Noteworthy impacts of COVID-19 pandemic on cancer screening: A systematic review
Bibliographic record
Abstract
The sudden onset of the coronavirus disease 2019 (COVID-19) in January 2020 has affected essential global health services. Cancer-screening services that can reduce cancer mortality are strongly affected. However, the specific role of COVID-19 in cancer screening is not fully understood. This study aimed to assess the efficiency of global cancer screening programs before and during the COVID-19 pandemic and to promote potential cancer-screening strategies for the next pandemic. Electronic searches in PubMed, Embase, and Web of Science, and manual searches were performed between January 1, 2020 and March 1, 2023. Cohort studies that reported the number of participants who underwent cancer screening before and during the COVID-19 pandemic were included. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. Differences in cancer-screening rates were estimated using the incidence rate ratio (IRR). Fifty-five cohort studies were included in this meta-analysis. The screening rates of colorectal cancer using invasive screening methods (Pooled IRR=0.52, 95% CI: 0.42 to 0.65, p<0.01), cervical cancer (Pooled IRR=0.56, 95% CI: 0.47 to 0.67, p<0.01), breast cancer (Pooled IRR=0.57, 95% CI: 0.49 to 0.66, p<0.01) and prostate cancer (Pooled IRR=0.71, 95% CI: 0.56 to 0.90, p<0.01) during the COVID-19 pandemic were significantly lower than those before the COVID-19 pandemic. The screening rates of lung cancer (Pooled IRR=0.77, 95% CI: 0.58 to 1.03, p=0.08) and colorectal cancer using noninvasive screening methods (Pooled IRR=0.74, 95% CI: 0.50 to 1.09, p=0.13) were reduced with no statistical differences. The subgroup analyses revealed that the reduction in cancer-screening rates varied across economies. Our results suggest that the COVID-19 pandemic has had a noteworthy impact on colorectal, cervical, breast, and prostate cancer screening. Developing innovative cancer-screening technologies is important to promote the efficiency of cancer-screening services in the post-COVID-19 era and prepare for the next pandemic.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".