Characterizing the Impact of Procedure Funding on the Covid-19 Generated Procedure Gap in Ontario: A Population-Based Analysis
Bibliographic record
Abstract
BACKGROUND: Surgical procedures in Canada were historically funded through global hospital budgets. Activity-based funding models were developed to improve access, equity, timeliness, and value of care for priority areas. COVID-19 upended health priorities and resulted in unprecedented disruptions to surgical care, which created a significant procedure gap. We hypothesized that activity-based funding models influenced the magnitude and trajectory of this procedure gap. METHODS: Population-based analysis of procedure rates comparing the pandemic (March 1, 2020-December 31, 2021) to a prepandemic baseline (January 1, 2017-February 29, 2020) in Ontario, Canada. Poisson generalized estimating equation models were used to predict expected rates in the pandemic based on the prepandemic baseline. Analyses were stratified by procedure type (outpatient, inpatient), body region, and funding category (activity-based funding programs vs. global budget). RESULTS: In all, 281,328 fewer scheduled procedures were performed during the COVID-19 period compared with the prepandemic baseline (Rate Ratio 0.78; 95% CI 0.77-0.80). Inpatient procedures saw a larger reduction (24.8%) in volume compared with outpatient procedures (20.5%). An increase in the proportion of procedures funded through activity-based programs was seen during the pandemic (52%) relative to the prepandemic baseline (50%). Body systems funded predominantly through global hospital budgets (eg, gynecology, otologic surgery) saw the least months at or above baseline volumes, whereas those with multiple activity-based funding options (eg, musculoskeletal, abdominal) saw the most months at or above baseline volumes. CONCLUSIONS: Those needing procedures funded through global hospital budgets may have been disproportionately disadvantaged by pandemic-related health care 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".