The ophthalmic surgical backlog associated with the COVID-19 pandemic: a population-based and microsimulation modelling study
Bibliographic record
Abstract
BACKGROUND: Jurisdictions worldwide ramped down ophthalmic surgeries to mitigate the effects of COVID-19, creating a global surgical backlog. We sought to predict the long-term impact of COVID-19 on the timely delivery of non-emergent ophthalmology sub-specialty surgical care in Ontario. METHODS: This is a microsimulation modelling study. We used provincial population-based administrative data from the Wait Time Information System database in Ontario for January 2019 to May 2021 and facility-level data for March 2018 to May 2021 to estimate the backlog size and wait times associated with the COVID-19 pandemic. For the postpandemic recovery phase, we estimated the resources required to clear the backlog of patients accumulated on the wait-list during the pandemic. Outcomes were accrued over a time horizon of 3 years. RESULTS: A total of 56 923 patients were on the wait-list in the province of Ontario awaiting non-emergency ophthalmic surgery as of Mar. 15, 2020. The number of non-emergency surgeries performed in the province decreased by 97% in May 2020 and by 80% in May 2021 compared with the same months in 2019. By 2 years and 3 years since the start of the pandemic, the overall estimated number of patients awaiting surgery grew by 129% and 150%, respectively. The estimated mean wait time for patients for all subspecialty surgeries increased to 282 (standard deviation [SD] 91) days in March 2023 compared with 94 (SD 97) days in 2019. The provincial monthly additional resources required to clear the backlog by March 2023 was estimated to be a 34% escalation from the prepandemic volumes (4626 additional surgeries). INTERPRETATION: The estimates from this microsimulation modelling study suggest that the magnitude of the ophthalmic surgical backlog from the COVID-19 pandemic has important implications for the recovery phase. This model can be adapted to other jurisdictions to assist with recovery planning for vision-saving surgeries.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".