British Journal of Cancer (2012) 107, 234–242
& 2012 Cancer Research UK All rights reserved 0007 – 0920/12
www.bjcancer.com
Clustering of health behaviours in adult survivors of childhood
cancer and the general population
Clinical Studies
CE Rebholz1, CS Rueegg1, G Michel1, RA Ammann2, NX von der Weid3, CE Kuehni*,1,4 and BD Spycher1,4
for the Swiss Paediatric Oncology Group (SPOG)5
1
Institute of Social and Preventive Medicine, Swiss Childhood Cancer Registry, University of Bern, Bern, Switzerland; 2Department of Paediatrics, University
of Bern, Bern, Switzerland; 3Paediatric Hematology-Oncology Unit, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
BACKGROUND:
Little is known about engagement in multiple health behaviours in childhood cancer survivors.
Using latent class analysis, we identified health behaviour patterns in 835 adult survivors of childhood cancer (age 20–35
years) and 1670 age- and sex-matched controls from the general population. Behaviour groups were determined from replies to
questions on smoking, drinking, cannabis use, sporting activities, diet, sun protection and skin examination.
RESULTS: The model identified four health behaviour patterns: ‘risk-avoidance’, with a generally healthy behaviour;
‘moderate drinking’, with higher levels of sporting activities, but moderate alcohol-consumption; ‘risk-taking’, engaging in several
risk behaviours; and ‘smoking’, smoking but not drinking. Similar proportions of survivors and controls fell into the ‘risk-avoiding’ (42%
vs 44%) and the ‘risk-taking’ cluster (14% vs 12%), but more survivors were in the ‘moderate drinking’ (39% vs 28%) and fewer in
the ‘smoking’ cluster (5% vs 16%). Determinants of health behaviour clusters were gender, migration background, income and
therapy.
CONCLUSION: A comparable proportion of childhood cancer survivors as in the general population engage in multiple healthcompromising behaviours. Because of increased vulnerability of survivors, multiple risk behaviours should be addressed in targeted
health interventions.
British Journal of Cancer (2012) 107, 234–242. doi:10.1038/bjc.2012.250 www.bjcancer.com
Published online 21 June 2012
& 2012 Cancer Research UK
METHODS:
Keywords: childhood cancer survivors; health behaviour; cluster analysis; smoking; alcohol consumption
Engagement in health protective behaviour is important for
preventing chronic diseases and early mortality (Centers for
Disease Control and Prevention, 2004; Khaw et al, 2008) and is of
particular importance for childhood cancer survivors (White et al,
2005; Children’s Oncology Group, 2008; Demark-Wahnefried and
Jones, 2008; Gritz and Demark-Wahnefried, 2009). Although about
80% are cured from cancer (Horner et al, 2009), survivors are at
increased risk for second malignancies and early mortality
(Armstrong et al, 2009; Meadows et al, 2009), and two thirds
suffer from chronic conditions, such as endocrine disorders, heart
problems, neurocognitive impairment and musculoskeletal disorders
(von der Weid et al, 1996; Hewitt et al, 2003; Oeffinger et al, 2006).
Only few studies have investigated health behaviours in young
adult survivors of childhood cancer (Mulhern et al, 1995;
Larcombe et al, 2002; Butterfield et al, 2004; Bauld et al, 2005;
Clarke and Eiser, 2007). In general, these studies reported a lower
or similar level of engagement in single risk behaviours compared
with the general population and controls (Mulhern et al, 1995;
Larcombe et al, 2002; Bauld et al, 2005; Clarke and Eiser, 2007).
From a public health perspective, it is important to know whether
there are groups of individuals who engage in multiple health
behaviours simultaneously, and whether such behaviour patterns
differ between survivors and controls. Answers to these questions
could provide a basis for targeted interventions, using a personcentred approach rather than focusing on single health behaviours.
Clustering methods, including latent class analysis (LCA), have
been used to identify and characterise health behaviour patterns
in various populations (Schneider et al, 2009; Sutfin et al, 2009;
Huh et al, 2011). In childhood cancer survivors, LCA has recently
been used to classify them according to modifiable cognitive,
affective and motivation indicators for future medical follow-up
(Cox et al, 2011).
This study aimed to (i) identify and characterise different
patterns of health behaviour in a mixed population of childhood
cancer survivors and matched controls from the general population using LCA, (ii) assess differences in the prevalence of these
behaviour patterns between survivors and controls, and (iii)
identify risk factors for health-compromising behaviour patterns
in survivors.
MATERIALS AND METHODS
*Correspondence: Professor CE Kuehni; E-mail: kuehni@ispm.unibe.ch
4
These authors shared the last authorship.
5
See Appendix.
Received 28 February 2012; revised 30 April 2012; accepted 4 May 2012;
published online 21 June 2012
This analysis included 835 adult survivors of childhood cancer
from the Swiss Childhood Cancer Survivor Study (SCCSS) and
1670 controls from the Swiss Health Survey (SHS) matched on
gender, age, language region and migration background; both
surveys were conducted in 2007–2009.
Behaviour clusters in childhood cancer survivors
CE Rebholz et al
235
The SCCSS is a nationwide population-based long-term follow-up
study of all childhood cancer patients registered in the Swiss
Childhood Cancer Registry (Michel et al, 2007; Kuehni et al, 2011),
who were diagnosed with cancer between 1976 and 2003 before age
16 years, and who survived at least 5 years since diagnosis.
