Impact of Covid-19 Related Disruptions to Colorectal Cancer Screening Programs in Three Countries: A Comparative Modelling Study
Bibliographic record
Abstract
Aims Colorectal cancer (CRC) screening programs worldwide have been disrupted during the COVID-19 pandemic. This study aimed to estimate the impact of hypothetical disruptions to organized FIT-based CRC screening programs on short- and long-term CRC incidence and mortality in three countries using microsimulation modelling. Methods Using CRC microsimulation models for Australia (Policy1-Bowel), Canada (OncoSim) and the Netherlands (ASCCA and MISCAN-Colon) participating in the COVID-19 and Cancer Global Modelling Consortium (CCGMC), we simulated a range of scenarios to assess the potential impact of disruptions to screening on CRC incidence and mortality. Modelled scenarios varied by disruption duration (3-, 6- and 12-months), post-disruption participation reduction, and catch-up screening strategy (no catch-up, immediate and 6-month delayed catch-up). Results Without catch-up screening, CRC incidence increased by 0.1-0.3 %, 0.2-0.6 %, and 0.4-1.2 % over 2020-2050 among individuals aged 50 years and older in the three modelled countries after 3-, 6-, and 12- month disruptions, respectively, compared to undisrupted screening and CRC mortality increased by 0.2-0.5 %, 0.4-1.0 %, and 0.8-2.0 % over 2020-2050 among individuals aged 50 years and older compared to undisrupted screening. A 6-month disruption without catch-up resulted in an estimated 3,552, 2,844 and 803-1,803 additional CRC diagnoses and 1,961, 1,319, and 678-881 additional CRC-related deaths in Australia, Canada and the Netherlands, respectively. A post-disruption reduction in participation increased CRC diagnoses by 0.2-0.9 % and CRC-related deaths by 0.5-1.6 % compared to undisrupted screening. Providing catch-up screening minimized this impact to 0.0-0.2 %. Conclusions Although the relative impact of the modelled CRC screening disruptions due to the COVID-19 pandemic appears modest, given a high burden of CRC, there is a substantial impact on CRC diagnoses and deaths across all countries considered. It is crucial that, if disrupted, screening programs ensure participation rates return to previously observed rates and provide catch-up screening wherever possible, as the impact of any disruption could be considerably larger otherwise. Citation: de Jonge L, Worthington J, van Wifferen F et al. OP85 IMPACT OF COVID-19 RELATED DISRUPTIONS TO COLORECTAL CANCER SCREENING PROGRAMS IN THREE COUNTRIES: A COMPARATIVE MODELLING STUDY. Endoscopy 2021; 53: S36. Publication History Article published online: 19 March 2021 © 2021. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".