Estimated Impact of COVID-19 on Preventive Care Service Delivery: An Observational Cohort Study
Bibliographic record
Abstract
Abstract Background: COVID-19 has caused significant healthcare service disruptions. Surgical procedure backlogs have been calculated but not those for other healthcare services. This study aims to estimate the backlog of key primary preventive care services caused by COVID-19. Methods: This observational study assessed preventive care screening rates at three primary care clinics in Ottawa, Ontario from March to November 2020 using data from 22,685 electronic medical records. The change in cervical cancer, colorectal cancer, and type 2 diabetes screening rates were extrapolated using 2016 census data, estimating the volume of key services delayed by COVID-19 across Ontario and Canada.Results: The mean percentage of patients appropriately screened for cervical cancer decreased by 7.5% (-0.3% to -14.7%; 95% CI), colorectal cancer decreased by 8.1% (-0.3% to -15.8%; 95% CI), and type 2 diabetes decreased by 4.5% (-0.2% to -8.7%; 95% CI). Extrapolation estimated that Ontarians delayed 288,000 cervical cancer (11,000 to 565,000; 95% CI), 326,000 colorectal cancer (13,000 to 638,000; 95% CI), and 274,000 type 2 diabetes screenings (13,000 to 535,000; 95% CI). Extrapolation to the Canadian population nearly triples these numbers. Re-opening measures have not reversed these trends.Interpretation: COVID-19 decreased the delivery of preventive care services, which may cause delayed diagnoses, increased mortality, and increased health care costs. Virtual care and reopening measures have not restored the provision of preventive care services. Electronic medical record data could be leveraged to improve screening via panel management. Additional, system-wide primary care and laboratory capacity will be needed to restore pre-COVID-19 screening rates.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".