Health and Quality of Life Outcomes Health related quality of life and psychosocial function among patients with carcinoid tumours. A longitudinal, prospective, and comparative study

Camilla Fröjd
Department of Public Health and Caring Sciences
Psychosocial oncology
Uppsala University
UppsalaSweden

Gunnel Larsson email@example.com
Department of Medical Sciences
Uppsala University
UppsalaSweden

Claudia Lampic firstname.lastname@example.com
Department of Public Health and Caring Sciences
Uppsala University
Caring SciencesUppsalaSweden

Department of Caring Sciences and Sociology
Gävle University
Sweden

Louise Von Essen
Department of Public Health and Caring Sciences
Psychosocial oncology
Uppsala University
UppsalaSweden

Health and Quality of Life Outcomes Health related quality of life and psychosocial function among patients with carcinoid tumours. A longitudinal, prospective, and comparative study
10.1186/1477-7525-5-18BioMed Central Page 1 of 9 (page number not for citation purposes) Research * Corresponding author
Background:The aim was to investigate HRQoL and psychosocial function among patients with carcinoid tumours, longitudinally and prospectively, and to compare HRQoL among patients with carcinoid tumours to that of the Swedish general population. The aim was also to investigate the prevalence of distress during the first year after diagnosis.Methods:At four assessments during the first year after diagnosis, HRQoL was measured by the EORTC QLQ-C30 3.0, anxiety and depression by the HADS, and prevalence, and worst aspects of distress by an interview guide. ANOVA was performed in order to study changes over time with regard to HRQoL, anxiety and depression. Comparisons regarding HRQoL between patients and the Swedish population were made by the use of one-sample t-tests and changes over time regarding the prevalence of distress was investigated by means of Cochran's Q.Results: High levels of physical-, emotional-, cognitive-, and social function and somewhat lower levels of role function and global quality of life were reported at all assessments. Role-and emotional function increased over time. Patients reported lower role function and global quality of life and more problems with fatigue and diarrhoea than the Swedish general population, at all assessments. Fatigue, limitations to work and pursue daily activities, and worry that the illness will get worse were among the most prevalent aspects at all assessments. At all assessments the majority reported worrying about the family's situation, the ability to care for the family, and worrying before the check-up.Conclusion:It is concluded that HRQoL and psychosocial function among patients with carcinoid tumours remains stable during the first year, that the patients report a lower HRQoL than the Swedish general population, and that a majority of the patients report a number of aspects of emotional distress. In the clinical care, it should be considered that the majority of patients report not only fatigue and diarrhoea but also worries about their prognosis, their families, tests, and examinations. Efforts to reduce these worries should be made.

Background

Carcinoid tumours are slow-growing malignancies belonging to the family of neuroendocrine tumours, the incidence is 2.5 in 100 000 [1]. The most common type is midgut carcinoid tumours with a 5-year survival rate of 61% [1]. Neuroendocrine tumours can be divided into functioning and non functioning tumours. Functioning tumours cause symptoms related to an overproduction of hormones while non-functioning tumours produce peptides that do not cause symptoms [1]. Symptoms caused by functioning tumours are flush, diarrhoea, bronchial constriction, and right heart failure caused by overproduction of varying hormones. Abdominal pain due to the tumour itself, lymph node or liver metastases may occur [1]. Surgery is the only treatment that can cure the patients. Some, but not all benefit from chemotherapy. The management usually includes treatment with biological agents such as somatostatin analogues and interferonα [1]. Both agents control the disease but do not provide a cure. The aims of treatment are to reduce hormone levels, control hormonal symptoms, prevent further tumour growth, and possibly also tumour reduction [1].

Common adverse effects of somatostatin analogues are nausea, abdominal cramps, loose stools, mild steathorrea, and flatulence. These symptoms usually decrease within a few weeks. Flu-like symptoms are very common, usually short lasting, consequences of interferon treatment. Chronic fatigue and mild depression may develop in approximately 50% of the patients treated with interferon [2]. The treatment should be carefully monitored by a complete history and physical examination approximately every 3 months [2]. Biochemical parameters (tumour markers) are tested every 3 to 6 months and the patients should be examined using conventional imaging studies (CT, MRI, ultrasonography, octreoscan) every 6 months [2,3].

It has been reported that patients with carcinoid tumours enjoy a good health related quality of life (HRQoL) and low levels of anxiety and depression, in early as well as later stages of the disease [4][5][6]. However, the findings of a longitudinal, prospective study in which 24 patients with carcinoid tumours were included indicate that they, during the first year of treatment with somatostatin analogues and/or interferon-α, report a lower emotional-and role function, lower global quality of life and more problems with fatigue, nausea/vomiting, appetite loss, and diarrhoea compared to the Swedish general population [6].

Since carcinoid tumours may be presented with problems typical for the disease it was questioned whether the EORTC QLQ-C30 and Hospital Anxiety and Depression Scale, previously used to measure patients' with carcinoid tumours psychosocial function [4][5][6], included all problems that may be of relevance for the patients. Therefore, an interview study with patients with carcinoid tumours, and staff caring for them was undertaken [7]. The findings indicate that the patients report a number of physical, social, and emotional aspects of distress [7]. Pain and flush are examples of aspects of physical distress [7] whereas limited possibilities to work/pursue daily activities are examples of aspects of social distress and worry that the disease will get worse an example of emotional distress [7]. Several of the identified aspects of distress are not included in the EORTC QLQ-C30 [8] or the Hospital Anxiety and Depression Scale [9]. In the studies [4][5][6][7] referred to above, the time the patients had been living with their disease varied from 1 to 96 months which limits the possibilities to make conclusions regarding the patients' HRQoL, and levels of anxiety and depression, at certain time-points during the course of the disease. This circumstance, together with the fact that the prevalence of disease-related aspects of distress among patients with carcinoid tumours has not been investigated indicates that there is room for more knowledge about how patients with carcinoid tumours experience their life situation.

The present study sets out to add to knowledge about HRQoL and psychosocial function among patients with carcinoid tumours by following a group of individuals over time, starting shortly after diagnosis. The following research questions were posed: 1. Do patient ratings of HRQoL, anxiety, and depression change over time from diagnosis?; 2. How do patients rate their HRQoL compared to the Swedish general population shortly after each of the first four admissions to hospital?; 3. What is the prevalence of physical, social, and emotional aspects of distress shortly after each of the first four admissions to hospital?; 4. Which physical, social, and emotional aspects of distress are reported as the worst shortly after each of the first four admissions to hospital? and 5. Does the prevalence of physical, social, and emotional aspects of distress change over time from diagnosis?

Methods

Design

This study is based on a longitudinal, prospective design with four assessments (T1-T4). Each of these occurred shortly after each of the patients' first four admissions to the Department of Endocrine Oncology (DepEO), Uppsala University Hospital, Uppsala, Sweden.

Sample and setting

To be eligible patients should be referred to the DepEO for the first time, age ≥18 years, have a good command of the Swedish language, and not be cognitively impaired. During the inclusion period (April 2001-August 2003) 103 patients with a suspected diagnosis of carcinoid were referred to the DepEO. Five of these were not asked about participation due to administrative reasons, of the remaining patients 76 accepted participation. At T1, 17 patients were excluded for the following reasons: 7 were not diagnosed with carcinoid, 6 would not be further referred to the DepEO, and 4 were too ill to participate. Thus, 59 patients were included in the study at T1, 36 of these participated at all assessments (T1-T4). Data from these patients are reported in this study and their medical and demographic characteristics are presented in Table 1. For a presentation of reasons for attrition at T2-T4, see Table 2. The patients in the study were referred to the DepEO from several hospitals in Sweden, for a diagnosis or a verification of a suspected diagnosis of carcinoid tumours. The patients participating in the study were all in-patients, the mean duration of each hospital admission was 6 days (SD = 2) at the first, 4 days (SD = 1) at the second, 3 days (SD = 1) at the third, and 3 days (SD = 0.7) at the fourth admission. During these admissions, patients' biochemical parameters (tumour markers) could be tested, and patients could be subjected to conventional imaging studies such as CT, MRI, ultrasonography, octreoscan [2,3] and in some cases patients received treatment (see Table 1). The mean time between the first and the second admission was 3 months (SD = 1.3), 4 months (SD = 1.4) between the second and the third, and 5 months (SD = 1.7) between the third and the fourth admission.

