Assessing Quitter Effects: Disaggregating Current Nondrinkers
Notice bibliographique
Résumé
To the Editor: The recent article by Lee et al.1 provides an interesting analysis using more than 12,000 respondents to the U.S. Health and Retirement Study (HRS) aged 55 and older interviewed in 2002. The study focuses on alcohol consumption and all-cause mortality while taking into account a number of risk factors that may confound this relationship. However, we have substantial reservations about their management of a key variable, namely, long-term drinking status. They state: “Subjects who reported no alcohol intake in 2002 but reported alcohol use in 2000 or 1998 were excluded (n=2,323) to minimize the chances of classifying subjects who stopped drinking because of worsening health as nondrinkers.” In our view, this is far from adequate in that it still leaves people in the nominal “nondrinker” category who drank some years and not others or who drank before 1998 and not more recently. The authors' exclusion of “recent quitters,” as noted in their description of the sensitivity analysis, does not adequately address this issue. Persons who were heavy drinkers for a number of years, quit some years before, and were abstainers in 2002 may still be at higher risk of long-term chronic complications from alcohol and of early mortality. Including them with nondrinkers confounds the interpretation. For example, in heavy drinkers who stop drinking, the risk of alcohol-related cancer can take up to 20 years to decrease to that of an abstainer.2 Overall, the mortality risk of ex-drinkers based on a comprehensive systematic meta-analysis of data sets mainly stemming from the United States was estimated to be RR1.44 for women (95% confidence interval (CI)=1.28–1.61) and RR1.21 for men (95% CI=1.10–1.32).3 A more-refined approach would have been to include all persons in the analysis with a disaggregation of the “current nondrinker” category into three groups, such as lifetime abstainer, current abstainer—former heavy drinker, and current abstainer—former moderate drinker, and to include these three groups in the analysis in Tables 1 and 2. These tables show that the nondrinker category had 5,672 subjects, the largest of the six categories under “number of alcoholic drinks consumed”; therefore, a breakdown into three groups would not complicate the analysis. The main problem with analyses of older populations that do not adequately control for sick-quitter effects is that they may lead to overall false conclusions and recommendations. Searching for the best control group in alcohol epidemiology is cumbersome,4,5 but without an adequate control, such recommendations are not valid. Most population-level studies fail to find beneficial health effects of moderate consumption,6 and this also needs to be taken into consideration when making recommendations. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this letter. Financial Disclosure: Funded by the Centre for Addiction and Mental Health, Toronto, Ontario, Canada. Author Contributions: Both authors contributed substantially to this letter. J. Rehm noted some of the challenges in the paper by Lee et al. that this letter focuses on. N. Giesbrecht developed a draft. J. Rehm revised the letter. Sponsor's Role: None.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,184 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,006 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».