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Development of Risk Perception and Substance Use of Tobacco, Alcohol and Cannabis Among Adolescents and Emerging Adults: Evidence of Directional Influences

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Background: While several studies have investigated the relationship between risk perception and substance use, surprisingly little is known about mutual influences between both variables over time. Objectives: The present study aimed to explore two different hypotheses separately for tobacco, alcohol and cannabis: influences from risk perception on behavior (motivational hypothesis) and influences from behavior on risk perception (risk reappraisal hypothesis). Methods: A prospective and longitudinal cross-lagged panel design was used with substance use and risk perception measured five times over the course of 10 years. Participants were 318 German youths aged 14-15 at the beginning of the study. Risk perception and substance use frequency were measured using self-reports. Results: Structural equation modeling indicated significant influences of risk perception on substance use behavior for all substances, which supports the motivational hypothesis. Changes in risk perception predict changes in future substance use of tobacco, alcohol and cannabis. Specifically for cannabis, influences of substance use on risk perception can also be shown, thus, supporting the risk reappraisal hypothesis. Conclusions: While there is support for the rationale behind adequate risk perception as a goal of preventive interventions, the possibility of risk reappraisal should not be neglected, especially regarding illicit substances.
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Running Head: RISC PERCEPTION AND SUBSTANCE USE 1
Development of Risk Perception and Substance Use of Tobacco, Alcohol and Cannabis
Among Adolescents and Emerging Adults: Evidence of Directional Influences.
Published 2015 in Substance Use and Misuse, 50, 376-386, doi:
10.3109/10826084.2014.984847
Dennis Grevenstein, Ede Nagy, Henrik Kroeninger-Jungaberle
Institue of Medical Psychology, Centre for Psychosocial Medicine, University Hospital
Heidelberg, Germany
Word count: 4844
Declaration of interest: the authors declare that they have no conflict of interest
Acknowledgments: This research is part of a longitudinal study on salutogenesis and drug
consumption patterns funded by the German Research Council (DFG) from 2002-2013 within
its Collaborative Research Centre (Sonderforschungsbereich) 619.
Thanks go to Mathias Ostergaard for help with the literature search and Markus Nagler for
valuable discussion. Correspondence should be addressed to dennis.grevenstein@med.uni-
heidelberg.de.
RISC PERCEPTION AND SUBSTANCE USE
2
Abstract
Background: While several studies have investigated the relationship between risk
perception and substance use, surprisingly little is known about mutual influences between
both variables over time.
Objectives: The present study aimed to explore two different hypotheses separately for
tobacco, alcohol and cannabis: influences from risk perception on behavior (motivational
hypothesis) and influences from behavior on risk perception (risk reappraisal hypothesis).
Methods: A prospective and longitudinal cross-lagged panel design was used with substance
use and risk perception measured five times over the course of 10 years. Participants were 318
German youths aged 14 to 15 at the beginning of the study. Risk perception and substance use
frequency were measured using self-reports.
Results: Structural equation modeling indicated significant influences of risk perception on
substance use behavior for all substances, which supports the motivational hypothesis.
Changes in risk perception predict changes in future substance use of tobacco, alcohol and
cannabis. Specifically for cannabis, influences of substance use on risk perception can also be
shown, thus, supporting the risk reappraisal hypothesis.
Conclusions: While there is support for the rationale behind adequate risk perception as a
goal of preventive interventions, the possibility of risk reappraisal should not be neglected,
especially regarding illicit substances.
Keywords: Alcohol, tobacco, cannabis, risk perception, cross-lagged, longitudinal
RISC PERCEPTION AND SUBSTANCE USE
3
Development of Risk Perception and Substance Use of Tobacco, Alcohol and Cannabis
Among Adolescents: Evidence of Directional Influences.
Perceptions of risk and vulnerability have been discussed as a major part of decision
making processes (Slovic, 1987; Weinstein, 1984). Assessing the consequences of any
decision as well as one’s own vulnerability to problematic outcomes is also a central aspect of
several formalized models in health psychology, including the Social Cognitive Theory
(Bandura, 1977), the Health Belief Model (Rosenstock, Strecher, & Becker, 1988; Strecher,
Champion, & Rosenstock, 1997) or the Theory of Planned Behavior (Ajzen, 1991). The
concept of risk perceptions is commonly used in many areas of health psychology, including
vaccination (Brewer et al., 2007) and sexually transmitted diseases (Ijadunola, Abiona, Odu,
& Ijadunola, 2007). Risk perceptions have been linked to substance use as well (Thornton,
Baker, Johnson, & Lewin, 2013), notably to alcohol consumption (Chomynova, Miller, &
Beck, 2009; Lundborg & Lindgren, 2002; Miller, Chomcynova, & Beck, 2009; Sjöberg,
1998), smoking (Borrelli, Hayes, Dunsiger, & Fava, 2010; Gerking & Khaddaria, 2012; Song,
Glantz, & Halpern-Felsher, 2009; Viscusi, 1991) and cannabis use (Apostolidis, Fieulaine,
Simonin, & Rolland, 2006; Kilmer, Hunt, Lee, & Neighbors, 2007; Piontek, Kraus,
Bjarnason, Demetrovics, & Ramstedt, 2013). Thus, risk perceptions are an important aspect of
health behavior and specifically substance use in adolescence (Larsman, Eklöf, & Törner,
2012; Millstein & Halpern-Felsher, 2002). As a result, a lot of effort has been put into the
creation of prevention programs to positively influence adolescent risk perceptions regarding
the damaging outcomes of substance use (Soole, Mazerolle, & Rombouts, 2008). For
example, warning labels on cigarette packages are supposed to inform users and increase their
risk perception (Strahan et al., 2002). These labels, even though they may be accurate,
essentially operate as fear appeals. They aim to persuade people by inducing fear of negative
outcomes, ultimately leading to self-protective action (Floyd, Prentice-Dunn, & Rogers, 2000;
Rogers, 1983). More positively framed, these practices should enable users to make informed
RISC PERCEPTION AND SUBSTANCE USE
4
choices with regard to their own substance use and its potentially damaging effects (Resnicow
et al., 2002; Turoldo, 2009). The core problem with information based campaigns is that they
have been shown to be little effective (Foxcroft & Tsertsvadze, 2011; Thomas, McLellan, &
Perera, 2013). This may in part be due to the common difficulty of transforming intentions
into actual behavior (Webb & Sheeran, 2006). Moreover, experimental studies have presented
evidence that in some cases, negative or threatening information may in fact have opposite
effects, such as defensive responses (Glock & Kneer, 2009), psychological reactance
(ErcegHurn & Steed, 2011), or even completely adverse effects, such as a reduced risk
perception (Myers, 2014). Pure knowledge about dangers and risk, as it is often provided by
prevention programs or warning labels, was often found to have very little predictive value for
actual risk behavior (Rosendahl, Galanti, Gilljam, & Ahlbom, 2005). Therefore, it is
important to show that a change in general risk perceptions can manifest in actual behavior.