Procedure

Ethical approval was obtained from the local ethics committee at the faculty of Medicine at Uppsala University. The first author (CF) provided potential participants with oral and written information about the aim and procedure of the study at their first admission to the DepEO. They were informed that participation was voluntary, that confidentiality was guaranteed, and that neither participation, non-participation or withdrawal would affect their treatment and care. Shortly thereafter CF asked patients about consent. Patients who accepted participation and were eligible at T1, T2, T3, and T4 received the instruments (see Instruments) at the preceding admission to the DepEO. Patients were asked to complete these at home, after their hospital-stay, and a time when the patient should be asked to provide his/her responses via telephone, was agreed upon with CF. Data was collected from one week up to one month after each admission. Medical and demographic data were collected from the medical journals by CF.

Instruments

HRQoL HRQoL was measured with the EORTC QLQ-C30 3.0 [8] which includes 30 questions incorporated in five functional scales: physical (PF), role (RF), cognitive (CF), emotional (EF), and social (SF), three symptom scales: fatigue (FA), pain (PA), and nausea and vomiting (NV), and a global health and quality of life scale (QL). Also included are single items assessing: dyspnoea (DY), appetite loss (AP), sleep disturbance (SL), constipation (CO), diarrhoea (DI), and perceived financial impact (FI) of the disease and treatment. Responses were provided on Likert scales, coded 1-4 with the response alternatives not at all, a little, quite a bit, and very much. Responses for global health and quality of life were provided on scales with the end-points 1 (very poor) and 7 (excellent). The scores were transformed in accordance with the EORTC guidelines to 0-100 scores [10]. A higher score on functional scales represents a higher level of function, while a higher score on symptom scales represents a higher level of symptoms. Good reliability and validity has been shown for the instrument [8]. In this study, Cronbach's alpha values for the subscales were >.70 at T1-T4, with the exception of RF (.60 at T2), CF (.35 at T2, .46 at T3, and .67 at T4), NV (.43 at T1, .65 at T3, and impossible to compute alpha at T4). Patients were asked to answer the questions according to the last week.

Anxiety and depression

Anxiety and depression was measured with the Hospital Anxiety and Depression Scale (HADS) [9]. The instrument consists of two subscales (7 items in each subscale), one measuring anxiety (A) and one depression (D). Responses are provided on verbal scales, coded 0-3. Subscale scores range from 0 to 21. The HADS has proved to be a useful clinical indicator of anxiety and depression [11][12][13] among patients with somatic illnesses. Cronbach's alpha values at T1-T4 were high for the anxiety (.80-.90) and the depression subscale (.82-.92.). Patients were asked to answer the questions according to the last week.

Aspects of distress

In a previous study [7] patients and nurses were posed semi structured interview questions about distress related to the disease and treatment. The data was analysed using content analysis (7), and 24 aspects of distress were identified. These aspects were related to 3 dimensions; physical (10 aspects), social (5), and emotional (10) aspects of distress [7] (see Table 5 for a presentation of the dimensions and aspects of distress). By using these aspects of distress as a starting point, an instrument was constructed for the present study, aiming at measuring the prevalence, and worst aspects of distress. The instrument consists of 3 dimensions, a physical (10 aspects), social restrictions (5 aspects), and emotional (9 aspects) dimension (in the present study the aspect worry before illness was omitted from the emotional dimension). For each aspect patients were asked to rate the level of distress, and provide their responses on a Likert scale from 0 (no distress) to 5 (very much distress). Thereafter the patients were asked, by a separate question, to identify which of the aspect that had been the worst, within each dimension. For the dimension social distress, the patients were asked to state which of the 10 aspects of physical distress they perceived had caused social distress. Patients were encouraged to identify only one aspect as worst, within each dimension, but when patients chose to report more than one aspect as being the worst, all reported aspects were included in the presentation of the results. Patients were asked to answer the questions according to the last four weeks.

Background data

Patients were asked to report age, gender, civil status and occupational status. Data on medical variables such as treatment, metastasis, surgery, and biochemical parameters were gathered through the patients' medical records.

Data analysis

All statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 14.0 (SPSS Inc, Chicago, IL). Research question 1 was answered by repeated measures ANOVA whereas research question 2 was answered by one-sample t-test. As suggested by others [14] a difference of more than 10 points between two values for the same variable in EORTC QLQ-C30 was considered as indicating a difference of clinical interest. For the comparison between patient scores on the EORTC QLQ-C30 and corresponding Swedish population norms [15], corrections for age and gender were made. When calculating prevalence of aspects of distress it was assumed that if a patient answered "no distress" in response to a question about an aspect, the same aspect was not prevalent for the same individual. Remaining response alternatives were assumed to indicate that the aspect, at least to some extent, was experienced as distressing and thus prevalent. Research question 5 was answered by Cochran's Q.

Missing values for the EORTC QLQ C-30 were replaced according to the manual for the EORTC QLQ C-30 [10]. After replacing missing values the internal dropout for EORTC QLQ C-30 was <2% at T2. Missing values for the HADS were replaced by the mean values of the patient's answers to the remaining items of the respective subscale, provided that at least four of the items in the subscale had been answered. Missing values for the interview guide  were not replaced. The internal dropout for aspects of physical distress was 0.5-3% at T1-T4, social distress 0-3% at T1-T4, and emotional distress 0-4% at T1-T4.

A significance level of p < .01 was chosen for all statistical analyses.

Results

See Table 1 for a presentation of demographic and medical background data for the patients participating in the study.

HRQoL, anxiety, and depression

Mean values and standard deviations for HRQoL, anxiety, and depression at T1-T4 are presented in Table 3. The mean values for role function and global quality of life were somewhat lower than for the remaining scales at all assessments. At all assessments the highest level of problems were reported for fatigue followed by pain, dyspnoea, and diarrhoea. Low mean values were reported for anxiety and depression at all assessments.

Patients reported lower emotional function at T3 compared to T4 [F(3, 102) = 3.50; p < .01].

The mean value for role function increased more than 10 points, i.e. of clinical interest, from T1 to T2, T3, and to T4.

See Table 4 for a presentation of the observed scores and population norms for HRQoL at T1-T4. The difference between scores was larger than expected by chance for role function at T1-T3 (t = 3.26-4.99, df = 34-35, p < .01, .001), social function at T1 (t = 3.17, df = 35, p < .01), and global quality of life at T1-T4 (t = 3.74-5.75, df = 34-35, p < .01, .001). The observed score was more than 10 points below the population norms for role-and social function, and global quality of life at all assessments and at all assessments, the observed score was more than 10 points above the population norms for fatigue and diarrhoea. All these differences were larger than expected by chance (t = 3.16-5.71, df = 34-35, p < .01, .001). At T2 the observed score was more than 10 points higher than the population norms for appetite loss (t = 2.93, df = 34, p < .01), and at T3 and T4 the observed score was more than 10 points higher than the population norms for dyspnoea.

Aspects of distress

See Table 5 for a presentation of prevalence and worst aspects of distress at T1-T4. Fatigue was the most prevalent, and flush, pain-stomach, dyspnoea, dry skin/mucous membranes, diarrhoea, and appetite loss were among the most prevalent aspects of physical distress at all assessments. At all assessments fatigue and diarrhoea were reported as the worst aspects of physical distress by most patients. A difference over time with regard to the prevalence of flush (Q (3) 13.2, p < .005), with a lower prevalence at T4 than at T1-T3, and dry skin/mucous membranes (Q (3) 12.7, p < .005), with a higher prevalence at T4 than at T1 was demonstrated.

