Health Education Research Advance Access originally published online on March 16, 2005
Health Education Research 2005 20(5):586-599; doi:10.1093/her/cyh020
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Determinants of smoking cessation among adolescents in South Africa
Departments of 1 Health Education and Health Promotion and 2 Experimental Psychology, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands, 3 National Health Promotion Research and Development Group, Medical Research Council, PO Box 19070, Tygerberg, 7505, South Africa and 4 Department of Public Health and Clinical Medicine, Epidemiology and Public Health Sciences and Department of Clinical Sciences, Pediatrics, Umeå University, Born-och ungdomskliniken, Norrland 5 Universitetssjukhus, S-901 85, Umeå, Sweden
5 Correspondence to: S. Panday, Child, Youth and Family Development, Human Science and Research Council, Private Bag X07, Dalbridge, 4014, Durban, South Africa E-mail: spanday{at}hsrc.ac.za
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Abstract |
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Data is required on the motivational determinants of smoking cessation among a multi-ethnic sample of adolescents in South Africa. The I-Change Model was used to explore the determinants of smoking cessation among a sample of 1267 Black African, Colored and White Grade 9–11 monthly smokers and former smokers in the Southern Cape-Karoo region. Across the ethnic groups, former smokers displayed a more positive attitude toward non-smoking, were surrounded by a social environment that was more supportive of non-smoking, displayed higher self-efficacy not to smoke in stressful, routine and social situations, and were more positive about their intention not to smoke in the next year. The I-Change Model can be used to address the cognitions of smoking in a multi-ethnic society like South Africa. However, some ethnic tailoring will be required. Black African students will benefit from a focus on attitudinal cognitions and cultural factors that motivate smoking. Colored students require the involvement of their social environment, while White students will benefit from the development of refusal skills in social situations.
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Introduction |
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Smoking is one of the chief preventable causes of premature morbidity and mortality (US Department of Health and Human Services, 1994
). Tobacco is responsible for 540 000 deaths each year in the EU and 461 000 deaths in the US (Peto et al., 1994). Cardiovascular disease, for which a causal relationship with tobacco use is known (US Department of Health and Human Services, 2004), is the second largest cause of death in South Africa (SA) (16.6% in 2000) (Bradshaw et al., 2003).
The US Surgeon General (US Surgeon General, 1994), building on the work of Flay (Flay, 1993) and others, summarized the process of smoking onset. Adolescents progress from the preparatory phase, where beliefs about smoking are formulated, to trying a few cigarettes, followed by experimenting with smoking on an irregular basis and then onto regular smoking on at least a weekly basis. While some adolescents exit this continuum after having smoked regularly, many adolescents stop smoking after experimenting with only a few cigarettes. Kremers et al. (Kremers et al., 2001a) recently labeled the latter group as non-smoking deciders. An understanding of the motivational determinants of this group will provide exit points earlier in the smoking continuum before adolescents progress onto dependence where quitting is known to be more difficult (Colby et al., 2000). Furthermore, the emergence of signs of dependence earlier in the trajectory of smoking even among monthly smokers (DiFranza et al., 2000; Panday et al., 2005) may provide insight into the limited effectiveness of existing cessation programmes that focus primarily on daily or weekly smokers (Backinger et al., 2003a).
Effective techniques have been identified for adult cessation (Lancaster et al., 2000). Yet a limited number of studies have addressed cessation techniques for youth (Backinger et al., 2003b). Psychological models such as the integrated model of change (I-Change Model) (De Vries et al., 2003b) may be an effective way to identify the motivational determinants of smoking. The model incorporates insights from the Theory of Reasoned Action (Fishbein and Ajzen, 1975), Social Cognitive Theory (Bandura, 1986) and the Transtheoretical Model (Prochaska and DiClemente, 1983). The model as well as its previous versions (Attitude–Social influence–Self-efficacy Model) have been used to assess the determinants of smoking, and to develop and evaluate smoking prevention programmes in several European countries (De Vries et al., 1988, 1994, 1995; Dijkstra et al., 1999). The model assumes that the most important determinant of behavior is behavioral intention, which, in turn, is influenced by one's overall evaluation of the behavior (attitude), one's beliefs concerning the beliefs and behaviors of significant others (social influences), and the control that people perceive themselves to have over performing a behavior (self-efficacy). Distal variables such as demographic (e.g. age, gender) and psychological factors (e.g. depression) are assumed to influence behavior via the motivational factors.
