Mortality in the Americas from 2013 to 2015 resulting from diseases, conditions and injuries which are 100% alcohol‐attributable
Bibliographic record
Abstract
AIMS: To describe mortality in the Americas from 2013 to 2015 inclusive resulting from diseases, conditions and injuries which are 100% attributable to alcohol consumption. DESIGN AND SETTING: Mortality registry, population-based study. The data come from 30 of the 35 countries of the Americas for the triennium of 2013 to 2015. PARTICIPANTS AND CASES: A total of 18 673 791 deaths coded by three-digit ICD-10 codes were analyzed. MEASUREMENTS: Cause (underlying), and age-specific and age-adjusted mortality rates were calculated by sex and country. FINDINGS: From 2013 to 2015 inclusive, among 30 of the 35 countries of the Americas, an average of 85 032 deaths per year were entirely attributable to alcohol. Men accounted for 83.1% of all 100% alcohol-attributable deaths, and death rates were higher for men than for women across all countries; however, the ratios of 100% alcohol-attributable deaths by sex varied by country. The majority of all 100% alcohol-attributable deaths occurred among those aged under 60 years (64.9%) and were due to liver disease (63.9%) followed by neuropsychiatric disorders (27.4%). Age-adjusted 100% alcohol-attributable mortality rates were highest in Nicaragua (23.2 per 100 000) and Guatemala (19.0 per 100 000), although the majority of all 100% alcohol-attributable deaths occurred in the United States 36.9%), Brazil (24.8%), and Mexico (18.4%). CONCLUSIONS: From 2013 to 2015, more than 85 000 deaths in the Americas were 100% attributable to alcohol. Most of those occurred in people under 60 years and the highest mortality rates occurred in the United States, Brazil and Mexico.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".