HEALTH EDUCATION RESEARCH
Theory & Practice
Vol.17 no.4 2002
Pages 415–424
Learning the relationship between smoking, drinking
alcohol and the risk of esophageal cancer
Sylvie Bonnin-Scaon, Peggy Lafon1, Gérard Chasseigne1, Etienne Mullet and
Paul Clay Sorum2,*
Abstract
Introduction
This study examined the effect of outcome
feedback on learning the multiplicative relationship between daily intakes of tobacco and alcohol, and the risk of esophageal cancer. In the first
of two experiments, 65 French adults judged
the risk of esophageal cancer associated with
combinations of five levels of intake of tobacco
and five of wine. They made these judgments
both before and after learning sessions in which
they were shown the actual risk for each vignette. In the second experiment, 35 French adults
underwent the same testing and learning, and
were re-tested twice 1 month later. The study
hypotheses were supported. First, prior to the
learning sessions, the participants used a subadditive rule to combine the perceived risk of
esophageal cancer from smoking and drinking.
Second, they learned after only one training
session to change to the multiplicative rule that
is consistent with epidemiological data. Third,
this learning persisted for 1 month. This methodology may prove useful in correcting people’s
underestimation of their health risks.
Drinking alcohol and smoking tobacco commonly
occur together (Hu et al., 1994; Batel et al., 1995;
Burton and Tiffany, 1997). Most people, whether
non-smokers, smokers or alcoholics, perceive the
combined effects on health of smoking and
drinking as subadditive (Hermand et al., 1995,
1997, 2000). When one substance is already consumed at a moderate or high level, the consumption
of the other substance is estimated to have only a
small incremental impact on health risk. This
subadditive model bears little resemblance to what
is expected on the basis of epidemiological studies.
The health risks of combining drinking and
smoking, particularly the risk of cancer, are multiplicative (Tuyns et al., 1977; Rosengren et al.,
1988; Zambon et al., 2000). Accordingly, there is
a crucial need to teach the public—and, in particular, to teach heavy drinkers and heavy smokers—
not only about the deleterious effects of each
individual substance, but also about their summative (non-antagonistic) effects in general and their
synergistic combination in the case of some
diseases.
One of the most important of these diseases is
cancer of the esophagus (Tuyns et al., 1977; Gao
et al., 1994; Chyou et al., 1995; Launoy et al.,
1997; Castellsagué et al., 1999; Parkin et al., 1999;
Zambon et al., 2000). Esophageal cancer is the
eighth most common cancer around the world
and, because of the poor survival of those with
esophageal cancer, it is the sixth most common
cause of death from cancer (Parkin et al., 1999).
There are two types of esophageal cancer, squamous cell carcinoma and adenocarcinoma; the former
Laboratoire Cognition et Décision, Ecole Pratique des
Hautes Etudes, 37000 Tours, 1Département de
Psychologie, Université François-Rabelais, 37000 Tours,
France and 2Departments of Medicine and Pediatrics,
Albany Medical College, Albany, New York 12208, USA
*To whom correspondence should be addressed at Albany
Med Primary Care Network, 724 Watervliet-Shaker
Road, Latham, NY 12110, USA
© Oxford University Press 2002. All rights reserved
415
S. Bonnin-Scaon et al.
predominates in most of the world, but the latter
has been increasing in the US and western Europe,
so that it now accounts for over half of new
esophageal cancers in the US, especially among
white males (Blot and McLaughlin, 1999). The
incidence of squamous cell carcinoma of the esophagus rises exponentially as people combine
increasing levels of exposure to tobacco smoke
and alcohol (Tuyns et al., 1977; Zambon et al.,
2000). The incidence of adenocarcinoma rises with
increased smoking, but not with increased alcohol
intake (Gammon et al., 1997; Blot and McLaughlin,
1999). The overall effect of combined smoking and
drinking on esophageal cancer rates, particularly
among non-whites and worldwide, is multiplicative.
The aim of this study was to devise a learning
technique to improve people’s assessment of the
health risks from multiple substance abuse; in
particular, to teach them to combine more accurately the effects of the various exposures. The aim
was also to examine the extent to which this
learning technique was sensitive to gender and age.
