A Multilevel Model of Alcohol Outlet Density, Individual Characteristics and Alcohol-Related Injury in Argentinean Young Adults
Bibliographic record
Abstract
ABSTRACT Objectives. Previous research from high-income countries has consistently shown an association between alcohol-related harms and neighborhood characteristics such as alcohol outlet density, but this research has not been extended to middle- and low-income countries. We assessed the role of neighborhood characteristics such as alcohol outlet density, overcrowding and crime rates, and individual characteristics including gender, age, alcohol and marijuana use, and geographic mobility associated with alcohol-related injuries in university students in Argentina. Methods. Data were collected from a randomized sample of students attending a national public university (n = 1346). Descriptive, bivariable, and multilevel logistic regression analyses were performed. Results. In the final model, on-premises alcohol outlet density—but not off-premises outlet density, overcrowding or crime—was associated with past-year and lifetime alcohol-related injury (median odds ratio = 1.16). At the individual level, quantity (odds ratio (OR) = 1.05, 95% CI = (1.01, 1.10)) and frequency (OR = 1.66, 95% CI = (1.41,1.97)) of alcohol consumption and age (OR = 0.81, 95% CI = (0.74, 0.88)) were associated with past-year and lifetime alcohol-related injury. Conclusions. This study contributes to an area with a paucity of information from non-high-income countries, finding differences with previous literature. Objectifs: Des recherches antérieures menées dans des pays à revenu élevé ont constamment montré une association entre les méfaits liés à l’alcool et les caractéristiques du quartier telles que la densité des points de vente d’alcool, mais cette recherche n’a pas été étendue aux pays à revenu moyen et faible. Nous avons évalué le rôle des caractéristiques du quartier telles que la densité des points de vente d’alcool, la surpopulation et les taux de criminalité, et les caractéristiques individuelles, y compris le sexe, l’âge, la consommation d’alcool et de marijuana, et la mobilité géographique associée aux blessures liées à l’alcool chez les étudiants universitaires en Argentine. Méthodes: Les données ont été recueillies auprès d’un échantillon aléatoire d’étudiants fréquentant une université publique nationale (n = 1 346). Des analyses de régression logistique descriptives, bivariables et multi-niveaux ont été effectuées. Résultats: Dans le modèle final, la densité des points de vente d’alcool sur place - mais pas la densité des points de vente hors établissement, le surpeuplement ou la criminalité - était associée aux blessures liées à l’alcool au cours de la dernière année et au cours de la vie (rapport de cotes médian = 1.16). Au niveau individuel, quantité (OR = 1.05, IC à 95% = (1.01, 1.10)) et fréquence (OR = 1.66, IC à 95% = (1.41,1.97)) de consommation d’alcool et âge (OR = 0.81, 95% IC = (0.74, 0.88)) étaient associés à des blessures liées à l’alcool au cours de la dernière année et de leur vie entière. Conclusions: Cette étude contribue à un domaine où les informations sur les pays qui ne sont pas à revenu élevé sont rares, trouvant des différences avec la littérature précédente.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".