative outcomes of the interventions for populations experiencing the greatest disparities. Results: Our search yielded 2681 articles. We included 22 peer review articles dated from January 2000 to January 2020. Interventions occured primarily in the USA, Europe and Canada. We used the ‘Patient Centered Access to Healthcare' conceptual framework by Khanassov et al 2016 to synthesize our findings. Three main themes summarise current interventions designed to increase the uptake of LCS: (i) a focus on individuals and their ability to engage with the healthcare system; (ii) inadequate targeting of populations experiencing greatest disparities and (iii) a lack of conceptual underpinning in the design of interventions so that the social patterning of lung cancer risk and ability to access care is ignored. Conclusion: LCS interventions must take into consideration the disproportionate burden of lung cancer risk in populations experiencing social disadvantage. Designing interventions that are cognisant of the social distribution of risk and targeted to support the uptake in high-risk populations can prevent an inadvertent widening of health disparities. Citation Format: Ambreen Sayani, Muhanad Ahmed Ali, Pooja Dey, Ann Marie Corrado, Carolyn Ziegler, Alex Sadler, Christina Williams, Aisha Lofters. Interventions designed to increase the uptake of lung cancer screening and implications for populations experiencing the greatest burden of lung cancer disparities: A scoping study [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-262.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".