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Drinking events are dynamic. The interactions of individuals, groups, and the environment as they relate to drinking behaviour are overwhelmingly complex. This paper presents an empirically grounded dynamic conceptual model to better understand drinking events. Using a collaborative mixed-methods approach, we developed an aggregated system dynamic model of drinking events. The process began with identification of system elements and boundaries. Once the first aspects of the model were completed, we constructed a causal loop diagram, an aggregated causal loop diagram, and stock and flow diagrams. Finally, we developed and ran computer simulations of the dynamical models. The model presented here can be used to guide future agent-based, system dynamics, or differential equation-based models. Such models can help inform future empirical work and modelling to increase the understanding of drinking events and provide solutions to the problems that happen proximal to these events. Copyright © 2018 John Wiley & Sons, Ltd.
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Systems Research and Behavioral Science
This is a pre-print (pre-refereeing) version of the paper. The
final version of the paper can be found here:
A System Dynamic Model of Drinking Events: Multi-Level
Ecological Approach
JD Clapp, D Ruderman, H Gonzalez, LF Giraldo, KM Passino, M. Reed, I Fernandes
Drinking events are dynamic. The interactions of individuals, groups, and the
environment as they relate to drinking behavior is overwhelmingly complex. This
paper presents an empirically-grounded dynamic conceptual model to better
understand drinking events. Using a collaborative mixed-methods approach, we
developed an aggregated system dynamics model of drinking events. The process
began with identification of system elements and boundaries. Once the first aspects of
the model were completed, we constructed a causal loop diagram (CLD), an
aggregated CLD, and stock and flow diagrams. Finally, we developed and ran
computer simulations of the dynamical models. The model presented here can be used
to guide future agent-based, system dynamics, or differential equation based models.
Such models can help inform future empirical work and modeling to increase the
understanding of drinking events and provide solutions to the problems that happen
proximal to these events.
Keywords: conceptual models, drinking events, drinking behavior, theoretical constructs
Systems Research and Behavioral Science
What is a Drinking Event? Toward a System Dynamics Model
Over the past half-century, a small but growing body of research has emerged
with the goal of better understanding drinking behavior as it naturally occurs.
Researchers hoping to better understand how and why drinkers become intoxicated
and experience related problems have struggled to untangle the complexities of an
inherently ecological problem. Reflecting the multi-disciplinary nature of alcohol
research, event studies vary in conceptual foci, methodology, and operational
definitions. Independently, studies on “drinking contexts,” “drinking situations,” and
“drinking environments” offer related but unique insights into drinking behavior in
situ. As a collective body of work, such studies suggest the need for and offer the
empirical basis of a conceptual modeling approach reflecting the complex and
dynamic nature of drinking events.
Although conceptual models and theory have long guided social science in
general (Lewin, 1951; Kuhn, 1974) and alcohol studies specifically (Gusfield, 1996;
Denzin, 1987), models for drinking events rarely build on previous work, transcend
theoretical streams, or acknowledge dynamics and complexity (e.g., non-linearity,
random effects, feedback loops). Although there is a small body of system dynamics
alcohol studies at the community-level (Holder, 2006; Gorman et al., 2001; Scribner et
al., 2009; Gruenewald, 2006), and some recent notable exceptions employing agent-
based modeling (Gorman et al., 2006; Fitzpatrick & Martinez, 2012) at the population
and event levels, dynamical modeling in alcohol research is still largely
Systems Research and Behavioral Science
underdeveloped. At the event-level, this may well be an artifact of the difficultly of
measuring drinking events (Kuntsche et al., 2014; Clapp et al., 2007).
Although recent advances in data collection technologies (Riley et al., 2011;
Leffingwell et al., 2013) have the potential to advance our understanding of event-
level drinking behavior, Riley et al. (2011, p.54) notes that our ability to collect
individualized, context-specific data and to intervene in situ has surpassed our current
theories. The authors note that “health behavior models that have dynamic, regulatory
system components to guide rapid intervention adaptation based on the individual’s
current and past behavior and situational context are greatly needed. Recent studies
have begun to embrace mobile continuous monitoring of physiological measures, like
heart rate, as a means of monitoring drug and alcohol relapse triggers prior to having
a solid theoretical understanding of the underlying relationship between this
indicator and relapse triggers (Kennedy et al., 2015). A clearer understanding of
dynamical relationships during drinking events will likely complement the future uses
of such technologies by identifying key leverage points for targeted intervention.
In this paper, we offer a dynamic conceptual model of drinking events that is
grounded in the extant literature. Before presenting the conceptual framework, we
briefly review the historical approaches to understanding drinking events. We also
discuss the conceptual importance of drinking events for understanding drinking
behavior as a whole and preventing acute problems.
Why are Drinking Events Important?
Drinking events are direct antecedents to numerous acute alcohol-related
Systems Research and Behavioral Science
problems including injuries, sexual and other violence, burns, falls, crashes, and crime,
among many other problems (NIH, 2000). In aggregate, drinking events represent
patterns of consumption that drive disease and premature death (Holder, 2006).
Acute problems have a huge global impact (Rehm et al., 2009); for instance,
approximately 25% of all unintentional, and 10% of intentional injuries in the world
can be attributed to drinking events. When alcohol-related disease and death are
considered, 3-4% of all deaths in the world are alcohol-related (Rehm et al., 2009).
Although heavier drinkers or those with alcohol-use disorders are at higher
risk to experience acute problems, lighter and moderate drinkers who engage in
heavy episodic drinking account for the bulk of acute alcohol-related problems
(Stockwell et al., 1996). This so-called “prevention paradox” (Kreitman, 1986)
suggests that universal environmental approaches (e.g., DUI campaigns, responsible
beverage service, taxation, regulation) have historically been the primary means for
preventing acute alcohol problems. Although a solid evidence base exists for
environmental alcohol interventions (Holder, 2006; Saltz et al., 2010), newer “smart”
interventions (e.g., geo-fencing and SMS prompts) have the potential to complement
universal environmental prevention efforts by targeting group or individual level
“leverage points” (Stokols, 2000) in real time while considering the current behavioral
Past Approaches to Understanding Drinking Events
The conceptualization, definition, and measurement of drinking events have
evolved little in recent decades. Over 30 years ago, the National Institute on Alcohol
Systems Research and Behavioral Science
Abuse and Alcoholism (NIAAA) published a monograph titled Social Drinking Contexts
(Harford & Gaines, 1982). In the introduction to that collection of conference papers,
Hartford and Gaines noted, “While context, or frame of reference, may hold the key to
understanding drinking behavior, no single idiom describes context” (p.1). The
authors go on to note that the multi-disciplinary nature of alcohol studies related to
context reflect a spectrum of terms and units of analysis. The nomenclature and
taxonomies used to frame drinking events still reflects such diversity.
