Attributable Costs of Stroke in Ontario, Canada and Their Variation by Stroke Type and Social Determinants of Health
Bibliographic record
Abstract
BACKGROUND: Estimates of attributable costs of stroke are scarce, as most prior studies do not account for the baseline health care costs in people at risk of stroke. We estimated the attributable costs of stroke in a universal health care setting and their variation across stroke types and several social determinants of health. METHODS: We undertook a population-based administrative database-derived matched retrospective cohort study in Ontario, Canada. Community-dwelling adults aged ≥40 years with a stroke between 2003 and 2018 were matched (1:1) on demographics and comorbidities with controls without stroke. Using a difference-in-differences approach, we estimated the mean 1-year direct health care costs attributable to stroke from a public health care payer perspective, accounting for censoring with a weighted available sample estimator. We described health sector-specific costs and reported variation across stroke type and social determinants of health. RESULTS: The mean 1-year attributable costs of stroke were Canadian dollars 33 522 (95% CI, $33 231-$33 813), with higher costs for intracerebral hemorrhage ($40 244; $39 193-$41 294) than ischemic stroke ($32 547; $32 252-$32 843). Most of these costs were incurred in acute care hospitals ($15 693) and rehabilitation facilities ($7215). Compared with all patients with stroke, the mean attributable costs were higher among immigrants ($40 554; $39 316-$41 793), those aged <65 years ($35 175; $34 533-$35 818), and those residing in low-income neighborhoods ($34 687; $34 054-$35 320) and lower among rural residents ($29 047; $28 362-$29 731). CONCLUSIONS: Our findings of high attributable costs of stroke, especially in immigrants, younger patients, and residents of low-income neighborhoods, can be used to evaluate potential health care cost savings associated with different primary stroke prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".