Psychological Methods, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands.
Institute of Advanced Studies, University of Amsterdam, Amsterdam, 1012 GC, The Netherlands.
Sci Rep. 2024 Feb 24;14(1):4499. doi: 10.1038/s41598-024-54155-0.
We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by [Formula: see text] and [Formula: see text], respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.
我们利用弗雷明汉心脏研究的纵向社交网络数据,考察了饮酒行为在多大程度上受到网络结构的影响。我们在三状态 SIS 型模型中评估了酒精使用的传播,将个体分类为不饮酒者、适度饮酒者和重度饮酒者。我们发现,三状态的使用优于更规范的两状态分类,因为数据表明所有三种状态都非常稳定,并且具有不同的社会动态。我们表明,在对酒精使用的传播进行建模时,通过纳入网络结构来对社交互动的拓扑结构进行建模是很重要的。人群并非均匀混合,并且不饮酒者和重度饮酒者的聚类程度很高。我们发现,不饮酒者和重度饮酒者对其社交环境都有很强的影响;每有一个重度饮酒者和不饮酒者的连接,中度饮酒者采用他们饮酒行为的概率就会分别增加[Formula: see text]和[Formula: see text]。我们还发现,不饮酒者的连接对重度饮酒者戒酒有显著的积极影响。通过模拟,我们发现虽然两者都有效,但与减少重度饮酒者的影响相比,增加不饮酒者的影响似乎是更有效的干预措施。