There are several variations of conceptualizing risk perceptions (Brewer et al., 2007).
Participants are commonly asked to rate risks either with regard to themselves or with regard
to people in general. Many studies have shown differential associations between perceived
risk and risk behavior, depending on the risk target. Perceived personal risk is commonly
found to be smaller than perceived general risk, which was described as a self-serving bias
(Weinstein, 1984). Additionally, personal risk perception was sometimes found to be
positively correlated to risk behavior, such as alcohol use (Sjöberg, 1998). Even though
subjectively perceived personal susceptibility may be a stronger predictor of risk behavior
than a general assessment of risks, general risk perceptions are still a major target of public
health interventions. The step from general risk perceptions, such as the knowledge about
smoking being a risk factor for lung cancer, to a change in health behavior, that is quitting
smoking, is rarely tested.
The question arises whether risk perceptions are the cause of substance use or maybe
its result. As described earlier, the former interpretation is most common. When people learn
RISC PERCEPTION AND SUBSTANCE USE
5
about risks, they are motivated to change their behavior or engage in preventive action to
avoid negative outcomes, which has been referred to as the motivational hypothesis (Brewer,
Weinstein, Cuite, & Herrington, 2004; Weinstein, 1993). An increase of risk perceptions
should also lead to an increase of safety behavior or to a decrease of risky, possibly harmful
behavior.
There is however, another possibility. Risk behavior might influence the perception of
how hazardous a certain behavior might be. Two possible outcomes can be the result of this
reappraisal process. First, people who increasingly perform health threatening behaviors,
might accurately realize that they are endangering their health. For example, when you start
drinking alcohol on a regular basis, you may experience negative consequences and those
could make you assess the riskiness and dangers of alcohol differently, which would
ultimately lead to an increase of risk perception.
The second possible outcome would be a systematic decrease of risk perceptions. Over
the course of adolescence, which is characterized by several developmental tasks (Havighurst,
1972), young people make their own experiences with psychoactive substances. During this
process, substance use can be an important part of adolescent development (Silbereisen,
Noack, & Reitzle, 1987) and can represent, for instance, an individual’s quest for autonomy
from the (adult or peer) mainstream, achieving peer-group acceptance, or the development of
coping strategies (Hurrelmann & Quenzel, 2012). The experiences made, may therefore be
positive, rather than negative, which could lead to a decrease in risk perception. This could
also be important with regard to illicit substances such as cannabis, which may be perceived
as more risky due to its illicit nature, rather than in accordance with its scientifically proven
danger.
Additionally, there is the possibility of cognitive dissonance (Festinger, 1957). In this
paradigm, people experience a feeling of unease or dissonance when their attitudes and
behaviors do not match. If a person smokes cigarettes, but knows that smoking tobacco may
RISC PERCEPTION AND SUBSTANCE USE
6
actually increase her chance of getting lung cancer, she will experience dissonance. To
resolve the situation she can either change her behavior (and quit smoking) or change her
attitude (and “adjust” her perception of risks). The same process could explain why awareness
of health risks tends to wear out. Often, people become aware that they are at risk, but fail to
take pre-cautions. Even if you know that your consumption of alcohol has reached an
unhealthy level, you may not easily be able to change your behavior at a given moment and
subsequently lose interest in the issue or actively deny the risk. Therefore, if a person’s risk
perception decreases after changing her behavior, it may be just the result of people’s need to
justify their actions in order to make them feel at ease. Any effect of changes in people’s
behavior on their risk perception can be termed the risk reappraisal hypothesis (Brewer et al.,
2004).
There have been general methodological problems associated with risk perception
research. Weinstein et al. (1998) discuss three major aspects: (1) The use of a longitudinal
design is necessary to study change as implied by the motivational or self-protective
hypotheses. A cross-sectional correlation cannot tell anything about the process of change and
may in fact document the relative accuracy of risk perceptions. (2) Controlling for prior
behavior is necessary to differentiate change and stability in prospective designs. (3) One has
to acknowledge that the relationship between behavior and risk perception may not be
constant, but is rather likely to change over time. This is an important issue since substance
use is an age dependent phenomenon and serves multiple purposes over the course of
adolescence (Silbereisen et al., 1987; Viscusi, 1991).
The following study uses structural equation modeling to fulfill the aforementioned
requirements. A longitudinal cross-lagged panel design was used which followed participants
from the age of 14 years into young adulthood at the age of 24. Models were computed
separately for tobacco, alcohol, and cannabis. Both possible directions of influence are of
interest. Risk perceptions influencing subsequent substance use would reflect the motivational
RISC PERCEPTION AND SUBSTANCE USE
7
hypothesis. Influences of substance use behavior on later risk perception would reflect the risk
reappraisal hypothesis.
Methods
Study sample
The following research is part of a ten-year-longitudinal study of drug use patterns
(RISA) conducted in the Rhine/Neckar metropolitan region in the South of Germany from
2003 to 2012. The study was approved by the ethics committee of the University Hospital
Heidelberg (No. 218/2005). Informed consent and written permission from legal guardians
were obtained. Participants were 318 students with a mean age of 14 at the beginning of the
study. The sample included 164 female (51.6%) and 154 male (48.4%) participants. The
study comprised 14 data collection events. 65.4% of the participants (n = 208) grew up in a
traditional family, which was defined as living with both biological parents up to the age of 18
years. Level of education spanned equally across the three-tier German school system. The
sample was ethnically diverse. Of all participants, 54.1% (n = 172) were of German
nationality, 15.7% (n = 50) did not possess German nationality, while 30.2% (n = 96) did not
provide that information. These data are comparable to the official census which denotes
19.3% of all students in south-west Germany having a migration background
(Statistisches_Landesamt_Baden-Württemberg, 2014).The sample can be characterized as
rural or sub-urban with participants living in smaller cities up to 100000 inhabitants.
