A systematic comparison of the global comparative risk assessments for alcohol
Bibliographic record
Abstract
AIMS: To compare systematically the alcohol-attributable mortality and burden of disease estimates for 2016 from a recent study by Shield and colleagues and the Global Burden of Disease study 2017 (GBD). METHOD: This study compared estimates of alcohol-attributable mortality and disability adjusted life years (DALYs) lost for 2016 with regards to absolute and relative differences, by region and by cause of disease or injury. Relative differences between the two studies are reported herein as percentage (%) differences. A difference of 10% or more was considered meaningful. RESULTS: The studies estimated similar global levels of overall alcohol-attributable mortality for 2016 (Shield and colleagues estimated 5.1% more alcohol-attributable mortality than the GBD study) but not alcohol-attributable DALYs lost (18.3% difference). There were marked differences by region and cause of disease or injury. Compared with the results from Shield and colleagues, the GBD study estimated a lower alcohol-attributable burden in Eastern Europe by 252 770 alcohol-attributable deaths (45.2% difference) and 6.1 million alcohol-attributable DALYs lost (32.9% difference) and in Western sub-Saharan Africa by 124 200 alcohol-attributable deaths (55.7% difference) and 7.0 million alcohol-attributable DALYs lost (63.4% difference), and estimated a higher alcohol-attributable burden in East Asia by 227 100 alcohol-attributable deaths (48.0% difference) and 2.2 million DALYs lost (11.0% difference). With regard to the cause of disease or injury, Shield and colleagues attributed an overall detrimental effect to alcohol on ischaemic heart disease mortality, whereas the GBD study attributed a net beneficial effect. The GBD study, as compared with Shield and colleagues' study, estimated a lower alcohol-attributable mortality because of liver cirrhosis and injuries by 262 500 (44.6% difference) and 398 800 (46.2% difference), respectively. CONCLUSIONS: Differences in estimates of the alcohol-attributable burden of disease in two recent studies indicate the need to improve the accuracy of underlying data and risk relations to obtain more consistent estimates and to formulate, advocate for, and implement alcohol policies more effectively.
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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 | 0.000 | 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".