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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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This is a pre-print (pre-refereeing) version of the paper. The
final version of the paper can be found here:
http://onlinelibrary.wiley.com/doi/10.1002/sres.2478/abstract
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
Abstract
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
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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
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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
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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
environment.
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
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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
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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
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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.
METHODS
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).
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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
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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
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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).
RESULTS
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
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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,
2004).
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
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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
stock.
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.
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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
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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.
DISCUSSION
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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.
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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
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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;
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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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Table 1. Description of Elements in the Model
BAC
Desired BAC
Environmental
Wetness
Ecological
Level of
Abstraction
Biological
Micro
Psychological
Micro
Social/Physical
Macro
Definition
Represents the
drinker’s blood
alcohol
concentration.
The state of
intoxication a drinker
hopes to obtain at
any given point
during the drinking
event.
Represents the
extent the
environment
promotes heavy
drinking.
Parameters
Grams of
ethanol/100 ML of
blood.
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
drunk.”
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).
Assumptions
Influenced by: rate
and volume of
drinking.
Influences desired
state.
Food delays
metabolism.
Delay drinking and
ability to perceive
BAC level.
May be conscious or
subconscious.
Can shift over the
course of event.
Initial strength of
desire relates to
likelihood of change.
Influences and is
influenced by Group
Wetness.
Some group
members might have
more influence than
others.
Influences and is
influenced by group
wetness.
Influences and is
influenced by
individual drinker
BAC and Desired
State.
Is selected based on
the weighted mean of
a group’s Desired
State (giving more
weight to influential
group members.
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Table 2. Model Parameters
Figure
Parameter
Value
3.A
Elimination parameter
0.0067
Absorption parameter
0.05
Effect of food
0
BAC rate perception parameter
5.4
Decision Commitment
0.1125
Alcohol in CNS/Blood ratio
0.6451
Inhibitory effect parameter
0.002
Env strength on individual
0
Group strength on individual
0
Initial Desired BAC
0.1
Initial GAC, BAC
0
3.B
Inhibitory effect parameter
0.004
Initial Desired BAC
0.03
3.C
Env strength on individual
0.002
Group strength on individual
0.005
Ind strength on group
0.0005
Ind strength on Env
0.0005
Group strength on Env
0.0005
Env strength on group
0.002
Initial Desired BAC
0.06
Note: Parameters not listed in Figures 3.B and 3.C remain the same as in Figure 3.A
Systems Research and Behavioral Science
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Figure 1. Conceptual Model of Drinking Events
Environment 1
Environment 2
A
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Figure 2. Causal Loop Diagram for Drinking Event Syste
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30
Figure 3. BAC Curves Under Different Conditions
3. A
3. B
Systems Research and Behavioral Science
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Systems Research and Behavioral Science
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3. C
Systems Research and Behavioral Science
33
Appendix A
Figure A.1. Full Stock and Flow Diagram for Drinking Event System
Systems Research and Behavioral Science
34
Appendix B
Table B.1. Potential Random Variables and Disturbances by Level of Abstraction
Level
Random Variable
Micro/Individual
Taking medication
Level of hydration
Level of rest
Drinking history (e.g., binge drinker, etc)
Genetic markers
Tolerance
Mezzo/Group
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
Macro/Environment
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.
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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).
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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.
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