Sample attrition amounted to n = 134 (42.1%) over the course of ten years with signs
of systematic drop out. At age 14 to 15, in comparison to participants remaining in the study
until age 24, those who dropped out consumed moderately more tobacco, Ms = 3.59 vs. 2.70
(SDs = 2.49 vs. 2.11), t(248.14) = 3.30, p = .001, Cohen’s d = 0.39, more cannabis, Ms = 1.49
vs. 1.26 (SDs = 1.00 vs. 0.70), t(214.03) = 2.27, p = .024, d = 0.27 and had a lower tobacco
risk perception Ms = 3.55 vs. 3.88 (SDs = 1.16 vs. 1.09), t(308) = -2.61, p = .009, d = 0.29.
RISC PERCEPTION AND SUBSTANCE USE
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There have been no signs of systematic dropout with regard to socio-demographic variables.
Despite noticeable sample attrition over the course of ten years, participant dropout was
comparable to other studies on adolescents’ development (Honkinen et al., 2009).
Measures
Socio-demographic variables: participants were asked to provide information on
several demographic variables, including gender, age, nationality, type of school and family
situation. Subsequently, gender and family situation were chosen as covariates in the model,
as neither nationality nor type of school were correlated with substance use or risk perception.
Risk perception: risk perception was measured using a single item: “how dangerous
do you think is the consumption of this substance for people in general?” Answers were given
separately for tobacco, alcohol and cannabis on 6-point scales with the end points marked (1)
“harmless” and (6) “very dangerous”.
Substance use frequency: the scale was adapted from the national survey on drug use
among adolescents (BZgA, 2004). It is similar to the brief self-report drug use frequency
measure provided by O'Farrell, Fals-Stewart and Murphy (2003). Substance use frequency
was measured using a single item question: “how often have you used this substance in the
last 6 months?” Answers were given separately for tobacco, alcohol, and cannabis on a 7-
point scale with the following options: (1) “not used in last 6 months”, (2) “1-2 times in the
last 6 months”, (3) “3-5 times in the last 6 months”, (4) “1-3 times a month”, (5) “1-2 times
a week”, (6) “several times a week” and (7) “several times a day”.
Statistical analysis
The descriptive data analysis was carried out using SPSS 20. Mplus 5.21 (Muthén,
1998-2007) was used for Structural Equation Modeling (SEM). Using SEM (Hoe, 2008;
Kline, 2011), all the study variables can be investigated at the same time in the same model.
Therefore, it is possible to model interconnections and mutual influences of the variables as
well as the development of variables over time while controlling for individual differences in
RISC PERCEPTION AND SUBSTANCE USE
9
prior behavior and initial covariation between variables. Most important are the diagonal
(longitudinal, cross-lagged) paths from one type of variable to another type of variable at the
next time point. Vertical (cross-sectional) paths between variables and horizontal
(autocorrelative, longitudinal) paths within a variable are needed to control for statistical
covariation. Thus, the diagonal, cross-lagged paths are partial regressions that allow
estimating the unique predictive influence of a variable at a given time. We included cross-
sectional covariation between risk perception and substance use at the beginning and at the
end of the study to control for covariation between both types of variables. To model the
longitudinal aspect, every variable at a given point in time was regressed on every variable at
the preceding point in time. Covariates were also controlled for at the beginning of the study.
The models included covaration paths with gender and family setting for both, substance use
and risk perception, at the first data point. Controlling for covariates is only needed once at
the beginning as all subsequent paths are partial regressions.
To estimate whether the model accurately represented the empirical data, goodness-of-
fit was evaluated by (1) the—ideally non-significant—χ2 test (Bentler & Bonett, 1980) and as
low as possible a χ2/df ratio, ideally as low as 2 (Tabachnick & Fidell, 2007). The χ2 test is
highly sensitive to sample size and therefore it is in most cases significant; (2) the
comparative fit index (CFI) with values of .90/.95 and above indicating appropriate/good
model fit (Bentler, 1990; Hu & Bentler, 1999); (3) the root mean square error of
approximation (RMSEA) with values of .00–.05/.06–.08/.09–.10 indicating
good/reasonable/poor model fit (Browne & Cudeck, 1993). Deviation from close fit (< .05)
can be additionally tested with a significance test (‘p-close’); and (4) the standardized root
mean square residual (SRMR) with values less than .08 (Hu & Bentler, 1999) or .05
(Schumacker & Lomax, 2010) considered to reflect good fit. A robust Maximum Likelihood
(MLR) estimator was used for parameter estimation and imputation of missing data. To
RISC PERCEPTION AND SUBSTANCE USE
10
address the problem of possible bias due to missing data imputation, all models were also
inspected using listwise deletion. The overall pattern of results remained the same, while
model fit decreased due to the reduced sample size. We therefore chose to use the usual
imputation of missing data, which is also the default setting in Mplus.
In SEM variances of variables as well as paths between variables have to be estimated
(Kline, 2011). As a result, the required sample sizes can be quite large when models include a
large number of variables. Due to our rather small sample, we had to reduce the number of
model parameters to be estimated and therefore chose to aggregate data over time by
computing mean scores. Scores in the model (see Table 1) thus represent average substance
use and risk perception during the specified time. The RISA study included 14 data collection
events. In the first four cases we aggregated three data collection events to a single data point
(T0, T1, T2, T3), while the last data point (T4) in the SEM comprised only two data collection
events. Cronbach Alpha for the aggregated data points ranged from .89 to .93 for tobacco use,
from .80 to .85 for alcohol use, from .73 to .90 for cannabis use, from .59 to .84 for tobacco
risk perception, from .63 to .80 for alcohol risk perception, and .68 to .85 cannabis risk
perception. This justifies the aggregation of data, but also documents that there is some
change in both substance and risk perception. Post-hoc power analysis according to
MacCallum, Browne, & Sugawara (1996) using an online calculator (Preacher & Coffman,
2006) indicated that the final models needed a minimum N of 235 with Alpha = .05, df = 44,
desired power = .80, RMSEAH0 = .05, RMSEAH1 = .08.