In that same volume, drawing from the basic social psychology theory of Lewin
(1951), Jessor (1982) offered a simple multi-level representation:
DB=f (P,E)
In this formula, drinking behavior is a function of the interaction of person-level
variables and environment influences. Jessor explicitly defines “context” as
“environment. In his discussion, he notes two important considerations. First, “(the
environment) persists in being a concept of disturbing complexity (p.230; emphasis
added). And then, “the dynamics of situations give rise to changes in situations and
behavior over time…an obvious source of such change is…alcohol ingestion…and its
disinhibition effects(p. 231; emphasis added).
Some three decades later, understanding drinking events from a systems
perspective remains a vexing problem. Since the publication of Social Drinking
Contexts (Harford & Gaines, 1982), there has been great variation in the
conceptualization, measurement, and analysis of drinking events. To start, it is
important to note that there is no standard definition of “drinking event.” Consistent
Systems Research and Behavioral Science
with Jessor’s (1982) basic model, we conceptualize drinking events as including a
drinker interacting socially with a network of other drinkers (and non-drinkers),
embedded in larger social and physical environments. Conceptually, we view the
event as a system that activates when drinking begins, and achieves entropy when
both active drinking has ceased and the social purpose of the event has concluded.
This approach differs from the now-common practice of segmenting drinking events
into time-specific (e.g., pre-gaming), social (e.g., drinking games), and/or geo-spatial
(e.g., bars) elements. Although analytically useful, such segmentation may obscure our
understanding of the system as a whole and the complex nature of these events
(Miller & Page, 2007). For instance, over the course of an individual’s drinking event,
pre-gaming can occur in a small private setting, followed by drinking games in a larger
party setting, and culminating with drinking in a public setting like a bar. Each activity
and setting comes with its own dynamics (Fitzpatrick & Martinez, 2012; Clapp et al.,
2008; Clapp et al., 2009), resulting in complexity (i.e., multi-level) and transitory risk
(and protection) across an entire event. Individuals interact socially with peers, while
their personal decisions and desires are potentially influenced by group dynamics, the
larger environment, and their own level of intoxication. Segmented approaches to
studying drinking events miss much of this behavior.
This paper hopes to further the scientific understanding of the dynamics
surrounding drinking behavior as it naturally occurs. There are several dynamic
problems related to drinking. First, although the biological dynamics associated with
metabolism and blood alcohol content have been modeled, little is known about how
Systems Research and Behavioral Science
individual desires relate to drinking effects (i.e., the rate of drinking, peak blood
alcohol levels, and blood alcohol concentration curves) or how the consumption of
alcohol impacts one’s personal desires over the course of an event. Further, our model
addresses the dynamical problem related to how a drinker’s drinking companions
influence a drinker’s desires; and in turn, the model postulates the dynamics of how a
drinker influences their drinking companions. Finally, the model addresses how the
drinking group influences, and is influenced by, the drinking environment. As a
system, we view these dynamics as being critical to better understanding the complex
nature of drinking as a social behavior that is inherently ecological.
Our general approach to developing a dynamics-driven framework for drinking
events is consistent with Pentland’s (2014, pg.5) approach to social physics: “Just as
the goal of traditional physics is to understand how the flow of energy translates into
changes in motion”, our aim is to understand how different social or environmental
factors translate into changes in the dynamics of a drinking event. While there is no
standard approach to developing dynamical models, we followed an approach similar
to others (Richardson & Pugh, 1981; Sterman, 2002) by engaging in the following
steps to develop our model: (1) problem definition, (2) system conceptualization, (3)
model formulation, (4) testing and simulation, and (5) model evaluation. We note that
steps 2-5 follow an iterative process.
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The model presented below represents our work to date. Consistent with
others (Sterman, 2002; Richmond, 2004), we are approaching this modeling as an
ongoing process that includes both conceptual and empirical stages. Here, we present
our conceptual work in a form we hope will facilitate its use by others.
Modeling Context
Our team consists of two full professors (one in social work, one in
engineering) and four doctoral students (one in social work, three in engineering)
working at the same university. In addition, we have a social work professor working
at another institution who did not participate in all modeling activities, but served as a
reviewerevaluating the logic of the models relative to the existing literature, in
addition to other tasks as the model developed. The social work members of the team
have extensive experience in studying drinking events and drinking behavior in situ
(Clapp et al., 2003; Clapp & Shillington, 2001; Clapp, et al., 2007; Clapp et al., 2008;
Clapp et al., 2009); and the engineering members have extensive experience in system
dynamics modeling of behavior (Passino et al., 2008; Passino & Seeley, 2006).
The team met twice monthly (about 90 minutes per session) to work on
modeling. Group members took extensive notes and minutes, including graphic
depictions of concepts, and those notes were circulated regularly. It was common for
group members to take on assignments between meetings. An in-depth description of
the team science aspects of this process can be found in Clapp, Passino, Giraldo, and
Ruderman (2017b).
Systems Research and Behavioral Science
Problem Definition: In the early phases of the collaboration, the social work
partners presented alcohol research related to drinking events (Clapp et al., 2008;
Clapp et al., 2014; Wells et al., 2008; Thombs et al., 2010; Neighbors et al., 2011;
Kuntsche et al., 2015). These presentations helped to define the problem by
specifically identifying the key elements of the drinking event system. Examples
related to the influence of variables at different levels of abstraction were discussed in
depth. For instance, the relationship between a drinker’s motivation and blood alcohol
content (BAC) at the individual-level (see: O’Grady et al., 2011; Neal & Carey, 2007;
Wetherill & Fromme, 2009) was discussed. Similarly, studies related to group
influence on drinking (see: Cullum et al., 2012; Wells et al., 2015; Reed et al., 2013)
and environmental influences on drinking (see: Clapp et al, 2008; Clapp et al., 2009)
were discussed. Additionally, the engineering team read key papers related to BAC
metabolism (Wilkinson, 1980; Jones, 2010; Lundquist & Wolthers, 1958; Norberg et
al., 2003).