Limited possibilities to work/pursue daily activities and to perform physical activities were the most prevalent aspects of social distress at all assessments. At T1-T3 limited possibilities to work/pursue daily activities was reported as the worst aspect of social distress by most patients. At T4 an equal number of patients reported this aspect and limited possibilities to perform physical activities and to dine out/go to theatre as the worst aspect of social distress. Fatigue followed by diarrhoea and dyspnoea were reported most frequently as the aspects causing social distress at all assessments.

Worry that the illness will get worse was the most prevalent, and depression, irritation, worry that the family cannot cope with the illness, and worry before check-up were among the most prevalent emotional aspects of distress at the respective assessment. At all assessments worry that the illness will get worse was reported as the worst aspect of emotional distress by the majority of the patients. A difference over time with regard to the prevalence of bother by changed appearance (Q (3) 12.0, p < .01) with a higher prevalence at later assessments than at T1 was demonstrated.

When looking at the prevalence of all aspects of distress together, worry that the illness will get worse was the most prevalent aspect followed by depression, worry that the family cannot cope with the illness, irritation, and worry before check-up at T1. At T2 the corresponding aspects were worry that the illness will get worse, fatigue, worry that the family can not cope with the illness, depression, and irritation, at T3 fatigue, worry that the illness will get worse, depression, irritation, limited possibility to perform physical activities, and worry that the family can not cope with the illness, and at T4 worry that the illness will get worse, fatigue, limited possibility to perform physical activities, irritation, worry before check-up, and limited possibility to work/pursue daily activities. Most of these aspects are of an emotional nature.

When taking together all aspects of distress that patients reported as being the worst, worry that the illness will get worse, fatigue, and limited possibility to work/pursue daily activities were the aspects considered the overall worst by most patients at T1-T3, when looking at the three dimensions together. At T4 the corresponding aspects were: fatigue, worry that the illness will get worse, and diarrhoea.  Scores range from 0 to 100, a higher score represents a higher level of symptoms. c Scores range from 0-21, a higher score represents a higher level of problems.

Discussion

At all assessments patients reported high levels of physical-, emotional-, cognitive-, and social function however somewhat lower levels of role function and global quality of life. Most problems were reported with fatigue, dyspnoea, diarrhoea, and pain. The results support previous results and taking the present results into consideration, together with previous results, [5,6,16] it may be concluded that patients with carcinoid tumours enjoy good HRQoL from the early stages of the disease as well as later on. Patients' role-and emotional function increased over time. Apart from these changes patients' HRQoL and psychosocial function did not change during the first year after diagnosis. On one hand it could be expected that once patients receive their diagnosis and treatment aiming at reducing the hormone-related symptoms is initiated, patients' HRQoL and psychosocial function should increase. On the other hand it could be expected that patients' HRQoL and psychosocial function, as a consequence of the illness itself and disruption of the previous normal life, should decrease over time. However, the results do not support any of these expectations. The lack of significant and/or clinically relevant changes could also be explained by methodological circumstances such as a small sample and inappropriate instruments and/or by, if it is assumed that HRQoL and psychosocial function ought to deteriorate over time, an adaptation to a difficult life situation.

When comparing the patients' HRQoL to the Swedish population norms, it is revealed that the patients' HRQoL is significantly lower in a statistical sense than that reported by the Swedish population for fatigue, diarrhoea, and overall quality of life at all assessments and for role function at the first three assessments. The findings support previous results [6]. In this study clinical significance, in most cases, coincides with statistical significance which highlights that identified differences between patients with carcinoid tumours and the Swedish population are of clinical relevance.

Some of the patients in the present study were treated with interferon and it is known that interferon may cause fatigue and mild depression [2]. In this study we found it difficult to analyze potential differences in HRQoL and psychosocial function between patients treated with interferon versus patients treated with chemotherapy, octreotide or no treatment at all, due to small sub-groups.

The sample may be considered small from a statistical point of view. However, when considering the size of the sample it should be remembered that carcinoid tumours are very rare. The patients were consecutively included during approximately two and a half years at the Department of Endocrine Oncology, Uppsala University Hospital, at the time of the study the main centre in Sweden specialized in treating endocrine tumours. In addition, the sample is unique since it is homogenous with regard to time since diagnosis and the timing of the assessments.

The present study is to the best of our knowledge one of only a few studies in which patients with carcinoid tumours have been asked to rate their HRQoL, anxiety, and depression repeatedly after diagnosis. When considering the generalizability of the findings it is important to consider the number of non-participants and drop-outs and whether these persons differ from the participants. It is possible that if health should deteriorate, patients may choose to withdraw. However, analyses investigating whether the participating patients' HRQoL and psychosocial function differed from that of the dropouts at T1 did not show any differences.

This study investigated the prevalence of some inductively derived aspects of physical, social, and emotional distress previously found to be of relevance for patients with carcinoid tumours [7]. By presenting the prevalence of these aspects and which of these that were considered to be the worst, the psychosocial function of patients with carcinoid tumours was further illuminated. The findings demonstrate that fatigue was the most prevalent and diarrhoea among the four most prevalent aspects of physical distress at all assessments as well as considered the worst aspects of physical distress by most patients at all assessments. The findings with regard to physical aspects of distress correspond with the EORTC QLQ C-30 data as these demonstrate at least high levels of problems with fatigue and diarrhoea at all assessments and a higher level of problem with these concerns than that for the Swedish general population at all assessments.

Limitations with regard to pursuing work, and daily-and physical activities were the most prevalent aspects of social distress at all assessments and limitations with regard to work and daily activities was considered the worst of these aspects by most patients at all assessments. Fatigue and diarrhoea were, at all assessments, reported as the most frequent reasons for social restrictions, which underscores the importance of realizing the important consequences of these problems.

Worry that the illness will get worse was the most prevalent as well as considered the worst aspect of emotional distress by most respondents at all assessments. The results show that the majority of the patients, at all assessments, worried, at least to some extent, about how their families would cope with the disease and about whether they would be able to care for their families in spite of the disease. A substantial number of the patients also reported worrying before troublesome tests and examinations and about what the test results would show. None of these aspects of emotional distress are included in the EORTC QLQ-C30, at the time of the study, the HRQoL instrument that previously had been used when investigating HRQoL among patients with endocrine gastrointestinal tumours [5,6,16]. A majority of the patients worry about a number of disease-and treatment related concerns at all assessments as well as report feeling depressed and irritated at all assessments.

Although the reported prevalence of physical, social, and emotional distress does not provide knowledge about the significance of the aspects of distress, the results illustrates that there are several aspects of emotional distress likely to be of concern for the patients. While the EORTC QLQ C-30, and especially the new carcinoid module [17] well covers the physical aspects of the carcinoid disease, and the HADS cover the levels of anxiety and depression, many concerns of a more emotional matter may be unrecognized by doctors who care for these patients. Thus, in this study, sensitivity has been prioritized over specificity, as it is our belief that it is important to be aware of what problems patients with carcinoid tumours may be presented with, and which of these problems patients report as being the worst. Further, these findings with regard to aspects of distress give another impression concerning the psychosocial function of patients' with carcinoid tumours than those from the EORTC QLQ-C30 which indicate that the patients enjoy good psychosocial function at all assessments. However, on the basis of the findings from this study it cannot be concluded which picture that best illustrates the life situation of these patients -they do however illustrate that patients with carcinoid tumours may perceive certain disease-and treatment related aspects not included in the EORTC QLQ-C30 as troublesome. The EORTC HRQoL-group has recently developed a disease-specific module for patients with gastrointestinal neuroendocrine tumours [17]. In this module, some aspects of emotional distress identified in this study, for instance concerning family life and worry about results from tests and examinations are included.

function, RF = Role function, CF = Cognitive function, EF = Emotional function, SF = Social function, QL = Global quality of life, FA = Fatigue, NV = Nausea/vomiting, P = Pain, DY = Dyspnoea, SL = Sleep disturbances, AP = Appetite loss, CO = Constipation, DI = Diarrhoea, FI = Perceived financial impact, A = Anxiety, D = Depression a Scores range from 0 to 100, a higher score represents a higher level of function. b