This resulted in five data points over the course of the 10 year study with T0
representing ages 14 to 15, T1 representing 16 to 17, T2 representing 18 to 19, T3
representing 20 to 22 and finally T4 representing ages 23 to 24.
Results
Descriptive data analysis
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Several 1 X 5 (time) repeated measurement ANOVAs were conducted to examine
mean differences. Mauchly’s test indicated that for all ANOVAs the assumption of sphericity
had been violated. Greenhouse-Geisser corrected degrees of freedom will be reported below.
Note that for the ANOVAs only those subjects could be used, where valid data was available
at all times. Substance use significantly increased from T0 to T4 for tobacco, F(2.47, 317.98)
= 14.85, p < .001. (Ms = 2.63, 2.89, 3.42, 3.53, 3.65); alcohol, F(2.97, 454.29) = 73.53, p <
.001. (Ms = 2.57, 3.40, 3.87, 3.91, 3.93), as well as cannabis, F(3.14, 314.44) = 6.86, p < .001.
(Ms = 1.20, 1.30, 1.53, 1.63, 1.62). In the same way, risk perception increased significantly
from T0 to T4 for tobacco, F(3.46, 525.60) = 6.45, p < .001. (Ms = 3.92, 4.09, 4.05, 4.23,
4.33) and for alcohol, F(3.29, 506.07) = 20.56, p < .001. (Ms = 3.79, 3.98, 3.97, 4.30, 4.44).
In contrast, cannabis risk perception had a decreasing trend over the course of ten years,
F(3.03, 420.50) = 2.91, p = .034. (Ms = 4.68, 4.61, 4.48, 4.43, 4.45).
There were noticeable gender differences. Considering the whole sample, males
tended to consume more alcohol and cannabis, while females tended to have higher risk
perceptions concerning alcohol and cannabis. Means and standard deviations for risk
perception and substance use frequency can be seen in Table 1.
Structural equation modeling (SEM)
Tobacco: Figure 1 shows the standardized estimates of the SEM for tobacco
consumption and risk perception. The model fitted the data well, χ2(44) = 74.47, χ2/df = 1.69,
p < .01, RMSEA = .047 [CI90% = .027 - .065], p-close = .597, CFI = .973, SRMR = .057.
SRMR was slightly above the value for optimal fit. As expected, consumption as well as risk
perception are quite stable over time. A significant cross-sectional correlation can be shown at
T0. More importantly, consumption at T1 is significantly influenced over time by risk
perception at T0. Additionally, there is a marginally significant influence of risk perception at
T2 on consumption at T3. No other paths are significant. Concerning the covariates, tobacco
RISC PERCEPTION AND SUBSTANCE USE
12
had a marginally significant correlation with family setting. Participants from non-traditional
homes smoked slightly more often.
Alcohol: Standardized estimates of the SEM for alcohol consumption and risk
perception can be seen in Figure 2. The model also fitted the data well, again with SRMR
being slightly above the value for optimal fit, χ2(44) = 91.39, χ2/df = 2.08, p < .001, RMSEA
= .058 [CI90% = .041 - .075], p-close = .200, CFI = .947, SRMR = .066. Once more,
consumption and risk perception are stable over time. Cross-sectional covariations are
significant at T0 and T4. Consumption at T2 is significantly influenced by risk perception at
T1. Additionally, consumption at T3 is influenced by risk perception at T2. No other paths
were significant. Risk perception at the beginning of the study was significantly correlated
with both gender and family setting. Female participants and those coming from a non-
traditional family setting had a higher alcohol risk perception.
Cannabis: Standardized estimates of the SEM for cannabis consumption and risk
perception can be seen in Figure 3. The model still fitted the data appropriately, χ2(44) =
116.02, χ2/df = 2.64, p < .001, RMSEA = .072 [CI90% = .056 - .088], p-close = .013, CFI =
.909, SRMR = .083. It has to be noted though, that this model showed noticeably lower fit
than the other models. The relative stability of consumption and risk perception is evident
again. Cross-sectional correlations are significant at T0 and T4. Paths from risk perception to
consumption are significant from T2 to T3 and from T3 to T4. Contrasting other substances,
paths from substance use to risk perception are also significant from T0 to T1, from T2 to T3
and from T3 to T4. No other paths are significant. At last, frequency of cannabis use was
significantly related to gender. Male participants used cannabis more often.
Discussion
Our analysis showed that there are indeed directed influences between risk perception
and substance use. Results were mixed in that both, the motivational hypothesis, as well as the
RISC PERCEPTION AND SUBSTANCE USE
13
risk reappraisal hypothesis could be supported. The results concerning the motivational
hypothesis provide evidence for the importance of adequate risk perceptions and support the
rationale behind information-based interventions. Our findings therefore support the
protection motivation approach (Rogers, 1983) in a longitudinal study. Even though prior
research has consistently shown negative associations between risk perceptions and substance
use behavior, intervention studies have only yielded small effects or no effects at all.
Therefore, some researchers have argued that users are still ill-informed about the negative
consequences of substance use (Cummings, 2004; McIntosh, MacDonald, & McKeganey,
2003). For example, users still tend to believe that “light” or “natural” cigarettes could be less
harmful (Czoli & Hammond, 2014; Mutti et al., 2011). Users have also been shown to engage
in active risk denial (Peretti-Watel et al., 2007) or to be overly optimistic about their ability to
quit, thus denying the risk of addiction (Weinstein, Slovic, & Gibson, 2004). There may be
ample opportunity to improve users’ knowledge about substance use and following that,
increase their risk perception.
