@article{ref1,
title="Development and validation of a risk predictive model for student harmful drinking-a longitudinal data linkage study",
journal="Drug and alcohol dependence",
year="2019",
author="Ngo, Duc Anh and Rege, Saumitra V. and Ait-Daoud, Nassima and Holstege, Christopher P.",
volume="197",
number="",
pages="102-107",
abstract="BACKGROUND: This study aimed to develop a predictive model to quantify the risk of student harmful drinking associated with emergency department (ED) visits and/or campus-wide incidents reported to campus authorities in a U.S. public university. <br><br>METHODS: Six-year (2010/11-2015/16) student enrollment data were linked to subsequent harmful drinking events defined as either alcohol intoxication associated with ED visits or alcohol-related incidents reported to authorities within 1 year following the annual (index) enrollment. Multivariable logistic regression analysis was used to develop a risk predictive model based on the first 3-year student cohort (n = 93,289), which was then validated in the following 3-year student cohort (n = 85,876). <br><br>RESULTS: A total of 2609 students in the derivation cohort and 2617 students in the validation cohort had at least 1 harmful drinking event within 1 year following the index enrollment, providing an incidence of 2.8% and 3.1%, respectively. Student demographics (gender, age, ethnicity, parental tax dependency), academic level, Greek life member, transfer students, first-time enrolled students, having been diagnosed with depression or injury, and violence involvement were statistically significant predictors. C-statistics of the model were 0.86 in both cohorts, with excellent calibration and no evidence of over- or under-prediction observed from calibration plots. <br><br>CONCLUSIONS: By linking routinely collected student data, a robust risk predictive model was developed and validated to quantify absolute risk of harmful drinking for every student. This model can provide a useful tool for clinicians or health educators to make real time decision to plan target interventions for students at elevated risk.<br><br>Copyright © 2019. Published by Elsevier B.V.  Keywords: College students <p /> <p>Language: en</p>",
language="en",
issn="0376-8716",
doi="10.1016/j.drugalcdep.2019.01.016",
url="http://dx.doi.org/10.1016/j.drugalcdep.2019.01.016"
}