Study participants received an extensive questionnaire in
German, French or Italian. Non-responders were sent a reminder
questionnaire after 2 months and subsequently contacted by phone
to encourage them to participate. Ethics approval was provided
through the general cancer registry permission of the Swiss
Childhood Cancer Registry (The Swiss Federal Commission of
Experts for Professional Secrecy in Medical Research) and a
statement of no objections was obtained from the ethics committee
of the Canton of Bern.
Of 1699 eligible survivors, 1497 could be contacted and 1067
responded (response rate 63% of eligible, 72% of contacted
survivors). We included participants aged 20–35 years at the time
of survey. Of the 860 eligible respondents, we dropped 10 because
of missing values in the question on alcohol consumption—the
model required complete data for this variable, because information on frequency of drinking and binge drinking were conditional
to a positive reply to this question—and another 15 because of
missing information on migration background, which was
required for matching controls, leaving 835 survivors for the
analysis (Supplementary Figure S1).
Swiss Health Survey
The SHS is a national representative health survey repeated in
5-year intervals. The 2007 survey included a random sample of
30,179 Swiss households with a telephone landline. A stratified (by
region) and stepwise (first selection of households, then of an
individual within each household) sampling procedure was
applied, with oversampling of households in the French- and
Italian-speaking regions of Switzerland. Within each household,
one person aged X15 years was randomly chosen for the
interview. The response rate was 66% (Bundesamt für Statistik,
2008). For each survivor, two controls from the SHS were matched
for gender, age, language, region and migration background,
resulting in 1670 controls.
Health behaviours
The SCCSS used a questionnaire similar to that of childhood
cancer survivor studies in the US and the UK (Robison et al, 2002;
Hawkins et al, 2008). For comparison with the Swiss population,
health behaviours were assessed with standardised questions of the
SHS. The following health-compromising and protective behaviours were assessed in both populations and included in the LCA
to identify health behaviour patterns: smoking, alcohol consumption including binge drinking, cannabis use, skin examination, sun
protection, sporting activities and vegetable/fruit consumption
(Table 1).
Potential determinants of health-behaviour patterns
In both populations, we examined the following potential
determinants of health behaviour: gender, age, marital status,
parenthood and socio-economic variables, including income,
educational attainment and migration background (one or both
parents originating from another country; Table 2). For survivors,
we additionally included parents’ education and disease-related
information, including age at diagnosis, ICCC-3 code of diagnosis
(Steliarova-Foucher et al, 2005), treatment and relapse history.
Treatment was categorised into four categories: surgery only,
chemotherapy (without radiotherapy, irrespective of surgery),
& 2012 Cancer Research UK
radiotherapy (irrespective of surgery and chemotherapy) and bone
marrow transplantation (BMT; irrespective of other therapies).
Statistical analysis
We first identified different patterns of health behaviour in the
combined population of survivors and controls, and subsequently
assessed the prevalence and determinants of these behaviours
separately in each population. To identify behaviour patterns,
we used LCA (Lazarsfeld and Henry, 1968; Skrondal and
Rabe-Hesketh, 2008), a clustering method that is based on a
statistical model and is particularly suited for data collected
through questionnaire surveys, because it can appropriately treat
categorical data and missing values. Latent class analysis assumes
that the population consists of distinct subpopulations (latent
classes), which cannot be observed directly, but are inferred from
the observed variables. After fitting the model, posterior probabilities of belonging to the identified classes can be computed for
each subject (McLachlan and Peel, 2000). We applied LCA to the
combined data from survivors and controls (n ¼ 2505) on the
health behaviours described in Table 1. After fitting the model,
subjects were then allocated to the behaviour patterns for which
they had the largest membership probability. We refer to the
groups thus formed as ‘health-behaviour clusters’. We fitted the
models with 1–6 classes and used the Bayesian Information
Criterion (BIC) to select the final model (McLachlan and Peel,
2000). Selecting the model with lowest BIC optimises model fit
while at the same time avoiding over-fitting.
We compared proportions of survivors and controls allocated to
the identified health behaviour patterns using w2-tests. We then
assessed associations of potential determinants (demographic,
socio-economic and disease related) with health-behaviour
patterns using w2-tests. We subsequently included all variables
with significant associations (Po0.05) in the first step in a
multinomial logistic regression model with health behaviour
clusters as the outcome levels. We investigated whether income
and educational attainment (assessed at the time of survey) lie on
the causal pathway between potential determinants assessed in
childhood (demographic- and disease-related variables, and
parents’ education) and health-behaviour patterns by comparing
multinomial regression models with and without income and
educational attainment.
The Mplus software version 6 (Muthén & Muthén, Los Angeles,
CA, USA) was used for LCA and Stata version 10 (StataCorp,
College Station, TX, USA) for all other analyses.
RESULTS
Characteristics of study population
Mean age was 26.1 years (s.d. ¼ 4.1 years; range 20.0–35.0 years)
and 53% were male in both study populations (because of
matching; Table 2). Fewer survivors were married (12% vs 23%),
had children (12% vs 21%), or had a university degree (8% vs
12%). Among survivors, mean age at diagnosis was 7.9 years
(s.d. ¼ 4.7 years; range 0.0–16.0 years) and mean time since
diagnosis was 18.1 years (s.d. ¼ 5.8 years; range 5.8–32.5 years);
36% were treated with radiotherapy and 10% had surgery only. A
relapse of their primary cancer occurred in 15% of the survivors
(Table 2).