The specific risk used in the learning task was
the risk of esophageal cancer in relation to smoking
and drinking. The learning technique was functional learning, as derived from Social Judgment
Theory (SJT) (Hammond and Stewart, 2001).
Functional learning is different from, and complementary to, the better-known technique of associative learning. Associative learning typically takes
place when a reduced set of stimuli is used. In a
typical associative learning experiment, the participant is presented with two sets of stimuli (usually
words such as tree or elephant). The task is to
learn through feedback (correct–not correct) to
associate these two sets of stimuli in an appropriate
way (e.g. to associate tree and elephant). Learning
is considered to be achieved when all the required
associations have been memorized.
In many daily-life situations, this kind of learning
is not practicable, especially when the two sets are
composed of many different stimuli. In these cases,
functional learning can be more adaptive. For
functional learning to take place, however, two
conditions must be fulfilled. One is that some
416
abstract property of each set of stimuli can be
extracted. The other is that some kind of correspondence can be established between the abstract
properties extracted from both sets of stimuli. In
an SJT learning experiment (Chasseigne et al.,
1997, 1999), the participant is presented with a
number of different cue configurations, such as, in
this case, different amounts of wine and cigarettes
consumed each day. Each configuration represents
one possible description of a person or situation,
or, as in this case, of a person’s habits. The
participant’s task is to infer from these cues the
possible value taken by a criterion, which in this
study is the degree of risk of esophageal cancer.
Once the participant has responded, the correct
criterion value is provided (outcome feedback).
After several presentations of cue configurations
and associated criterion values, the participants are
usually able to learn the relationship between the
dimensions reflected by the cues and the criterion.
Outcome feedback has been used in numerous
contexts and has enabled participants to learn
complex relationships between variables—inverse,
U-shaped, N-shaped—as well as to integrate
several information items according to simple or
complex weighting schemes (Hammond and
Stewart, 2001).
In the present study, the model for the relationship between the cue values (the intakes of alcohol
and tobacco) and the criterion (the degree of risk
of esophageal cancer) was provided by Tuyns et al.
(Tuyns et al., 1977) and confirmed recently by
Zambon et al. (Zambon et al., 2000). This model
is shown in Figure 1. The different levels of
tobacco intake are seen on the horizontal axis. The
five curves correspond to five levels of alcohol
intake. The degree of objective risk, computed
according to the rule of Tuyns et al. (Tuyns et al.,
1977), is shown on the vertical axis (on an arbitrary
scale of 0–100). The five curves are ascending—
the more the daily tobacco consumption, the higher
the risk. The curves are separate—the more the
daily alcohol consumption, the higher the risk.
The curves are diverging on the right—the two
consumption levels do not simply add their effects.
The valid combination rule in this case is thus a
Relationship between smoking, alcohol and esophageal cancer
Fig. 1. Ecological model of the relationship between the combined daily intakes of cigarettes (the horizontal axis) and wine
(the curves), and the risk of esophageal cancer (expressed on a 0–100 scale on the vertical axis).
multiplicative one. The alcohol was, in the scenarios, imbibed in the form of wine. This is the
most commonly consumed alcoholic beverage in
France. Its use in this study was, therefore, appropriate even if debate continues over the relative
negative and positive health effects of different
types of alcoholic beverages (Zambon et al., 2000;
Grønbæk et al., 2000).
We conducted two experiments to examine the
learning by adults of the relationship between
smoking, drinking alcohol and the risk of esophageal cancer. We had two hypotheses in the
first experiment. The first, based on the work of
Hermand et al. (Hermand et al., 1995, 1997,
2000), was that participants would initially use a
subadditive rather than a multiplicative rule when
inferring the risk of esophageal cancer from various
levels of tobacco and alcohol consumption. The
second hypothesis, derived from SJT (Chasseigne
et al., 1997), was that, after several learning
sessions with outcome feedback, participants
would learn to use a multiplicative rule. The
aim of the second experiment was to assess the
durability of the type of learning evidenced in
Experiment 1.
