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Types of drinkers and drinking settings: an application of a mathematical model

Authors

  • Anuj Mubayi,

    Corresponding author
    1. Mathematical and Computational Modeling Sciences Center, Arizona State University, Tempe, AZ, USA
    2. School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA
    3. Department of Mathematics, The University of Texas, Arlington, TX, USA
    4. Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, USA
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  • Priscilla Greenwood,

    1. Mathematical and Computational Modeling Sciences Center, Arizona State University, Tempe, AZ, USA
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  • Xiaohong Wang,

    1. Mathematical and Computational Modeling Sciences Center, Arizona State University, Tempe, AZ, USA
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  • Carlos Castillo-Chávez,

    1. Mathematical and Computational Modeling Sciences Center, Arizona State University, Tempe, AZ, USA
    2. School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA
    3. School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
    4. Sante Fe Institute, Sante Fe, NM, USA
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  • Dennis M. Gorman,

    1. Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX, USA
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  • Paul Gruenewald,

    1. Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, USA
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  • Robert F. Saltz

    1. Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, USA
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Anuj Mubayi, JJN3-01, Department of Quantative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA. E-mail: anujmubayi@yahoo.com

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.

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