Physical function, RF = Role function, CF = Cognitive function, EF = Emotional function, SF = Social function, QL = Global quality of life, FA = Fatigue, NV = Nausea/vomiting, P = Pain, DY = Dyspnoea, SL = Sleep disturbances, AP = Appetite loss, CO = Constipation, DI = Diarrhoea, FI = Perceived financial impact, A = Anxiety, D = Depression aScores range from 0 to 100, a higher score represents a higher level of function. b Scores range from 0 to 100, a higher score represents a higher level of symptoms. * p < .01, ** p < .001

Table 2 :
2Reasons for attrition at T2-T4Reason for attrition

Table 1 :
1Background data at T1 for patients (N = 36). All figures represent number of patients unless statedT1
T2
T3
T4

Table 3 :
3Mean values (M) and standard deviations (SD) for the EORTC QLQ-C30 scales and single items and the HADS subscales at(T1-T4) (n = 36)

Scales/single items
T1
T2
T3
T4
M, SD
M, SD
M, SD
M, SD

PF a
80 (22)
80 (20)
81 (22)
79 (22)
RF a
53 (39)
64 (31)
69 (28)
69 (35)
EF a
77 (20)
75 (23)
75 (21)
82 (19)
CF a
85 (21)
85 (17)
83 (18)
84 (18)
SF a
77 (26)
80 (28)
80 (26)
79 (27)
QL a
58 (19)
61 (23)
58 (24)
58 (25)
FA b
35 (26)
38 (27)
35 (24)
34 (24)
NV b
3 (8)
10 (20)
6 (12)
7 (12)
PA b
20 (28)
23 (31)
27 (33)
21 (27)
DY b
27 (31)
22 (24)
30 (36)
29 (34)
SL b
18 (24)
11 (18)
13 (18)
18 (26)
AP b
10 (19)
18 (28)
8 (17)
13 (24)
CO b
9 (22)
13 (22)
10 (21)
9 (23)
DI b
25 (35)
21 (29)
23 (31)
30 (27)
FI b
14 (23)
15 (26)
12 (23)
14 (26)
A c
5.1 (4.1)
5.1 (4.7)
4.4 (3.9

Table 4 :
4Mean (M) for observed scores (O) and population norms (P) and differences (D) between the observed scores and population
norms for the EORTC QLQ C-30 scales and single items at T1-T4

T1
T2
T3
T4
O
P
D
O
P
D
O
P
D
O
P
D
M
M
D
M
M
D
M
M
D
M
M
D

PF a
80
87
-7
80
87
-7
81
87
-6
79
87
-8
RF a
53
85
-32**
64
85
-21**
69
85
-16*
69
85
-16
EF a
77
83
-6
75
83
-8
75
83
-8
82
83
-1
CF a
85
88
-3
85
88
-3
83
88
-5
84
88
-4
SF a
77
91
-14*
80
91
-11
80
91
-11
79
91
-12
QL a
58
76
-18**
61
76
-15**
58
76
-18**
58
76
-18**
FA b
3 5
2 1
1 4 *
3 8
2 1
1 7 * *
3 5
2 1
1 4 * *
3 4
2 1
1 3 *
NV b
3
3
0
1 0
3
7
6
3
3
7
3
4
P b
2 0
2 0
0
2 3
2 0
3
2 7
2 0
7
2 1
2 0
1
DY b
27
17
10
22
17
5
30
17
13
29
17
12
SL b
18
19
-1
11
19
-8
13
19
-6
18
19
-1
AP b
10
4
6
18
4
14*
8
4
4
13
4
9
CO b
9
6
3
1 3
6
7
1 0
6
4
9
6
3
DI b
25
5
20*
21
5
16*
23
5
18*
30
5
25**

Table 5 :
5Prevalence and worst aspects of physical, social, and emotional distress at T1-T4 (n = 36)

T1
T2
T3
T4
Prevalence Worst 1 Prevalence Worst 1 Prevalence Worst 1 Prevalence Worst 1

Physical distress
Fatigue
25 (69%)
11
31 (86%)
15
32 (89%)
12
30 (83%)
17
Diarrhoea
18 (50%)
10
20 (56%)
10
22 (61%)
11
25 (69%)
13
Dry skin/mucous membranes
14 (39%)
2
22 (61%)
5
22 (61%)
3
25 (69%)
6
Pain-stomach
18 (50%)
4
20 (56%)
3
20 (56%)
8
23 (64%)
4
Dyspnoea
18 (50%)
3
21 (58%)
5
20 (56%)
8
21 (58%)
10
Flush
19 (53%)
8
21 (58%)
1
21 (58%)
5
11 (30%)
1
Appetite loss
14 (39%)
1
17 (47%)
5
21 (58%)
4
15 (42%)
4
Pain-muscles
15 (42%)
1
17 (47%)
6
15 (42%)
5
18 (50%)
6
Nausea
14 (39%)
1
15 (42%)
3
19 (53%)
5
17 (47%)
6
Insomnia
13 (36%)
4
15 (42%)
3
17 (47%)
1
18 (50%)
5
Social distress
13
Work/pursue daily activities
23 (64%)
15
24 (67%)
15
24 (67%)
13
26 (72%)
6
Perform physical activities
23 (64%)
7
23 (64%)
3
25 (69%)
10
27 (75%)
6
To travel
20 (56%)
2
17 (47%)
5
23 (64%)
8
21 (58%)
2
Dine out/go to theatre etc
17 (47%)
-
18 (50%)
3
19 (53%)
6
19 (53%)
6
Associate with friends
17 (47%)
3
17 (47%)
3
20 (56%)
6
18 (50%)
Emotional distress
Worry that the illness will get worse
33 (92%)
20
32 (89%)
10
30 (83%)
16
31 (86%)
16
Depression
32 (89%)
1
27 (75%)
5
29 (80%)
9
26 (72%)
10
Irritation
27 (75%)
5
26 (72%)
5
29 (80%)
6
27 (75%)
6
Worry that the family can not cope with the illness
29 (80%)
6
29 (80%)
4
25 (69%)
5
23 (64%)
7
Worry before check-up
27 (75%)
4
25 (69%)
3
24 (67%)
7
26 (72%)
7
Worry that the illness will interfere with ability to
care for the family

25 (69%)
5
21 (58%)
3
24 (67%)
5
22 (61%)
1

Troublesome tests/examinations
25 (69%)
4
17 (47%)
4
20 (56%)
1
24 (67%)
2
Bother by changed appearance
7 (19%)
-
14 (39%)
1
18 (50%)
2
17 (47%)
3
Bother by changed sexual activity
13 (36%)
-
14 (39%)
-
16 (44%)
1
19 (53%)
2

(page number not for citation purposes)
14 patients had surgery prior to the first admission to the Department of Endocrine Oncology.2 3 patients had surgery between the first and second admission to the Department of Endocrine Oncology.
Some patients reported more than one aspect of distress as being worst.
Publish with Bio Med Central and every scientist can read your work free of charge"BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." Sir Paul Nurse, Cancer Research UK Your research papers will be: available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours -you keep the copyright Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.aspBioMedcentral
AcknowledgementsThis study was made possible by grants from the Swedish Cancer Society and the Vardal foundation. There exists no conflict of interest that would prejudice the impartiality.Conclusion and implicationsIt is concluded that HRQoL and psychosocial function among patients with carcinoid tumours remains stable during the first year after diagnosis, that the patients report a lower HRQoL than the Swedish general population, and that a majority of the patients report a number of aspects of emotional distress. In the clinical care of these patients, in addition to paying attention to the physical problems such as diarrhoea and fatigue it should also be considered that the majority of the patients also worry about their prognosis, their families, tests, and examinations. Efforts to reduce these worries should be made.Competing interestsThe author(s) declare that they have no competing interests.Authors' contributionsLvE designed the study and assisted in the data analysis and the critical revision of the manuscript. CF carried out the data collection, analysed the data and wrote the manuscript. CL assisted in the critical revision of the manuscript. GL assisted in the data analysis, and critical revision of the manuscript.
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Consensus report on the use of somatostatin analogs for the management of neuroendocrine tumors of the gastroenteropancreatic system. K Öberg, L Kvols, M Caplin, G Delle Fave, W De Herder, G Rindi, P Ruszniewski, E A Woltering, B Wiedenmann, Ann Oncol. 156Öberg K, Kvols L, Caplin M, Delle Fave G, de Herder W, Rindi G, Ruszniewski P, Woltering EA, Wiedenmann B: Consensus report on the use of somatostatin analogs for the management of neuroendocrine tumors of the gastroenteropancreatic sys- tem. Ann Oncol 2004, 15(6):966-973.