Several studies (US Department of Health and Human Services, 1994; Epstein et al., 1998; Riedel et al., 2003; Markham et al., 2004), including studies in SA (Swart et al., 2004), have reported lower smoking rates among Black adolescents than among White adolescents. However, little is known about the mechanisms underpinning these differences or whether programmes developed primarily for White adolescents are applicable to other ethnic groups (US Department of Health and Human Services, 1998). Black African and Colored smokers in SA considered their smoking peers as barriers to stop smoking, while White students indicated that they would support their peers to stop (Panday et al., 2003). [During the Apartheid years, all South Africans were classified into ethnic groups in accordance with the Population Registration Act of 1950, i.e. Black African (people of African descent), Colored (people of mixed descent), Indian (people of Indian descent) and White (people of European descent). The authors in no way subscribe to this classification.] Studies in the UK also showed that the social environment through modeling and social norms can, in fact, account for ethnic differences in smoking (Markham et al., 2004). Research among adolescents in SA has primarily focused on documenting the prevalence of tobacco use (Swart et al., 2003, 2004) and to our knowledge data is unavailable on the determinants of smoking. Our study investigated ethnic differences in the determinants of smoking cessation with the view to develop evidence-based and culturally sensitive tobacco control programmes for adolescents (US Department of Health and Human Services, 1998; Siqueira et al., 2001; Swart et al., 2004).
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Methods |
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Participants and sampling
A cross-sectional study was conducted in 2002 in the Southern Cape-Karoo Region, Western Cape Province. Grade 9–11 students (general age range of 14–16 years) completed a self-administered questionnaire. Forty-two public schools were eligible to participate in the study. School selection was stratified by ethnicity in accordance with the school's previous race classification under the apartheid government, i.e. Black African (six schools), Colored (17 schools) or White (19 schools). Previous research (Swart et al., 2003
) was used to estimate the sample size of 100 smokers required for each ethnic and gender group. A total of 23 schools were selected to participate in the study, consisting of all six Black African schools, eight randomly selected Colored schools and nine randomly selected White schools. The number of classes selected was proportional to the number of grade 9–11 classes in the school, based on an estimated class size of 40 students. Class selection was stratified by grade and an equal number of classes were randomly selected from each grade. Due to the presence of older students in lower grades, additional classes were selected from the lowest grade. All students in the selected classes were eligible to participate in the study. A total of 121 classes representing 4768 students were selected to participate in the study.
The Research Ethics Committees of the South African Medical Association and the Medical Faculty of Umeå University granted ethical approval for the study. Permission was also obtained from the Education Department, school principals, and from parents and students in the selected classes. Parents and students were informed in writing of the purpose of the study, and that all answers would be treated confidentially and only viewed by the researchers.
Questionnaire
The questionnaire was administered in three languages during two regular classroom periods either to individual classes or to groups of classes. The questionnaire was prepared in English, and translated from English to Afrikaans and Xhosa. To ensure the accuracy of the translations, the Afrikaans and Xhosa versions of the questionnaire were back translated to English.
To guarantee students' confidentiality, trained research assistants administered the survey in the classroom simulating ‘examination-like’ conditions; teachers were asked to leave the classroom during survey administration. An introductory letter was read out to the students informing them of the purpose of the study, that participation was voluntary and reassuring them that their confidentiality would be maintained. Additionally, to ensure their anonymity, students were requested not to write their names on the questionnaires.
The European Smoking Prevention Framework Approach (ESFA) questionnaire (De Vries et al., 2003a,b) was used as the core of the questionnaire. The questionnaire assessed smoking behavior, attitude, social influences, self-efficacy expectations, intention not to smoke in the next year and several demographic items. Item selection and item formulation used in the ESFA questionnaire were validated and localized to the SA context through data obtained from prior qualitative research (Panday et al., 2003), through focus group discussions conducted during questionnaire development, and from a pilot study conducted among 292 Grade 9 English, Afrikaans and Xhosa speaking students in the study area.
