Long-term trends in population-based hospitalisation rates for myocardial infarction in England: a national database study of 3.5 million admissions, 1968–2016
Bibliographic record
Abstract
AIM: To analyse the timing and scale of temporal changes in rates of hospitalised myocardial infarction (MI) in England by age and sex from 1968 to 2016. METHODS: MI admissions for adults aged 15-84 years were identified from electronic hospital data. We calculated age-standardised and age-specific rates, and examined trends using joinpoint. RESULTS: From 1968 to 2016, there were 3.5 million admissions for MI in England (68% men). Rates increased in the early years of the study in both men and women, peaked in the mid-1980s (355 per 100 000 population in men; 127 in women) and declined by 38.8% in men and 37.4% in women from 1990 to 2011. From 2012, however, modest increases were observed in both sexes. Long-term trends in rates over the study period varied by age and sex, with those aged 70 years and older having the greatest and most sustained increases in the early years (1968-1985). During subsequent years, rates decreased in most age groups until 2010-2011. The exception was younger women (35-49 years) and men (15-34 years) who experienced significant increases from the mid-1990s to 2007 (range +2.1%/year to 4.7%/year). From 2012 onwards, rates increased in all age groups except the oldest, with the most marked increases in men aged 15-34 years (7.2%/year) and women aged 40-49 (6.9%-7.3%/year) . CONCLUSION: Despite substantial declines in hospital admission rates for MI in England since 1990, the burden of annual admissions remains high. Continued surveillance of trends and coronary disease preventive strategies are warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".