Jul 7, 2014 - Elevated Alcohol Demand Is Associated with Driving After. Drinking Among College Student Binge Drinkers. Jenni B. Teeters, Alison M.
ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH
Vol. 38, No. 7 July 2014
Elevated Alcohol Demand Is Associated with Driving After Drinking Among College Student Binge Drinkers Jenni B. Teeters, Alison M. Pickover, Ashley A. Dennhardt, Matthew P. Martens, and James G. Murphy
Background: Alcohol-impaired driving among college students represents a significant public health concern, yet little is known about specific theoretical and individual difference risk factors for driving after drinking among heavy drinking college students. This study evaluated the hypothesis that heavy drinkers with elevated alcohol demand would be more likely to report drinking and driving. Method: Participants were 207 college students who reported at least 1 heavy drinking episode (4/5 or more drinks in 1 occasion for a woman/man) in the past month. Participants completed an alcohol purchase task that assessed hypothetical alcohol consumption across 17 drink prices and an item from the Young Adult Alcohol Consequences Questionnaire that assessed driving after drinking. Results: In binary logistic regression models that controlled for drinking level, gender, ethnicity, age, and sensation seeking, participants who reported higher demand were more likely to report driving after drinking. Conclusions: These results provide support for behavioral economics models of substance abuse that view elevated/inelastic demand as a key etiological feature of substance misuse. Key Words: Impaired Driving, Alcohol, Behavioral Economics, Demand, College.
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LCOHOL-impaired driving among college students represents a significant public health concern. Despite widespread prevention efforts, approximately 3.4 million (30%) college students report driving after drinking alcohol (Hingson et al., 2009), with rates of alcohol-impaired driving increasing substantially after the 21st birthday (Beck et al., 2010; Fromme et al., 2010). Driving after drinking can be fatal; 74% of alcohol-related student deaths result from alcohol-impaired traffic accidents, and alcohol was involved in 50% of traffic-related deaths among 18- to 24-year-olds in 2009 (Hingson et al., 2009; LaBrie et al., 2011). College students are more likely to drive after drinking than their same-aged peers who do not attend college (Paschall, 2003). Heavy episodic drinking (i.e., 4/5 drinks or more per occasion for females/males) is a strong predictor of drinking and driving and is implicated in over 80% of all impaired driving occurrences (Flowers et al., 2008). Considering the multitude of potential serious negative consequences associated with impaired driving, it is important to investigate whether there From the Department of Psychology (JBT, AMP, AAD, JGM), The University of Memphis, Memphis, Tennessee; and Department of Educational, School, and Counseling Psychology (MPM), The University of Missouri, Columbia, Missouri. Received for publication December 18, 2013; accepted March 21, 2014. Reprint requests: James G. Murphy, PhD, Department of Psychology, The University of Memphis, 202 Psychology Building, Memphis, TN 38152-3232; Tel.: 901-678-2630, Fax: 901-678-2579; E-mail: jgmurphy@ memphis.edu Copyright © 2014 by the Research Society on Alcoholism. DOI: 10.1111/acer.12448
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are individual difference factors associated with driving after drinking, above and beyond drinking level, to elucidate why some young adult drinkers drive after drinking while others refrain. Identifying such factors may inform targeted intervention efforts designed to reduce impaired driving among college students. To date, researchers have identified several other individual difference factors associated with driving after drinking, including male gender, fraternity or sorority affiliation, living off-campus, younger age of onset, stronger selfapproval of driving after drinking, stronger perception of peer approval of driving after drinking, and decreased perceptions of risk of alcohol-impaired driving (Hingson et al., 2009; LaBrie et al., 2011; Quinn