System Conceptualization: Consistent with Richmond (2004), we developed
hypothesized dynamical “behavior over time graphs” and causal loop diagrams (CLD)
along with other visuals to illustrate concepts and potential dynamics during this
stage of the work. In turn, the engineering members presented simulations of
computational modelsbased on the materials presented by the social work
membersand explained underlying mathematical concepts. Early simulations were
based on field data collected by one of the team members in a previous project (Clapp
et al., 2009). Mathematical models, proofs, and simulations related to the conceptual
Systems Research and Behavioral Science
elements of the model presented in this paper have been published in engineering
journals (Giraldo et al., 2017a; Giraldo et al., 2017b).
Model Formulation: During this phase of the work, the group jointly
constructed stock and flow diagrams based on the CLDs. We discussed the dynamics
relative to what would be expected logicallyfor example, one with strong motives to
get “drunk” would likely have a higher BAC than a drinker with motives to get
“buzzed. Once the qualitative assessment of the initial stock and flow diagrams was
complete, the engineering team developed a computational model to illustrate the
dynamics of BAC metabolism and the group influence aspects of the model (Giraldo et
al., 2017a).
Testing and Simulation: Computational models were built for environmental
dynamics as well. Those models and simulations were all performed in Simulink. To
facilitate the social work team’s understanding of mathematical models, and to ensure
the engineering team had gotten drinking concepts correctly represented, generic
versions using representative parameters of variables were constructed in STELLA
and Vensim, collaboratively by our team.
Model Evaluation: The final stage of the initial modeling process involved a
qualitative and quantitative assessment of the simulation results. Qualitatively, the
team assessed simulation results relative to the extant literature on drinking events.
For instance, researchers (Trim et al., 2011) have shown that a drinker’s desired level
of intoxication at the beginning of an event often fails to match the outcome (i.e., they
get more or less intoxicated than they intended), suggesting that the interaction of the
Systems Research and Behavioral Science
individual’s drinking and endogenous factors (i.e., group influences or environmental
influences) are dynamically linked. As such, our model needed to allow for both
“under-shooting” and “over-shooting” of BAC over the course of an event. The models
developed in previous stages were reviewed for this characteristic and others
described in the results. Mathematically, the underlying differential equations had to
have sound and demonstrated proofs (Giraldo et al., 2017a; Giraldo et al., 2017b).
Figure 1 represents an overall conceptual model of drinking events. The figure
depicts individuals during a drinking event (smallest circles), who are situated in
groups (enclosed in the same medium-sized circle), drinking in specific environments
(the largest circles). Lines between individuals represent communication between
group members, across groups, or between people in different locations. Our model
focuses on the aspects of a drinking event for only one of these hypothetical drinkers
(for example, the individual marked A). The model is conceptually an extension of
Jessor’s simple heuristic path model of drinking behavior. That is, we have begun to
fill in the various ecological elements at the person level (i.e., biological variables,
psychological variables) that theoretically drive the dynamics within the overall
drinking event system.
Figure 2 presents the CLD for the drinking event system. Conceptually, our model
included four key stocks: (1) BAC, (2) Desired State (of intoxication) or Desired BAC,
(3) Group Wetness, and (4) Environmental Wetness. From a social ecological
Systems Research and Behavioral Science
framework, BAC, and Desired State are micro-level variables; Group Wetness is a
mezzo-level variable; and Environmental Wetness is a macro-level variable. Together,
these stocks represent the various levels of abstraction found in the literature
examining drinking events (Clapp et al., 2009; Reed et al., 2013; Jessor, 1982) in a
highly aggregated model consistent with a 10,000 foot view of the system (Richmond,
Beginning from the bottom of the CLD (micro-level) in Figure 2, there is a
balancing causal feedback loop between the BAC stock and the metabolic rate (Giraldo
et al., 2017a).
Moving up a level is the feedback loop related to drinking motives and
perceptions of intoxication are represented. ,. As shown as BAC increases, a
reinforcement loop is generated via the disinhibitory effects of alcohol on cognitive
control, specifically on inhibitory control causing increased motivation to drink
(Steele & Josephs, 1990, Field et al., 2010). A delay is introduced before the BAC stock,
representing the transit of alcohol through the gastrointestinal tract before entering
the blood by an absorption process (it could also be delayed further by food intake).
Put in practical terms, decisions by drinkers whether to have another drink to
maintain or obtain a “buzz” are often based on a misperception of the amount of
alcohol they have in their system (Richmond, 2004). A full operational model
including all stocks, flows, and connectors is presented in the appendix.
Continuing with Figure 2 and moving up a level of abstraction, the drinker’s
Desired BAC stock is also influenced by the Group Wetness stock in a reinforcing
Systems Research and Behavioral Science
feedback structure. As described in Table 1, Group Wetness is a form of social
influence that includes the average BAC of a drinker’s companions at the drinking
event, the average Desired BAC of the drinker’s companions, and the relative influence
of each member of the group on the drinker. In our earlier work (Giraldo et al.,
2017a), we modeled how peer influence varies in strength and interacts with a
drinker’s own desired state to alter drinking trajectories. Through a series of
computer simulations we showed that a strong influence within a peer network pulls
all but those with very strong desires toward a BAC trajectory similar to the peer
exerting the influence. In turn, completing this reinforcing feedback loop as a member
of the group, the drinker’s BAC and Desired BAC also influence the Group Wetness
Finally, moving to the top of the CLD in Figure 2 to the group and
environmental levels, we posit a reinforcing feedback loop between Group Wetness
and Environmental Wetness. Our earlier studies of drinking events (Clapp et al., 2000;
Clapp et al., 2009) found that the presence of “many intoxicated people” (whether
observed by researchers or reported by survey respondents) consistently contributed
to high BAC or self-reported heavy drinking. We also found that heavier drinkers seek
out wetter environments (Trim et al., 2011), suggesting that influence flows in both
directions. As Group Wetness increases, Environmental Wetness increases. In turn,
Environmental Wetness, which represents the overall average BAC among bar patrons
coupled with alcohol availability in the environment (Clapp et al., 2009), influences
Group Wetness in a reinforcing way.
Systems Research and Behavioral Science
Table 1 describes each element in the model, except for the GAC stock, which
simply represents the aggregate amount of alcohol (for example, the unit of measure
could be standard drinks) residing in the gastrointestinal tract. We offer basic
definitions of each element, theoretical parameters (based on our mathematical
models), how the elements might be measured (or have been), and some assumptions
about how each element operates in the system based on the literature and our
previous research.
