Stroke mortality in Greece (2001–2021): Trends, sex differences, and the impact of population aging
Bibliographic record
Abstract
BACKGROUND: Despite stroke being a leading cause of mortality in Greece, long-term national data on stroke mortality trends remain limited. This study aimed to describe trends in stroke mortality in Greece between 2001 and 2021, accounting for demographic shifts and changes in care delivery, using nationwide mortality and population data. METHODS: We analyzed cause-of-death data from the Hellenic Statistical Authority (ELSTAT) for 2001-2021. Stroke deaths were defined using International Classification of Diseases, Ninth Revision (ICD-9) (430-438) and Tenth Revision (ICD-10) (I60-I69, G45x) codes. Crude and age-standardized mortality rates (using the GBD 2019 standard) were computed annually and stratified by sex. Negative binomial regression was used to estimate the average annual percent change (AAPC) in mortality. Sex-specific trends, age-specific rate ratios, and time interactions were examined. A decomposition analysis using the Das Gupta method was conducted to quantify the contribution of population aging versus changes in age-specific stroke mortality. RESULTS: Between 2001 and 2021, crude stroke mortality declined from 171.1 to 116.5 per 100,000, and age-standardized mortality declined from 90.5 to 36.4 per 100,000. Crude mortality remained higher in women, but age-standardized mortality was consistently lower compared with men. The female advantage in age group-specific stroke mortality has narrowed over time and reversed in the ⩾80 age group where females experience higher mortality than men. The estimated AAPC for the overall population was -1.90%, corresponding to approximately 292 fewer stroke deaths per year. Decomposition analysis revealed that improvements in age-specific mortality outweighed the adverse effects of population aging. CONCLUSION: Despite substantial population aging and rising crude all-cause mortality, stroke mortality in Greece has declined significantly over the past two decades. This trend likely reflects improvements in acute stroke care and reduced case fatality, rather than a decrease in incidence. Although recent efforts have expanded access to acute reperfusion therapies and stroke units, further gains are possible. Continued investment in national stroke systems and implementation of a comprehensive stroke registry are essential for sustaining and accelerating progress.Data access statement:The study utilized publicly available aggregate data (https://www. STATISTICS: gr/en/home).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".