Generally, there were no significant influences at all the points in time. This might
imply age specific development processes. The tobacco model showed the least amount of
directed influence from risk perception to consumption over time. While it is evident that risk
perception plays a role at the onset of smoking behavior, it appears to be much less important
at later stages. This may be due to the stable patterns of tobacco use once initiated which
reflect the addictive qualities of this substance (Galambos & Silbereisen, 1987). For alcohol,
influences of risk perception on substance use were slightly more evident, which could be
seen later in life than for tobacco.
However, our results also document that mutual influences between substance use and
risk perception are quiet small. There are numerous possible explanation for this.
Acknowledging the dangers of substance use, can only be the first step towards a healthier,
reduced consumption. Pure knowledge has almost no predictive value (Rosendahl et al.,
RISC PERCEPTION AND SUBSTANCE USE
14
2005), has to be understood within the social context of an individual (Rumpold et al., 2006)
or may be mostly protective for higher frequency users (Aguilar-Raab, Heene, Grevenstein, &
Weinhold). The way information is conveyed may be one of the most important aspects
(Glock, Müller, & Ritter, 2013). In the context of adolescent substance use and due to the
subjective importance of psychoactive substances, prevention may also provoke unintended
effects when adolescents’ beliefs are threatened (Hansen, Winzeler, & Topolinski, 2010).
Additionally, adolescents and emerging adults are prone to risk behavior, which also has a
strong neuropsychological basis (Steinberg, 2008). Especially peer influence might be one of
the strongest predictors of adolescent substance use (Chassin, Hussong, & Beltran, 2009;
Gardner & Steinberg, 2005).
The risk reappraisal hypothesis can also be substantiated for cannabis. In this regard,
cannabis seems to be different than the other substances. While there were influences from
risk perception on consumption, there was even stronger evidence for influences from
consumption on risk perception. This finding may be explained by the theory of cognitive
dissonance (Festinger, 1957). However, it may also reflect the readjustment of unrealistic risk
perceptions when a person actually comes in contact with a substance. Sussman et al. (2004)
have argued that risk perceptions include myths and social influence. Others propose that they
are an object of unsteady situational decision making on bases of emotional appraisal
(Loewenstein, Weber, Hsee, & Welch, 2001) rather than reflecting realistic, or even accurate,
risk assessments. When using a substance, users might notice that the negative consequences
of substance use are not as severe or not as immediate as expected. This has been shown for
cannabis use before. Kilmer et al. (2007) reported that non-users have a significantly higher
risk perception than users. However, when looking only at the subgroup of users, risk
perception did not relate to actual frequency of marijuana use or personal experience of
negative consequences. This is mirrored in several studies concerning alcohol expectancies.
Adolescents’ beliefs about alcohol tend to change once they start consuming (Aas, Leigh,
RISC PERCEPTION AND SUBSTANCE USE
15
Anderssen, & Jakobsen, 1998; Leigh & Stacy, 2004). This is especially important since
substance use is one example of health behavior, where immediate experiences are often
positive (Barron, Leider, & Stack, 2008; Lee et al., 2010; Park, 2004).
The influence of consumption on risk perception may be connected to the illicit nature
of cannabis in most (legal) contexts. The mere fact of being illicit may create an
unrealistically high risk perception which is normalized over time (Roy, Wibberley, & Lamb,
2005), but then again, might also increase possible effects of cognitive dissonance.
Nonetheless, we conclude that raising general risk perceptions may be a valuable component
of a comprehensive prevention approach, which in later adolescence could include harm
reduction for high frequency users (Futterman, Lorente, & Silverman, 2004).
Our research has implications for the debate about adjusting preventive interventions
to specific age groups, i.e. about the ideal onset of modern risk centered messages. Prevention
research has shown differential effects as far as the optimal stage for drug related intervention
programs is concerned (Scheier, 2012; Soole et al., 2008; Tobler et al., 2000). Some have
even argued to start early in childhood (Frankel, 1998). Looking at our data, we can see an
interesting possibility to influence the onset of substance use, especially for tobacco. Early
onset has been shown to be an important predictor of later problematic consumption
(Behrendt, Wittchen, Höfler, Lieb, & Beesdo, 2009). Influencing general risk perceptions
towards drugs (through risk information and social-influence programs) may indeed have an
impact on future consumption patterns. Though, depending on the substance, we can also see
a reasonable possibility for influence in later years, especially for cannabis.
Limitations
The small sample size was just large enough for structural equation modeling.
Aggregating data collection events over time was a necessary means of dealing with the
sample size of 318 participants. This approach, however, might mask short-term changes.
RISC PERCEPTION AND SUBSTANCE USE
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The models exhibited overall good model fit, with the SRMR index being a noticeable
exception. The SRMR is a measure of the mean absolute value of the covariance residuals and
may therefore highly depend on the many insignificant cross-lagged paths in the models.
However, we chose not to respecify the models and to retain all these paths, as they were
theoretically and explicitly predicted paths that we wanted to test.
Measurement can also be subject to criticism. Both consumption and risk perception
were measured with only a single item. The operationalization of substance use as substance
use frequency, although done on a regular basis, may not cover a critical part of substance
use, that is, how much is consumed at a single event. Even though the models fitted the data
well, more reliable measures with more items to form latent variables should be used in the
future. Taken together, the development of psychometrically sound measures of substance use
still seems a pressing issue.
With regard to risk perception, the current study used what others have called “vague”
quantifiers of danger, rather than true numerical estimates of the likelihood of a certain
negative outcome. Still, these verbally labeled scales have been shown to be a valid measure
of risk perceptions with reasonable predictive validity (Baghal, 2011).
We reported signs of systematic dropout in the study. Participants who dropped out
consumed more tobacco and cannabis and also had lower tobacco risk perception at ages 14 to
15. Even though we can use missing data imputation, this might systematically underestimate
variance at the end of the study and thus possibly strengthen the surprising stability of both
consumption and risk perception.
Further research
The target of the present study was general risk perception, rather than self-perceived
vulnerability. Karlsson (2008) argued that prevention programs focus too much on general
risk perception. A person who already has a high general risk perception may not sufficiently
benefit from a prevention program. The initial level of risk perception should therefore be
RISC PERCEPTION AND SUBSTANCE USE
17
taken into account, when evaluating intervention programs. As there are noticeable gender
differences in substance use as well as risk perception, future studies should examine gender
differences in the relationship between risk perception and substance use as well, possibly
using multigroup SEM. This method requires larger samples, so that we were unable to look
at this issue more thoroughly in this study.