Figure 3 shows a series of our hypothesized reference behavior of time graphs for
various BAC outcomes generated via simulation of the model found in Figure A.1.
Although we are interested in conceptually understanding the entire drinking event
system, understanding how different elements of the model affect the BAC is
particularly important for guiding prevention efforts. The graphs shown in Figure 3.A.
portray the effect of metabolism on BAC. The plots in Figure 3.B. shed light on the
reinforcing cognitive effects on BAC while the plots in Figure 3.C shows the effect of
peer and environment influence on individual’s intoxication. In all cases, it is assumed
that the drinking period lasts for three hours. The model parameters are provided in
Table 2.
While the graphs in Figure 3.B. and 3.C. only illustrate “peak” BAC, and do not
show the decline of BAC back to zero, Figure 3.A. shows the entire BAC curve for a
drinker who desired to get “very buzzed or drunk.” As presented, the drinker reaches
a peak BAC of over 0.1 by hour three of the event, before the BAC begins to decrease
slowly. The fluctuations in the decreasing BAC are due to consumption of more
Systems Research and Behavioral Science
alcohol after stopping drinking for a period. The steep growth of BAC in the early
hours of the event is related to the rate of drinking. That is, to obtain the BAC shown,
the rate of drinking would be fairly fast.
The graph shown in Figure 3.B. illustrates the BAC curve for a drinker with the
desire to “get slightly buzzed (Desired BAC = 0.03).” In this graph, the drinker reaches
an initial peak BAC of about 0.035 and decreases his intake rate towards his initial
desired BAC. However, due to the disinhibitory effect which increases his desire to
drink, the individual resumes drinking at a lower rate which increases his BAC level.
The final graph in the series, Figure 3.C., illustrates a drinker who desires to “get
buzzed (Desired BAC = 0.06),” but is pulled off that trajectory later in the event.
Conceptually, such “overshooting” is a function of group and/or environmental
dynamics. Empirically, we found this to be fairly common during drinking events
(Giraldo et al., 2017a; Clapp et al., 2009; Trim et al., 2011). The mathematics
underlying the dynamics of overshooting or undershooting desired intoxication levels
is presented in our more technical work (Giraldo et al., 2017a).
Finally, Appendix B presents potential random variables (disturbances), at each
level of abstraction that, theoretically, might alter the dynamics in the model. The list
is provided as both a means to set the exogenous boundaries of the model and to
guide potential simulations in the future.
Systems Research and Behavioral Science
Drinking events remain an important area of study for alcohol researchers.
Understanding drinking events and the complex dynamics that underlie them is
important both conceptually and to help guide prevention efforts that utilize “smart”
technologies in situ. Our model and previous work (Giraldo et al., 2017a; Giraldo et al.,
2017b), advance a conceptual approach which we hope will aid understanding of
drinking behavior while guiding the development of prevention approaches.
By considering the underlying interactions among the biological, psychological,
social, and environmental interactions related to drinking behavior as it occurs, the
model presented here is one of the few attempts to address the inherent complexity of
drinking events noted by Jessor (1982) and Harford & Gaines (1982), over three
decades ago. Our conceptual and mathematical results thus far begin to illustrate the
potential of dynamics at several levels resulting in individuals drinking heavily, and
more than they initially intended. Initial intentions are important. In our model, we
posit a reinforcing feedback loop between perceptions/cognitive effects, desired state,
drinking, and BAC. In theory, a drinker with the motivation to have a no buzz or a
“slight buzz” who drank slowly enough to have fairly accurate perceptions of their
intoxication (or stopped drinking after a drink or two) could be represented by a
balancing feedback loop where equilibrium is achieved. However, as noted by other
researchers (Trim et al., 2006; Giraldo et al., 2017a), drinkers’ initial motives for a
level of “buzz” often do not reflect their actual BAC (i.e., drinkers get more intoxicated
than intended). Further field work is needed to better understand how these
dynamics might differ based on initial desires for intoxication.
Systems Research and Behavioral Science
The modeling efforts and simulation results presented here (figure 3) illustrate
the importance of dynamics in drinking behavior. For instance, figure 3.B shows how
a drinkers desire impacts and is impacted by BAC levels. The issue of how one’s BAC
curve impacts an overall drinking event was raised by Jessor over 30 years ago
(Jessor, 1982) yet little work to date has focused on this issue. Figure 3.B illustrated
an example of “overshooting” where the drinker ends up drinking more than intended
resulting in a higher BAC. Figure 3.C, illustrates how the GI tract results in a delay in
BAC, the mechanism that theoretically accounts for overshooting. Taken together, the
simulation leaves in a manner consistent with the CLD presented in figure 2.
Future workboth empirical and computationalwill be needed to validate
and refine the conceptual model. Field studies with high ecological validity (Clapp et
al., 2007) are needed to examine social network influences as they relate to desired
intoxication and actual drinking outcomes. Similarly, more work is needed to
biologically validate BAC curves as they relate to elements of the drinking event
system. Likewise, better understanding of how individuals decide to continue or stop
drinking to achieve a desired level of intoxication must be better developed. Better
measures of environmental wetness also must be created and tested. Finally, applying
the knowledge generated by the dynamical modeling and validation process must
identify leverage points to guide the development and testing of preventive
interventions. Such work is never complete; nor is it easy.
Our approach required the collective effort of a team of scientists from
disparate disciplines working together in a highly collaborative manner for a
Systems Research and Behavioral Science
considerable period. Pulling together the relevant aspects of the drinking event
literature with appropriate mathematical formulations drawn from physics and
engineering required both parties to be simultaneously open and critical. The extant
literature on drinking events, with a few notable exceptions (Gorman et al., 2006;
Fitzpatrick & Martinez, 2012), rarely considers multiple levels of abstraction and is
almost exclusively grounded in static linear modelsmaking the jump to developing a
dynamical model challenging. Similarly, the application of principles related to physics
(like force and attraction) used in our computational work (Giraldo et al., 2017a), had
to be carefully applied to a social behavior. As this line of work continues and, we
hope, expands to other groups, the multi-disciplinary method described here must
evolve into a transdisciplinary approach.