Measures
Smoking behavior
Adolescents were asked to pick a statement that best described them out of a set of specific smoking-related questions. Adolescents were then categorized as never smokers (never smoked not even one puff), triers (tried smoking once in a while, but not monthly), non-smoking deciders (tried smoking less than once a week, but not smoking anymore), experimenters or monthly smokers (smoking at least once a month, but not weekly), regular smokers (currently smoking cigarettes weekly or more) and quitters (quit smoking after having smoked at least once a week) (Kremers et al., 2004). Self-reports could not be biologically validated due to logistical and financial constraints. However, when anonymity is assured, self-reports have been shown to be reliable and in agreement with biochemical markers (Dolcini et al., 1996). Self-reported smoking was cross-validated using an algorithm consisting of four additional concepts measuring current smoking and lifetime smoking. When incongruent answers were found, participants were given the most unfavorable response (De Vries et al., 2003a).
Attitudes
Attitudes were assessed by two five-point scales measuring the pros and cons of non-smoking that were identified through factor analyses, and in accordance with previous research (see Table I) (Kremers et al., 2001a,b; De Vries et al., 2003a). The pros of non-smoking were measured with a seven-item scale [e.g., ‘If I do not smoke it will be very good for my health (+2) or very bad for my health (–2)’; Cronbach's 
= 0.86]. The cons of non-smoking were measured with a five-item scale [e.g., ‘If I do not smoke it will make me feel very relaxed (–2) or very stressed (+2)’; = 0.68]. One item referring to drug use was excluded due to ambiguity in the Xhosa version of the questionnaire.
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Social influences
Influences from the social environment were measured by assessing social norms, social modeling and social pressure from important others: mother, father, grandmother, grandfather, brother(s), sister(s), friends, best friend, classmates and teachers. Social norms were measured on a five-point scale assessing students' perceptions of whether important others (combined into family,
= 0.89, friends, = 0.79, and teacher, see Table I) thought that they definitely should (–2) or should not (+2) smoke. Social modeling assessed the perceived smoking behavior of mother, father, grandmother, grandfather, brother(s), sister(s) and best friends using a two-point scale (0 = smoking, 1 = non-smoking). The seven items were analyzed separately as they did not load uniquely on the one social modeling factor. Social pressure was measured on a five-point scale to assess how often social pressure not to smoke was encountered (combined into family, = 0.90, friends, = 0.87 and teacher, see Table I) (0 = never, 4 = very often).
Self-efficacy
Multiple item scales measured how sure (+2 = sure that I will not smoke, –2 = sure that I will smoke) students' felt that they could refrain from smoking in stressful situations (stress self-efficacy, 10 items = 0.98), routine situations (routine self-efficacy; five items = 0.94) and social situations (social self-efficacy; four items = 0.93) (see Table I). The items were identified through previous research (De Vries et al., 1988; Lawrance, 1998).
Demographic variables
Characteristics of the participants were provided by asking for ethnicity (1 = Black African, 2 = Colored, 3 = White), gender (0 = boys, 1 = girls), age (continuous scores), school performance (0 = lowest, 1 = average, 2 = best), spending money (0 = no, 1 = yes) and religion (0 = no religion, 1 = religious).
Depressive mood
A scale developed by Kandel and Davies (Kandel and Davies, 1982) was used to measure depressive mood. Six items using a five-point scale (0 = never, 4 = always) assessed ‘How often adolescents were bothered or troubled by the following states’, i.e. ‘feeling too tired to do things’, ‘having trouble going to sleep or staying asleep’, ‘feeling unhappy, sad or depressed’, ‘feeling hopeless about the future’, ‘feeling nervous or tense’ and ‘worrying too much about things’. The scores were summed to produce an index of depressive mood with a range of 0 – 24 ( = 0.98).
Intention
Participants' intention not to smoke in the next year was measured by one item on a five-point scale (–2 = definitely, +2 = definitely not).
