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Journal Article

Citation

Foster S, Gmel G, Mohler-Kuo M. Drug Alcohol Depend. 2022; 234: e109410.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.drugalcdep.2022.109410

PMID

35364420

Abstract

BACKGROUND: Previous research has demonstrated a preventive effect of the alcohol policy environment on alcohol consumption. However, little is known about the heterogeneity of this effect. Our aim was to examine the extent of heterogeneity in the relationship between the strictness of alcohol policy environments and heavy drinking and to identify potential moderators of the relationship.

METHODS: Cross-sectional data from 5986 young Swiss men participating in the cohort study on substance use risk factors (C-SURF) were analysed. The primary outcome was self-reported risky single-occasion drinking in the past 12 months (RSOD, defined as 6 standard drinks or more on a single occasion at least monthly). A previously-used index of alcohol policy environment strictness across Swiss cantons was analysed in conjunction with 21 potential moderator variables. Random forest machine learning captured high-dimensional interaction effects, while individual conditional expectations captured the heterogeneity induced by the interaction effects and identified moderators.

RESULTS: Predicted subject-specific absolute risk reductions in RSOD risk ranged from 16.8% to - 4.2%, indicating considerable heterogeneity. Sensation seeking and antisocial personality disorder (ASPD) were major moderators that reduced the preventive relationship between stricter alcohol policy environments and RSOD risk. They also were associated with the paradoxical observation that some individuals displayed increased RSOD risk in stricter alcohol policy environments.

CONCLUSION: Whereas stricter alcohol policy environments were associated with reduced average RSOD risk, additionally addressing the risk conveyed by sensation seeking and ASPD would deliver an interlocking prevention mix against young Swiss men's RSOD.


Language: en

Keywords

Young adults; Machine learning; Alcohol drinking; Alcohol control policy; Moderator variables

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