In this spirit, others have noted that system dynamics maps and CLDs are
essentially heuristic devices to explain complex behavior in an elegant and aggregate
form (Richmond, 2004) and guide applied intervention work (BeLue et al., 2012). One
challenge of both the current work presented and future work will be to develop a
common system of visual explanation (e.g., conventions for drawing CLDs, stock and
flow diagrams). CLDs and stock and flow diagrams can be useful when carefully
presented. They can, however, be overly complex and confusing. Finding the correct
balance of system specification that is ecologically valid without sacrificing
accessibility can be challenging. Similarly, developing a common nomenclature for
discussing and studying drinking events will be important. Beyond the scientific
community, using collaborative model building approaches (BeLue et al., 2012;
Systems Research and Behavioral Science
Hovmand, 2013) and visual simulations using free software packages like Vensim and
Mental Modeler, might facilitate the understanding of these complex systems. Like
others working on collaborative models to tackle important real-world problems, we
hope that such efforts will help move science into applied situations faster and in a
more ecologically valid way.
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Systems Research and Behavioral Science
Table 1. Description of Elements in the Model
Desired BAC
Level of
Represents the
drinker’s blood
The state of
intoxication a drinker
hopes to obtain at
any given point
during the drinking
Represents the
extent the
promotes heavy
Grams of
ethanol/100 ML of
Values range from
0.0--.30. (Bounded
not to go below .0.0
or above. 0.30 to
reflect typical values)
A standard drink
=.02 (+) in BAC.
Metabolism= .02 (-)
in BAC per hour.
(Dubowski, 1985)
Measured on Likert
scale with values
ranging from “drink
but not get buzzed”
to “drink to get very
Can also measure
strength of desire by
Likert scale.
(Clapp et al., 2009;
Thombs et al., 2010)
Measured by an
index of availability
including: average
dollar amount per
standard drink;
average time to
obtain a drink; and
the number of fixed
and temporary
servers. Could also
include the social
aspects of the
environment such as
the presence of many
intoxicated people
(Clapp et al., 2009).
Influenced by: rate
and volume of
Influences desired
Food delays
Delay drinking and
ability to perceive
BAC level.
May be conscious or
Can shift over the
course of event.
Initial strength of
desire relates to
likelihood of change.
Influences and is
influenced by Group
Some group
members might have
more influence than
Influences and is
influenced by group
Influences and is
influenced by
individual drinker
BAC and Desired
Is selected based on
the weighted mean of
a group’s Desired
State (giving more
weight to influential
group members.
Systems Research and Behavioral Science
Table 2. Model Parameters
Elimination parameter
Absorption parameter
Effect of food
BAC rate perception parameter
Decision Commitment
Alcohol in CNS/Blood ratio
Inhibitory effect parameter
Env strength on individual
Group strength on individual
Initial Desired BAC
Initial GAC, BAC
Inhibitory effect parameter
Initial Desired BAC
Env strength on individual
Group strength on individual
Ind strength on group
Ind strength on Env
Group strength on Env
Env strength on group
Initial Desired BAC
Note: Parameters not listed in Figures 3.B and 3.C remain the same as in Figure 3.A
Systems Research and Behavioral Science
Figure 1. Conceptual Model of Drinking Events
Environment 1
Environment 2
Systems Research and Behavioral Science
Figure 2. Causal Loop Diagram for Drinking Event Syste
Systems Research and Behavioral Science
Figure 3. BAC Curves Under Different Conditions
3. A
3. B
Systems Research and Behavioral Science
Systems Research and Behavioral Science
3. C
Systems Research and Behavioral Science
Appendix A
Figure A.1. Full Stock and Flow Diagram for Drinking Event System
Systems Research and Behavioral Science
Appendix B
Table B.1. Potential Random Variables and Disturbances by Level of Abstraction
Random Variable
Taking medication
Level of hydration
Level of rest
Drinking history (e.g., binge drinker, etc)
Genetic markers
New group member(s) during event
Exiting group members during event
Sexual attraction among group members
Social media or SMS connection among group
members during event
Moving locations during event
Fights or aggression at events
Introduction of music or dancing during event
Influx or outflow of other groups during event
Note: This table provides potential variables and disturbances and does not reflect specifically what is in
our model. This is not an exhaustive list.
... Drinking events are dynamic and complex. [7][8][9] When viewed as a system, drinkers are influenced by personal characteristics (ie, motives), their direct peer network and multiple social and physical environments. 7 In a typical drinking event system (Figure 1) each activity and setting are dynamic 5,10,11 and risk and protective factors for heavy drinking vary across drinking environments 5 and time. ...
... [7][8][9] When viewed as a system, drinkers are influenced by personal characteristics (ie, motives), their direct peer network and multiple social and physical environments. 7 In a typical drinking event system (Figure 1) each activity and setting are dynamic 5,10,11 and risk and protective factors for heavy drinking vary across drinking environments 5 and time. 12 Similarly, as an event progresses, drinking plans 13 and drinking groups 14 often change. ...
... Items were developed for this study based on theoretical assumptions of what may shape intoxication momentarily. 7 For instance, the presence of food in the stomach can slow the absorption of ethanol. 50 On the other hand, co-consumption of diet soda or energy drinks may increase the risk for injury during an event. ...
Full-text available
Objectives: Despite the substantial influence these acute alcohol-related problems cause globally, past research has failed historically to capture the dynamic nature of drinking events, including how multiple factors (ie, individual, group, and environmental) interact to affect event-level intoxication. Fortunately, technology (eg, transdermal alcohol monitors) and smartphone surveys have provided researchers with new avenues to measure the complex nature of alcohol consumption. This paper presents the methods of a pilot study that sought to measure event-level alcohol consumption in a natural drinking group of college students. Methods: Ten groups of friends (N=49) were followed for 2 weeks with daily diary surveys, continuous activity trackers, hourly geographic ecological momentary assessments (EMAs) on 4 separate drinking occasions, and a transdermal alcohol monitor during one group-based social event. Results: On average, participants responded to > 75% of both daily diaries and EMAs and were compliant with activity trackers on 96% of monitoring days. Over 90% of the sample had usable transdermal data and after smoothing, peak transdermal alcohol contents ranged from 0.13 to 0.395 during the observation evening. Conclusion: The lessons learned during this pilot study can provide a building block for future work in this area, especially as data collection in alcohol research rapidly advances.
