A Multi-Country Effort to Understand Temperature Associations with Stroke-Specific Mortality
Bibliographic record
Abstract
BACKGROUND AND AIM: Globally, extreme temperatures contribute to millions of deaths each year, including stroke-related deaths. However, existing environmental evidence has overlooked the distinct causes and mechanisms of different stroke outcomes. Stroke-specific studies have been limited to single-city or single-country analyses, that are limited by publication bias and generalizability. Within the Multi-country Multi-City (MCC) Network, we built a new mortality database for ischemic and hemorrhagic stroke to conduct a multi-national, multi-decade analysis on the relationship between extreme temperatures and the two most common causes of stroke. METHOD: We used a two-stage protocol to analyze stroke-specific deaths. In the first stage, we fitted conditional quasipoisson regression for daily mortality counts with distributed lag non-linear models for the temperature exposure in each city. In the second stage, the cumulative risk from each city was pooled using mixed effects meta-analysis, accounting for potential higher-level effect modification and clustering of cities with similar features. We compared temperature-stroke associations across country-level gross domestic product (GDP) per capita. We computed excess deaths in each city that are attributable to the hottest and coldest 2.5% of days. RESULTS: We collected a total of 3,443,969 ischemic stroke and 2,454,267 hemorrhagic stroke deaths from 522 cities in 25 countries. For every 1000 ischemic stroke deaths, we found that extreme cold and hot days contributed 9.1 (95% eCI:8.6,9.4) and 2.2 (95%eCI:1.9,2.4) excess deaths, respectively. For every 1000 hemorrhagic stroke deaths, extreme cold and hot days contributed 11.2 (95% eCI:10.9,11.4) and 0.7 (95% eCI:0.5,0.8) excess deaths, respectively. The study found that countries with low GDP per capita were at higher risk of heat-related hemorrhagic stroke mortality than countries with high GDP per capita (p=0.02). CONCLUSIONS: As climate change is driving more extreme temperatures and weather events, urgent attention to meaningful clinical outcomes can help identify and minimize the risk of death from stroke, especially in low-income countries.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".