Prevalence of health behaviours in survivors and controls
More survivors than controls were non-smokers (76% vs 65% in
controls) and had preventive skin examinations by a physician
(46% vs 35%; Table 1). In contrast, fewer survivors than controls
reported protecting themselves from sun exposure (78% vs 87%)
British Journal of Cancer (2012) 107(2), 234 – 242
Clinical Studies
Swiss Childhood Cancer Survivor Study
Behaviour clusters in childhood cancer survivors
CE Rebholz et al
236
Table 1
Questions used to assess behaviours and prevalence of behaviours among survivors and controls
Survivors
(n ¼ 835)
Clinical Studies
Behaviour
Measurement
Recoded categories
Smoking
Do you smoke? If yes, how many cigarettes a day?
Cannabis use
Have you ever consumed marijuana?
Drinking
Do you drink alcohol?
None
Up to 9 cigarettes a day
10–19 cigarettes a day
One or more packs a day
No, never
Previously
Currently
No alcohol consumption
Alcohol consumption
Rarely
1–2 times a week
42 times a week
1 or more drinks a day
No binge drinking
Less than once a month
Once a month or more
None
Low to moderate intensity
Quite intensively
Very intensively
None (0 to o1 portion a day)
Vegetable or fruit consumption
(X1 portion a day)
Vegetable and fruit consumption
(X1 portion a day each)
No
Yes
No, never
Yes, more than 12 months ago
Yes, in the last 12 months
How frequently do you usually consume alcoholic drinks
(such as wine, beer, schnapps or any other hard liquor)?c
How many times have you drunk more than 8 units (males)/
6 units (females) at a time in the past year?c
Sporting
activities
Do you engage in physical exercise or sporting activities?
If yes, how intensively do you pursue these activities?
Diet
How many portionsd of fruit do you eat a day on average?
How many portions of vegetables do you eat a day on average?
Skin protection
Do you protect yourself from sun exposure?
Have you ever had your skin or moles examined by a physician?
Controls
(n ¼ 1670)
n
%a
n
%a
P-valueb
634
106
50
40
469
275
74
86
749
299
270
126
51
273
272
170
290
269
198
63
63
156
76
13
6
5
56
33
9
10
90
36
33
15
6
33
33
20
35
32
24
8
8
19
987
289
200
141
988
512
167
177
1493
517
784
138
54
986
321
144
556
480
417
217
118
358
65
17
12
8
59
31
10
11
89
31
47
8
3
59
19
9
33
29
25
13
7
21
o0.001
604
72
1140
68
188
647
429
275
112
23
78
51
33
13
214
1455
1046
442
127
13
87
63
27
8
0.309
0.818
o0.001
o0.001
0.001
0.187
o0.001
o0.001
a
Percentages don’t always add up to 100% due to missing values. bw2-test. cAsked only to those with alcohol consumption (percentages don’t add up to 100%).
1 portion ¼ size of your fist.
d
and fewer intensively pursued sporting activities (8% vs 13%).
More survivors engaged in binge drinking (20% vs 9%).
Identification of health-behaviour clusters
We fitted LCA models with 1–6 classes (Figure 1). The 2-class
model distinguished between a ‘low-risk’ group (B1) and a ‘highrisk’ group that engaged in smoking and alcohol use (B2). In the
3-class model, the high-risk group was separated into two new
groups, the first characterised by sporting activities and moderate
to frequent drinking (C2), and the second by frequent drinking
and smoking (C3). In the 4-class model, a new group emerged
characterised by frequent smoking, but low alcohol consumption
(D4). According to the BIC, the models including 3 and 4 classes
were optimal, with BIC values: 36 007 and 36 008 for the 3 and 4,
compared with 36 203 and 36 062 for the models with 2 and 5
classes, respectively.
This manuscript reports results for the 4-class model, which
highlights differences in behaviour patterns between survivors
and controls that are less evident from the 3-class model. Results
of the 3-class model are shown in the online supplement
(Supplementary Table 1).
‘moderate drinking’ (n ¼ 797, 32%), D3 ‘risk-taking’ (n ¼ 316,
13%) and D4 ‘smoking’ (n ¼ 303, 12%).
Cluster D1: ‘risk-avoiding’ This cluster includes individuals who
did not, or only to a minor extent, engage in risk behaviours, and
who reported health-protective behaviours (sporting activities,
vegetable and fruit consumption, sun protection and skin
examination; Figure 2, green dashed).
Cluster D2: ‘moderate drinking’ This cluster had a similar
tendency for health-protective behaviours as the ‘risk-avoiders’,
but engaged more frequently in sporting activities and in alcohol
consumption, including binge drinking (Figure 2, blue).
Cluster D3: ‘risk-taking’ These individuals tended to engage in all
assessed risk behaviours: smoking, marijuana consumption and
alcohol use, including binge drinking. In addition, they reported
lower engagement in health-protective behaviours compared with
the ‘risk-avoiding’ Cluster D1 and ‘moderate-drinking’ Cluster D2
(Figure 2, red).
Description of health-behaviour clusters
Cluster D4: ‘smoking’ These individuals had low engagement in
health-protective behaviours and were likely to smoke, but not to
drink (Figure 2, yellow dashed).
We labelled the four behaviour clusters as: D1 ‘risk-avoiding’
(number of individuals allocated n ¼ 1089, 44% of sample), D2
The clusters varied little with respect to sun protection and skin
examination (Figure 2; Supplementary Table 2).
