The contribution of unrecorded alcohol to health harm
Bibliographic record
Abstract
We thank Drs Lachenmeier & Walch 1 for their commentary on our review 2, where they highlight quality of unrecorded alcohol as a relevant dimension for alcohol-attributable burden of disease. Unrecorded alcohol includes some categories 3 which have been produced mainly in the same circumstances as recorded alcoholic beverages and then diverted or brought across jurisdictions legally. However, other unrecorded categories often are not controlled for quality in the same way as usual alcoholic beverages, either because they are produced at home or illegally or because they are not intended for human consumption (such as medicinal tinctures 4). In many high-income countries, these non-standardized products probably account for less than half of the unrecorded alcohol (e.g. 5, 6), but in low- and middle-income countries the proportion is usually much higher. An estimated 25% of the global alcohol consumption is estimated as unrecorded 7, and this proportion is highest in low- and middle-income countries 8. Estimating the contribution of unrecorded alcohol to the alcohol-attributable burden of disease correctly is thus crucial for comparative risk assessments 9, 10. The arguments of Lachenmeier & Walch 1 raise two questions: (1) should we estimate attributable burden for all or part of unrecorded alcohol with different relative risk functions than for recorded alcohol and (2) are we confident about the inclusion of all the deaths from substances such as methanol under alcohol-attributable deaths? To answer the first question, at the moment, based on prior reviews 3, 11, the same relative risk functions are used for unrecorded consumption as for recorded consumption in the estimation of the burden of disease 2. This approach still seems justified, as no new evidence on differential relative risks is available and, to date, the few studies that have found higher risks 12 could potentially be explained by heavy or very heavy drinking occasions, due in part to the lower price of unrecorded alcohol. We believe that the burden lies with those claiming higher risks for unrecorded consumption to provide empirical evidence in support of this assertion. The second question raised 1 is more complicated to answer. Overall, the contribution of alcohol poisoning is underestimated globally for four reasons: first, many estimates are restricted to ethanol and do not include substances such as methanol (i.e. methanol poisoning; see 1); secondly, there is frequent miscoding of alcohol poisoning deaths as cardiovascular deaths, albeit not to the degree that could explain the detrimental impact of alcohol on cardiovascular disease 13, 14; thirdly, incidence and prevalence of all fully alcohol-attributable causes of death are underestimated because of stigma 15; and finally, there is underestimation because alcohol as a contributory cause to illicit drug overdose deaths is usually not reflected on death certificates (e.g. 16). In this sense, Lachenmeier & Walch 1 should be taken as a plea to improve future statistics on alcohol poisonings to include unrecorded consumption. Finally, we agree strongly 1 that additional research on unrecorded consumption in relation to burden of disease is needed. This research needs to recognize that ‘unrecorded alcohol’ includes subcategories with varying likelihoods of contamination, and that contaminants may vary with customary methods of brewing or distilling the alcohol. Based on toxicological knowledge and past research 3, most of these contaminants will not impact upon risk for disease above alcohol, but some may, varying considerably across cultures and by geography (e.g. 17, 18). Thus, it would probably be a long time before the results are robust enough to be used in global burden of disease estimates, even if new research is initiated. None.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".