Longitudinal retrospective study of real-world adherence to colorectal cancer screening before and after the COVID-19 pandemic in the USA
Bibliographic record
Abstract
Introduction: At-home stool tests are an increasingly popular practice for colorectal cancer screening, especially when access to healthcare facilities is challenging. However, there is limited information about whether stool tests provide sufficient coverage when patients must undergo repeat testing. This study evaluates repeat preventative stool tests over 2 year periods in a healthcare system with 51 hospitals and over 1000 clinics across seven western US states, before and after the onset of the COVID-19 pandemic. Methods: We conduct a real-world, observational, retrospective and longitudinal study based on electronic medical records. We measure the rate of repeat screening and mean delay in repeat screening among patients who receive an initial stool test. We estimate the changes in the likelihood of colorectal cancer screening using a Cox proportional hazard model. Results: Our sample included 4 03 085 patients. The share of patients with an initial negative stool test who received a repeat screening ranged from 38% to 49% across different years. Among patients who received a repeat screening, there is a delay of 3 months on average. The volume of stool tests increased during the pandemic: the HR of screening after the onset of the pandemic to that before the pandemic was 1.18 (95% CI (1.15, 1.20), p<0.001). Conclusions: Our findings show that less than 50% of patients received a repeat stool test, creating gaps in their screening coverage. The increase in stool tests during the pandemic is partly due to a substitution away from colonoscopies, underscoring the increasing importance of stool tests in CRC screening. Programmes that aim to increase CRC screening uptake should focus on repeated testing after an initial screening.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".