Experiment 1
Method
Participants
Sixty-five individuals (26 males and 39 females)
participated in this experiment. They were aged
18–74, with a mean age of 44.25 (SD ⫽ 8.75).
The youngest participants (11 males and 12
females) were university undergraduates and
volunteers recruited while walking along the street.
The middle-aged people (eight males and 14
females) were employed and were recruited
through personal contacts. The older people (seven
males and 13 females) consisted of retired persons
who lived at home. The older adults (65–74 years
old) were tested with the Mini-Mental State Examination (Folstein et al., 1975) in order to exclude
participants with early signs of dementia. No
observed score was less than 27 (cut-off ⫽ 24).
Material
The material consisted of two sets of cards—one
set used in the learning phases and one set used
417
S. Bonnin-Scaon et al.
in the test phases. Each card showed two cue
values in the form of alcohol and tobacco consumption levels, and a response scale graduated from 1
to 100: 1 corresponded to a low risk level and 100
corresponded to an extremely high risk level.
The set used during the learning phases was
composed of 30 cards showing various tobacco–
alcohol combinations. The design was a representative design. The values for tobacco consumption
varied from 0 to 45 cigarettes a day, with a normal
distribution. The values for alcohol consumption
varied from 0 glasses of wine a day to 2 bottles
of wine a day, with a normal distribution. Between
the two cues, the correlation was 0.20 in order to
mimic the ecological relationship between tobacco
and alcohol consumption.
The test set was composed of 25 cards. The
design was orthogonal: 5⫻5. The five exact values
for the tobacco consumption levels were 0, 0.5, 1,
1.5 and 2 packs a day. The exact values for the
alcohol consumption levels were 0 glasses, 2
glasses, 1 bottle, 1.5 bottles and 2 bottles a day.
None of these 25 test cards was identical to any
of the 30 cards in the learning set.
Procedure
The participants were told that their task was to
assess the risk of esophageal cancer associated
with the various consumption levels by examining
scenarios that would be presented to them. For
each vignette, the participants were told (1) to pay
attention to the tobacco–alcohol combination, (2)
to judge the level of risk associated with the cue
configuration and (3) to indicate their response by
marking an ‘⫻’ at the appropriate point on the
response scale. The experiment was self-paced,
and the participants did the task individually. They
took about 1–1.5 h to complete the experiment.
Four identical packs of 25 test cards and two
identical packs of 30 learning cards were needed
for each participant. There were six consecutive
sessions. Session 1 was a session of familiarization
in which a set of 25 test cards was presented in
random order. Participants were told to judge the
level of risk associated with each tobacco–alcohol
418
combination. At the end of the session, the participants could return to previous cards, change their
responses, and ask questions about the experimental material and procedure. The experimenter
made certain that every participant understood the
nature of the judgment task.
Session 2 was the first test session. It was
an assessment of the state of the participants’
knowledge before any learning process. A set of
25 test cards was presented in random order and
the participants were again asked to judge the
level of risk associated with each tobacco–alcohol
combination. This time the participants could not
look at previous cards nor change their responses.
Session 3 was the first learning session. A set
of 30 learning cards was presented to the participants in random order. The participants were asked
to judge the level of risk associated with each
tobacco–alcohol combination. They were also told
that, to do this, they had to learn the relationship
between the levels of the two indicators and the
level of overall risk. After each judgment, the
experimenter showed, on a similar 1–100 scale,
the actual risk value associated with this particular
tobacco–alcohol combination (outcome feedback).
Participants had to read the feedback value out
loud.
Sessions 4 and 6 were additional test sessions,
and session 5 an additional learning session. For
each session, the order of presentation of cards
varied randomly. At the end of the sixth session,
the participants completed a questionnaire (see
Table II).
Results
The participants’ mean estimations of risk were
graphed in the manner described above for Figure
1. In addition, the raw data were subjected to an
ANOVA, with a design of Block (second, fourth
and last)⫻Tobacco⫻Alcohol⫻Age⫻Gender, 3⫻5
⫻5⫻2⫻2.
Graphs
Figure 2 shows the results from the second, fourth
and last blocks, the testing sessions.
