Risk of <scp>COVID</scp>‐19 death for people with a pre‐existing cancer diagnosis prior to <scp>COVID</scp>‐19‐vaccination: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract While previous reviews found a positive association between pre‐existing cancer diagnosis and COVID‐19‐related death, most early studies did not distinguish long‐term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher‐quality evidence on risk of COVID‐19‐related death for people with recent/active cancer (compared to people without) in the pre‐COVID‐19‐vaccination period. We searched the WHO COVID‐19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk‐of‐bias assessment was based on the Newcastle‐Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse‐variance random‐effects models. Random‐effects meta‐regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID‐19‐related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36‐1.61, I 2 = 0; people with COVID‐19: aOR = 1.58, 95% CI: 1.41‐1.77, I 2 = 0.58; inpatients with COVID‐19: aOR = 1.66, 95% CI: 1.34‐2.06, I 2 = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4‐4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68‐2.68, I 2 = 0.43), and for metastatic cancers. Meta‐regression suggested risk of COVID‐19‐related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37‐1.75) at 1 year and aOR = 0.98 (95% CI: 0.80‐1.20) at 5 years post‐cancer diagnosis/treatment. In conclusion, before COVID‐19‐vaccination, risk of COVID‐19‐related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".