National and subnational burden of stroke in Iran from 1990 to 2019
Bibliographic record
Abstract
BACKGROUND: Data on the burden of stroke and changing trends at national and subnational levels are necessary for policymakers to allocate recourses appropriately. This study presents estimates of the stroke burden from 1990 to 2019 using the results of the Global Burden of Disease (GBD) 2019 study. METHODS: For the GBD 2019, verbal autopsy and vital registration data were used to estimate stroke mortality. Cause-specific mortality served as the basis for estimating incidence, prevalence, and disability-adjusted life years (DALYs). The burden attributable to stroke risk factors was calculated by a comparative risk assessment. Decomposition analysis was applied to determine the contribution of population aging, population growth, and changes in the age-specific incidence rates. RESULTS: In 2019, the number of prevalent cases, incident cases, and deaths due to stroke in Iran were 963,512; 102,778; and 40,912, respectively. The age-standardized incidence rate (ASIR) and the age-standardized death rate (ASDR) decreased from 1990 to 2019. Of national stroke ASDRs in 2019, 44.7% (35.7-54.7%) were attributable to hypertension and 28.8% (15.2-57.4) to high fasting plasma glucose. At the subnational level, the trend of the stroke incidence and mortality rate decreased in all provinces. Stroke was responsible for 4.48% of total DALYs in 2019 (3.38% due to ischemic stroke, 0.87% due to intracerebral hemorrhage, and 0.22% due to subarachnoid hemorrhage). CONCLUSION: ASIR and ASDR of stroke are decreasing nationally and subnationally; however, the number of incident cases and deaths are increasing in all SDI quintiles, possibly due to population growth.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".