Rehm et al. Respond to "Never, or Hardly Ever?"
Notice bibliographique
Résumé
In this issue of the Journal, Dr. Klatsky (1) raised a couple of open questions with respect to defining the best control group for alcohol epidemiology in his insightful commentary regarding our finding (2) that more than half of the people stating lifetime abstention in a representative US panel survey had elsewhere reported drinking before. Here, we respond to his comments, trying to further illuminate the best way to define this control group. We completely agree with Dr. Klatsky that the exact wording of the questions plays an important role. Much work in alcohol epidemiology has been done on defining the best way to elicit drinking reports (3, 4). Work on the wording of the introduction to these questions usually dealing with distinguishing whether the respondent is a lifetime or a “current” abstainer has been less methodological. However, as laid out in our paper (2), defining this control group has important implications for alcohol epidemiology; thus, the wording of such queries should receive the same methodological attention as other measures of exposure. Therefore, we believe that the exact wording should be reported (1). Dr. Klatsky's commentary (1) deals mainly with the relation between alcohol consumption and chronic disease. For this outcome, an adequate control group would be people for whom alcohol exposure could not reasonably have a biologic impact, that is, a mixture of lifetime abstention and very low levels of infrequent drinking (1, 2). However, almost half of the mortality and morbidity burden from alcohol stems from injuries (5), with different dimensions of alcohol consumption being relevant, especially amount of intake before the event. The best control group here is no drinking before the event or, in cohort studies, its best correlate. Even moderate drinking has some effects on psychomotor abilities (6), and its risk would then be determined by the overall frequency of different kinds of drinking occasions, each associated with a specific relative risk of injury (7). While moderate drinking is already associated with an elevated risk compared with abstention, the relative risks tend to increase exponentially with increasing intake (refer, for example, to Borkenstein et al. (8)). Finally, we could not agree more with Dr. Klatsky's contention (1) that other measurement errors, such as underreporting, could have an even more important effect on epidemiologic indicators of public health importance, such as alcohol attributable fractions (2, 9). We see 3 consequences for future work here: first, even for complex indicators, we should always give confidence intervals and conduct sensitivity analyses (e.g., for confidence intervals around attributable fractions, refer to Natarajan et al. (10)). Second, alcohol epidemiology should start correcting for regression dilution bias based on measurement error of exposure, as is now standard in epidemiologic research on other risk factors (11). Third, triangulation of different data sources will help avoid some of the main problems of measurement error, such as underreporting (12). Overall, we hope that the above-mentioned steps will provide some guidance for future research in alcohol epidemiology and will help reduce the effects of measurement errors. Author affiliations: Centre for Addiction and Mental Health, Toronto, Ontario, Canada (J. Rehm, H. Irving); Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany (J. Rehm); Public Health Sciences, University of Toronto, Toronto, Ontario, Canada (J. Rehm); Alcohol Research Group, Public Health Institute, Emeryville, California (Y. Ye, W. C. Kerr, J. Bond, T. K. Greenfield); and Clinical Services Research Training Program, Department of Psychiatry, University of California, San Francisco, California (T. K. Greenfield) Support for this study was provided by a grant from the National Institute on Alcohol Abuse and Alcoholism (“Drinking Patterns & Ethnicity: Impact on Mortality Risks”; R01AA016644) to the Public Health Institute. Conflict of interest: none declared.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».