A Bayesian spatio-network model for multiple adolescent adverse health behaviours

Gerogiannis, G., Tranmer, M. , Lee, D. and Valente, T. (2022) A Bayesian spatio-network model for multiple adolescent adverse health behaviours. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(2), pp. 271-287. (doi: 10.1111/rssc.12531)

Abstract

The use of alcohol, cigarettes and marijuana among adolescents are major public health concerns, and a number of epidemiological studies have been conducted to understand the drivers of these individual health behaviours. However, there is no literature that jointly models these health behaviours with the aim of understanding the relative importance of individual factors, friendship effects and spatial effects in determining the prevalence of alcohol, cigarette and marijuana use among adolescents. To address this gap in the literature, we propose a novel multivariate spatio-network model for jointly modelling all three of these behaviours, with inference conducted in a Bayesian setting using Markov chain Monte Carlo simulation. The model is motivated by survey data from five schools in Los Angeles, California, and the results indicate the important roles that individual factors and friendship networks play in driving the uptake of these health behaviours.

• Item Type: Articles
• Additional Information: This publication was supported by the University of Glasgow's Lord Kelvin/Adam Smith (LKAS) PhD Scholarship.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lee, Professor Duncan and Tranmer, Professor Mark and Gerogiannis, George
• Authors: Gerogiannis, G., Tranmer, M., Lee, D., and Valente, T.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics; College of Social Sciences > School of Social and Political Sciences; College of Social Sciences > School of Social and Political Sciences > Sociology Anthropology and Applied Social Sciences
• Journal Name: Journal of the Royal Statistical Society: Series C (Applied Statistics)
• Publisher: Wiley
• ISSN: 0035-9254
• ISSN (Online): 1467-9876
• Published Online: 27 November 2021
• Copyright Holders: Copyright © 2021 Royal Statistical Society
• First Published: First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 71(2): 271-287
• Publisher Policy: Reproduced in accordance with the publisher copyright policy