Targeting alcohol use in high-risk population groups: a US microsimulation study of beverage-specific pricing policies
Notice bibliographique
Résumé
BACKGROUND: Raising retail prices on alcoholic beverages preferred by high-risk groups (males, those of low socioeconomic status, and those with heavy alcohol use) might selectively reduce their alcohol consumption. However, the differential impact of beverage-specific price increases on US population groups has yet to be studied. This study aimed to simulate the effect of beverage-specific price increases on alcohol use within subgroups of the adult US population defined by sex, educational attainment, and alcohol use category. METHODS: An individual-level microsimulation of the US population (aged 18-79 years) was used to simulate alcohol consumption from 2000 to 2019 based on individual characteristics (ie, sex, age, race, ethnicity, and educational attainment as a proxy for socioeconomic status categorised as high school degree or less, some college, and college degree or more) and previous alcohol use. The microsimulation model was generated via integration of diverse data sources including decennial US Census data, annual data from the American Community Survey, annual data from the National Vital Statistics System, annual data from the Behavioral Risk Factor Surveillance System, and biennial, longitudinal data from the Panel Study of Income Dynamics. Policy parameters were informed by the existing literature. Four national policy scenarios were compared with a reference scenario without price change in 2019: a uniform price increase of 10% (scenario 1), a uniform price increase of 30% (scenario 2), a beverage-specific price increase of 30% for beer and spirits and 10% for wine (scenario 3), and a beverage-specific price increase of 50% for beer and spirits and 10% for wine (scenario 4). Individual-level effects on alcohol consumption were simulated using beverage-specific own-price elasticities. Sensitivity analysis assessed assumption-based correlation coefficient between alcohol consumption and the individual-level percent reduction in alcohol consumed; and the application of the beverage-non-specific own-price participation elasticity. FINDINGS: Scenario 4 had the strongest effect on alcohol use overall and most effectively reduced consumption in high-risk groups: males and females with high alcohol use (more than 60 g of pure alcohol per day for males and 40 g of pure alcohol per day for females) and low educational attainment (high school degree or less) reduced their alcohol use by -17·30% (-17·62 g per day, credible interval [CI] -21·77 to -13·20) and -17·49% (-12·25 g per day, CI -14·72 to -9·58), respectively. In comparison, smaller relative changes were observed among groups at less risk of harm. INTERPRETATION: Disproportionate increases in retail prices for the cheapest beverages, beer and spirits, might lead to a greater decline in consumption among high-risk groups. Pricing policies could thus be used as a powerful public health tool to mitigate the unequal alcohol-attributable burden of disease. FUNDING: National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,000 |
| 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 ».