... Drinking events are dynamic and complex. [7][8][9] When viewed as a system, drinkers are influenced by personal characteristics (ie, motives), their direct peer network and multiple social and physical environments. 7 In a typical drinking event system (Figure 1) each activity and setting are dynamic 5,10,11 and risk and protective factors for heavy drinking vary across drinking environments 5 and time. ...
... [7][8][9] When viewed as a system, drinkers are influenced by personal characteristics (ie, motives), their direct peer network and multiple social and physical environments. 7 In a typical drinking event system (Figure 1) each activity and setting are dynamic 5,10,11 and risk and protective factors for heavy drinking vary across drinking environments 5 and time. 12 Similarly, as an event progresses, drinking plans 13 and drinking groups 14 often change. ...
... Items were developed for this study based on theoretical assumptions of what may shape intoxication momentarily. 7 For instance, the presence of food in the stomach can slow the absorption of ethanol. 50 On the other hand, co-consumption of diet soda or energy drinks may increase the risk for injury during an event. ...
... System models typically include domains (or parts), at multiple levels of abstraction, which are interconnected and influence each other over time (Meadows, 2008). Although systems can be strictly linear, complex systems tend to have feedback loops that result in bidirectional causation and non-linear outcomes (see Clapp et al, 2018). One feature of complex systems of particular interest for health behavior intervention research is the notion of "leverage points," which represent strategic places in complex systems where an intervention will likely lead to a large change in the behavior of the system (Meadows, 1999;Stokols, 2000). ...
... He explained that the modeling process would be an intensive and joint effort that would require time. Our collaboration in developing system dynamics models of drinking events has been described elsewhere (Clapp et al., 2018). For the purposes of this paper it's enough to note that for the past four or so years, Kevin's team and my team have worked to develop and refine a complete dynamical model of the drinking event system. ...
... For the purposes of this paper it's enough to note that for the past four or so years, Kevin's team and my team have worked to develop and refine a complete dynamical model of the drinking event system. Much of this work has been published in engineering journals Giraldo, Passino, Clapp & Ruderman, 2017;Gonzalez Villasanti, Passino, Clapp & Madden, 2017) with the most accessible piece detailing the conceptual model being Clapp et al., 2018. Conceptually, this work addressed numerous questions generated by our earlier field work. ...
Full-text available
This paper is based on my Research Laureate Address to the American Academy of Health Behavior, Portland Oregon, March 4th, 2018. The paper follows the basic content and structure of my address but has been written in a style more consistent with a scientific essay rather than a transcript of a verbatim speech.
... Healthrelated topics are quite rich, and their list is full of particular issues. Addiction represents one of them, and a focus on alcohol use [240,241] or cocaine addiction [242] can be beneficial for both medical research and SD methodology. Stress and depression represent a ubiquitous characteristic of the current lifestyle. ...
Full-text available
System dynamics, as a methodology for analyzing and understanding various types of systems, has been applied in research for several decades. We undertook a review to identify the latest application domains and map the realm of system dynamics. The systematic review was conducted according to the PRISMA methodology. We analyzed and categorized 212 articles and found that the vast majority of studies belong to the fields of business administration, health, and environmental research. Altogether, 20 groups of modeling and simulation topics can be recognized. System dynamics is occasionally supported by other modeling methodologies such as the agent-based modeling approach. There are issues related to published studies mostly associated with testing of validity and reasonability of models, leading to the development of predictions that are not grounded in verified models. This study contributes to the development of system dynamics as a methodology that can offer new ideas, highlight limitations, or provide analogies for further research in various research disciplines.
... • our plans for validation and future steps. <normal>Data for this case study comes from field notes of the senior author, group discussions among the team members, review of simulations and models, and a review of the published papers and presentations related to the work (Clapp et al., 2018;Giraldo, Passino, & Clapp, 2017;Giraldo, Passino, Clapp, & Madden, 2016). The case study is presented sequentially and we conclude with a brief discussion section. ...
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This chapter details a three-year collaboration between a team of social scientists and a team of engineers to develop a system dynamics framework to study and model drinking events. Beyond the goal of advancing knowledge, the collaboration is geared toward informing the development of real-time solutions to address alcohol-related problems as they happen. Much of our work has focused on young adults and college students—one of the heaviest drinking demographics in society. Prior to discussing our modeling approach, we provide some background on drinking among college students, research related to drinking events and why that focus is important, and where the field is heading given recent technological advances.
Background Drinking events are characterized by social and physical contexts that are associated with level of alcohol consumption. Ecologically valid data is needed to delineate aspects of the drinking context that are most likely to precipitate excessive alcohol consumption. Methods We utilized event-level data from a longitudinal study that included repeated daily surveys administered in two 28-day bursts. Data from 341 college student past-month alcohol and cannabis users (Mage=19.79; 53% women; 74% White) produced a total of 4,107 alcohol use days. Generalized linear mixed effects models were used to predict drinking level (moderate: 1-3/1-4 for women/men; heavy-episodic drinking (HED): 4-7/5-9; high-intensity drinking (HID), 8+/10+) by social (e.g., with friends) and physical (e.g., at a party) contexts. We conducted analyses for the first and last drink reported, controlling demographic and study characteristics. Results Being at a party, friend’s house, or with strangers at the last drink reported were associated with HID compared to HED, while being at home, alone, or with family were protective for HID. No first drink contexts were associated with HID relative to HED. Witnessing others who were intoxicated was consistently associated with HID. Conclusions Social settings such as parties and those with intoxicated persons were associated with risk for HID. The context of drinks at the end of an event are salient signals of level of alcohol consumption. Preventive interventions, particularly those that deliver strategies in real time, should consider accounting for contextual risk factors to reduce harms associated with excessive alcohol consumption.
