The burden of Ischemic Heart Disease and the Epidemiologic Transition in the Eastern Mediterranean Region: 1990-2019
Bibliographic record
Abstract
Abstract Introduction The purpose of this study is to investigate the trends of the burden of ischemic heart disease (IHD) in the Eastern Mediterranean Region (EMR) countries from 1990 to 2019. Method Prevalence, disability-adjusted life years (DALYs), death, DALYs attribution risk factors, healthcare access and quality index (HAQ), and universal health care (UHC) were extracted from the database of the Global Burden of Disease study (GBD) for the EMR countries. Data stratification is based on the social demographic index (SDI). Cardiac rehabilitation data was obtained from the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR) and other information was e obtained by an advanced search of individualized countries’ data. Result IHD age-standardized prevalence increased from 4.96% to 5.31% in the EMR from 1990-2019 while it decreased at the global level. In the EMR, the trend of age-standardized IHD death and DALYs rates decreased by 11.39% and 15.36% between 1990 and 2019 respectively, however, both rates were higher than the global rates. The burden of IHD in males was higher than females. The highest decrease of IHD age-standardized prevalence, death and DALYs rate in the EMR countries occurred in Bahrain (-3.72%, -64.95%and-69.08%, respectively). However, the most increase of prevalence happened in Oman with a change of 14.40% and for death and DALY rates was in Pakistan (29.62% and 31.93%, respectively) in the studied period. The top three attributed risk factor to IHD DALYs in the EMR in 2019 were high systolic blood pressure, high low-density lipoprotein cholesterol, and particular matter pollution. The 29-year trend of an attributed risk factor to IHD DALYs in the EMR (1990-2019) showed that the two factors of high fasting plasma glucose (64.03%) and high BMI (23.39%) had an increasing trend, respectively. Conclusion Our results showed an increased trend of the prevalence of IHD in the EMR that requires well planned prevention and treatment strategies. Developing and implementing programs to address the risk factors through health promotion and education, preventive programs, and medical care should be a priority for countries in this region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".