Time trends in cardiovascular and all-cause mortality in the ‘old’ and ‘new’ European Union countries
Bibliographic record
Abstract
AIMS: There are large differences in all-cause and cardiovascular disease (CVD) mortality between eastern and western countries in Europe. We reviewed the development of these mortality trends in countries of the European Union (EU) over the past 40 years and evaluated available data regarding possible determinants of these differences. METHODS AND RESULTS: We summarized all-cause mortality and specific cardiovascular mortality for two country groups - 10 countries that joined the European Union (EU) after 2004 (East), and 15 countries that joined before 2004 (West). Standardized mortality rates were retrieved from the World Health Organization "European Health for All" database for each country between 1970 and 2007. Currently (in the 2000s), mortality due to circulatory system disease, ischemic heart disease (IHD), cerebrovascular disease (CBVD), and all-causes in the 'new' EU countries (East) is approximately twice that in the 'old' EU countries (West). These differences were much smaller in the 1970s. The increasing gap in mortality between West and East is primarily the result of a continuous and rapid improvement in the West. CONCLUSION: Differences in lifestyle (i.e. diet, alcohol consumption, physical activity, and smoking) provide insufficient explanation for the observed mortality gap in these two groups of EU countries. Higher expenditures on health, better access to invasive and acute cardiac care, and better pharmacological control of hypertension and hypercholesterolemia in the West are well documented. Socioeconomic and psychosocial factors may also contribute to the changes in mortality trends.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".