Assessing the impact of the COVID-19 pandemic on breast cancer screening and diagnosis rates: A rapid review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The ongoing COVID-19 pandemic has caused an indefinite delay to cancer screening programs worldwide. This study aims to explore the impact on breast cancer screening outcomes such as mammography and diagnosis rates. METHODS: We searched Ovid MEDLINE, Ovid Embase, medRxiv and bioRxiv between January 2020 to October 2021 to identify studies that reported on the rates of screening mammography and breast cancer diagnosis before and during the pandemic. The effects of 'lockdown' measures, age and ethnicity on outcomes were also examined. All studies were assessed for risk of bias using the Newcastle-Ottawa Scale (NOS). Rate ratios were calculated for all outcomes and pooled using standard inverse-variance random effects meta-analysis. RESULTS: We identified 994 articles, of which 7 registry-based and 24 non-registry-based retrospective cohort studies, including data on 4,860,786 and 629,823 patients respectively across 18 different countries, were identified. Overall, breast cancer screening and diagnosis rates dropped by an estimated 41-53% and 18-29% respectively between 2019 and 2020. No differences in mammogram screening rates depending on patient age or ethnicity were observed. However, countries that implemented lockdown measures were associated with a significantly greater reduction in mammogram and diagnosis rates between 2019 and 2020 in comparison to those that did not. CONCLUSION: The pandemic has caused a substantial reduction in the screening and diagnosis of breast cancer, with reductions more pronounced in countries under lockdown restrictions. It is early yet to know if delayed screening during the pandemic translates into higher breast cancer mortality.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".