and Fromme, 2012; Weschler et al., 2003). In addition, sensation seeking has been shown to be associated with alcohol-impaired driving in both the general population and among young adults (for review, see Jonah, 1997). However, these associations are confounded with alcohol use itself, suggesting that the association between sensation seeking and driving after drinking may be better accounted for by drinking level. This study attempted to extend this literature by investigating whether or not a theoretically based variable that has shown robust relations with a variety of other indices of alcohol-related risk—behavioral economic measures of alcohol demand—predicts risk for drinking and driving among young adult heavy drinkers above and beyond known covariates. Behavioral economics views drug consumption as choice behavior maintained by the reinforcing properties of drugs and assumes that substance misuse and ultimately addiction Alcohol Clin Exp Res, Vol 38, No 7, 2014: pp 2066–2072
DRIVING AFTER DRINKING AMONG COLLEGE BINGE DRINKERS
entails a consistent overvaluation of substance-related rewards relative to substance-free rewards. Demand refers to the amount of a commodity consumed by an individual at a particular price. A multidimensional assessment of a commodity’s relative value can be visualized by generating a demand curve, which plots consumption across a range of drug prices. Hypothetical demand curve measures, such as the alcohol purchase task (APT) and the related cigarette and cocaine purchase tasks, have been used in clinical research to generate demand and expenditure curves that illustrate participants’ hypothetical rate of consumption across a range of drink or drug prices and generate a number of valid and reliable estimates of alcohol’s reinforcing properties (Bruner and Johnson, 2014; Heinz et al., 2012; MacKillop and Murphy, 2007; Murphy and MacKillop, 2006). These indices are conceptually related yet empirically discrete and include intensity (consumption level when drinks are free), breakpoint (the price that suppresses consumption to zero), Omax (maximum expenditure on alcohol), and Pmax (the price associated with Omax). Individual differences in sensitivity to changes in price can be quantified by measuring elasticity of demand, which can range from elastic (sensitive to price) to inelastic (insensitive to price) and may reflect the “essential value” of the commodity (Hursh and Roma, 2013; Hursh and Silberberg, 2008). Multiple factor analytic studies suggest that the 5 demand indices form 2 latent factors: Persistence and Amplitude (Bidwell et al., 2012; MacKillop et al., 2009; Skidmore et al., 2014). Persistence reflects relative sensitivity to escalating prices of alcohol and is composed of Omax, Pmax, breakpoint, and elasticity. Amplitude reflects the total quantity consumed and spent and is comprised of intensity and Omax. Although increases in price typically lead to decreases in demand, there are important individual differences reflected in the demand indices that may provide a unique measure of substance use severity. Elevated alcohol demand is significantly associated with increased alcohol consumption (Murphy and MacKillop, 2006), impulsivity (Kiselica and Borders, 2013), drinking to cope (Yurasek et al., 2011), craving (MacKillop et al., 2010a,b), and depression and posttraumatic stress disorder (PTSD) symptoms (Murphy et al., 2013). Additionally, individual differences in alcohol demand predict treatment success and responsiveness to interventions (Heinz et al., 2012; MacKillop and Murphy, 2007). Although previous research indicates that elevated demand is associated with increased overall levels of alcohol problems (Murphy et al., 2009), no studies have examined whether demand is associated with driving after drinking specifically. Behavioral economic theory would predict that drinkers with elevated/inelastic demand for alcohol would be less likely to modify their drinking in order to avoid the health and legal risks associated with drinking and driving. If this is true, it would provide further support for behavioral economic models of addiction and suggest that demand may be a clinically relevant marker of risk for substance abuse and need for intervention.
