Selection and Socialization Influences on Adolescent Alcohol Use: The Individual and Joint Contexts of Neighborhood Disadvantage and Population Density

Abstract

Background: Neighborhood disadvantage (ND) and population density (PD) are two community characteristics contextualizing friends’ influence on adolescent alcohol use. However, these community characteristics are rarely examined for potential joint contributions, although it is possible that the way friends are selected or influence alcohol use are shaped by both ND and PD. In addition, prior studies examining ND or PD contexts on friend influence rarely discern between socialization and selection. Objectives: The current study examined how selection and socialization influences on adolescent alcohol use are shaped by unique and joint contexts of ND and PD. Methods: Adolescents from Waves I and II of the National Longitudinal Study of Adolescent and Adult Health (Add Health) were included in three models assessing friends’ socialization of alcohol us initiation and binge drinking, and selection of drinking friends. ND and PD were tested for mediation and moderation individually and jointly. Results: Results indicated that socialization of drinking initiation was stronger in high ND contexts, and that continued binge drinking was stronger in low ND contexts. PD indirectly influenced socialization of initiation and binge drinking maintenance via a negative association with number of drinking friends. PD and ND jointly influenced the association between initial binge drinking and next-year selection of drinking friends, such that selection was stronger within areas related to lower levels of drinking friends. Conclusions/Importance: Current results indicate that PD and ND shape friends’ influence on alcohol use in unique ways. These must be accounted for to better understand bidirectional effects of friend influence.

Keywords:

• Alcohol
• adolescence
• peer influence
• neighborhood

Acknowledgements

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. This work was supported by National Institute of General Medical Sciences grant 5P20GM121341-02. The author also thanks T.L. Burch for manuscript feedback and editing.

Notes

1 Block groups are collections of blocks, which are statistical areas bounded by visible (e.g., streets, roads, rivers) and nonvisible (e.g., property lines, district lines) boundaries (US Census, 2017). They are the smallest unit for published sample data, and are not always analogous to size or population of a region. For example, while a city block may be a block of a city bounded on all sides by streets, blocks in suburban or rural areas are larger and more irregular – in remote areas, census blocks may be hundreds of square miles. For the 1990 Census, block groups had on average 1,100 people.

2 Model fit for Poisson regression was compared to zero-inflated and negative binomial models, and was selected as the best fitting model. Comparisons/results available upon request.