Conclusions
The present research provides evidence for the importance of risk perception when
predicting substance use. Going beyond correlational relationships, our data supports directed
influences of risk perceptions on substance use over time. Cannabis is the only substance
where influences of consumption on risk perception can be seen, which contrasts cannabis
with both tobacco and alcohol. When planning health interventions, especially concerning
illicit substance use, the possibility of risk reappraisal should therefore be taken into account.
RISC PERCEPTION AND SUBSTANCE USE
18
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28
Table 1:
Sample characteristics, significance tests, and effect sizes (Cohen’s d) for study variables at T0 (age 14-15), T1 (age 16-17), T2 (age 18-19), T3
(age 20-22) and T4 (age 23-24). Sample sizes reflect the maximum number of participants. Different degrees of freedom indicate missing data
and/or unequal variances.
Total (N = 318)
M (SD)
Men (n = 154)
M (SD)
Women (n = 164)
M (SD)
p
d
Tobacco use T0
3.07 (2.32)
3.07 (2.27)
3.07 (2.37)
.980
0
Tobacco use T1
3.32 (2.43)
3.41 (2.49)
3.24 (2.38)
.561
0.07
Tobacco use T2
3.76 (2.54)
3.72 (2.47)
3.79 (2.60)
.837
0.03
Tobacco use T3
3.72 (2.55)
4.05 (2.56)
3.45 (2.53)
.115
0.24
Tobacco use T4
3.85 (2.65)
4.09 (2.65)
3.64 (2.64)
.269
0.17
Alcohol use T0
2.74 (1.23)
2.84 (1.30)
2.64 (1.16)
.135
0.16
Alcohol use T1
3.45 (1.27)
3.58 (1.34)
3.32 (1.19)
.092
0.21
Alcohol use T2
3.82 (1.25)
4.10 (1.32)
3.60 (1.15)
.002
0.40**
Alcohol use T3
3.94 (1.12)
4.26 (1.19)
3.67 (1.13)
< .001
0.51***
Alcohol use T4
3.90 (1.19)
4.31 (1.09)
3.54 (1.16)
< .001
0.68***
Cannabis use T0
1.36 (0.84)
1.49 (1.00)
1.23 (0.65)
.008
0.31**
Cannabis use T1
1.56 (1.21)
1.76 (1.43)
1.38 (0.93)
.008
0.32**
29
Cannabis use T2
1.62 (1.34)
1.98 (1.64)
1.35 (0.97)
.001
0.47**
Cannabis use T3
1.70 (1.26)
2.04 (1.53)
1.41 (0.88)
.003
0.50**
Cannabis use T4
1.66 (1.20)
2.12 (1.48)
1.24 (0.63)
< .001
0.77***
Tobacco risk perception T0
3.74 (1.13)
3.78 (1.11)
3.71 (1.16)
.561
0.06
Tobacco risk perception T1
3.92 (1.15)
3.89 (1.12)
3.93 (1.19)
.729
0.03
Tobacco risk perception T2
3.94 (1.13)
3.95 (1.14)
3.92 (1.12)
.829
0.03
Tobacco risk perception T3
4.22 (1.17)
4.12 (1.25)
4.30 (1.10)
.280
0.15
Tobacco risk perception T4
4.31 (1.15)
4.26 (1.08)
4.36 (1.21)
.570
0.09
Alcohol risk perception T0
3.74 (1.10)
3.58 (1.05)
3.89 (1.12)
.013
0.29*
Alcohol risk perception T1
3.90 (1.13)
3.74 (1.13)
4.05 (1.12)
.019
0.28*
Alcohol risk perception T2
3.95 (1.07)
3.85 (1.11)
4.02 (1.04)
.246
0.16
Alcohol risk perception T3
4.32 (1.14)
4.18 (1.22)
4.45 (1.05)
.091
0.24
Alcohol risk perception T4
4.44 (1.09)
4.18 (1.06)
4.66 (1.07)
.002
0.45**
Cannabis risk perception T0
4.65 (1.15)
4.58 (1.19)
4.71 (1.12)
.338
0.11
Cannabis risk perception T1
4.54 (1.13)
4.40 (1.18)
4.66 (1.08)
.046
0.23*
Cannabis risk perception T2
4.47 (1.18)
4.26 (1.19)
4.63 (1.15)
.017
0.32*
Cannabis risk perception T3
4.43 (1.30)
4.07 (1.35)
4.75 (1.17)
< .001
0.54***
Cannabis risk perception T4
4.43 (1.33)
3.87 (1.40)
4.90 (1.07)
< .001
0.83***
Note: Men and women differ significantly at †p < 0.10, *p < 0.05, **p < .01, ***p < .001.
30
Figure 1: tobacco risk perception (RP) and tobacco use frequency (TOB) with covariates gender and home (traditional family setting). Paths are
significant at †p < .10, *p < .05 and **p < .01.
31
Figure 2: alcohol risk perception (RP) and alcohol use frequency (ALC) with covariates gender and home (traditional family setting). Paths are
significant at †p < .10, *p < .05 and **p < .01.
32
Figure 3: cannabis risk perception (RP) and cannabis use frequency (CAN) with covariates gender and home (traditional family setting). Paths
are significant at †p < .10, *p < .05 and **p < .01.
... Risk perception is a subjective judgment that can be defined as the "degree of risk associated with a given behavior" and is often used as a predictive measure to gauge one's likelihood of engaging in a behavior as well as their perception of potential associated consequences (Clapper et al., 1995). The level of risk an individual associates with the use of a particular substance may impact their intention to use, and thus, their behaviors, as changes in risk perception have been shown to predict changes in future use of substances including tobacco, alcohol and, especially, marijuana (Grevenstein et al., 2015). ...
... Research shows that factors such as age, personality variables, and values influence risk perception (Hampson et al., 2001). Moreover, the way risk is determined may vary by substance (Grevenstein et al., 2015) and be impacted by proximal factors (Johnston et al., 2012). Though a large proportion of respondents may use frequency of use as a key determinant of how they assess risk, others may perceive inherent risk in the use of the substance no matter the frequency. ...
