INEQUITIES IN ACCESS TO ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION AND TIME TO SURGERY BASED ON SOCIOECONOMIC STATUS: A PROVINCIAL COHORT STUDY
Bibliographic record
Abstract
A limited but growing pool of evidence from orthopaedic literature suggests that inequalities persist in health care systems around the world. A primarily publicly funded health care system such as there is in Canada should ideally help to modulate these inequalities. The purpose of this study was to investigate the effects of socioeconomic status (SES) on availability and timing of ACL reconstruction (ACLR) surgery based on the Manitoba experience. A retrospective population-based study was conducted using data assembled through the Manitoba Centre for Health Policy (MCHP). A comparison of patients that underwent ACLR and those that were diagnosed with an ACL injury by a surgeon but did not proceed to surgery was conducted (2015–2020). Time from primary care physician diagnosis to orthopaedic consult (time to consult) and time from consult to surgery (time to surgery) were also evaluated with respect to SES for patients (1990–2020). Logistic regression was performed using surgery as the dependent and SES as the independent variable. SES was operationally defined using the Socioeconomic Status Factor Index-2, a factor value established by MCHP that takes into account average household income, percent of single parent households, unemployment rate for those 15 years or older, and rate of high school education based on postal code. SEFI-2 scores range from −5 to +5 with lower scores being more positive SES. Logistic regression was also performed using time to surgery of more or less than a year as the dependent variable. From 2015 to 2020, 1454 patients underwent ACLR in Manitoba compared to 915, with the same diagnostic codes given by the surgeon, who did not. The mean SEFI-2 score for those that underwent ACLR was −0.28 (95% CI 0.04) and for those that did not it was −0.005 (95% CI −0.07; p < 0 .001). The odds ratio for those with a score greater than +1 of undergoing ACLR was 0.37 (95% CI 0.12; p < 0 .001) and between 0 and +1 was 0.60 (95% CI 0.15; p < 0 .001) compared to other SEFI-2 score levels. Of the 7,868 patients that underwent ACLR between 1990 and 2020, only 6% were in the lowest SES category compared to 16% in the highest (Figure 1). Mean time to consult was 0.71 (95% CI 0.05) years and mean time to surgery from consult was 0.54 (95% CI 0.03). The odds of those in the lowest SES category having their surgical consult within 1 year of diagnosis was not different than those in other categories (0.77 (95% CI 0.20) years; p=0.09). The odds of having surgery within 1 year of consult was significant (0.72 (95% CI 0.18) years; p=0.02). Patients of lower SES were less likely to undergo ACLR compared to those of higher SES. Furthermore, patients of lower economic status were less likely to have their surgery within 1 year of initial consultation. There was no difference in time from initial diagnosis by a primary care physician to surgical consult. For any figures or tables, please contact the authors directly.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".