PD177 The Effect Of COVID-19 On Cancer Screening In Brazil, Canada, And The USA: A Cross-National Study
Bibliographic record
Abstract
Introduction The COVID-19 pandemic strained hospital systems and diverted resources, prompting a reorientation of healthcare priorities. This shift disrupted patient access to preventive cancer screenings and curtailed interactions between medical professionals and patients. This study aimed to examine changes in cancer screening during the COVID-19 pandemic period (2019 to 2021) in Brazil, Canada, and the USA. Methods The study included a literature review of academic articles, health reports, and government data that focused on the impact of the pandemic on cancer screening. Official health data in Brazil, Canada, and the USA were collected from medical records, national health databases, and screening statistics. A comparative analysis was conducted to unveil the changes in access to screening services for colorectal cancer (CRC), breast cancer, hepatocellular carcinoma (HCC), and cervical cancer. Results During the COVID-19 period, significant declines in cancer screening were observed globally. In Canada, CRC diagnoses dropped by 55 percent and remained 20 percent lower than averages from previous years, with an estimated 467 cases undiagnosed by August 2020. In the USA, HCC screenings were reduced by 44 percent, while cervical cancer screenings for women aged 21 to 29 years plummeted by 78 percent. Additionally, mammography screenings fell drastically from 180,724 in March to May 2019 to just 1,681 in the same period of 2020, leading to fewer breast cancers detected and a surge in symptomatic, aggressive tumors. Similarly, Brazil saw a 39 percent drop in breast cancer screenings. Conclusions The COVID-19 pandemic significantly disrupted cancer screening programs across Brazil, Canada, and the USA, resulting in marked declines in the number of diagnoses of various cancers. This reduction highlights the extensive impact of the pandemic on preventive health care, necessitating strategies to address the backlog and ensure timely cancer detection and treatment in the post-pandemic era.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".