Association of socioeconomic status with medical assistance in dying: a case–control analysis
Bibliographic record
Abstract
OBJECTIVES: Economic constraints are a common explanation of why patients with low socioeconomic status tend to experience less access to medical care. We tested whether the decreased care extends to medical assistance in dying in a healthcare system with no direct economic constraints. DESIGN: Population-based case-control study of adults who died. SETTING: Ontario, Canada, between 1 June 2016 and 1 June 2019. PATIENTS: Patients receiving palliative care under universal insurance with no user fees. EXPOSURE: Patient's socioeconomic status identified using standardised quintiles. MAIN OUTCOME MEASURE: Whether the patient received medical assistance in dying. RESULTS: A total of 50 096 palliative care patients died, of whom 920 received medical assistance in dying (cases) and 49 176 did not receive medical assistance in dying (controls). Medical assistance in dying was less frequent for patients with low socioeconomic status (166 of 11 008=1.5%) than for patients with high socioeconomic status (227 of 9277=2.4%). This equalled a 39% decreased odds of receiving medical assistance in dying associated with low socioeconomic status (OR=0.61, 95% CI 0.50 to 0.75, p<0.001). The relative decrease was evident across diverse patient groups and after adjusting for age, sex, home location, malignancy diagnosis, healthcare utilisation and overall frailty. The findings also replicated in a subgroup analysis that matched patients on responsible physician, a sensitivity analysis based on a different socioeconomic measure of low-income status and a confirmation study using a randomised survey design. CONCLUSIONS: Patients with low socioeconomic status are less likely to receive medical assistance in dying under universal health insurance. An awareness of this imbalance may help in understanding patient decisions in less extreme clinical settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".