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Healthrelated quality of life, anxiety and depression in patients with midgut carcinoid tumours. G Larsson, P O Sjödén, K Öberg, B Eriksson, L Von Essen, Acta Oncol. 407Larsson G, Sjödén PO, Öberg K, Eriksson B, von Essen L: Health- related quality of life, anxiety and depression in patients with midgut carcinoid tumours. Acta Oncol 2001, 40(7):825-831.

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The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. N K Aaronson, S Ahmedzai, B Bergman, M Bullinger, A Cull, N J Duez, A Filiberti, H Flechtner, S B Fleishman, J C De Haes, J Natl Cancer Inst. 855Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, Filiberti A, Flechtner H, Fleishman SB, de Haes JC, et al.: The Euro- pean Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in interna- tional clinical trials in oncology. J Natl Cancer Inst 1993, 85(5):365-376.

The hospital anxiety and depression scale. A S Zigmond, R P Snaith, Acta Psychiatr Scand. 676Zigmond AS, Snaith RP: The hospital anxiety and depression scale. Acta Psychiatr Scand 1983, 67(6):361-370.

P Fayers, N Aaronson, K Bjordal, M Groenvold, D Curran, A Bottomley, EORTC QLQ-C30 Scoring Manual. BrusselsThird edition editionFayers P, Aaronson N, Bjordal K, Groenvold M, Curran D, Bottomley A: EORTC QLQ-C30 Scoring Manual. Third edition edition. Brussels ; 2001.

Screening for anxiety and depression in cancer patients: the effects of disease and treatment. T Ibbotson, P Maguire, P Selby, T Priestman, L Wallace, Eur J Cancer. 1Ibbotson T, Maguire P, Selby P, Priestman T, Wallace L: Screening for anxiety and depression in cancer patients: the effects of disease and treatment. Eur J Cancer 1994, 1:37-40.

Hospital Anxiety and Depression Scale (HAD): some psychometric data for a Swedish sample. J Lisspers, A Nygren, E Soderman, Acta Psychiatr Scand. 964Lisspers J, Nygren A, Soderman E: Hospital Anxiety and Depres- sion Scale (HAD): some psychometric data for a Swedish sample. Acta Psychiatr Scand 1997, 96(4):281-286.

The validity of the Hospital Anxiety and Depression Scale. An updated literature review. I Bjelland, A A Dahl, T T Haug, D Neckelmann, J Psychosom Res. 522Bjelland I, Dahl AA, Haug TT, Neckelmann D: The validity of the Hospital Anxiety and Depression Scale. An updated litera- ture review. J Psychosom Res 2002, 52(2):69-77.

Interpreting the significance of changes in health-related quality-of-life scores. D Osoba, G Rodrigues, J Myles, B Zee, J Pater, J Clin Oncol. 161Osoba D, Rodrigues G, Myles J, Zee B, Pater J: Interpreting the sig- nificance of changes in health-related quality-of-life scores. J Clin Oncol 1998, 16(1):139-144.

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Parents' and peers' normative influence on adolescents' smoking: results from a Swiss- Italian sample of middle schools students

Francesca Scalici
Peter J Schulz
Parents' and peers' normative influence on adolescents' smoking: results from a Swiss- Italian sample of middle schools students
10.1186/s13011-017-0089-2R E S E A R C H Open Access
Background and method: Adolescents observe and imitate people to whom they are associated in their social context, and the normative factors sent out by reference groups are crucial determinants of their decision to smoke. The aim of the study is to investigate how adolescents' smoking changes when they are exposed to factors of pro-smoking normative influence by parents and peers, and how age moderate this relation. A cross sectional survey collected data from 5657 students, aged between 11 and 14, from public and private middle schools in the Italian region of Switzerland (Ticino) on their smoking habits, perceived parents' and peers' approval and smoking. Results: Multinomial logistic regression show that, as adolescents get older, more of the pro-smoking factors come from peers and parents, the higher the risk gets of being a "heavy smoker" has compared against having no experience with smoking. Living in a context with no factor of normative influence toward smoking play a protective role against smoking, and this effect becomes more important than more harmful the smoking behavior in question is. Furthermore, peers' descriptive norms are more influential for adolescents to become "light" and "heavy smokers", while smoking being approved by peers is important for adolescents to become accustomed to smoking. Conclusions: Findings support the different influence of parents' and peers' norms on adolescents' smoking, and highlight the importance of peers' model behavior as the most important factor influencing smoking during adolescence. Such results have implications for programs that aim to prevent or reduce smoking in early adolescence when friendship choice starts to become crucial.

Background

Smoking prevalence among adolescents is a factor of major concern in Switzerland as well as in other countries. To study the dynamics that cause adolescents' tobacco use is an important issue for public health research intending to develop more effective and targeted antismoking interventions that aim at the adolescents' social environments. Adolescents interact with different people and groups, and they are exposed to different behavioral models and opinion coming from different referents in their social environment. Literature is unanimous that adolescents' exposure to norms coming from their own social environment is the major factor in their taking up smoking [1]. Nevertheless, some gaps are still to be filled. In fact, research so far has treated social norms as single causal agents of adolescents' tobacco consumption [2][3][4], but rarely have these norms been researched together, and studied as factors the effects of which might be interacting.

As the social learning theory suggests [5], young people observe and learn by watching the behaviors, and their consequences, of others with whom they live. The adolescents' decision to embark on a particular behavior depends on the exposure to norms, values and behavioral attitudes of other humans with whom they interact [1,[5][6][7][8][9].

Adolescents' social environment can be separated into parents and peers (or friends). Behavioral norms are related to adolescents by conversing with both [6][7][8][9][10][11][12], and by observing their behaviors [2][3][4][12][13][14][15][16][17][18]. Normative ideas acquired from conversing are called injunctive, and norms obtained from observing others are called descriptive. Both are crucial in developing the smoking habit [7,9,10,11,19].

The influence of descriptive norms works through imitation. Adolescents imitate, and are influenced by models who smoke and to whom they are exposed. They smoke more frequently when they are associated with others, family members or peers, who smoke or who have a prosmoking attitude [9,10,11,19,20]. Research shows that both types of norms (injunctive and descriptive) and both referents (parents and peers) influence adolescents' smoking, and play a key role in adolescents' decision to engage in such behavior [21][22][23][24][25][26][27]. There is no agreement which of the referents, parents or peers, exert the stronger influence on adolescents' smoking behaviors, and neither is there complete agreement about which of the two kinds of norms exerts the stronger impact. Literature agrees smoking by peers is the most important factor predicting tobacco use. Adolescents whose peers smoke are more likely to smoke and to embark on such behavior [28][29][30][31][32]. Similar to peers' smoking, parents' smoking is identified as a factor in adolescent smoking [33][34][35], but this association decreases as adolescents get older [2,30].

Research provides evidence that supports the influence of peers' (dis)approval and parents' (dis)approval on adolescents' smoking and intention [20-23, 36, 37], but while peer influence increases with adolescents' age, parent influence decreases [21,36].

Supported by the literature, we can conclude that both types of norms and both referents have an impact on adolescents' smoking, and play a key role in adolescents' decision to engage in such behavior. As evidence suggests, living in an environment where norms from parents and peers are consensual and not in conflict, gives a maximal chance that adolescents will behave according to these norms [4,11]. If the parents' and peers' norms are conflicting, the question arises which side will prevail when it comes to affecting adolescents' smoking behavior. This study is concerned with two factors of normative influence and their relative impact upon adolescents' smoking behavior. One factor is the origin of the normative influence, which can come from either parents or peers. The other is the type of norm, which can be either descriptive or injunctive. The influence of the two reference groups and the two types of norms have rarely been researched together. Our study intends to contribute to closing this gap in research in a Central European sample of adolescents. As a new element, we include age as a possible moderator of parents' and peers' influence on adolescents' smoking behaviors, in expectation that parents' influence decreases and friends' influence increases as adolescents get older [2,24,36,[38][39][40][41].

