Global reporting of pulmonary embolism–related deaths in the World Health Organization mortality database: Vital registration data from 123 countries
Bibliographic record
Abstract
INTRODUCTION: Pulmonary embolism (PE) has not been accounted for as a cause of death contributing to cause-specific mortality in global reports. METHODS: We analyzed global PE-related mortality by focusing on the latest year available for each member state in the World Health Organization (WHO) mortality database, which provides age-sex-specific aggregated mortality data transmitted by national authorities for each underlying cause of death. PE-related deaths were defined by International Classification of Diseases, Tenth Revision codes for acute PE or nonfatal manifestations of venous thromboembolism (VTE). The 2001 WHO standard population served for standardization. RESULTS: We obtained data from 123 countries covering a total population of 2 602 561 422. Overall, 50 (40.6%) were European, 39 (31.7%) American, 13 (10.6%) Eastern Mediterranean, 13 (10.6%) Western Pacific, 3 (2.4%) Southeast Asian, and 2 (1.6%) African. Of 116 countries classifiable according to population income, 57 (49.1%) were high income, 42 (36.2%) upper-middle income, 14 (12.1%) lower-middle income, and 3 (2.6%) low income. A total of 18 726 382 deaths were recorded, of which 86 930 (0.46%) were attributed to PE. PE-related mortality rate increased with age in most countries. The reporting of PE-related deaths was heterogeneous, with an age-standardized mortality rate ranging from 0 to 24 deaths per 100 000 population-years. Income status only partially explained this heterogeneity. CONCLUSIONS: Reporting of PE-related mortality in official national vital registration was characterized by extreme heterogeneity across countries. These findings mandate enhanced efforts toward systematic and uniform coverage of PE-related mortality and provides a case for full recognition of PE and VTE as a primary cause of death.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".