... However, findings on these assumptions are mixed. Often, people who binge drink seem to perceive less risk of heavy alcohol use than those who do not (Chen, 2018;Grevenstein et al., 2015;Hanauer et al., 2019;Khushalani et al., 2019). Other studies conflict with these results, instead finding that people who drink heavily rate the risks of binge drinking similarly to others (Davies & Joshi, 2018;Lopez et al., 2019;Pettigrew et al., 2016). ...
... This research departs from prior models of alcohol use in which the outcome of excessive drinking is conceptualized as the direct result of a decision to consume a large number of drinks (e.g., Finn et al., 2017;Martínez-Loredo et al., 2021), which may be motivated by perceptions that heavy drinking is low risk (Chen, 2018;Grevenstein et al., 2015;Hanauer et al., 2019;Khushalani et al., 2019), or by inadequate knowledge of possible consequences of heavy alcohol use (Bonar et al., 2012). Drawing on FTT's model of the fuzzy processing preference (Klein et al., 2017;Reyna, 2012a;Reyna & Lloyd, 2006), we propose an alternate model in which excessive drinking is the cumulative result of a series of choices about one drink, wherein high-risk choices are an ironic result of the arguably correct notion that a single drink carries little risk. ...
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Background Understanding the decision factors that drive harmful alcohol use among young adults is of practical and theoretical importance. We apply fuzzy‐trace theory (FTT) to investigate a potential danger that may arise from the arguably correct notion that a single drink carries no meaningful risk. Decisions that are mentally represented as one drink at a time could contribute to excessive drinking. Methods College students (N = 351) made a series of decisions to take or decline eight hypothetical drinks presented one at a time. Outcome measures included each decision, recent alcohol consumption (weekly drinks, peak blood alcohol content, and binges), and alcohol‐related harms (scores on the Brief Young Adult Alcohol Consequences Questionnaire and Alcohol Use Disorders Identification Test). Linear regression models predicted each outcome from sex, perceived risk of a single drink, perceived risk of heavy drinking, perceived consequences of drinking, and general health‐related risk sensitivity. Results Consistent with FTT, decisions to have a first drink and up to four additional drinks in short succession were each associated with lower perceived risk of one drink—a “just‐one drink” effect—independent of perceived risks of heavy drinking, perceived consequences of drinking, and general risk sensitivity. Similarly, all measures of recent alcohol consumption and consequent harms were associated with perceived risk of one drink. Participants reporting “zero risk” of a single drink had worse outcomes on all measures than those reporting at least “low risk.” Conclusions Results are consistent with the theoretically informed premise that consumption decisions are typically made one drink at a time rather than by deciding the total number of drinks to be consumed in a sitting. When decisions about alcohol use proceed one drink at a time, a perception of zero risk in a single drink may contribute to heavy drinking.
... In addition, unexpectedly, we observed a significant negative correlation between risk perception and the behavioral intentions of individuals with SUD. This finding is inconsistent with previous findings on physical exercise behavior acquisition (Schwarzer et al., 2007) but reflects the previously identified strong relationship between risk perception and addictive substance use and withdrawal (Grevenstein et al., 2015). This finding suggested that individuals with SUD who perceive greater risk will have a decreased probability of quitting physical exercise to seek more rapid quitting substance use. ...
... In the behavior maintenance stage in the integrated HAPA-TPB model, both behavioral intention and maintenance self-efficacy were significant predictors of planning, together explaining 32% of the variance. The current findings are consistent with a meta-analysis of studies applying the HAPA (Grevenstein et al., 2015). This finding implies that self-efficacy in coping with obstacles one may face when engaging in physical exercise and the intention to engage in that behavior are important factors in the development of a plan among individuals with SUD. ...
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Introduction Physical exercise is considered a useful non-pharmacological adjunctive treatment for promoting recovery from substance use disorders (SUD). However, adherence to physical exercise treatments is low, and little is known about what factors are associated with the initiation and maintenance of physical exercise behaviors. The aim of this study was to explore the psychosocial factors underlying these behaviors in individuals with SUD using an integrated theoretical model based on the health action process approach (HAPA) and the theory of planned behavior (TPB). Methods A total of 1,197 individuals with SUDs (aged 37.20 ± 8.62 years) were recruited from 10 compulsory isolation drug rehabilitation centers in Zhejiang Province via convenience sampling according to a set of inclusion criteria. Self-reported data were collected to assess task self-efficacy (TSE), maintenance self-efficacy (MSE), recovery self-efficacy (RSE), outcome expectations (OE), action planning (AP), coping planning (CP), social support (SS), subjective norms (SN), attitude behavior (AB), behavioral intention (BI), perceived behavioral control (PBC), risk perception (RP), exercise stage, and exercise behavior in this integrated model. ANOVA and structural equation modeling (SEM) were used to evaluate this model. Results One-way ANOVA revealed that the majority of the moderating variables were significantly different in the exercise phase. Further SEM showed that the model fit the data and revealed several important relationships. TSE, RP, SS, AB, and SN were indirectly associated with physical exercise behavior in individuals with SUD through the BI in the SUD initiation stage. In addition, PBC was directly related to physical exercise behavior in individuals with SUD. In the maintenance stage, MSE, AP, CP and exercise behavior were significantly related. Moreover, AP and CP were mediators of BI and MSE. Conclusion This study is the first attempt to integrate patterns of physical exercise behavior in individuals with SUD. The HAPA-TPB integration model provides a useful framework for identifying determinants of physical exercise behavioral intentions and behaviors in individuals with SUD and for explaining and predicting the initiation and maintenance of physical exercise behaviors in these individuals. Moreover, the model provides scientific guidance for the enhancement of physical exercise adherence in individuals with SUD.
... The findings from our study provide critical insights that can significantly inform policy and intervention strategies aimed at reducing cannabis use and mitigating its associated risks. Given the importance of health risk perceptions identified in our analysis, public health campaigns should emphasize the potential health risks associated with substance use (Grevenstein et al., 2015;Mariani and Williams, 2021). Our results indicate that society has different risk perception about experimental use and regular use. ...