Statistical analyses
For the purposes of this analysis, former smokers (non-smoking deciders and quitters, coded as 1) were compared to monthly smokers (coded as 0). Monthly smokers and former smokers for each ethnic group were compared with regard to demographic variables using logistic regression for dichotomous variables and F-tests for linear variables. Significant differences (P < 0.05) between monthly smokers and former smokers were found for age, gender, school performance, spending money, religion and depressive mood. These variables were included as covariates in subsequent analyses. Differences in attitude, social influences and self-efficacy between the smoking categories and ethnic groups were analyzed using 2 (Smoking Categories: monthly smokers versus former smokers) x 3 (Ethnic Groups: Black African versus Colored versus White) covariance analyses. Where mean scores were dependent on the interaction of Smoking Categories and Ethnic Groups, simple main effect analyses were conducted to test the relationship between Smoking Categories and the dependent variable within each ethnic group. Where a significant effect for Ethnic Group was found, contrast analyses were conducted to determine which ethnic groups differed significantly.
To identify the correlates of former smoking, separate logistic regression analyses were conducted for the three ethnic groups using smoking status as the dependent variable. Demographic as well as other distal variables were entered in Block 1, attitude, social influences and self-efficacy in Block 2, and intention not to smoke in the next year in Block 3 (Holm et al., 2003). For the regression analyses, the subscales for social norms, social pressure and self-efficacy were combined into one scale each, due to the high correlations between the subfactors. A backward deletion procedure was used to determine the final model of variables that relate to smoking status for each ethnic group. The significance level was set at P < 0.05.
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Results |
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Characteristics of the sample
Of the 4768 students selected to participate in the study, 3869 completed the questionnaire. Two-hundred and thirty eight cases were excluded from the analyses due to non-completion of 10% or more of the questionnaire as well as missing or incomplete data on key variables. A total of 1267 participants (567 monthly smokers and 700 former smokers) were eligible for the present study (see Table II). The mean age of the participants was 16.06 years (SD = 1.53), and the overall distribution of males and females was 51.0% for males and 49.0% for females. Most students were Colored (58.1%), followed by Black African (27.1%) and White (14.8%) students. Most students reported an average school performance (64.6%), that they received spending money (83.2%) and that they were religious (80.8%). The mean level of depressive mood for the sample was 6.88 (SD = 5.32).
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Differences were also found between monthly smokers and former smokers across the ethnic groups as well as within the ethnic groups (see Table II). Among the Black African students, the percentage of males who smoked monthly was higher than the percentage of females who smoked monthly (P < 0.05), while among the Colored students, the percentage of females who smoked monthly was higher than the percentage of males who smoked monthly (P < 0.05). Across the ethnic groups, students in the monthly smoking group were older than students in the former smoking group (P < 0.05). More Colored monthly smokers than former smokers reported an average school performance (P < 0.05), not receiving spending money (P < 0.05) and not being religious (P < 0.05). Additionally, across ethnic groups, monthly smokers reported higher scores on the depressive mood scale than former smokers (P < 0.05).
Differences between monthly smokers and former smokers
The analyses showed that independent of ethnic group, former smokers reported more pros of non-smoking (P < 0.001), fewer cons of non-smoking (P < 0.001), more social norms from family (P < 0.001), friends (P < 0.001) and teachers not to smoke (P < 0.01), more social pressure from friends (P < 0.05) and teachers (P < 0.05) to refrain from smoking, fewer important others who smoke (P < 0.001), greater self-efficacy during stressful (P < 0.001), routine (P < 0.001) and social situations (P < 0.001), and a more positive intention not to smoke in the next year (P < 0.001) (see Table III).
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The differences between monthly smokers and former smokers on the attitude and social influence scales were no different within each of the ethnic groups as interaction patterns were not found between Smoking Categories and Ethnic Groups. There were, however, significant interaction patterns on the stress (P < 0.001), routine (P < 0.001) and social (P < 0.001) self-efficacy scales. Simple main effect analyses showed that former smokers displayed higher stress, routine and social self-efficacy than monthly smokers in all three ethnic groups (P < 0.001), but that these patterns were more pronounced among Colored and White students than among Black African students.
