Types of drinkers and drinking settings: an application of a mathematical model
Article first published online: 23 DEC 2010
© 2010 The Authors, Addiction © 2010 Society for the Study of Addiction
Volume 106, Issue 4, pages 749–758, April 2011
How to Cite
Mubayi, A., Greenwood, P., Wang, X., Castillo-Chávez, C., Gorman, D. M., Gruenewald, P. and Saltz, R. F. (2011), Types of drinkers and drinking settings: an application of a mathematical model. Addiction, 106: 749–758. doi: 10.1111/j.1360-0443.2010.03254.x
- Issue published online: 3 MAR 2011
- Article first published online: 23 DEC 2010
- Accepted manuscript online: 26 OCT 2010 03:07AM EST
- Submitted 14 October 2009; initial review completed 18 January 2010; final version accepted 15 October 2010
- College drinking;
- drinking environments;
- drinking reproduction number;
- social influence;
- uncertainty and sensitivity analyses
Aims US college drinking data and a simple population model of alcohol consumption are used to explore the impact of social and contextual parameters on the distribution of light, moderate and heavy drinkers. Light drinkers become moderate drinkers under social influence, moderate drinkers may change environments and become heavy drinkers. We estimate the drinking reproduction number, Rd, the average number of individual transitions from light to moderate drinking that result from the introduction of a moderate drinker in a population of light drinkers.
Design and Settings Ways of assessing and ranking progression of drinking risks and data-driven definitions of high- and low-risk drinking environments are introduced. Uncertainty and sensitivity analyses, via a novel statistical approach, are conducted to assess Rd variability and to analyze the role of context on drinking dynamics.
Findings Our estimates show Rd well above the critical value of 1. Rd estimates correlate positively with the proportion of time spent by moderate drinkers in high-risk drinking environments. Rd is most sensitive to variations in local social mixing contact rates within low-risk environments. The parameterized model with college data suggests that high residence times of moderate drinkers in low-risk environments maintain heavy drinking.
Conclusions With regard to alcohol consumption in US college students, drinking places, the connectivity (traffic) between drinking venues and the strength of socialization in local environments are important determinants in transitions between light, moderate and heavy drinking as well as in long-term prediction of the drinking dynamics.