British Journal of Cancer (2012) 107(2), 234 – 242
& 2012 Cancer Research UK
Behaviour clusters in childhood cancer survivors
CE Rebholz et al
237
Table 2 Socio-demographic and clinical characteristics of the two study
populations, survivors and controls
n
Socio-demographic characteristics
Age (years)
20–25
26–30
31–35
%
Controls
(n ¼ 1670)
n
%
P-valuea
44.7
34.1
21.2
746
570
354
44.7
34.1
21.2
n.a.b
Gender
Male
Female
441
394
52.8
47.2
882
788
52.8
47.2
n.a.b
Language
German
French/Italian
633
202
75.8
24.2
1266
404
75.8
24.2
n.a.b
b
Migration backgroundc
No
Yes
648
187
77.6
22.4
1296
374
77.6
22.4
na
Civil status
Single, divorced or widowed
Married
732
95
88.5
11.5
1284
385
76.9
23.1
o0.001
Education
Compulsory schooling
Vocational training
Higher secondaryd
University
70
379
304
63
8.0
46.0
36.4
7.5
69
945
449
200
4.1
56.6
26.9
12.0
o0.001
Income
Unemployed
0–3000 CHF
3001–6000 CHF
46000 CHF
110
239
402
45
13.2
28.6
48.1
5.4
43
573
819
235
2.6
34.3
49.0
14.1
o0.001
Number of children
None
One
Two or more
708
63
41
84.8
7.5
4.9
1325
169
176
79.3
10.1
10.5
o0.001
Body mass index (kg m2)
o25
X25
606
203
72.6
24.3
1266
388
75.8
23.2
0.372
228
205
183
219
27.3
24.6
21.9
26.2
Diagnosis
Leukaemia
Hodgkin lymphoma
Non-Hodgkin lymphoma
CNS tumours
Embryonal tumourse
Bone tumours and soft tissue sarcomas
Otherf
310
73
83
101
127
87
54
37.1
8.7
9.9
12.1
15.2
10.4
6.5
Therapy
Surgery only
Chemotherapy, but no radiotherapy
Any radiotherapy
BMT
81
394
257
95
9.7
47.2
30.8
11.4
Relapse
No
Yes
710
125
85.0
15.0
Model 3
B2
959
38.3%
C1
1362
54.4%
C2
679
27.1%
D1
Model 4 1089
43.5%
D2
797
31.8%
Cluster 1
Risk-avoiding
BIC=36 203
C3
464
18.5%
D4
303
12.1%
BIC=36 007
D3
316
12.6%
Cluster 2
Cluster 4
Moderate drinking Smoking
BIC=36 008
Cluster 3
Risk-taking
Figure 1 Illustration of behaviour groups identified by LCA as the
number of classes was increased. The boxes in a given layer represent the
behaviour groups identified in that model. Numbers of individuals and
percentage of sample allocated to the group are reported next to the
boxes.
Prevalence of health-behaviour clusters in survivors and
controls
Abbreviations: CHF ¼ Swiss Francs; CNS ¼ central nervous system; n.a. ¼ not
applicable; BMT ¼ bone marrow transplantation. Numbers do not always sum up
to the total because of missing values. aw2-test. bPopulation matched for gender, age,
language, region and migration background. cDoes not have a Swiss passport or has
received the Swiss passport after date of birth or parents originate from another
country. dHigher secondary education includes high school, teachers training colleges,
technical colleges and higher vocational education. eIncludes neuroblastoma,
retinoblastoma, Wilms tumour, liver tumour and germ cell tumour. fIncludes
epithelial neoplasms, malignant melanomas, unspecified malignant tumours and
Langerhans cell histiocytosis.
& 2012 Cancer Research UK
B1
1546
61.7%
Model 2
373
285
177
Clinical characteristics
Age at diagnosis (years)
r4
5–8
9–12
412
BIC=37 125
The prevalence of the four health-behaviour clusters differed
between survivors and controls (P-value for w2-testo0.001).
Similar proportions of survivors and controls were allocated to the
‘risk-avoiding’ Cluster D1 (42% of survivors and 44% of controls)
and ‘risk-taking’ Cluster D3 (14% of survivors and 12% of
controls), a higher proportion of survivors was allocated to the
‘moderate drinking’ Cluster D2 (39% of survivors and 28% of
controls) and a smaller proportion to the ‘smoking’ Cluster D4
(5% of survivors and 16% of controls).
The membership probabilities tended to be high for the groups
to which subjects were allocated. Mean membership probabilities
were 0.89, 0.76, 0.82 and 0.78, for Cluster D1 (‘risk-avoiding’),
Cluster D2 (‘moderate drinking’), Cluster D3 (‘risk-taking’) and
Cluster D4 (‘smoking’), respectively, and did not differ substantially between survivors and controls.
Socio-demographic characteristics of health-behaviour
clusters in survivors and controls
In both populations, gender, education, income and migration
background were significantly associated with health-behaviour
clusters (Table 3; Supplementary Table S3). Female gender was
common in the ‘risk-avoiding’ Cluster D1 (64% of survivors, 62% of
controls) and less frequent in the ‘moderate drinking’ Cluster D2
(38% of survivors and 34% of controls) and ‘risk-taking’ Cluster D3
(25% of survivors and 24% of controls). The proportion of
individuals with a university degree was highest in the ‘moderate
drinking’ Cluster D2 (10% of survivors and 17% of controls).
Members of Cluster D2 (‘moderate drinking’) and Cluster D3 (‘risktaking’) tended to have a higher income than those in other clusters,
whereas the percentage of individuals with a migration background
was highest in the ‘smoking’ Cluster D4 (45% of survivors and 38%
of controls). These associations remained similar in multinomial
logistic regression models (Supplementary Table 3).
