Interruption of cancer screening services due to COVID-19 pandemic: lessons from previous disasters
Bibliographic record
Abstract
PURPOSE: To review the scientific literature seeking lessons for the COVID-19 era that could be learned from previous health services interruptions that affected the delivery of cancer screening services. METHODS: A systematic search was conducted up to April 17, 2020, with no restrictions on language or dates and resulted in 385 articles. Two researchers independently assessed the list and discussed any disagreements. Once a consensus was achieved for each paper, those selected were included in the review. RESULTS: Eleven articles were included. Three studies were based in Japan, two in the United States, one in South Korea, one in Denmark, and the remaining four offered a global perspective on interruptions in health services due to natural or human-caused disasters. No articles covered an interruption due to a pandemic. The main themes identified in the reviewed studies were coordination, communication, resource availability and patient follow-up. CONCLUSION: Lessons learned applied to the context of COVID-19 are that coordination involving partners across the health sector is essential to optimize resources and resume services, making them more resilient while preparing for future interruptions. Communication with the general population about how COVID-19 has affected cancer screening, measures taken to mitigate it and safely re-establish screening services is recommended. Use of mobile health systems to reach patients who are not accessing services and the application of resource-stratified guidelines are important considerations. More research is needed to explore best strategies for suspending, resuming and sustaining cancer screening programs, and preparedness for future disruptions, adapted to diverse health care systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".