Predicted long-term impact of COVID-19 pandemic-related care delays on cancer incidence and mortality in Canada
Bibliographic record
Abstract
Abstract Objectives The COVID-19 pandemic has affected cancer care worldwide. This study aimed to estimate the long-term impacts of the pandemic on cancer incidence and mortality in Canada using a mathematical model. Methods We developed a stochastic microsimulation model to estimate the cancer care disruptions and its long-term impact on cancer incidence and mortality in Canada. The model reproduces cancer incidence, survival, and epidemiology in Canada, by using cancer incidence, stage at diagnosis and survival data from the Canadian Cancer Registries. We modeled reported declines in cancer diagnoses and treatments recorded in provincial administrative datasets from March 2020-June 2021. We assumed that diagnostic and treatment delays lead to an increased rate of death. Based on the literature, we assumed each 4-week delay in diagnosis and treatment would lead to a 6% to 50% higher rate of cancer death. Results are the median predictions of 10 stochastic simulations. Findings The model predicts that cancer care disruptions during the COVID-19 pandemic could lead to 21,247 (2·0%) more cancer deaths in Canada in 2020-2030, assuming treatment capacity is recovered to 2019 pre-pandemic levels in 2021. This represents 355,172 life years lost expected due to pandemic-related diagnostic and treatment delays. The highest absolute expected excess cancer mortality was predicted in breast, lung, and colorectal cancers, and in the provinces of Ontario, Québec, and British Columbia. Diagnostic and treatment capacity in 2021 onwards highly influenced the number of predicted cancer deaths over the next decade. Interpretation Cancer care disruptions during the Covid-19 pandemic could lead to significant life loss; however, most of these could be mitigated by increasing diagnostic and treatment capacity in the post-pandemic era to address the service backlog. Funding Canadian Institutes of Health Research Research in context Evidence before this study We performed a review of modeling studies predicting the impact of pandemic-induced disruptions to cancer care on cancer survival outcomes. We searched MEDLINE on 2 July 2021 for records published from 1 January 2020 with no language restrictions. Our search consisted of index keywords [ Cancer AND COVID-19 AND [( delay AND diagnosis ) OR ( delay AND screening ) OR ( delay AND treatment )] AND outcomes AND modelling study ]. We identified 14 studies that model the long-term effect of disruptions to cancer screening programs, diagnostic intervals, and to treatment intervals for common cancers. Most studies (9/14) assessed the impact of cancer screening disruptions but did not assume any treatment disruptions. Disruptions to cancer screening services in high income health systems were estimated to lead to small increases in cancer incidence and mortality, even with immediate resumption of screening to services after disruption periods. Fewer studies examined the impact of diagnostic referral and treatment disruptions; these are similarly predicted to lead to increases in cancer incidence and mortality, with varying impacts depending on cancer site. Due to difficulties in obtaining real-time healthcare data, previous studies have relied on assumptions regarding the duration of health care disruptions (1-, 3-, 6-, 12-, to 24-months) rather than on empirical data. All studies restricted their analysis to the impact on a single or a few cancer sites. Added value of this study Our stochastic microsimulation model is the first to assess the population-level impact of diagnostic and treatment disruptions on overall cancer mortality across all sites. Using Canadian cancer statistics and expert validation of treatment modalities, we constructed a model that reproduced pre-pandemic cancer mortality data. An important added value of this analysis compared to previous studies was that we were able to integrate empirical data on cancer-related procedures during the pandemic era to model disruptions to cancer care. Implications of all the available evidence We estimate there could be a 2·0% increase over expected cancer mortality between 2020-2030 in Canada due to pandemic-related disruptions to diagnostic and treatment intervals. Our results identified that a 10-20% increase in cancer care service capacity over pre-pandemic levels could prevent a considerable amount of the predicted excess cancer-related deaths by reducing diagnostic and treatment backlogs. By stratifying our reported outcomes by sex, age, province, and cancer site, we provide a long-term perspective that can inform post-pandemic public health policy or aid in prioritization of patients in the event of a resurgence of COVID-19. While our model is specific to Canada, it could be applied to countries that have experienced comparable COVID-19-related healthcare disruptions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".