British Journal of Cancer (2012) 107(2), 234 – 242
Clinical Studies
Survivors
(n ¼ 835)
A1
2505
100%
Model 1
Behaviour clusters in childhood cancer survivors
CE Rebholz et al
238
Smoking
1.0
Cannabis
use
Alcohol
consumption
Binge
drinking
Sporting
activities
Diet
Sun
protection
Skin
exams
0.8
0.6
0.4
0.2
N
ev
e
Ev r
m er
on
th
s
12
st
La
N
o
Ye
s
Pr Ne
ev ve
i r
C ous
ur ly
re
nt
ly
N
1–
ev
2
t R er
>2 ime ere
tim s/w ly
es ee
/w k
ee
D k
ai
ly
. <1
O p N
nc e o
e r m ne
pe o
r m nth
on
Lo
th
w
to
m
od No
e
Ve In rate
ry ten
in se
te
ns
e
N
o
v
or
v f
v or f
an
d
f
Clinical Studies
- N
10 10/ o
–1 da
y
. 9/da
20 y
/d
ay
0.0
Figure 2 Prevalence of health behaviours within the four health-behaviour patterns identified. The prevalence of the response categories of a given
variable are connected with lines to better visualise differences between the behaviour patterns.
, Cluster D1: risk-avaiding;
, Cluster D2: moderate
, Cluster D3: risk-taking;
, Cluster D4: smoking. Abbreviations: v ¼ vegetables; f ¼ fruits.
drinking;
Clinical characteristics of health-behaviour clusters in
survivors
Comparison with other health behaviour studies in the
general population
In survivors, determinants of health-behaviour clusters in adjusted
multinomial logistic regression were gender, diagnosis, therapy,
relapse, having a migration background and income (Table 4). In
females, the odds for having a behaviour pattern other than ‘riskavoiding’ (Cluster D1) was a third or less of that in males (odds
ratio (OR) 0.33 for ‘moderate drinking’ Cluster D2; 0.17 for ‘risk
taking’ Cluster D3; 0.33 for ‘smoking’ Cluster D4). Compared with
survivors of leukaemia, survivors of a central nervous system
tumour were less likely to belong to the ‘moderate drinking’
Cluster D2 (OR 0.38) and ‘risk-taking’ Cluster D3 (0.26).
Individuals treated by surgery only were more likely to belong to
one of the three risk behaviour clusters D2, D3 and D4 (OR42)
than those treated with chemotherapy, but no radiotherapy, and
BMT was associated with the ‘smoking’ Cluster D4 (OR 3.60).
Survivors who had a relapse were less likely to belong to a risk
cluster (ORso0.6 for D2, D3 and D4). A migration background
was associated with an increased risk for the ‘smoking’ Cluster D4
(OR 2.60).
In additional analysis (Supplementary Table 4), we investigated
whether potential effects of diagnosis and treatment were mediated
via education and income (assessed at time of survey) by excluding
the latter variables from the regression models. Estimated associations did not change substantially, suggesting that associations
between health-behaviour patterns and diagnosis or therapy were
not mediated by educational attainment and income.
Several authors have reported evidence for the clustering of health
behaviour in the general population, including in children, adults
and the elderly (Karvonen et al, 2000; Chiolero et al, 2006;
Poortinga, 2007; Schneider et al, 2009; Sutfin et al, 2009; Huh et al,
2011). Our results are consistent with findings of a previous
analysis of risk behaviours in the general Swiss population
showing that, with increasing number of cigarettes, smokers
engage less in leisure time physical activity, eat less fruits/
vegetables and drink more alcohol (Chiolero et al, 2006).
Determinants of multiple-risk behaviours in these studies were
male gender and lower social class (Chiolero et al, 2006; Poortinga,
2007; Schneider et al, 2009). In agreement with these findings, we
found that male gender was also associated with all three clusters
involving risk behaviours. As in a previous study using data from
the SCCSS (Rebholz et al, 2012), we found that high income and
education were associated with alcohol consumption patterns. In
student populations, increased alcohol use has previously been
reported (O’Malley and Johnston, 2002), particularly among
better-off students (Wicki et al, 2010). Students consume alcohol
mostly for social and enhancement motives during social
gatherings (Wicki et al, 2010). These may include gatherings in
connection with sporting activities. Pupils engaging in a lot of
sports more often reported episodes of drunkenness in Switzerland
(Annaheim et al, 2006). In agreement with a study of Schneider
et al, 2009 in a population 50 years plus, we found that the
‘smoking’ cluster contained many individuals with lower education
and a migration background.
DISCUSSION
This study used LCA to determine how health behaviours cluster in
young adult childhood cancer survivors and controls from the
general population. Four health-behaviour clusters were identified:
(i) ‘risk-avoiding’ with a healthy behaviour throughout, (ii)
‘moderate drinking’ with a similar profile, but engaging in more
exercise and binge drinking, (iii) ‘risk-taking’ engaging in all risk
behaviours and (iv) ‘smoking’ with a risk profile comparable with
‘risk-taking’, but low alcohol consumption. Fewer survivors than
controls were part of the ‘smoking’ cluster, but more fell into the
‘moderate-drinking’ cluster. A considerable proportion, comparable to that in the general population (14%), engaged in multiple
health-compromising behaviours.