Relationship between smoking, alcohol and esophageal cancer
Fig. 2. Experiment 1. The participants’ estimations of the risk of esophageal cancer (the vertical axis) associated with the combined
daily intakes of cigarettes (the horizontal axis, in packs) and wine (the curves): (a) before any learning session (by outcome
feedback), (b) (the fourth block) after the first learning session and (c) (the sixth block) after the second learning session.
Figure 2(a) shows the results from the second
session, prior to any feedback. The five levels of
tobacco consumption are on the horizontal axis.
The five curves correspond to the five levels
of alcohol (specifically wine) consumption. The
judged risk of esophageal cancer is on the vertical
axis. The curves are ascending: even before receiving any feedback, the participants perceived that
the higher the tobacco consumption level, the
higher the associated risk. The curves are clearly
separated: even before receiving any feedback, the
participants perceived that the higher the alcohol
consumption level, the higher the associated risk.
In addition, the curves are not parallel; they form
a fan-shaped graph, open to the left. The higher
the tobacco consumption level, the less the effect
of alcohol consumption, and the higher the alcohol
consumption level, the less the effect of tobacco
consumption.
Figure 2(b) shows the results from the fourth
block, after completing one learning session. The
curves are again ascending, clearly separated and
not parallel. This time, however, the fan-shaped
graph is open to the right. The higher the tobacco
consumption level, the greater the effect of alcohol
consumption, and the higher the alcohol consumption level, the greater the effect of tobacco consumption. The pattern is similar to that for the
actual risks, as shown in Figure 1.
Figure 2(c) shows the results observed in the
last block, after completing two learning sessions.
The graph formed by the five curves is very similar
to the one in Figure 2(b). The fan-shaped form of
the graph is, however, more accentuated, and the
pattern is even closer to that in Figure 1.
Statistical analyses
The results of the ANOVA are shown in Table I.
The more important results concern the
Block⫻Tobacco⫻Alcohol interaction, which is
significant and concentrated in its trilinear component (80% of the variance). The form of the
419
S. Bonnin-Scaon et al.
Table I. Results of the ANOVA: results shown are those which are significant plus the two four-way interactions involving age
and gender
Source
d.f.
Mean square
F
P
Alcohol
Tobacco
Age⫻Alcohol
Gender⫻Alcohol
Block⫻Alcohol
Block⫻Tobacco
Alcohol⫻Tobacco
Block⫻Alcohol⫻Tobacco
Age⫻Block⫻Alcohol⫻Tobacco
Gender⫻Block⫻Alcohol⫻Tobacco
4
4
8
4
8
8
16
32
64
32
330030
120618
3854
4653
13591
1179
1586
1126
107
70
247.07
319.10
2.89
3.25
37.43
5.92
12.18
12.66
1.20
0.79
0.00001
0.00001
0.005
0.01
0.00001
0.00001
0.00001
0.00001
0.13
0.79
Tobacco⫻Alcohol interaction was thus significantly dependent on the block considered. Neither
four-way interaction involving Age or Gender was
significant.
Several complementary ANOVAs were performed. An ANOVA on the data from the first block
showed that the Tobacco⫻Alcohol interaction for
this block was significant, F(16,1008) ⫽ 6.96,
P ⬍ 0.00001, i.e. the left-opening shape observed
in Figure 2(a) was significantly different from
parallelism.
An ANOVA on the data from the third block
showed that the Tobacco⫻Alcohol interaction for
this block was significant, F(16,1008) ⫽ 17.23,
P ⬍ 0.00001, i.e. the right-opening shape observed
in Figure 2b was significantly different from parallelism.
Finally, an ANOVA on the reduced set of data
corresponding to the three upper curves in each
panel showed that the Block⫻Tobacco⫻Alcohol
interaction in this reduced set of data was significant, F(16,944) ⫽ 2.76, P ⬍ 0.0003, i.e. the opening
to the right of these three curves from the left
panel to the right panel was significant.
and alcohol. A strong majority of participants
favored a multiplicative rule.
Experiment 2
The aim of the second experiment was to assess
the durability of the type of learning evidenced in
Experiment 1.