Hypotheses and research questions

The following hypotheses will be tested:

Adolescents whose parents smoke will themselves smoke more than adolescents whose parents do not smoke (H1).

Adolescents whose peers smoke will themselves smoke more than adolescents whose peers do not smoke (H2). Adolescents whose peers approve of smoking will smoke more than adolescents whose peers disapprove of it (H3). Adolescents whose parents approve of smoking will smoke more than adolescents whose parents disapprove of it (H4). The more factors of pro smoking normative influence an adolescent is exposed to, the more will he or she smoke (H5, cumulative effect). As said, the study is concerned with the relative influence of origin and types of norms. Lacking a basis for formulating hypotheses, we address that subject as a research question:

Which is the stronger influence on adolescents' smoking, parent or peer model behavior? (RQ1).

Which is the stronger influence on adolescents' smoking, peer model behavior or peer approval? (RQ2). Finally, we argue that with the adolescents' age the influence of parent norms grows weaker, relatively speaking, while the influence of peer norms gets stronger. In other words, it is hypothesized that age moderates the effect of model behavior as well as of injunctive norms. Formally, we formulate:

As adolescents get older the influence of peer norms increases relative to the influence of parent norms with regard to smoking (H6).

Method

The study protocol was submitted and approved by the cantonal Department of Education (DECS) because the Università della Svizzera italiana did not have an IRB system in 2011. Data were collected, without asking parental consent, in the Italian region of Switzerland (Ticino) middle schools, after we had received the written cantonal approval.

Data collection and sample description

The Institute of Communication and Health at the university of Lugano (Università della Svizzera italiana), in collaboration with the Swiss Non-smokers' Association in Ticino and the DECS, conducted a cross-sectional survey from October 2011 to January 2012. Four rounds of pre-test were carried out in May and June 2011 with 10 students, aged between 11 and 12, from the first and second grades, and with 8 students, aged between 13 and 14, from the third and fourth grades up [36]. Questions related to the socioeconomic status of the respondents' families were present in the questionnaire at the pre-test phase. Results of pre-test showed that students of the first and the second grade were not able to answer them. The questions were therefore deleted from the final version of the questionnaire.

Students aged between 11 and 14, from all 42 public and private middle schools in Ticino, participated in the survey. Out of a total of 598 classes, 285 were randomly selected, namely 69 for the first grade, 69 for the second grade, 73 for the third grade and 74 for the fourth grade ( Table 1). The school director personally informed students about the survey, and teachers detailed the study protocol. Students, after having given their consent to participate, filled in an anonymous paper and pencil questionnaire during the class hours. To ensure anonymity, the completed questionnaire was put in a covered box handed over to the researchers. The questionnaire took 25 min to fill out. A total of 5890 students were members of the selected classes, and 5657 correctly completed questionnaires (response rate 96%) were included in our dataset, which represents the 46.3% of the total middle school students in Ticino (Total N: 12210), [36].

Measures

The following measures were employed in the analyses reported in this article:

Injunctive norms were measured as perceived parents' and peers' approval, perceived that is by the respondent student [36].

Parents' smoking: students answered to two items, "Does your father smoke?" and "Does your mother smoke?" Answers were combined in a dummy variable 0 = "neither parent smokes" and 1 = "one or both smoke".

Perceived peers' smoking was measured with one item, "How many of your friends smoke? Answers were recorded on a 5-point scale from 1 = "all my friends smoke" to 5 = "nobody smokes". The variable was recoded in a dummy variable 0 = "nobody smokes" and 1 = "my friends smoke (all smoke, the majority smoke, some smoke, few smoke)," in order to be consistent with the other independent variables.

Respondents' tobacco consumption was measured with three items: "have you ever smoked in your life? (Answers were recorded as 0 = "no" and 1 = "yes"); "how frequently do you smoke?" (Answers were recorded on a 4-point scale from 1 = "every day" to 4 = "never); "if you currently smoke, how many cigarettes do you smoke in a week?" (Answers were recorded on a 7-point scale from 1 = "more than one pack" to 7 "none"). The variables were recoded in a single smoking behavior scale with 1 = "never tried, no smoking experience", 2 = "non-smokers with smoking experience (adolescents who have tried smoking but have not continued)", 3 = "light smokers (smoke once a week or less and less than 3 cigarettes)", 4 = "heavy regular smokers (smoke every day or 3 or 4 or more cigarettes in a week)."

Grade level was used as representative of the students' age, ranging from first to fourth grade (11 to 14 years), [36].

Data analysis

Multinomial Logistic Regression is used as regression analysis to conduct when the dependent variable is nominal with more than two levels. The multinomial regression might be interpret in terms of predictive analysis. It is used to describe data and to explain the relationship between one dependent nominal variable and one or more continuous-level (interval or ratio scale) independent variables. Sometimes a probit model is used instead of a logit model for multinomial regression. Both models are commonly used as the link function in ordinal regression. However, most multinomial regression models are based on the logit function (like the one implemented in SPSS). The difference between both functions is typically only seen in small samples because probit assumes normal distribution of the probability of the event, when logit assumes the log distribution. At the center of the multinomial regression analysis is the task estimating the k-1 log odds of each category.

As we had a nominal dependent variable with four levels and four independent variables with two levels each, multinomial logistic regression was applied to evaluate the effect of parents or peers approval and parents or peers smoking attitude on adolescents smoking. Hence, after choosing/setting the group of never-smoking adolescents as reference, three models were been estimated comparing respectively "non-smokers with smoking experience" relative to "never-smokers", "light smokers" relative to "never-smokers" and "heavy smokers" relative to neversmokers. The basic idea is to use, within a general linear model setting, logits as link function. When we use logits we restrict the probability values to (0, 1). Technically this is the log odds (the logarithmic of the odds of y = 1).

Results

Overview

Based on N = 5649 cases, the independent variables that measure various aspects of normative influence distribute quite differently. More than two in five respondents (42%) declared that at least one of his or her parents smoked. A considerably larger share of adolescents (62%) reported that few or more (some, the majority, all) of their friends smoked. In contrast to this perception, hardly any respondent said that either his or her parents or friends approved of adolescent smoking. In fact, 99% perceived their parents and 97% their friends as disapproving smoking, including taking a neutral position. Only 32 respondents saw their parents and 150 their friends as approving teenage smoking. This lack of variance in the perceptions of approval precludes some analyses we would have liked to run, systematically comparing the influence of injunctive and descriptive norms. Three quarters of the respondents said they did not smoke and never had, not even tried. Every seventh respondent (14%) had had some experience with smoking but did not classify as smoker presently. Another 4% were classified as light, 5% as heavy smokers.

Effect of single variables

Non-smokers with smoking experience relative to adolescents who never smoke This chapter compares the probability of having some experience with smoking without developing a habit of it with the probability of having made no experience at all with smoking. Each of the three independent variablespeer disapproval of smoking, non-smoking parents, non-smoking peers-reduces this probability and therefore represents a protective factor against smoking. When a youth has friends who approve of teenage smoking, his or her probability to have tried it (without developing habit of fit) is 1.593 times higher than when friends disapprove (p < 0.05). When parents smoke the selfsame probability is 1.794 times (p < 0.001) higher than when parents do not smoke, and when peers smoke it is 8.051 times higher than when peers do not smoke.

Light smokers relative to adolescents who never smoke

This chapter compares the probability of becoming a light smoker with the probability again of having made no experience at all with smoking. Both descriptive norms-non-smoking parents and non-smoking peersreduce this probability and therefore represent a protective factor against becoming a light smokers. When a youth's friends smoke, his or her probability to turn into a light smoker is 23.085 times higher than when friends abstain (p < 0.001), and when parents smoke the selfsame probability is 1.743 times (p < 0.001) higher than when parents do not smoke. Peer approval of teenage smoking, however, does not play a significant role in predicting who will become a light smoker.

