The Impact of the COVID-19 Pandemic on Wait-Times for Ophthalmic Surgery in Ontario, Canada: A Population-Based Study
Bibliographic record
Abstract
Michael Balas,1 Diana Vasiliu,2 Gener Austria,2 Tina Felfeli3– 5 1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; 2Health System Intelligence Team, Health System Performance & Support Portfolio, Ontario Health, Toronto, ON, Canada; 3Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada; 4Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, ON, Canada; 5The Institute of Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, ON, CanadaCorrespondence: Tina Felfeli, Toronto Western Hospital, 399 Bathurst Street, 6-East Room 432, Toronto, ON, M5T 2S8, Canada, Tel +1 647 678 1634, Fax +1 416 340 3459, Email tina.felfeli@mail.utoronto.caObjective: To investigate the effects of the COVID-19 pandemic on case volumes and wait-times for ophthalmic surgery in Ontario, Canada.Design: Population-based retrospective cohort study.Participants: Patients undergoing ophthalmic surgery in Ontario, Canada, from 2010 to 2021, collected from the Ontario Health Wait Times Information System (WTIS) database.Methods: The WTIS contains non-emergent surgical case volume and wait-time data for six ophthalmic subspecialty surgery types, three priority levels (low, medium, high) and 14 different regions in Ontario. Case volume and wait-times were compared between the COVID-19 pandemic (2020– 2021) and the preceding time period (2010– 2019) across all stratifications.Results: There was a significant decrease in case volumes and significant increase in wait-times across geographic regions, priority levels, and subspecialty surgeries from the pre-pandemic to pandemic period. Moreover, COVID-19 exacerbated pre-existing wait-time disparities between sexes, with females waiting 4.1 days longer than males overall to receive surgery in 2010– 2019 compared to waiting 8.8 days longer in 2020– 2021 (117% increase).Conclusion: These findings highlight the impact of the COVID-19 pandemic on ophthalmic surgical wait times in Ontario. Cataract, strabismus and oculoplastic surgeries, the Waterloo Wellington, Central, and South East regions of Ontario, and those with female sex had the greatest relative increases in wait-times during the pandemic.Keywords: ophthalmology, surgery, wait time
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".