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. Â\copyright 2010 The Authors, Addiction Â\copyright 2010 Society for the Study of Addiction.
College drinking; Drinking environments; Drinking reproduction number; Social influence; Uncertainty and sensitivity analyses
issn
09652140
correspondence_address1
Mubayi, A.; Department of Quantative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, United States; email: anujmubayi@yahoo.com
affiliation
Mathematical and Computational Modeling Sciences Center, Arizona State University, Tempe, AZ, United States; School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, United States; Department of Mathematics, The University of Texas, Arlington, TX, United States; Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, United States; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States; Sante Fe Institute, Sante Fe, NM, United States; Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A and M Health Science Center, College Station, TX, United States
%0 Journal Article
%1 Mubayi2011749
%A Mubayi, A.
%A Greenwood, P.
%A Wang, X.
%A Castillo-ChÃ!'vez, C.
%A Gorman, D.M.
%A Gruenewald, P.
%A Saltz, R.F.
%D 2011
%J Addiction
%K Alcohol Behavior; Drinking; Environment; Factors; Humans; Models, Risk Risk; Social States; Students; Theoretical; United Universities article; aspect; behavior; drinking environment; factor; human; model; psychological risk risk; social statistics; student; theoretical university,
%N 4
%P 749-758
%R http://dx.doi.org/10.1111/j.1360-0443.2010.03254.x
%T Types of drinkers and drinking settings: An application of a mathematical model
%U http://dx.doi.org/10.1111/j.1360-0443.2010.03254.x
%V 106
%X 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. Â\copyright 2010 The Authors, Addiction Â\copyright 2010 Society for the Study of Addiction.
@article{Mubayi2011749,
abstract = {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. {\^A}{\copyright} 2010 The Authors, Addiction {\^A}{\copyright} 2010 Society for the Study of Addiction.},
added-at = {2017-11-10T22:48:29.000+0100},
affiliation = {Mathematical and Computational Modeling Sciences Center, Arizona State University, Tempe, AZ, United States; School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, United States; Department of Mathematics, The University of Texas, Arlington, TX, United States; Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, United States; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States; Sante Fe Institute, Sante Fe, NM, United States; Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A and M Health Science Center, College Station, TX, United States},
author = {Mubayi, A. and Greenwood, P. and Wang, X. and Castillo-Ch{\~A}{!'}vez, C. and Gorman, D.M. and Gruenewald, P. and Saltz, R.F.},
author_keywords = {College drinking; Drinking environments; Drinking reproduction number; Social influence; Uncertainty and sensitivity analyses},
biburl = {https://www.bibsonomy.org/bibtex/22ed10ee5029f06078084ac7e90c9c2a9/ccchavez},
coden = {ADICE},
correspondence_address1 = {Mubayi, A.; Department of Quantative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, United States; email: anujmubayi@yahoo.com},
date-added = {2017-11-10 21:45:26 +0000},
date-modified = {2017-11-10 21:45:26 +0000},
document_type = {Article},
doi = {http://dx.doi.org/10.1111/j.1360-0443.2010.03254.x},
interhash = {fb8093efce1e90746dd42d1c6ac3b25f},
intrahash = {2ed10ee5029f06078084ac7e90c9c2a9},
issn = {09652140},
journal = {Addiction},
keywords = {Alcohol Behavior; Drinking; Environment; Factors; Humans; Models, Risk Risk; Social States; Students; Theoretical; United Universities article; aspect; behavior; drinking environment; factor; human; model; psychological risk risk; social statistics; student; theoretical university,},
language = {English},
number = 4,
pages = {749-758},
pubmed_id = {21182556},
timestamp = {2017-11-10T22:48:29.000+0100},
title = {Types of drinkers and drinking settings: An application of a mathematical model},
url = {http://dx.doi.org/10.1111/j.1360-0443.2010.03254.x},
volume = 106,
year = 2011
}