British Journal of Cancer (2012) 107(2), 234 – 242
Comparison with health-behaviour studies in childhood
cancer survivors
Several authors have compared single behaviours between
survivors and healthy adults. They usually found less engagement
in health-compromising behaviour among survivors, particularly
smoking (Emmons et al, 2002; Carswell et al, 2008; Frobisher et al,
2008) and alcohol consumption (Carswell et al, 2008; Lown et al,
2008; Frobisher et al, 2010; Rebholz et al, 2012). Rather than
focusing on single behaviours, we chose a multiple behaviour
approach. This allowed, for instance, to identify the group of ‘risktakers’, who engage in various unhealthy activities while neglecting
healthy behaviours, and to show that this group is as prevalent
& 2012 Cancer Research UK
Behaviour clusters in childhood cancer survivors
CE Rebholz et al
239
Socio-demographic and clinical characteristics of health behaviour clusters in survivors and controls
Cluster D1
‘risk-avoiding’
Cluster D2
‘moderate drinking’
Cluster D3
‘risk-taking’
Cluster D4
‘smoking’
P-valuea
survivors
P-valuea
controls
Survivors
(n ¼ 352)
Controls
(n ¼ 737)
Survivors
(n ¼ 327)
Controls
(n ¼ 470)
Survivors
(n ¼ 114)
Controls
(n ¼ 202)
Survivors
(n ¼ 42)
Controls
(n ¼ 261)
46.6
32.1
21.3
40.7
34.7
24.6
41.6
36.1
22.3
44.3
34.7
21.1
49.1
36.0
14.9
53.0
31.7
15.4
40.5
31.0
28.6
50.2
33.3
16.5
0.414
0.006
Gender
Male
Female
36.1
63.9
38.5
61.5
62.1
37.9
66.4
33.6
74.6
25.4
75.7
24.3
61.9
38.1
51.0
49.0
o0.001
o0.001
Language
German
French/Italian
77.8
22.2
76.4
23.6
76.2
23.9
79.6
20.4
69.3
30.7
71.8
28.2
73.8
26.2
70.5
29.5
0.316
0.023
Marital status
Single, divorced or widowed
Married
86.1
13.1
68.9
30.9
87.7
11.4
81.9
18.1
87.7
10.5
93.6
6.4
87.7
11.9
77.4
22.6
0.796
o0.001
Education
Compulsory schooling
Vocational training
Higher secondaryb
University
11.4
44.0
34.4
6.5
5.4
55.4
26.5
12.8
4.0
45.0
39.8
10.1
1.5
50.0
31.9
16.6
8.8
47.4
37.7
5.3
3.5
60.9
27.2
8.4
16.7
54.8
23.8
2.4
8.4
68.6
18.8
4.2
0.002
o0.001
Income
Unemployed
0–3000 CHF
3001–6000 CHF
46000 CHF
19.9
33.0
40.9
2.3
2.3
36.5
46.8
14.4
9.2
23.9
54.1
8.6
2.1
31.7
47.5
18.7
7.0
27.2
51.8
7.0
1.5
33.2
55.9
9.4
4.8
33.3
52.4
2.4
5.0
33.7
52.9
8.4
o0.001
0.001
Number of children
None
One
Two or more
75.0
8.8
5.4
72.7
12.9
14.4
82.6
5.2
4.6
86.0
7.9
6.2
83.3
8.8
3.5
91.1
5.0
4.0
83.3
7.1
4.8
77.0
10.3
12.6
0.265
o0.001
Migration backgroundc
No
Yes
75.9
24.2
79.4
20.6
81.4
18.7
82.1
17.9
80.7
19.3
81.2
18.8
54.8
45.2
61.7
38.3
0.001
o0.001
Body mass index (kgm2)
o25
X25
72.6
27.4
74.6
24.3
77.5
22.5
77.0
22.8
69.6
30.4
77.7
19.8
85.1
14.9
75.5
23.8
0.142
0.143
Parent’s educationd
Compulsory schooling
Vocational training
Higher secondaryb
University
8.6
47.3
29.1
9.4
8.0
43.3
32.5
12.9
4.4
47.8
30.4
13.9
25.0
29.2
31.3
4.2
0.009
Clinical characteristics
Age at diagnosis (years)
r4
5–8
9–12
412
29.6
21.9
21.3
27.3
26.3
28.1
22.3
23.2
25.4
21.9
26.3
26.3
21.4
26.2
11.9
40.5
0.209
Diagnosis
Leukaemia
Lymphoma
CNS tumour
Other solid tumoure
33.2
17.9
16.8
32.1
40.7
17.7
8.3
33.3
40.0
24.6
7.0
29.0
35.7
16.7
16.7
31.0
0.024
0.007
Socio-demographic characteristics
Age (years)
20–25
26–30
31–35
Therapy
Surgery only
Chemotherapy, but no
radiotherapy
Any radiotherapy
BMT
8.2
42.6
9.8
51.4
11.4
57.0
16.7
26.2
36.7
11.9
26.9
10.7
22.8
7.9
33.3
21.4
Relapse
No
Yes
80.1
19.9
89.3
10.7
89.5
10.5
81.0
19.1
0.003
Abbreviations: CHF ¼ Swiss Francs; CNS ¼ central nervous system; BMT ¼ bone marrow transplantation. aw2-test for differences in prevalence of characteristics between
clusters. bHigher secondary education includes high school, teachers training colleges, technical colleges and higher vocational education. cDoes not have a Swiss passport or has
received the Swiss passport after date of birth or parents originate from another country. dThe highest level of education of either father or mother. eIncludes neuroblastoma,
retinoblastoma, Wilms tumour, liver tumour, germ cell tumour, epithelial neoplasms, malignant melanomas, unspecified malignant tumours and Langerhans cell histiocytosis.