Differences between the ethnic groups
Independent of smoking status, Black African students were less convinced of the pros of non-smoking than Colored and White students (P < 0.001), while White students were least convinced of the cons of non-smoking, followed by Black African and, in turn, Colored students (P < 0.001) (see Table IV).
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Smoking within the context of the social environment was particularly important for Colored adolescents. Colored students experienced a stronger social norm from their friends not to smoke than Black African and White students, respectively (P < 0.01), while Colored and White students experienced stronger norms not to smoke from their family (P < 0.001) and teachers (P < 0.001) as opposed to Black African students. Colored students also reported more smoking in their social environment than Black African and White students (P < 0.001). Colored and Black African students as opposed to White students experienced more pressure from their family (P < 0.001) and friends (P < 0.001) not to smoke. Colored students as opposed to Black African and White students also experienced significantly more pressure from their teacher not to smoke (P < 0.001).
While White students reported greater routine self-efficacy than Black African and Colored students (P < 0.05), they reported lower social self-efficacy (P < 0.05) than the other two groups. White students also expressed a less positive intention not to smoke in the next year than Black African and Colored students (P < 0.01).
Correlates of former smoking
To determine the most important factors associated with former smoking for each of the ethnic groups, separate logistic regression analyses were conducted (see Table V).
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Among the Black African students, in the first step, gender and depressive mood were significant correlates (P < 0.001). In the second step, gender and depressive mood remained as significant correlates (P < 0.001), as were having a non-smoking brother (P < 0.05) and self-efficacy (P < 0.001). In the third step, gender (P < 0.001), depressive mood (P < 0.001), a non-smoking brother (P < 0.05) and self-efficacy (P < 0.001) remained as significant correlates. The emergence of religious practices as a marginally significant correlate (P < 0.05) aroused suspicion about a potential suppressor effect. Examination of the signs of the correlation between smoking status and religion (albeit a low correlation, r = –0.13, P < 0.05) and the ß of the logistic regression excluded the possibility of suppressor effects. Among the Black African students, the model correctly classified 76.4% of the monthly smokers and 79.2% of the former smokers, respectively.
Among the Colored students, in the first step, age (P < 0.05), gender (P < 0.01), spending money (P < 0.05), school performance (P < 0.05) and depressive mood (P < 0.001) were significant correlates of former smoking. In the second step, spending money (P < 0.05), perceived cons of non-smoking (P < 0.05), a non-smoking best friend (P < 0.001) and self-efficacy (P < 0.001) were significant correlates of former smoking. Pressure not to smoke (P < 0.01) was inversely related to former smoking. In the third step, spending money (P < 0.05), perceived cons of non-smoking (P < 0.05), social pressure not to smoke (P < 0.05), a non-smoking best friend (P < 0.01), self-efficacy (P < 0.001) and intention not to smoke (P < 0.001) were significant correlates of former smoking. Due to the inverted sign of the ß for social pressure not to smoke, the possibility of a suppressor effect was explored. Smoking status and social pressure did not correlate significantly (r = 0.03, P = 0.38), and low correlations were also found between social pressure and other correlates (r < 0.24). In-depth analyses showed that the suppressor effect was not directly bound to one specific variable. (Attempts were made to isolate the suppressor by excluding the congruent predictors one at a time. Social pressure and social norms were each in turn removed from the analyses; subfactors were also created for social pressure, social norms and self-efficacy. The low correlation between social pressure and self-efficacy refuted the hypotheses that self-efficacy was the suppressor. Several independent variables may make the isolation of the suppressor difficult and reference is made to suppressor situations rather than specific suppressor variables.) Among the Colored students, the model correctly classified 78.7% of the monthly smokers and 88.8% of the former smokers, respectively.
