The Impact of the COVID-19 Pandemic on Costs of Custom-made Devices Used for Advanced Endovascular Aneurysm Repair Procedures
Bibliographic record
Abstract
Advanced endovascular aneurysm repair (AEVAR) using custom-made devices (CMDs) is a critical intervention for complex abdominal aortic aneurysms (AAAs). However, CMDs are among the costliest components of these procedures, and recent global inflation, exacerbated by the COVID-19 pandemic may be impacting their affordability and accessibility in Ontario, Canada. This study investigates CMD cost trends during the peri-COVID-19 pandemic period. A retrospective review was conducted on AEVAR procedures involving CMDs at a single academic hospital in Ontario from April 2018 to March 2023. CMD costs were tracked and analyzed by fiscal year, CMD type, and vendor. CMDs implanted in multiple fiscal years with complete cost data were included. Percent change in average annual cost was calculated to assess inflationary trends. Of 193 AEVAR cases, 125 CMDs met inclusion criteria. CMD costs peaked in FY 2020/21, the first full COVID-19 year, with continued increases in subsequent years. Compared to FY 2018/19, average CMD costs rose by 6.15% in FY 2020/21, 7.85% in FY 2021/22, and 9.54% in FY 2022/23. Despite policy updates to improve AEVAR funding, CMD cost increases persist and may be outpacing adjustments in provincial funding models. CMD costs for AEVAR procedures have risen notably since the onset of the COVID-19 pandemic, revealing ongoing medical inflation pressures. Current funding mechanisms may not sufficiently account for these rising costs, risking inequitable access and institutional budget strain. Policy revisions are needed to align funding models with the evolving cost landscape of advanced vascular care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".