BURDEN OF CARDIOVASCULAR DISEASES, KIDNEY DISEASES AND DEMENTIA ATTRIBUTABLE TO HYPERTENSION IN FRANCE
Bibliographic record
Abstract
Objective: Cardiovascular and renal diseases and dementia constitute major hypertension-related conditions and have a high burden for public health. The aim of our study was to estimate the burden of these diseases attributable to hypertension in France by providing the number of hospital stays and deaths attributable to hypertension in 2017. Design and method: Age- and sex-specific attributable fractions were calculated by combining relative risks extracted from the literature with the prevalence of hypertension (defined as a systolic blood pressure higher than 140mmHg) estimated in the Esteban Study, a national representative survey. These fractions were applied to the nationwide statistics of death and hospitalization stays with a primary diagnosis for cardiovascular or renal diseases, or dementia whose risk is known to increase with hypertension. Data were extracted from the French National Health Insurance Information System (SNDS) which contains nationwide and exhaustive hospital stays and deaths occurring in France. Results: In France in 2017, 1 043 207 hospital stays and 185 459 deaths related to cardiovascular, renal or dementia condition occurred. Of these, 164 093 hospital stays (119 640 in men and 44 453 in women) and 35 017 deaths (17 436 in men and 16 142 in women) were estimated to be attributable to hypertension, representing 16% of all cardiovascular, renal and dementia-related hospital stays and 19% of all cardiovascular renal and dementia deaths. Regarding the attributable fraction, the largest impact was observed on hemorrhagic stroke (26.8% of cases attributable to hypertension in men and 15.8% in women) and on ischemic heart disease (25.0% in men and 15.4% in women). For all cardiovascular diseases the highest attributable fraction were reach in adults aged 55-74 years old. Conclusions: In France, the burden attributable to hypertension is high and is expected to increase in the coming years given the aging of the population. The reduction of the burden attributable to hypertension requires ambitious public health policies aimed at promoting healthy lifestyles but also an improvement in the awareness and management of hypertension, which has not improved over the last 20 years, unlike in several other European countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".