Heavy smokers relative to adolescents who never smoke

This chapter compares the probability of becoming a heavy smoker with the probability of having made no experience at all with smoking. Each of the three independent variables-peer disapproval of smoking, non-smoking parents, non-smoking peers-reduces this probability and therefore represents a protective factor against habitual heavy smoking. When a youth has friends who approve of teenage smoking, his or her probability to become a heavy smoker is 4.625 times higher than when friends disapprove (p < 0.001). When parents smoke the selfsame probability is 3.154 times (p < 0.001) higher than when parents do not smoke, and when peers smoke it is 49.552 times higher than when peers do not smoke (p < 0.001).

In general, we can conclude that the protective role of peers' disapproval and the effect of a smoke-free environment become more and more evident as the "severity" of the adolescent's smoking behavior increases [from having just tried to developing the habit of light and heavy smoking]. The results support H1, H2, H3, while H4 cannot be tested do to the low number of cases in the sample. The fact that for all three smoking behaviors discussed, the effect is strongest for peers' descriptive norms, second strongest for parents' descriptive norms and weakest for peers' injunctive norms gives a preliminary answer to RQ1 and RQ2. Table 2 presents the results of the multinomial logistic regression for the cumulative effect of norms. The four normative factors that are considered as independent variables in this study are conceptualized as presence or absence of pro-smoking norms. It was hypothesized that adolescents will smoke more the more pro-smoking factors are present in the environment they live in. To test this assumption the number of pro-smoking factors in a person's environment was added up, ranging theoretically from 0 to 4, but not a single respondent indicated he or she was living in an environment with all four prosmoking factors present.

Cumulative effects of norms and age interaction

The lowest risk to show any type of smoking behaviors against having no experience was reached by the group of adolescents who reported that neither their parents nor their peers smoked and neither approved of it, or in other words, by the group with no factor of normative influence directing them towards smoking (non-smoker with smoking experience: OR = 14.532, p < 0.001; light smoker: OR = 40.154, p < 0.001; heavy smoker: OR = 118.868, p < 0.001), ( Table 2).

The two groups in between reach the higher risk to be in one category of smokers the more pro factors are present.

With one normative factor present, the probability of either of the three smoking behaviors against having no experience with smoking become higher as smoking behavior becomes more severe, but only for adolescents having smoking peers (non-smoker with smoking experience: OR = 4.790, p < 0.001; light smoker: OR = 13.257, p < 0.001; heavy smoker: OR = 8.798, p < 0.001). For adolescents having smoking parents the change in the odds is only significant in the case of "non-smoker with smoking experience" compared to "never smokers" (OR = 2.273, p < 0.01), while it is not significant for "light smokers" and "heavy smokers", ( Table 2).

With two factors present, the probability of either of the three smoking behaviors against having no experience with smoking become higher as smoking behavior becomes more severe. This is true for adolescents having approving and smoking peers (OR = 7.352, p < 0.001; OR = 18.200, p < .001; OR = 68.111, p < 0.001) and for those having smoking parents and peers (OR = 8.889, p < .001; OR = 24.286, p < .001; OR = 33.307 p < 0.01) relative to those that are in the group with no factor of normative influence, ( Table 2).

In line with H5, we can conclude that the protective role of living in a context with no factor of normative influence toward smoking becomes more important than more harmful the smoking behavior in question is.

Moreover, adolescents tend to smoke more intensely as they get older. Increasing age significantly predicts the three smoking behaviors against having never smoked, and the effect increases, as the smoking behavior in question becomes more harmful. Nevertheless, the interaction model suggests that increasing age does not change the cumulative effect of normative factors for "non-smokers with smoking experience" and for "light smokers" relative to "never-smokers, while is a contributing factor for becoming a "heavy smoker" ( Table 2). As adolescents get older, the risk of being a "heavy smoker", given one unit of age increase, is by far most pronounced in a normative environment where both parents and peers smoke and peers approve (OR = 4.083, p < 0.01), where both referents smoke (OR = 2.236, p < 0.001), and where peers approve and smoke (OR = 3.490, p < 0.01), ( Table 2). The results in part confirms H6. As adolescents get older, more of the pro-smoking factors come from peers and parents have, and the higher the risk gets of being a "heavy smoker" has compared against having no experience with smoking. Presents the results of the multinomial logistic regression for the cumulative effect of norms * = indicate the product term for the interaction

Comparison of effects in conflicting situations

This analysis first compares adolescent smoking behavior in two situations in which parents and peers showed opposing model behavior with regard to smoking. Realistically assuming that the intensity of adolescent smoking behavior in situations with conflicting normative factors lies between the consonant situations of neither group smoking and both groups doing it, the question is simply, which of the two conflicting situations produces more smoking in adolescents. If adolescents who live in an environment that shows smoking peers and nonsmoking parents smoke more than their counterparts who know smoking parents and nonsmoking peers, peer model behavior is considered the more influential factor. A first multinomial logistic regression with a model interaction was run to compare conflictual situations of the two referent groups within one type of norm ( Table 3). The highest risk to be a "non-smoker with smoking experience", a "light smoker" or a "heavy smoker" was reached by the group of adolescents who reported their parents and peers smoke (Table 3). In case of conflictual situations, where parents smoke and peers do not smoke, the change in the odds is significant only for "non-smokers with smoking experience", while it is not significant for the other two smoking behaviors (Table 3). This means that the influence of parents' descriptive norms disappears as the smoking behavior in question becomes more harmful. In the opposite situation, when the conflict consists in having smoking peers and nonsmoking parents, the probability of any type of smoking behaviors against having no experience become higher as smoking behavior becomes more severe (non-smoker with smoking experience: OR = 8.835, p < 0.001; light smoker: OR = 20.025, p < 0.001; heavy smoker: OR = 22.893, p < 0.001), (Table 3).

A similar analysis compares the injunctive norms of both referent groups that is parent vs. peer approval of adolescent smoking. The probability of being a "nonsmoker with smoking experience" rather than a "never smoker" is 1.591(p < .05) times higher for adolescents having peers that approve and parents that do not approve relative to those where both disapprove smoking. This effect is not significant for "light smokers" relative to "never smokers", while it became highly significant for "heavy smokers" relative to "never smokers" (OR = 4.638, p < 0.001).

These calculations support the assumption that peer model behavior of smoking is clearly more influential than smoking parents.

A similar logic can be applied to compare the influence of two types of norms within one type of referent. It can, however, be done only for peers as all 32 cases in which adolescents perceived their parents as approving of their smoking had missing cases for parents' smoking behavior. This precludes a comparative analysis of parent injunctive vs. descriptive norms.

Again, the higher probability to be "non-smokers with smoking experience", "light-smokers" or "heavy-smokers" rather than "never smokers" is reached by the group where peers approve smoking and smoke, and this probability becomes higher as the smoking behavior in question becomes more serious (Table 3). In case of conflict between peers' norms (i.e. peers do not approve and smoke) the probability of either of the three smoking  (Table 3). Similar analysis for injunctive norms would be desirable but could be not computed due to the mentioned missing data.

In summary, the comparisons of the intensity of adolescent smoking in situations of conflicting normative influence show that peer model behavior is more influential than parent model behavior and also more influential than peer injunctive norms, with somewhat weaker evidence in the latter case.

The matter of interest, however, is the comparison between the environments where only peers' respectively only parents' smoke. It is obvious that the model behavior of peers is more influential in driving adolescents towards tobacco use than bad examples their parents give.

Furthermore, results shows that peers' behaviors (i.e. descriptive norms) are more influential for adolescents to become "light" and "heavy smokers", while smoking being approved by peers is important also for adolescents to become accustomed to smoking.

