Burden of ischemic heart disease and its attributable risk factors in 204 countries and territories, 1990–2019
Bibliographic record
Abstract
AIMS: To report the prevalence, deaths, and disability-adjusted life years (DALYs) associated with ischemic heart disease (IHD) and its attributable risk factors in 204 countries and territories from 1990 to 2019, by age, sex, and socio-demographic index (SDI). METHODS AND RESULTS: Ischemic heart disease was defined as acute myocardial infarction (MI) and chronic IHD (angina; asymptomatic IHD following MI). Cause of death ensemble modelling was used to produce fatality estimates. The prevalence of the non-fatal sequalae of IHD was estimated using DisMod MR 2.1. All estimates were presented as counts and age-standardized rates per 100 000 population. In 2019, IHD accounted for 197.2 million (177.7-219.5) prevalent cases, 9.1 million (8.4-9.7) deaths, and 182.0 million (170.2-193.5) DALYs worldwide. There were decreases in the global age-standardized prevalence rates of IHD [-4.6% (-5.7, -3.6)], deaths [-30.8% (-34.8, -27.2)], and DALYs [-28.6% (-33.3, -24.2)] from 1990 to 2019. In 2019, the global prevalence and death rates of IHD were higher among males across all age groups, while the death rate peaked in the oldest group for both sexes. A negative association was found between the age-standardized DALY rates and SDI. Globally, high systolic blood pressure (54.6%), high low-density lipoprotein cholesterol (46.6%), and smoking (23.9%) were the three largest contributors to the DALYs attributable to IHD. CONCLUSION: Although the global age-standardized prevalence, death, and DALY rates all decreased. Prevention and control programmes should be implemented to reduce population exposure to risk factors, reduce the risk of IHD in high-risk populations, and provide appropriate care for communities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".