Data are prevalence in %
& 2012 Cancer Research UK
British Journal of Cancer (2012) 107(2), 234 – 242
Clinical Studies
Table 3
Behaviour clusters in childhood cancer survivors
CE Rebholz et al
240
Table 4
Determinants of health behaviour clusters in survivors only (adjusted multinomial logistic regression model)
Cluster D1
‘risk-avoiding’ (n ¼ 352)
Gender
Male
Female
Clinical Studies
Diagnosis
Leukaemia
Lymphoma
CNS tumour
Other solid tumoursc
Cluster D3
‘risk-taking’ (n ¼ 114)
Cluster D4
‘smoking’ (n ¼ 42)
Reference
ORa
95% CI
ORa
95% CI
ORa
95% CI
1.00
1.00
0.33
(0.23–0.47)
1.00
0.17
(0.10–0.28)
1.00
0.33
(0.16–0.67)
1.00
0.74
0.38
0.83
(0.46–1.19)
(0.20–0.77)
(0.55–1.25)
1.00
1.13
0.26
0.72
(0.61–2.09)
(0.09–0.76)
(0.40–1.31)
1.00
0.91
0.55
1.04
(0.33–2.51)
(0.16–1.96)
(0.42–2.54)
1.00
Therapy
Surgery only
Chemotherapy, but no radiotherapy
Any radiotherapy
BMT
1.00
Relapse
No
Yes
1.00
Migration background
No
Yes
1.00
Income
Unemployed
0–3000 CHF
3001–6000 CHF
46000 CHF
Cluster D2 ‘moderate
drinking’ (n ¼ 327)
1.00
Education
Compulsory schooling
Vocational training
Upper secondaryd
University
1.00
Parent’s educatione
Compulsory schooling
Vocational training
Higher secondaryd
University
1.00
2.08
1.00
0.77
0.85
(1.02–4.24)
(0.51–1.16)
(0.48–1.50)
2.94
1.00
0.59
0.55
1.00
0.52
(0.32–0.87)
1.00
0.71
(0.47–1.08)
0.34
0.72
1.00
2.02
(0.22–0.66)
(0.48–1.10)
0.43
1.00
1.22
1.44
(0.21–0.89)
1.08
1.00
1.27
1.76
(0.57–2.06)
(1.09–7.67)
(0.83–1.80)
(0.73–2.81)
(0.86–1.87)
(0.97–3.17)
(1.13–7.63)
o0.001
0.122
(0.33–1.06)
(0.23–1.28)
5.77
1.00
1.47
3.60
(0.60–3.62)
(1.27–10.2)
1.00
0.57
(0.27–1.18)
1.00
0.56
(0.21–1.47)
1.00
0.76
(0.42–1.38)
1.00
2.60
(1.23–5.49)
0.25
0.86
1.00
1.89
(0.10–0.60)
(0.48–1.54)
0.74
1.00
1.14
0.76
(0.31–1.79)
0.73
1.00
0.99
1.83
(0.27–1.99)
(0.61–5.82)
(0.67–1.96)
(0.26–2.22)
(0.57–1.71)
(0.83–4.03)
P-valueb
(1.55–21.4)
0.003
0.064
0.009
0.18
0.92
1.00
0.94
(0.04–0.85)
(0.40–2.14)
1.69
1.00
0.75
0.33
(0.59–4.85)
2.51
1.00
1.97
1.05
(0.93–6.80)
0.001
(0.10–8.69)
0.102
(0.31–1.80)
(0.04–2.86)
0.483
(0.84–4.64)
(0.20–5.46)
Abbreviations: BMT ¼ bone marrow transplantation; CHF ¼ Swiss Francs; CI ¼ confidence interval; CNS ¼ central nervous system; CHF ¼ Swiss Francs; OR ¼ odds ratio.
Adjusted for all factors listed and age at survey. Reference group for ORs is the ‘risk-avoiding’ Cluster D1, for example, the odds of belonging to Cluster D2 rather than to
Cluster D1 (probability of Cluster D2/probability of Cluster D1) among females is 0.33 times that among males. bP-value of likelihood-ratio test. cIncludes neuroblastoma,
retinoblastoma, Wilms tumour, liver tumour, germ cell tumour, epithelial neoplasms, malignant melanomas, unspecified malignant tumours and Langerhans cell histiocytosis.
d
Higher secondary education includes high school, teachers training colleges, technical colleges and higher vocational education. eThe highest level of education of either father or
mother.
a
among survivors as in the general population. This would not have
been evident from a simple univariate comparison of health
behaviours between survivors and controls.
Few other studies have looked at engagement in multiple health
behaviours of childhood cancer survivors, finding that behaviours
were correlated with each other (Mulhern et al, 1995; Larcombe
et al, 2002; Butterfield et al, 2004). Butterfield et al, 2004 created a
risk factor variable out of five behaviours and found that the
majority (92%) of survivors who were enroled in a smoking
cessation trial engaged in other health-compromising behaviours.
Larcombe et al, 2002 used principal component analysis to create a
health-behaviour index based on smoking, drinking, recreational
drug use, diet, exercise and sun care, ranging from ‘most healthy’
to ‘least healthy’. The behaviour patterns ‘risk-avoiding’ and ‘risktaking’ identified in our study may correspond to the ends of this
spectrum. However, our approach using LCA identified two
British Journal of Cancer (2012) 107(2), 234 – 242
additional qualitatively distinct patterns ‘moderate drinking’ and
‘smoking’, which do not easily fit into a continuous spectrum.