Background: Wearable transdermal alcohol concentration (TAC) sensors allow passive monitoring of alcohol concentration in natural settings and measurement of multiple features from drinking episodes, including peak intoxication level, speed of intoxication (absorption rate) and elimination, and duration. These passively collected features extend commonly used self-reported drink counts and may facilitate the prediction of alcohol-related consequences in natural settings, aiding risk stratification and prevention efforts. Method: A total of 222 young adults aged 21-29 (M age = 22.3, 64% female, 79% non-Hispanic white, 84% undergraduates) who regularly drink heavily participated in a 5-day study that included the ecological momentary assessment (EMA) of alcohol consumption (daily morning reports and participant-initiated episodic EMA sequences) and the wearing of TAC sensors (SCRAM-CAM anklets). The analytic sample contained 218 participants and 1274 days (including 554 self-reported drinking days). Five features-area under the curve (AUC), peak TAC, rise rate (rate of absorption), fall rate (rate of elimination), and duration-were extracted from TAC-positive trajectories for each drinking day. Day- and person-level associations of TAC features with drink counts (morning and episodic EMA) and alcohol-related consequences were tested using multilevel modeling. Results: TAC features were strongly associated with morning drink reports (r = 0.6-0.7) but only moderately associated with episodic EMA drink counts (r = 0.3-0.5) at both day and person levels. Higher peaks, larger AUCs, faster rise rates, and faster fall rates were significantly predictive of day-level alcohol-related consequences after adjusting for both morning and episodic EMA drink counts in separate models. Person means of TAC features added little above daily scores to the prediction of alcohol-related consequences. Conclusions: These results support the utility of TAC sensors in studies of alcohol misuse among young adults in natural settings and outline the specific TAC features that contribute to the day-level prediction of alcohol-related consequences. TAC sensors provide a passive option for obtaining valid and unique information predictive of drinking risk in natural settings.
Background and Aims A complex systems perspective has been advocated to explore multi‐faceted factors influencing public health issues, including alcohol consumption and associated harms. This scoping review aimed to identify studies that applied a complex systems perspective to alcohol consumption and the prevention of alcohol‐related harms in order to summarise their characteristics and identify evidence gaps. Methods Studies published between January 2000 and September 2020 in English were located by searching for terms synonymous with ‘complex systems’ and ‘alcohol’ in the Scopus, MEDLINE, Web of Science and Embase databases, and through handsearching and reference screening of included studies. Data were extracted on each study’s aim, country, population, alcohol topic, system levels, funding, theory, methods, data sources, timeframes, system modifications and type of findings produced. Results Eighty‐seven individual studies and three systematic reviews were identified, the majority of which were conducted in the United States or Australia in the general population, university students or adolescents. Studies explored types and patterns of consumption behaviour and the local environments in which alcohol is consumed. Most studies focused on individual and local interactions and influences, with fewer examples exploring the relationships between these and regional, national and international sub‐systems. The body of literature is methodologically diverse and includes theory‐led approaches, dynamic simulation models and social network analyses. The systematic reviews focussed on primary network studies. Conclusions The use of a complex systems perspective has provided a variety of ways of conceptualising and analysing alcohol use and harm prevention efforts, but its focus ultimately has remained on predominantly individual‐ and/or local‐level systems. A complex systems perspective represents an opportunity to address this gap by also considering the vertical dimensions that constrain, shape and influence alcohol consumption and related harms, but the literature to date has not fully captured this potential.
The complex and dynamic interplay between an individual's psychophysiological processes and multilevel interactions with his/her group and environment during alcohol drinking events is analysed in this work. Our aim is to provide a system dynamics model to accurately represent a drinking event and provide guidelines for feedback‐based behavioural interventions. We employ a pharmacodynamics model of alcohol metabolism, with a self‐regulation approach of decision‐making to characterize the individual's drinking behaviour. The nonlinearities introduced by the acute effects of alcohol in cognition, along with social perception and influence, complete the individual's model, which serves as a basis for the group and environment's behaviour models. A sensitivity analysis revealed that influenceability and overestimation via descriptive social norms are key drivers of higher blood alcohol content levels. Furthermore, simulations showed that intervening early in the event, before cognition processes are inhibited, and targeting groups of individuals result in efficient implementations.
Background: Although most young adults drink alcohol, there are specific drinking contexts that are associated with increased risk for alcohol-related consequences. One such drinking context is pregaming, which typically involves heavy drinking in brief periods of time and has consistently been linked to consequences within the pregaming event itself, on a night after pregaming, and in the long-term. Intervention efforts that specifically target this risky behavior are needed, but these efforts need to be informed by empirical work to better understand what behaviors young people engage in that can protect them from pregaming-related harms. Purpose: We designed this study to create a measure of protective behavioral strategies that young people use before, during, and after pregaming to inform future intervention work. Methods: We tested an item pool with 363 young adult college students who engaged in pregaming in the past year and conducted exploratory factor analysis to develop a 19-item Protective Behavioral Strategies for Pregaming (PBSP) scale, which featured four subscales of safety and familiarity, setting drink limits, pacing strategies, and minimizing intoxication. Results: Each subscale negatively and significantly correlated with measures of alcohol use and consequences, though subscales differed in their associations with specific pregaming outcomes and by sex. Conclusion: This initial exploratory examination of the PBSP scale's psychometric properties suggests that use of protective behavioral strategies used specifically during pregaming events may protect young people from heavy drinking and harms. More research with the PBSP scale is encouraged to determine its practical utility as a clinical and assessment tool with young people.
Full-text available
This chapter details a three-year collaboration between a team of social scientists and a team of engineers to develop a system dynamics framework to study and model drinking events. Beyond the goal of advancing knowledge, the collaboration is geared toward informing the development of real-time solutions to address alcohol-related problems as they happen. Much of our work has focused on young adults and college students—one of the heaviest drinking demographics in society. Prior to discussing our modeling approach, we provide some background on drinking among college students, research related to drinking events and why that focus is important, and where the field is heading given recent technological advances.
Full-text available
Heavy alcohol consumption is considered an important public health issue in the United States as over 88,000 people die every year from alcohol-related causes. Research is being conducted to understand the etiology of alcohol consumption and to develop strategies to decrease high-risk consumption and its consequences, but there are still important gaps in determining the main factors that influence the consumption behaviors throughout the drinking event. There is a need for methodologies that allow us not only to identify such factors but also to have a comprehensive understanding of how they are connected and how they affect the dynamical evolution of a drinking event. In this paper, we use previous empirical findings from laboratory and field studies to build a mathematical model of the blood alcohol concentration dynamics in individuals that are in drinking events. We characterize these dynamics as the result of the interaction between a decision-making system and the metabolic process for alcohol. We provide a model of the metabolic process for arbitrary alcohol intake patterns and a characterization of the mechanisms that drive the decision-making process of a drinker during the drinking event. We use computational simulations and Lyapunov stability theory to analyze the effects of the parameters of the model on the blood alcohol concentration dynamics that are characterized. Also, we propose a methodology to inform the model using data collected in situ and to make estimations that provide additional information to the analysis. We show how this model allows us to analyze and predict previously observed behaviors, to design new approaches for the collection of data that improves the construction of the model, and help with the design of interventions.