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This study conducts an in-depth analysis of feature selection methods in AI, underscoring their importance in social science and public health research. Using the Finnish National Drug Survey's 2022 dataset, which comprises 76 selected features, the study aims to identify the main predictors of cannabis use among Finnish populations over the last 12 months. Fifteen feature selection techniques were applied, from simple K-Best filters to complex LSTM models, to determine the top 10 predictors. The methodology involves data preprocessing and uses various metrics—precision, recall, F1 score, accuracy, Cohen Kappa Score, MCC, and ROC AUC Score—for a comprehensive evaluation of each method. Results show a diversity in performance across 15 models, identifying 39 unique features. The analysis of the predictive model primarily examines a BiLSTM model and demonstrates considerable variations in the effectiveness of different feature selection methods. Findings suggest that while some models, like wrapper and sequential selections, may underperform due to their limitations, others like LSTM, RFE, embedded, and Information Gain excel by capturing complex relationships and refining feature sets. This research highlights the utility of different feature selection techniques in enhancing predictions and supporting prevention program designs. It aims to guide social science researchers in choosing appropriate methods that cater to the complexities of their subjects, improving understanding and interventions in areas like cannabis use.
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In Uruguay, there is a high prevalence of alcohol and marijuana use among adolescents. This study seeks to analyze the relationship between consumption, adolescent and parental risk perception, and subjective psychological well-being (SWB). The sample consisted of 102,490 school going adolescents from 12 to 21 years old. Analyses were performed based on sex, age, perception of drug use by family members, and access to marijuana. The findings indicate that alcohol consumption increases with age and is higher in girls. Marijuana use decreases toward late adolescence, showing no differences by gender. Girls have a higher perception of risk regarding frequent alcohol consumption. For marijuana, the perception of risk decreases with age and is higher in boys. Girls consider that it is easier to get marijuana. The declared SWB decreases toward 15 years old and increases in late adolescence; girls report a lower level generally. Adolescents with a higher risk perception and with parents who are intolerant to substance use reported a higher level of well-being. These results seek to provide evidence on possible factors that affect the consumption of substances among Uruguayan adolescents and their subjective well-being.
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Background: Alcohol and prescription opioid use are highly prevalent among chronic pain populations. One-fifth of individuals prescribed opioids report same-day use of alcohol and opioids. Alcohol use and alcohol/opioid co-use can have deleterious pain management and health outcomes. The extent to which individuals with chronic pain are aware of these deleterious outcomes is considerably understudied.Objectives: To explore individuals' understanding of seven health- and pain-related risks of alcohol/alcohol-opioid use. An exploratory aim was to examine whether greater risk awareness was associated with alcohol/opioid use patterns.Methods: Participants included 261 adults age ≥21(36.4% women) endorsing current alcohol use, chronic musculoskeletal pain, and opioid prescription who completed an online survey via Amazon Mechanical Turk.Results: Distribution of the total number of items for which a participant endorsed awareness was as follows: zero (10.7%), one (5.0%), two (13.0%), three (13.8%), four (13.8%), five (11.5%), six (10.0%), and seven items (22.2%). Awareness of the health consequences of alcohol/alcohol-opioid use was positively associated with opioid misuse behaviors (β = .525, ΔR2 = .251, p < .001), and higher-risk alcohol consumption (β = .152, ΔR2 = .021, p = .011).Conclusion: Many adults with chronic pain are unaware of the health consequences of alcohol/alcohol-opioid use. Findings of positive covariation between risk awareness and higher-risk alcohol/opioid use suggest that future interventions among this population should go beyond simple risk education and utilize motivational enhancement to help change decisional balance.
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College student cannabis use is at an all-time high. Although frequent heavy cannabis use is related to cannabis problems, perceived risk of cannabis use is rapidly decreasing. Yet, it is unknown whether specific domains of risk perceptions (general and domain-specific risk, risk to others and personal risk) are related to more cannabis use or related problems. Thus, among 130 undergraduates who reported past-month cannabis use, the present study conducted secondary analyses to test whether, for both perceived risk to others and perceived personal risk: (1) general perceived risk was associated with cannabis-related outcomes (i.e., use, negative consequences, cannabis use disorder (CUD) symptoms, motivation to change), (2) seven specific domains of perceived risk were related to cannabis outcomes, and (3) domain-specific perceived risk was related to cannabis use frequency. General perceived risk to others was negatively associated with cannabis use frequency whereas general perceived personal risk was positively associated with cannabis-related negative consequences, CUD symptoms, and importance and readiness to change. Greater legal and withdrawal/dependence risks were uniquely related to several outcomes (e.g., CUD symptoms). Participants who used cannabis frequently perceived more personal risk in most risk domains and less general risk to others than those who used infrequently. Findings suggest personal risk is an important component to consider when assessing perceived risk of cannabis use and focusing on both general and domain-specific risks may provide valuable insight for future prevention and intervention efforts.
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Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
Conference Paper
Aims Because expectancies about the effects of alcohol change as drinking experience is accumulated, it is likely that the relationship of expectancy to drinking will differ with age. In this study, we examine the prediction of drinking behavior from positive and negative outcome expectancy at different ages. Design Data were collected as part of the National Alcohol Survey, using a multi-stage area probability sample of the household population of the 48 contiguous United States. Participants US residents aged 12 and older (n = 2875). Measurements Survey questions included drinking habits (frequency, quantity, frequency of drunkenness, maximum quantity) and beliefs about the effects of alcohol (alcohol expectancies). Findings Structural equation models tested the relationship of positive and negative expectancy to drinking behavior in six age groups. Outcome expectancy accounted for a larger portion of the variance in drinking among younger respondents than among older respondents. However, suppression effects were common. When suppression effects were considered, positive expectancy predicted drinking better than negative expectancy only among respondents under 35, while negative expectancy was a better predictor of drinking status in most respondents over 35 years. Among drinkers, positive expectancy predominated over negative expectancy when suppression effects were considered. Conclusions These results suggest that negative expectancy predicts abstention, while positive expectancy predicts level of drinking among drinkers. In expectancy research, differences between drinkers and abstainers, age of participants and the presence of suppression effects should be taken into account.