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Present Study This study tested the hypothesis that driving after drinking would be associated with elevated alcohol demand among heavy drinking college students after controlling for relevant covariates (e.g., drinking level, ethnicity, age, gender, and sensation seeking). Although all 5 demand indices and both demand composites have shown unique associations with alcohol use and problems, we expected that the composite measure of sensitivity to price (Persistence) would be most predictive of the tendency to persist in driving after drinking regardless of the risks that may occur (i.e., lack of sensitivity to risk may be indicative of a more general lack of sensitivity to price or aversive consequences). Support for these hypotheses would contribute to the development of more specific models of risk for driving after drinking among college student drinkers and might also lead to intervention approaches that target demand. MATERIALS AND METHODS Participants Participants were 207 undergraduate students (53.1% female, mean age = 19.50, SD = 1.99) recruited from a university student health clinic or from university-wide introductory classes at a public university in the southern United States and who consented to participate in 1 of 2 randomized controlled trials. Participants identified as Caucasian (64.7%), African American (26.1%), Hispanic or Latino (2.4%), Asian (1.4%), American Indian or Alaska Native (0.5%), “Other” (0.5%), and/or multiethnic (4.3%) were eligible to participate if they were 18 years of age or older and reported 1 or more heavy drinking episodes (defined as having 4/5 drinks or more on 1 occasion for women/men) in the past month. Procedure All procedures were approved by the university’s Institutional Review Board, and written informed consent was obtained from all participants prior to participation. Data were obtained from the initial assessments of 2 randomized controlled trials designed to investigate the efficacy of brief interventions for college student binge drinkers (for details, see Murphy et al., 2010). Students were invited to complete a brief screening evaluation to determine eligibility status, and interested individuals who were deemed eligible were scheduled to complete the initial assessment in a private laboratory space. All measures were completed prior to the intervention. The initial assessment lasted approximately 1 hour. Measures Demographics. Participants completed a brief questionnaire regarding age, race/ethnicity, gender, current residence, and fraternity and sorority affiliation. Alcohol Use. Typical drinks per week were assessed by the Daily Drinking Questionnaire (DDQ; Collins et al., 1985). Students were asked to estimate the total number of standard drinks they consumed on each day during a typical week in the past month. The DDQ is frequently used to assess alcohol consumption patterns among college students and is correlated with self-monitoring and retrospective drinking measures (Kivlahan et al., 1990). A separate item was included to assess binge drinking. Students were asked to report how many times they had drank 4 or more
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(if female) or 5 or more (if male) standard drinks in 1 occasion during the past month. Driving After Drinking. Driving after drinking was assessed using a single item from the Young Adult Alcohol Consequences Questionnaire (Read et al., 2006); participants either endorsed or denied “I have driven a car when I knew I had too much to drink to drive safely” in the past 6 months. Single item measures represent a common modality for assessing this behavior (e.g., Fairlie et al., 2010; Gustin and Simons, 2008; LaBrie et al., 2011; Zakletskaia et al., 2009). Alcohol Demand. Alcohol demand indices were derived from the APT (Murphy and MacKillop, 2006), a hypothetical measure that assesses alcohol expenditures over a range of 17 prices ($0.00 to $20.00 in this study) and that can be used to generate alcohol demand curves. Participants were asked to indicate how many drinks they would purchase and consume at increasing monetary prices (e.g., “How many drinks would you have if they were $0.25 each?”) and received the following instructions: In the questionnaire that follows we would like you to pretend to purchase and consume alcohol. Imagine that you and your friends are at a party on a Thursday night from 9:00 PM until 2:00 AM to see a band. Imagine that you do not have any obligations the next day (i.e., no work or classes). The following questions ask how many drinks you would purchase at various prices. The available drinks are standard size domestic beers (12 oz.), wine (5 oz.), shots of hard liquor (1.5 oz.), or mixed drinks containing 1 shot of liquor. Assume that you did not drink alcohol or use drugs before you went to the party, and that you will not drink or use drugs after leaving the party. Also, assume that the alcohol you are about to purchase is for your consumption only during the party (you can’t sell or bring the drinks home). Please respond to these questions honestly, as if you were actually in this situation. Four observed indices (intensity, breakpoint, Omax, and Pmax) and 1 derived index (elasticity) were generated from the APT. Intensity was recorded as consumption at $0.00. Breakpoint was recorded as the price that suppressed consumption to zero. Omax was recorded as participant’s maximum expenditure on alcohol. Pmax was recorded as the price associated with Omax. Elasticity was derived in this study using GraphPad Prism v. 5.04 for Windows (GraphPad Software, San Diego, CA, www.graphpad.com) and the macro available online through the Institute for Behavioral Resources Web site (www.ibrinc.org). Elasticity was generated from Hursh and Silberberg’s (2008) exponential equation: logQ = 1ogQ0 + k (e aP 1). In this equation, Q = quantity consumed, Q0 = consumption at $0.00, k = range of alcohol consumption in logarithmic units, P = price, and a = elasticity. In this study, k was held constant across curve fits at 2.60. Larger values of a indicate greater elasticity (i.e., greater sensitivity to price). Consumption values of zero, which cannot be log transformed, were eliminated prior to calculating elasticity, as were participant data in which