Among the White students, in the first step, the identified correlate did not reach significance. In the second step, self-efficacy (P < 0.001) emerged as a significant correlate of former smoking. In the third step, self-efficacy (P < 0.001) and an intention not to smoke in the next year (P < 0.01) were significant correlates of former smoking. Social pressure not to smoke (P < 0.05) was again inversely related to former smoking. Suppressor effects were also explored for social pressure not to smoke even though the sign of the ß and that of the correlation with smoking status were congruent. Again in-depth analyses were unable to identify the specific suppressor. Among the White students, the model was able to correctly classify 81.5% of monthly smokers and 88.8% of former smokers, respectively.
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Discussion |
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This study describes the differences in attitude, social influences, self-efficacy and intention not to smoke in the next year between Black African, Colored and White monthly smokers and former smokers. Across the ethnic groups, former smokers displayed a more positive attitude toward non-smoking, were surrounded by a social environment that was supportive of non-smoking, and displayed higher self-efficacy not to smoke in stressful, routine and social situations. Former smokers were also more positive about their intention not to smoke in the next year. Chassin et al. (Chassin et al., 1985
) reported similar findings in that quitters had fewer smoking friends, and both their parents and friends were more negative about them smoking. Redmond (Redmond, 2002) also showed substantially higher intention not to smoke in future among reducers compared to maintainers of regular smoking. Although monthly smokers and former smokers differed on the self-efficacy subfactors in each ethnic group, these differences were more pronounced among Colored and White students than among Black African students. For all of the other cognitive factors, the lack of interaction patterns between smoking status and the ethnic groups suggest that the differences between monthly smokers and former smokers were the same within each of the ethnic groups. However, the lack of power to detect differences within the ethnic groups cannot be excluded.
Our study demonstrates that social cognitive models such as the I-Change Model that were primarily developed in western cultures can be used to address the motivational determinants of smoking cessation in a multi-ethnic society like SA. This finding is supported by the ability of the regression model to correctly classify monthly smokers and former smokers from all three ethnic groups. Markham et al. (Markham et al., 2004) also showed the appropriateness of the I-Change Model in an ethnically diverse population in the UK. However, our results do suggest the need for some ethnic sensitivity. Even though self-efficacy was a correlate of former smoking for all three ethnic groups, the covariance analyses showed that Black African and Colored students require a focus on routine self-efficacy, while White smokers require coping skills in social situations. Among the Colored students, the study highlights the importance of the social environment to stop smoking. A non-smoking best friend was a correlate of former smoking, as has been shown in other studies (Chassin et al., 1985; Ershler et al., 1989; Burt and Peterson, 1998; Arisa and Nebot, 2002). Colored students also reported higher perceived social norms, social pressure and modeling. King and colleagues (King et al., 2003) have also demonstrated the instrumental role that the social environment plays in adolescent smoking in South Africa.
Across the smoking categories, Black African students reported lower scores on the pros and cons of non-smoking, suggesting that they were less convinced of the detrimental effects of smoking. The lack of attitudinal and intention cognitions as correlates of former smoking in the regression analyses as well as the weaker fit of the model in this group also intimate towards distal correlates of former smoking, such as gender, religion and depressive mood.
Contrary to other studies (Brownson et al., 1990; Burt and Peterson, 1998; Arisa and Nebot, 2002), gender showed some cultural specificity in that Black African females were more likely to stop smoking than Black African males. Black African women in SA, however, have displayed greater propensity to quit smoking (Department of Health et al., 2002). Cultural resilience factors reflected by consistently low smoking rates in this group (Department of Health et al., 2002) and a social taboo against smoking may underpin this finding in our study. Similarly, a lower level of depressive mood as a correlate of former smoking, and the inverse association between religion and former smoking among Black African students requires further investigation. The finding among Colored students that increased spending money is a correlate of former smoking may be indicative of the inextricable link between socioeconomic status, ethnicity and smoking (World Bank, 1999). In a developing country, like SA, macro-economic development may positively enhance the effect of tobacco control programmes.
It is known that factors such as low socioeconomic status, poverty and educational attainment tend to cluster in certain ethnic groups (Tyas and Pederson, 1998). This is particularly relevant in the context of SA where apartheid resulted in large social, economic and spatial differentials amongst the ethnic groups (UN Population Development South Africa, 2003). Our study could not include reliable measures of socioeconomic status and poverty as they proved difficult to estimate among adolescents in a country in rapid transition. Future studies must investigate the extent to which ethnic differences in the determinants of smoking can be, in fact, explained by factors such as socioeconomic status and poverty. Furthermore, the power of the study to detect differences within the ethnic groups was possibly low. Alternative strategies must be identified to stratify by ethnicity as the increasing racial integration of schools makes stratification at the school level difficult.
