Trends towards increase of Cardiovascular diseases mortality in USA: A comparison with Europe and the importance of preventive care
Bibliographic record
Abstract
BACKGROUND: the aim of our study was to analyze exposure of the general population to established risk factors for cardiovascular disease (CVD), which might have determined the trend towards increased mortality rates related with CVD from 2015 to 2019 in USA. MATERIAL AND METHODS: We Analyzed epidemiological of data from the US National Health and Nutrition Examination Survey and from the European Health Interview Survey to determine trends for exposure to several established risk factors for CVD from 2000 to 2018-2019. Trends of prevalence of obesity, arterial hypertension, cigarettes smoking, high cholesterol level, diabetes in the period 2000 to 2018-2019 in USA were correlated with age adjusted mortality and burden related with CVD. We correlated these trends also with educational attainment, family income and national expenditure for preventive care. RESULTS: Cardiovascular Diseases Related Mortality And Burden Decreased Significantly In Usa In The Period 2000-2015; In The Period 2015-2019 there was a trend towards increasing mortality rates. The trend in the period 2015-2019 was associated with increased exposure to several established risk factors for CVD: obesity, diabetes, cigarettes smoking and arterial hypertension. Level of education attainment and family income, and national health expenditure for information, education and counseling were statistically correlated with reduced exposure to established risk factors. Similar trends were present in Western European countries. CONCLUSIONS: Attention is required to improve education and communication, health access and care for people with poor economic conditions, homeless, minorities, to reduce CVD related mortality and burden.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".