Method
Participants
A total of 35 individuals (15 males and 20 females)
participated in this experiment. They were recruited
in the same way as the participants of the experiment 1. They were aged 22–50, with a mean age
of 35.25 (SD ⫽ 9.15).
Material and procedure
The material and procedure used were the same
as in Experiment 1. The difference was that the
participants went through two additional testing
sessions, 1 month after the initial sessions. Two
more packs of 25 test cards were, therefore, needed
for these sessions.
Responses to the questionnaire
Results
Table II shows the number of participants who
agreed with each of the four propositions in the
final questionnaire. All participants (except one)
expressed their conviction that the risk of cancer
was dependent on the joint consumption of tobacco
Graphs
420
Figure 3(a) shows the results of the second block,
the initial testing session prior to any feedback.
As in Experiment 1, the participants judged already
that the higher the tobacco consumption level, the
Relationship between smoking, alcohol and esophageal cancer
Fig. 3. Experiment 2. The participants’ estimations of the risk of esophageal cancer (the vertical axis) associated with the combined
daily intakes of cigarettes (the horizontal axis, in packs) and wine (the curves, representing, from top to bottom, 2 bottles,
1.5 bottles, 1 bottle, 2 glasses and 0 glasses): (a) before any learning session, (b) (the fourth block) after the first learning session,
(c) (the sixth block) after the second learning session and (d and e) the two testing sessions 1 month later.
Table II. Responses given to the questionnaire
Questions
Gender
Male
To estimate the risk of cancer
...I have based my judgment on alcohol intake only
...I have based my judgment on tobacco intake only
...I have based my judgment on alcohol and tobacco intake: alcohol intake and
tobacco intake add their effects
...I have based my judgment on alcohol and tobacco intake: alcohol intake and
tobacco intake multiply their effects
Total
higher the associated risk, and also that the higher
the alcohol consumption level, the higher the
associated risk. As in Experiment 1, the curves are
not parallel; they form a fan-shaped graph, open
to the left. The higher the tobacco consumption
level, the less the effect of alcohol consumption,
and the higher the alcohol consumption level, the
less the effect of tobacco consumption.
Female
Total
0
0
6
1
0
9
1
0
15
20
29
49
26
39
65
Figure 3(b and c) shows the results of the fourth
and last blocks, after completing one and two
learning sessions, respectively. The shape of the
curves is very similar to the shape of the curves
in Figures 2(a and b) and 1. They form fan-shaped
graphs, open to the right. The higher the tobacco
consumption level, the greater the effect of alcohol
consumption, and the higher the alcohol consump-
421
S. Bonnin-Scaon et al.
tion level, the greater the effect of tobacco
consumption.
Figure 3(d and e) shows the results of the testing
sessions 1 month later. The shape of the curves
was very similar to the shape of the curves in
Figure 3(b and c).
Statistical analyses
An ANOVA was performed on the raw data with
a Block⫻Tobacco⫻Alcohol, 5⫻5⫻5 design. The
Block⫻Tobacco⫻Alcohol interaction was significant, F(64,2176) ⫽ 5.92, P ⬍ 0.00001. The
form of the Tobacco⫻Alcohol interaction was thus
significantly dependent on the block considered.
Several complementary ANOVAs were performed. An ANOVA on the data from the first block
showed that the Tobacco⫻Alcohol interaction for
this block was significant, F(16,544) ⫽ 7.06,
P ⬍ 0.00001. An ANOVA on the data from the
third block showed that the Tobacco⫻Alcohol
interaction for this block was significant,
F(16,544) ⫽ 8.68, P ⬍ 0.00001. Finally, two
ANOVAs were performed on the data from the
one-month testing sessions. The analyses showed
that the Tobacco⫻Alcohol interaction for the two
blocks were significant, F(16,544) ⫽ 3.88 and
4.34, P ⬍ 0.00001.
Discussion
In estimating the health risks associated with
potentially dangerous exposures, people tend to
combine two risk factors in a subadditive fashion.