Full-text available
High-risk drinking is considered a major concern in public health, being the third leading preventable cause of death in the United States. Several studies have been conducted to understand the etiology of high-risk drinking and to design prevention strategies to reduce unhealthy alcohol-consumption and related problems, but there are still major gaps in identifying and investigating the key components that affect the consumption patterns during the drinking event. There is a need to develop tools for the design of methodologies to not only identify such dangerous patterns but also to determine how their dynamics impact the event. In this paper, based on current empirical evidence and observations of drinking events, we model a human group that is in an alcohol-consumption scenario as a dynamical system whose behavior is driven by the interplay between the environment, the network of interactions between the individuals, and their personal motivations and characteristics. We show how this mathematical model complements empirical research in this area by allowing us to analyze, simulate, and predict the drinking group behaviors, to improve the methodologies for field data collection, and to design interventions. Through simulations and Lyapunov stability theory, we provide a computational and mathematical analysis of the impact of the model parameters on the predicted dynamics of the drinking group at the drinking event level. Also, we show how the dynamical model can be informed using data collected in situ and to generate information that can complement the analysis.
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Background: Ambulatory physiological monitoring could clarify antecedents and consequences of drug use and could contribute to a sensor-triggered mobile intervention that automatically detects behaviorally risky situations. Our goal was to show that such monitoring is feasible and can produce meaningful data. Methods: We assessed heart rate (HR) with AutoSense, a suite of biosensors that wirelessly transmits data to a smartphone, for up to 4 weeks in 40 polydrug users in opioid-agonist maintenance as they went about their daily lives. Participants also self-reported drug use, mood, and activities on electronic diaries. We compared HR with self-report using multilevel modeling (SAS Proc Mixed). Results: Compliance with AutoSense was good; the data yield from the wireless electrocardiographs was 85.7%. HR was higher when participants reported cocaine use than when they reported heroin use (F(2,9)=250.3, p<.0001) and was also higher as a function of the dose of cocaine reported (F(1,8)=207.7, p<.0001). HR was higher when participants reported craving heroin (F(1,16)=230.9, p<.0001) or cocaine (F(1,14)=157.2, p<.0001) than when they reported of not craving. HR was lower (p<.05) in randomly prompted entries in which participants reported feeling relaxed, feeling happy, or watching TV, and was higher when they reported feeling stressed, being hassled, or walking. Conclusions: High-yield, high-quality heart-rate data can be obtained from drug users in their natural environment as they go about their daily lives, and the resultant data robustly reflect episodes of cocaine and heroin use and other mental and behavioral events of interest.
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Gaining a better understanding of young adults' excessive drinking on nights out is crucial to ensure prevention efforts are effectively targeted. This study aims to identify Saturdays with similar evening drinking patterns and corresponding situation-specific and person-specific determinants. Growth mixture modeling and multilevel logistic regressions were based on 3,084 questionnaires completed by 164 young adults on 514 evenings via the Internet-based cell phone optimized assessment technique (ICAT). The results showed that the 2-group solution best fitted the data with a "stable low" drinking pattern (64.0% of all evenings, 0.2 drinks per hour on average, 1.5 drinks in total) and an "accelerated" drinking pattern (36.0%, increased drinking pace from about 1 drink per hour before 8 p.m. to about 2 drinks per hour after 10 p.m.; 11.5 drinks in total). The presence of more same-sex friends (ORwomen = 1.29, 95% CI [1.09-1.53]; ORmen = 1.35, 95% CI [1.15-1.58], engaging in predrinking (ORwomen = 2.80, 95% CI [1.35-5.81]; ORmen = 3.78, 95% CI [1.67-8.55] and more time spent in drinking establishments among men (ORmen = 1.46, 95% CI [1.12-1.90] predicted accelerated drinking evenings. Accelerated drinking was also likely among women scoring high on coping motives at baseline (ORwomen = 2.40, 95% CI [1.43-4.03] and among men scoring high on enhancement motives (ORmen = 2.36, 95% CI [1.46-3.80]. To conclude, with a total evening consumption that is almost twice the threshold for binge drinking, the identified accelerated drinking pattern signifies a burden for individual and public health. Promoting personal goal setting and commitment, and reinforcing self-efficacy and resistance skills training appear to be promising strategies to impede the acceleration of drinking pace on Saturday evenings. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
Community Based System Dynamics introduces researchers and practitioners to the design and application of participatory systems modeling with diverse communities. The book bridges community- based participatory research methods and rigorous computational modeling approaches to understanding communities as complex systems. It emphasizes the importance of community involvement both to understand the underlying system and to aid in implementation. Comprehensive in its scope, the volume includes topics that span the entire process of participatory systems modeling, from the initial engagement and conceptualization of community issues to model building, analysis, and project evaluation. Community Based System Dynamics is a highly valuable resource for anyone interested in helping to advance social justice using system dynamics, community involvement, and group model building, and helping to make communities a better place. © 2014 Springer Science+Business Media New York. All rights reserved.
Predrinking (preloading, pregaming) has been found to be related to alcohol use and intoxication. However, most research relies on estimates of blood alcohol concentration and does not control for usual drinking pattern. We assessed whether predrinking was associated with subsequent alcohol consumption and breath alcohol concentration (BrAC) among 287 young adult bargoers (173 men [60.3%], Mage = 21.86 years, SD = 2.55 years) who were recruited in groups in an entertainment district of a midsized city in Ontario, Canada. We also examined whether predrinking by other group members interacted with individual predrinking in relation to amount consumed/BrAC. Adjusting for nesting of individuals within groups in hierarchical linear models, predrinkers were found to consume more drinks in the bar district and over the entire night compared to nonpredrinkers and had higher BrACs at the end of the night controlling for drinking pattern. A group- by individual-level interaction revealed that individual predrinking predicted higher BrACs for members of groups in which at least half of the group had been predrinking but not for members of groups in which less than half had been predrinking. This study confirms a direct link of predrinking with greater alcohol consumption and higher intoxication levels. Group- by individual-level effects suggest that group dynamics may have an important impact on individual drinking. Given that predrinking is associated with heavier consumption rather than reduced consumption at the bar, initiatives to address predrinking should include more effective policies to prevent intoxicated people from entering bars and being served once admitted. (PsycINFO Database Record (c) 2015 APA, all rights reserved).