“Harm per litre” as a concept and a measure in studying determinants of relations between alcohol consumption and harm
Bibliographic record
Abstract
The term "harm per litre" has been increasingly used in alcohol research in recent years as a concept and a comparative measure of alcohol-attributable harm in comparisons between environments, circumstances, and patterns of drinking. This essay discusses the origins of the term in connection with analyses in terms of patterns as well as levels of drinking and with concerns about differential harms from drinking different beverage types. Also discussed is the term's current primary usage, in the context of epidemiological concerns about differentially severe harms for poorer persons who drink. It is noted that these same concerns have been discussed, particularly in Britain, using the phrase "alcohol harm paradox". "Harm per litre" was initially most often used in comparisons between rates of alcohol-attributable harm by beverage type. After 2010, the expression was applied more broadly, particularly after its use in various World Health Organization-related discussions and documents. In addition, and especially from 2018 onwards when most of the papers using this term were published, it has been used in comparisons by socioeconomic status at the individual level, and by level of socioeconomic development at the country level. Almost all the findings indicate that people with lower socioeconomic status, and countries with lower average income, e.g., low income and lower-middle income countries, incur considerably higher harm per litre (with harm being expressed in disease burden and mortality) than upper middle-income and high-income countries. "Harm per litre" is a practicable and easy-to-understand concept to compare groups of individuals or countries, and to quantify health inequalities. The next important step will need to be elucidating a better causal understanding of the processes underlying these inequalities, with an emphasis on factors which can be most easily changed by interventions.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".