Discussion and conclusions

Summarizing our findings, this research shows that each of four normative factors increased the inclination in adolescents aged 11 to 14 to smoke. The four factors are parents' and peers' model behavior, also referred to as descriptive norms, and their approval respectively disapproval of adolescent smoking, also referred to as injunctive norms. Smoking parents and peers as well as the expression of approval by these referents drives adolescents towards (increased or newly begun) tobacco consumption. The influence of these factors is cumulative; that is each of them has something to contribute to adolescent smoking. The issue of which is the most influential (and that means detrimental) factor can be answered only on incomplete evidence. It can be fairly certainly said that peer model behavior contributes to adolescents smoking more than parent model behavior and more than peer injunctive norms. That is to say, what one's pals do and show when it comes to smoking impresses more than what they think and say, and it is also more consequential than what parents do. Moreover, being already superior to parent influence, peer influence further gains ground for the regular smokers as children get older.

Our paper has the advantage of including simultaneously multiple causal agents that contribute to create a pro or anti-smoking environment. In contrast to the existing literature, we did not look at the effect on adolescents' smoking of a single social norm, but of relevant cases of different, partly conflicting norms. This allowed us to identify the stronger force when adolescents are exposed to conflictual or not consensual norms from their reference group. In that perspective, the analysis shows a pervasive influence of peer model behavior on young adolescents' smoking habits. This influence seems to increase, as the seriousness of smoking behavior increases, with age. The other two normative factors, peers' attitudes to smoking and parent model behavior do not affect adolescents' smoking in a comparable degree.

The analysis helps to enrich the debate regarding social norms and smoking, and to strengthen some points already established in the literature. In an environment where parents and peers are the main points of reference of adolescents, their norms and behaviors are both contributing factors of adolescents' smoking, with peers' normative model being a major determinant of adolescents' decision to smoke [9,18,[21][22][23][24][25][26][27][28][29].

In line with research that states smoking is more likely to occur when norms from referents are uniformly prosmoking [10], in our sample, adolescents who live in a consensual pro-smoking environment where peers' and parents' opinion and behavior are all pro-smoking are more likely to smoke than those who live in a nonsmoking normative environment. The simultaneous study of conflicting social norms suggests that, when the different factors of normative influence work and interact, the strength of the influence of parents' and peers' behaviors and opinions is different. In the debate on the role of parents and peers' social norms on adolescents' smoking, our results clearly support and reinforce the part of the literature that affirms adolescents behave based on what they see in their social environment, and they imitate, primarily, their peers [24,28,29,40].

Nevertheless, our findings should be read in light of some limitations. First, the most important weakness of the paper is that the measures of parents' and peers' smoking and approval are as perceived by adolescents.

Smoking among others is often overestimated by adolescents and depends on adolescents' own smoking [40][41][42]. Moreover, the communication of disapproval of a behavior, from the parent to their child, is part of a communication process that, during adolescence, can be complex and influenced by many factors. Moreover, it is just the complexity of this communication process that often gives rise to a gap between parents' message and adolescents' perception of it [43,44]. Second, due to the cross-sectional methodology, we cannot draw conclusions about the causality of relationships. Third, the lack of information about the respondents' socioeconomic status did not allow us to include in the tested models these variables. Socio-economic factors (SES), namely education level, income and/or occupational status [45], are strong predictors of smoking among adolescents. Past literature agrees that there is an inverse relationship between SES and adolescents' smoking; the higher the household income is, and the higher the parental education level is, the lower the possibility that children smoke [46][47][48][49]. The analysis of the effect on adolescents' smoking of the interaction among SES and social norms should be investigated in future studies. Finally, the lack of data concerning the measure of parents' approval does not allow us to draw firm conclusions concerning the role of parents' opinion.

Despite the limitations, the large sample size, the simultaneous inclusion of peers' and parents' descriptive and injunctive norms, and the examination of a Central European sample of adolescents, represent the major strengths of this paper, and our findings can have some implications for public health policies. Together with other measures and policies implemented by public and private actors active in the tobacco consumption prevention, our conclusions support the implementation of smoking prevention programs, starting from early adolescence, that intervene in the social environment adolescents live in. Adolescents smoke because they imitate other people they meet in private or public places they usually frequent. In that sense, we suggest tobacco prevention programs targeting adolescents and parents, addressed in schools and in public places that first help adolescents to avoid smoking initiation by making them attentive to the role that friends play in their choice to smoke. Second, programs that aim to improve the role of parents during adolescence making them aware that their intervention, communication and monitoring can be crucial in reducing the risk for their children to become smokers in early adolescence [50], and by providing them information about the importance of monitoring who their children are friends with. The cumulative nature of the effects of the normative factors suggests that no point of attack is completely futile when it comes to fighting juvenile tobacco consumption.

Table 1 Sample
1N (5657)
%

Gender

Male
2820
49.9

Female
2768
49.0

Nationality

Swiss
4177
73.9

Italian
506
9.0

Others
708
12.5

Grade

First (11 years)
1381
24.4

Second (12 years)
1370
24.3

Third (13 years)
1392
24.6

Fourth (14 years)
1501
26.6

Table 2
2Significant cumulative effects of parents' and peers' norms and age interactionNon-smokers with
smoking experience

Light smokers
Heavy smokers

B
Std.
Error

p
Odds B
Std.
Error

p
Odds B
Std.
Error

p
Odds

Table 3
3Comparison of effects in situations of conflicting normsNon-smokers with
smoking experience

Light smokers
Heavy smokers

B
Std. Error p
Odds B
Std. Error p
Odds B
Std. Error p
Odds

Within norms conflict between referents

Peers smoke, parents do
not smoke

vs Parents and peers do
not smoke

2.179 .187
.000 8.835 2.997 .513
.000 20.025 3.131 .590
.000 22.893

Peers do not smoke,
parents smoke

Parents and peers do
not smoke

.743 .240
.002 2.103 .264 .765
.731 1.302 -.555 1.156
.631 .574

Peers and parents smoke
Parents and peers do
not smoke

2.745 .187
.000 15.570 3.562 .512
.000 35.228 4.307 .585
.000 74.208

Peers approve, parents do
not approve

Parents and peers do
not approve

.465 .236
.049 1.592 .390 .409
.340 1.478 1.534 .255
.000 4.638

Between norms conflict within one referent

Peers approve and smoke
Peers do not approve
and do not smoke

2.504 .269
.000 12.236 3.509 .553
.000 33.404 5.387 .557
.000 218.530

Peers do not approve and
smoke

2.105 .129
.000 8.210 3.128 .387
.000 22.837 3.870 .506
.000 47.945

Peers approve and do not
smoke

1.184 .782
.059 4.392 -
-
-
-
-
-
-
-

Show results of a first multinomial logistic regression with a model interaction that compare the conflictual situations of the two referent groups within one type
of norm, and of a second multinomial logistic regression that compare the influence of two types of norms within one type of referent

AcknowledgementsWe thankfully acknowledge the Swiss Non-Smokers Association in Ticino and the cantonal Department of Education, Culture and Sport who support this study, the schools directors and teachers for their support during the data collection, as well as the students who were involved in the survey. We also thankfully acknowledge the Professor Clelia Di Serio, Università Vita-Salute San Raffaele, Milano, for the precious support in the phase of data analysis.FundingThe project has been funded by the Università della Svizzera Italiana and Swiss Non-Smokers Association in the Italian region of Switzerland.Availability of data and materialsThe datasets during and/or analyzed during the current study available from the corresponding author on reasonable request.Authors' contributionsThe authors of the paper both meet the following conditions: 1) substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; 2) drafting the article or revising it critically for important intellectual content; and 3) read and approved the final manuscript to be published.Competing interestsThe authors declare that they have no competing interests.Consent for publication Not applicableEthics approval and consent to participate The study protocol and the questionnaires were submitted and approved by the cantonal Department of Education (DECS) because the Università della Svizzera italiana did not have an IRB system in 2011 (this was done in Summer 2013). The Department provided to the University a formal declaration of agreement, which allowed us to collect data in schools. For a privacy policy, the schools and the cantonal Department of Education cannot provide the contacts of families but no parental consent was required for this study due to the cantonal approval. Submit your next manuscript to BioMed Central and we will help you at every step:
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