Global review of COVID-19 mitigation strategies and their impact on cancer service disruptions
Bibliographic record
Abstract
During the COVID-19 pandemic, countries adopted mitigation strategies to reduce disruptions to cancer services. We reviewed their implementation across health system functions and their impact on cancer diagnosis and care during the pandemic. A systematic search was performed using terms related to cancer and COVID-19. Included studies reported on individuals with cancer or cancer care services, focusing on strategies/programs aimed to reduce delays and disruptions. Extracted data were grouped into four functions (governance, financing, service delivery, and resource generation) and sub-functions of the health system performance assessment framework. We included 30 studies from 16 countries involving 192,233 patients with cancer. Multiple mitigation approaches were implemented, predominantly affecting sub-functions of service delivery to control COVID-19 infection via the suspension of non-urgent cancer care, modified treatment guidelines, and increased telemedicine use in routine cancer care delivery. Resource generation was mainly ensured through adequate workforce supply. However, less emphasis on monitoring or assessing the effectiveness and financing of these strategies was observed. Seventeen studies suggested improved service uptake after mitigation implementation, yet the resulting impact on cancer diagnosis and care has not been established. This review emphasizes the importance of developing effective mitigation strategies across all health system (sub)functions to minimize cancer care service disruptions during crises. Deficiencies were observed in health service delivery (to ensure equity), governance (to monitor and evaluate the implementation of mitigation strategies), and financing. In the wake of future emergencies, implementation research studies that include pre-prepared protocols will be essential to assess mitigation impact across cancer care services.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".