Global, regional, and national burden of ischaemic heart disease and its attributable risk factors, 1990–2017: results from the Global Burden of Disease Study 2017
Bibliographic record
Abstract
AIMS: The aim of this study was to estimate the burden and risk factors for ischaemic heart disease (IHD) in 195 countries and territories from 1990 to 2017. METHODS AND RESULTS: Data from the Global Burden of Disease Study 2017 were used. Prevalence, incidence, deaths, years lived with disability (YLDs), and years of life lost (YLLs) were metrics used to measure IHD burden. Population attributable fraction was used to estimate the proportion of IHD deaths attributable to potentially modifiable risk factors. Globally, in 2017, 126.5 million [95% uncertainty interval (UI) 118.6 to 134.7] people lived with IHD and 10.6 million (95% UI 9.6 to 11.8) new IHD cases occurred, resulting in 8.9 million (95% UI 8.8 to 9.1) deaths, 5.3 million (95% UI 3.7 to 7.2) YLDs, and 165.0 million (95% UI 162.2 to 168.6) YLLs. Between 1990 and 2017, despite the decrease in age-standardized rates, the global numbers of these burden metrics of IHD have significantly increased. The burden of IHD in 2017 and its temporal trends from 1990 to 2017 varied widely by geographic location. Among all potentially modifiable risk factors, age-standardized IHD deaths worldwide were primarily attributable to dietary risks, high systolic blood pressure, high LDL cholesterol, high fasting plasma glucose, tobacco use, and high body mass index in 2017. CONCLUSION: Our results suggested that IHD remains a major public health challenge worldwide. More effective and targeted strategies aimed at implementing cost-effective interventions and addressing modifiable risk factors are urgently needed, particularly in geographies with high or increasing burden.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".