Surgical wait times and socioeconomic status in a public healthcare system: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: One aim of publicly-funded health care systems is to provide equitable access to care irrespective of ability to pay. At the same time, differences in socioeconomic status (SES) are associated with health outcomes and access to care, including waiting times for surgery. In public systems where both high- and low-SES patients use the same resources, low-SES patients may be adversely impacted in surgical waiting times. The purpose of this study was to determine whether a publicly-funded health system can provide equitable access to surgical care across socioeconomic status. METHODS: Patient-level records were obtained from a comprehensive provincially-administered surgical wait time database, encompassing years 2006-2015 and 98% of Ontario hospitals. Patient SES was determined by linking postal code with the Material and Social Deprivation Index. Surgical waiting times (time in days between decision to treat and surgery) accounted for patient-initiated delays in treatment, and regression analysis considered age, SES, rurality, sex, priority level for surgical urgency (assigned by surgeons), surgical subspecialty, number of visits, and procedure year. RESULTS: For the 4,253,305 surgical episodes, the mean wait time was 62.3 (SD 75.4) days. Repeated measures least squares regression analysis showed the least deprived SES quintile waited 3 days longer than the most deprived quintile. Wait times dropped in the initial study period but then increased. The proportion of procedures exceeding wait time access targets remained low at 11-13%. CONCLUSIONS: The least deprived SES quintile waited the longest, although the absolute difference was small. This study demonstrates that publicly-funded healthcare systems can provide equitable access to surgical care across SES.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".