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Enregistrement W2019676133 · doi:10.1093/aje/kwn195

Rehm et al. Respond to "Never, or Hardly Ever?"

2008· article· en· W2019676133 sur OpenAlexaboutno aff
Jürgen Rehm, Hyacinth Irving, Yu Ye, William C. Kerr, Jason Bond, Thomas K. Greenfield

Notice bibliographique

RevueAmerican Journal of Epidemiology · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueAlcohol Consumption and Health Effects
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRespondentEpidemiologyMedicineControl (management)Alcohol consumptionAlcoholPsychologyComputer scienceLawPolitical sciencePathology

Résumé

récupéré en direct d'OpenAlex

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,014
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,405
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,014
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,222
Tête enseignante GPT0,484
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2008
Routes d'admission1
Résumé présentoui

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Même revueAmerican Journal of EpidemiologyMême sujetAlcohol Consumption and Health EffectsTravaux en français237 207