Incidence of childhood cancer in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has had a major impact on access to health care resources. Our objective was to estimate the impact of the COVID-19 pandemic on the incidence of childhood cancer in Canada. We also aimed to compare the proportion of patients who enrolled in clinical trials at diagnosis, presented with metastatic disease or had an early death during the first 9 months of the COVID-19 pandemic compared with previous years. Methods: We conducted an observational study that included children younger than 15 years with a new diagnosis of cancer between March 2016 and November 2020 at 1 of 17 Canadian pediatric oncology centres. Our primary outcome was the monthly age-standardized incidence rates (ASIRs) of cancers. We evaluated level and trend changes using interventional autoregressive integrated moving average models. Secondary outcomes were the proportion of patients who were enrolled in a clinical trial, who had metastatic or advanced disease and who died within 30 days. We compared the baseline and pandemic periods using rate ratios (RRs) and 95% confidence intervals (CIs). Results: Age-standardized incidence rates during COVID-19 quarters were 157.7, 164.6, and 148.0 per million, respectively, whereas quarterly baseline ASIRs ranged between 150.3 and 175.1 per million (incidence RR 0.93 [95% CI 0.78 to 1.12] to incidence RR 1.04 [95% CI 0.87 to 1.24]). We found no statistically significant level or slope changes between the projected and observed ASIRs for all new cancers (parameter estimate [β], level 4.98, 95% CI −15.1 to 25.04, p = 0.25), or when stratified by cancer type or by geographic area. Clinical trial enrolment rate was stable or increased during the pandemic compared with baseline (RR 1.22 [95% CI 0.70 to 2.13] to RR 1.71 [95% CI 1.01 to 2.89]). There was no difference in the proportion of patients with metastatic disease (RR 0.84 [95% CI 0.55 to 1.29] to RR 1.22 [0.84 to 1.79]), or who died within 30 days (RR 0.16 [95% CI 0.01 to 3.04] to RR 1.73 [95% CI 0.38 to 15.2]). Interpretation: We did not observe a statistically significant change in the incidence of childhood cancer, or in the proportion of children enrolling in a clinical trial, presenting with metastatic disease or who died early during the first 9 months of the COVID-19 pandemic, which suggests that access to health care in pediatric oncology was not reduced substantially in Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".