Heart failure in an ageing world: closing the evidence-equity gap
Bibliographic record
Abstract
This invited editorial refers to ‘Global burden of heart failure in older adults: trends, socioeconomic inequalities, and future projections from 1990 to 2035’, by J. Gu et al., https://doi.org/10.1093/ehjqcco/qcaf047. Heart failure (HF) is now seen as a litmus test for how well societies manage the twin challenges of an ageing population and the increasing burden of chronic diseases: United-Nations projections indicate that by 2035 > 1.1 billion people, one in eight of the planet’s inhabitants, will be aged ≥65 years,1 while contemporary epidemiology counts at least 64 million individuals already living with HF, a figure that has doubled since the turn of the millennium.2 In the present issue of this Journal, Gu et al. provide the most granular and comprehensive audit to date of HF in older adults across 204 countries, revealing that between 1990 and 2021 cases surged from 14.1 million to 36.2 million and years-lived-with-disability (YLDs) from 1.34 million to 3.45 million; Bayesian age-period-cohort modelling projects a further climb to 4935 prevalent cases and 473 YLDs per 100 000 older adults by 2035.3 Crucially, the results of the decomposition analysis show that 89% of this growth is related to demographic factors rather than biological ones: in high socio-demographic index (SDI) regions, population ageing actually reduced the age-standardized prevalence by 39%, whereas in middle-SDI regions it amplified the burden by 29%, a paradox that tracks neatly with the uneven penetration of guideline-directed medical therapy (GDMT).4
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".