In other words, as already demonstrated by
Hermand et al. [(Hermand et al., 1995, 1997,
1999); see also (Hampson et al., 2000)], they judge
that exposure to one dangerous substance has less
impact on health risk if the person is already
exposed to a second dangerous substance. This
combination rule is different from the ways in
which these risks—particularly the risk of esophageal cancer from exposure to tobacco and
alcohol—are combined in nature and revealed by
epidemiological studies. It is also differs from the
way people think they judge health risks (Hermand
422
et al., 1997). This cognitive error in estimating
personal health risks is likely to have detrimental
health effects; our goal was, therefore, to find a
way to correct it.
Our specific aim was to study the effect of
outcome feedback on learning the multiplicative
relationship between daily intakes of alcohol and
tobacco and the risk of esophageal cancer. Our first
hypothesis—that, prior to the learning sessions, the
participants would implement a subadditive rule—
was well supported by the data (Figures 2a and
3a). Our second hypothesis—that, after several
learning sessions, participants would be able to
implement the correct multiplicative rule—was
also well supported (Figures 2b and c and 3b and
c). Complete rule learning took place. After only
a limited amount of feedback (Figures 2b and
3b), the participants already learned to use a
multiplicative rule, indicating that the effect on the
perceived risk of esophageal cancer of increased
consumption of one substance was increasingly
great when combined with a larger consumption
of the other substance. Additional feedback did
little to alter the pattern of results (Figures 2c and
3c). Verbal reports from the participants were
consistent with the use of a uniform multiplicative
rule at the end of the learning sessions. Moreover,
our third hypothesis—that this learning of the way
to combine the two risks would persist over the
short run, i.e. at 1 month—was also demonstrated
to be correct (Figure 3d and e).
Limitations and implications
Our study has several limitations. First, the external
generalizability of our findings is limited by the
moderate size and the cultural homogeneity of the
samples. Yet, we expect to find similar results as
we extend this study to other groups of people
because we are studying mental processes, i.e. the
rules used to integrate information, rather than
opinions.
Second, the practical value of our intervention
is uncertain because we had no control group,
because the participants’ judgments were not
related to personal risks, because we do not know
if this training would affect the participants’ actual
Relationship between smoking, alcohol and esophageal cancer
behavior and because the training is costly in time
and energy. To resolve these issues will require
further research. To begin with, because of the
nature of the risks for esophageal cancer, specific
groups should be targeted for further study and
intervention, i.e. those who smoke or drink heavily
and for whom, therefore, esophageal cancer looms
as an important personal threat. We have already
started a study of the ability of alcoholics to learn
the multiplicative rule for integrating these risks.
More generally, we realize that the importance
of our ability to teach people how to judge health
risks more accurately depends, as suggested above,
on the superiority of functional learning over
simpler educational methods, the durability of
the cognitive changes we produce, their internal
generalizability and their impact on behavior.
People in France report, in line with cancer prevention campaigns, that the risks of smoking and
drinking are multiplicative, but these same people
have been shown, on a functional level, to combine
these risks in a subadditive fashion (Hermand
et al., 1997, 2000); in this sense, knowing is not
the same as applying that knowledge when making
judgments. We think, therefore, that functional
learning should result in a greater degree of learning
and of persistence over time than simpler, more
direct methods of helping people to learn health
risks; this, however, needs to be proved by future
studies. We also need to devise and evaluate a less
time-consuming method of functional learning. In
addition, although the persistence of our participants’ use of the multiplicative rule is encouraging,
we need to show that this learning endures for
longer than 1 month. Furthermore, the internal
generalizability of this learning—the diminution
of subadditivity when judging other health risks—
would be useful to demonstrate in light of people’s
general tendency to underestimate health risks
(Ayanian and Cleary, 1999). Most important, however, would be the follow-up of participants, such
as the alcoholics in our current study, to see if
learning about risks leads to less risky health
behaviors. Our methods could thereby be shown
to have broad applicability in health education.
Acknowledgements
Thanks are extended to Joël Cogneau for his
many helpful suggestions. This work was supported
by the UPRES Vieillissement, Rythmicité et
Developpement Cognitif (Université FrançoisRabelais) and the UMR Travail et Cognition
(Université du Mirail).
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Received on February 8, 2001; accepted on October 10, 2001