Trends and Drivers of Declining Stroke Mortality in British Columbia: A Population-Based Study (2002–2022)
Bibliographic record
Abstract
BACKGROUND: Stroke remains a leading cause of death in British Columbia (BC), Canada. Understanding whether mortality declines are driven by prevention (reduced incidence) or improved survival (treatment) can inform public health and acute care planning. METHODS: We conducted a population-based study of 123,075 stroke events from 2002 to 2022 among BC residents aged 35-110 years, using linked administrative datasets. We calculated age-standardized rates of stroke events, 30-day case fatality and mortality, stratifying the rates by sex, age, income and geography. Regression models estimated temporal changes and relative contributions of declining event rates and case fatality to mortality reductions. RESULTS: Age-standardized stroke event rates declined by 33% in females (208-140 per 100,000) and 25% in males (248-187) but increased among adults aged 35-54 (+14% females, +27% males). Females experienced a higher burden of stroke events as pre-admission deaths, particularly among 85+. Case fatality fell by 22% in females (40-31 per 100 events) and 15% in males (37-32), with the greatest improvements in younger adults. Mortality declined by 53% in females (72-34 per 100,000) and 43% in males (72-41) primarily driven by declines in case fatality. Disparities by sex, income and geography persisted. CONCLUSION: Improved survival is the main driver of declining stroke mortality in BC, particularly in recent years. Socioeconomic, sex and age disparities persist, warranting focused strategies to address inequities and the rising stroke burden among younger populations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".