Socioeconomic Status, Hospital Volume, and Stroke Fatality in Canada
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Low socioeconomic status is associated with stroke fatality; however, the mechanism behind this association is uncertain. We sought to determine whether residence in a low-income neighborhood was associated with admission to low-volume facilities and whether this contributed to differences in fatality after stroke. METHODS: All hospitalizations for ischemic stroke from April 2003 to March 2004 were identified from a national administrative database containing patient-level sociodemographic, diagnostic, procedural, and administrative information. Patients were assigned to income quintiles based on the median income of their primary neighborhood of residence and then categorized as low income (quintiles 1 and 2) or high income (quintiles 3 through 5). Hospitals were categorized as low or high volume on the basis of their annual number of stroke admissions. Multivariable analyses were performed to compare stroke fatality at 7 days and at discharge in patients in low- and high-income groups seen at low- and high-volume facilities. RESULTS: Overall, 25,228 patients with ischemic stroke were included in the analysis. Those from high-income areas were more likely to be admitted to high-volume hospitals. Fatality at 7 days was 8.4%, 8.2%, 7.7%, 7.1, and 6.6% (chi(2)=0.002) for income quintiles 1 (lowest) to 5 (highest), respectively. Low-income patients admitted to low-volume hospitals had the highest risk-adjusted stroke fatality. CONCLUSIONS: Patients from low-income areas presenting with acute stroke are more likely to be seen in low-volume facilities. This subgroup of patients had a higher risk-adjusted fatality than those from high-income areas seen at high-volume facilities. Understanding the pathways through which socioeconomic status affects health care may lead to strategies for quality improvement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".