Variations in incidence of venous thromboembolism in low-, middle-, and high-income countries
Bibliographic record
Abstract
AIMS: To examine the rates of venous thromboembolism (VTE) in high-income, upper middle-income, and lower middle/low-income countries (World Bank Classification). METHODS AND RESULTS: We examined the rates of VTE in high-income, upper middle-income, and lower middle/low-income countries (World Bank Classification) in a cohort derived from four prospective international studies (PURE, HOPE-3, ORIGIN, and COMPASS). The primary outcome was a composite of pulmonary embolism, deep vein thrombosis, and thrombophlebitis. We calculated age- and sex-standardized incidence rates (per 1000 person-years) and used a Cox frailty model adjusted for covariates to examine associations between the incidence of VTE and country income level. A total of 215 307 individuals (1.5 million person-years of follow-up) from high-income (n = 60 403), upper middle-income (n = 42 066), and lower middle/low-income (n = 112 838) countries were included. The age- and sex-standardized incidence rates of VTE per 1000 person-years in high-, upper middle-, and lower middle/low-income countries were 0.87, 0.25, and 0.06, respectively. After adjusting for age, body mass index (BMI), smoking, antiplatelet therapy, anticoagulant therapy, education level, ethnicity, and incident cancer diagnosis or hospitalization, individuals from high-income and upper middle-income countries had a significantly higher risk of VTE than those from lower middle/low-income countries [hazard ratio (HR) 3.57, 95% confidence interval (CI) 2.40-5.30 and HR 2.27, 95% CI 1.59-3.23, respectively]. The effect of country income level on VTE risk was markedly stronger in people with a lower BMI, hypertension, diabetes, non-White ethnicity, and higher education. CONCLUSION: The rates of VTE are substantially higher in high-income than in low-income countries. The factors underlying the increased VTE risk in higher-income countries remain unknown.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".