Burden of heart failure and underlying causes in 195 countries and territories from 1990 to 2017
Bibliographic record
Abstract
AIMS: To provide the first systematic analysis of the burden and underlying causes of heart failure (HF) in 195 countries and territories from 1990 to 2017. METHODS AND RESULTS: We collected detailed information on prevalence, years lived with disability (YLDs), and underlying causes of HF from the Global Burden of Disease study 2017. Numbers and age-standardized rates of HF prevalence and YLDs were compared by age, sex, socio-demographic index (SDI), and location. The proportions of HF age-standardized prevalence rates due to 23 underlying causes were also presented. Globally, the age-standardized prevalence and YLD rates of HF in 2017 were 831.0 and 128.2 per 100 000 people, a decrease of -7.2% and -0.9% from 1990, respectively. Nevertheless, the absolute numbers of HF prevalent cases and YLDs have increased by 91.9% and 106.0% from 1990, respectively. There is significant geographic and socio-demographic variation in the levels and trends of HF burden from 1990 to 2017. Among all causes of HF, ischaemic heart disease accounted for the highest proportion (26.5%) of age-standardized prevalence rate of HF in 2017, followed by hypertensive heart disease (26.2%), chronic obstructive pulmonary disease (23.4%). CONCLUSION: HF remains a serious public health problem worldwide, with increasing age-standardized prevalence and YLD rates in countries with relatively low SDI. More geo-specific strategies aimed at preventing underlying causes and improving medical care for HF are warranted to reduce the future burden of this condition.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| 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".