Burden of diseases and injuries attributable to alcohol consumption in the Middle East and North Africa region, 1990–2019
Bibliographic record
Abstract
Alcohol consumption is associated with a number of diseases and injuries, including cardiovascular diseases, cancers, mental and neurological disorders, as well as transport-related injuries. This article reports the alcohol-attributable burden of diseases and injuries at the regional and national levels in the Middle East and North Africa (MENA) region between 1990 and 2019, by sex, age, underlying cause, and Socio-demographic Index (SDI). The regional deaths and disability-adjusted life-years (DALYs) attributable to alcohol consumption were reported for the MENA region, between 1990 and 2019, using the methodological framework and analytical strategies adopted by the Global Burden of Disease (GBD) study 2019. The estimates were all reported as counts, population-attributable fractions, and age-standardised rates per 100,000 population, along with their corresponding 95% uncertainty intervals (UIs). Also, the average annual percentage changes were used to represent the trends of age-standardised rates. In 2019, there were an estimated 22.0 thousand deaths (95% UI: 16.1-29.4) and 1.1 million DALYs (0.8-1.3) attributable to alcohol consumption in the MENA region. The number of DALYs attributable to alcohol consumption were much higher in men (878.0 thousand, 691.4-1104.8) than among women (181.8, 138.6-232.0). The overall age-standardised death and DALY rates attributable to alcohol consumption decreased by 34.5% (13.2-48.3) and 31.9% (16.9-42.5), respectively, over the study period. Egypt (10.1 [5.7-16.6]) and Kuwait (1.1 [0.8-1.5]) had the highest and lowest age-standardised death rates attributable to alcohol consumption, respectively. In 2019, the number of deaths and DALYs in the MENA region were highest in those aged 60-64 and 50-54 years, respectively. A negative association was observed between a country's SDI and their corresponding age-standardised DALY rates over the period 1990 to 2019. Digestive diseases were the main contributor to the alcohol-attributable burden. Over 1990-2019, the regional deaths and DALYs of diseases and injuries attributable to alcohol consumption decreased with AAPC of - 1.45 (- 1.78 to - 1.12) and - 1.31 (- 1.46 to - 1.15), respectively. The death and DALY rates attributable to alcohol consumption in the MENA region have decreased over the past three decades. Further decreases can be facilitated by implementing country-level policies and increasing public awareness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".