Strengths and limitations
The SCCSS is a national population-based survey of childhood
cancer survivors with a response rate of 72% that well represents
young adult childhood cancer survivors in Switzerland. Questions
on health behaviours originated from the SHS and were assessed in
the SCCSS and SHS 2007 in the same study period. We used an
objective method (LCA) to derive health-behaviour patterns from
data on a set of pre-specified behaviour variables.
Several limitations should be considered. Health behaviours
were based on self-report in both surveys and were, because of
restrictions in length of the questionnaire, limited in detail.
Differences in the survey methods (paper questionnaires in the
& 2012 Cancer Research UK
Behaviour clusters in childhood cancer survivors
CE Rebholz et al
241
Implications for clinical practice
Our finding that the ‘risk-avoiding’ and ‘risk-taking’ behaviour
patterns were equally prevalent in survivors and controls suggests
that the experience of having had childhood cancer does not
change future health behaviour in the majority of survivors.
However, the higher proportion ‘moderate drinkers’ compared
with ‘smokers’ in survivors might represent a shift away from
smoking towards increased alcohol consumption in some. It is
possible that survivors are more aware of the health-compromising
effect of tobacco than of alcohol. In clinical guidelines on
follow-up, care counselling against smoking is recommended
(Hewitt et al, 2003; Scottish Intercollegiate Guidelines Network
(SIGN), 2004; Hewitt et al, 2005; United Kingdom Children’s
Cancer Study Group, 2005), while whereas alcohol is rarely
mentioned (Hewitt et al, 2003; Children’s Oncology Group, 2008).
Few health interventions have yet been conducted in childhood
cancer survivors (Clarke and Eiser, 2007; San Juan et al, 2011). Our
study suggests that there is a need for targeted health interventions
by showing that a significant proportion of cancer survivors
readily engaged in multiple harmful activities. Given the increased
vulnerability of childhood cancer survivors for chronic diseases
and late mortality, this is a reason for concern (Hewitt et al, 2003;
Oeffinger et al, 2006; Reulen et al, 2010). Engaging in multiple risk
behaviours simultaneously can have synergistic detrimental effects
on health (Mokdad et al, 2005), and health interventions should
therefore primarily target survivors showing multiple-risk behaviour pattern. Multicomponent health interventions may help
these survivors to adopt a healthy lifestyle (Prochaska, 2008).
Conversely improvement of single behaviours may serve as a
gateway: increasing physical activity and a healthier diet could, in
turn, increase motivation and confidence for reducing smoking
and alcohol consumption habits (Butterfield et al, 2004). Such an
intervention might also benefit the small group of survivors who
were allocated to the ‘smoking’ pattern, but not the ‘moderate
drinkers’.
Routine assessment of health behaviours and targeted counselling should be included in long-term follow-up for childhood
cancer survivors. In previous studies, survivors have expressed
interest in receiving lifestyle counselling, in particular for diet and
physical activity (Demark-Wahnefried et al, 2005b; Zebrack, 2008),
and follow-up care appointments may provide opportunities for
teachable moments (Demark-Wahnefried et al, 2005a). Special
attention should be given to male patients, to survivors from
immigrant families, who are at particular risk of smoking, but also
to survivors with a high educational attainment, who are at greater
risk of increased alcohol consumption including binge drinking.
Because of their high risk of late effects, survivors after BMT
should be regularly seen in follow-up appointments, and risky
behaviours should be strongly discouraged.
In conclusion, although engaging in health protective behaviour
is more common and smoking less common in childhood cancer
survivors than among young adults from the general population,
a comparable proportion of young adults in both populations engage in multiple health-compromising activities. As
childhood cancer survivors remain a vulnerable population,
targeted health interventions are needed for this multiple risktaking group.
& 2012 Cancer Research UK
ACKNOWLEDGEMENTS
This study was supported by the Swiss Cancer League (Grant No
KLS-01605-10-2004 and KLS-2215-02-2008), the Wyeth Foundation for the Health of Children and Adolescents, and the
Foundation for the Fight against Cancer. Gisela Michel and
Claudia Kuehni were funded by the Swiss National Science
Foundation (GM: Ambizione Grant PZ00P3_121682 and
PZ00P3_141722; CK: PROSPER Grant 3233-069348), Ben Spycher
by Asthma UK (Grant 07/048) and Cornelia Rebholz by a
scholarship of the Bernese Cancer League.
Conflict of interest
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on British
Journal of Cancer website (http://www.nature.com/bjc)
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APPENDIX
Bern; PD Dr med AH Ozsahin, Geneva; PD Dr med M Beck
Popovic, Lausanne; Dr med L Nobile Buetti, Locarno;
Dr med Pierluigi Brazzola, Bellinzona; Dr med U Caflisch, Lucerne;
Dr med J Greiner, Dr med H Hengartner, St Gallen; Professor Dr med
M Grotzer, Professor Dr med F Niggli, Zürich.
The Swiss Paediatric Oncology Group (SPOG): Dr med R Angst,
Aarau; Professor Dr med M Paulussen, Professor Dr med T Kühne,
Basel; Professor Dr med A Hirt, Professor Dr med K Leibundgut,
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British Journal of Cancer (2012) 107(2), 234 – 242
& 2012 Cancer Research UK