Although Kremers et al. (Kremers et al., 2001a) demonstrated that non-smoking deciders and ex-regular smokers are a heterogeneous groups, both groups in the study showed a similar pattern of lower risk for future smoking than experimenters and regular smokers. However, further studies are required to disentangle the motivational determinants of non-smoking deciders and ex-regular smokers.
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Acknowledgments |
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The authors would also like to thank the participating schools and students as well as the research assistants for assisting with data collection. We also thank the reviewers for their useful comments in revising the paper. We are grateful to the South African Medical Research Council for funding this study.
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References |
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Arisa, C.A. and Nebot, M.A. (2002) Factors associated with smoking progression among Spanish adolescents. Health Education Research, 17, 750–760.
Backinger, C.L., Fagan, P., Matthews, E. and Grana, R. (2003a) Adolescent and young adult tobacco prevention and cessation: current status and future directions. Tobacco Control, 12(Suppl. 4), Iv46–Iv53.
Backinger, C.L., McDonald, P., Ossip Klein, D.J., Colby, S.M., Maule, C.O., Fagan, P., Husten, C. and Colwell, B. (2003b) Improving the future of youth smoking cessation. American Journal of Health Behavior, 27(Suppl. 2), S170–S184.
Bandura, A. (1986) Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, New York.
Bradshaw, D., Groenewald, P., Laubscher, R., Nannan, N., Nojilana, B., Norman, P., Pieterse, D. and Schneider, M. (2003) Initial Burden of Disease Estimates for South Africa, 2000. South African Medical Research Council, Cape Town.
Brownson, R.C., DiLorenzo, T.M., Van Tuinen, M. and Finger, W.W. (1990) Patterns of cigarette and smokeless tobacco use among children and adolescents. Preventive Medicine, 19, 170–180.[CrossRef][Web of Science][Medline]
Burt, R.D. and Peterson, A.V. (1998) Smoking cessation among high school seniors. Preventive Medicine, 27, 319–327.[CrossRef][Web of Science][Medline]
Chassin, L., Presson, C.C. and Sherman, S. (1985) Cognitive and social influence factors in adolescent smoking cessation. Addictive Behaviors, 9, 383–390.
Colby, S.M., Tiffany, S.T., Shiffman, S. and Niaura, R.S. (2000) Are adolescent smokers dependent on nicotine? A review of the evidence. Drug and Alcohol Dependence, 59(Suppl 1), S83–S95.
De Vries, H., Backbier, E., Dijkstra, M., Van Breukelen, G., Parcel, G. and Kok, G. (1994) A Dutch social influence smoking prevention approach for vocational school students. Health Education Research, 9, 365–374.
De Vries, H., Backbier, E. and Kok, G. (1995) The impact of social influences in the context of attitude, self-efficacy, intention and previous behavior as predictors of smoking onset. Journal of Applied Social Psychology, 237–257.
De Vries, H., Dijkstra, M. and Kuhlman, P. (1988) Self-efficacy: the third factor besides attitude and subjective norm as a predictor of behavioral intentions. Health Education Research, 3, 273–282.
De Vries, H., Mudde, A., Kremers, S., Wetzels, J., Uiters, E., Ariza, C., Vitoria, P.D., Fielder, A., Holm, K., Janssen, K., Lehtuvuori, R. and Candel, M. (2003a) The European Smoking Prevention Framework Approach (ESFA): short-term effects. Health Education Research, 18, 649–663.
De Vries, H., Mudde, A., Leijs, I., Charlton, A., Vartiainen, E., Buijs, G., Clemente, M.P., Storm, H., Gonzalez Navarro, A., Nebot, M., Prins, T. and Kremers, S. (2003b) The European Smoking Prevention Framework Approach (EFSA): an example of integral prevention. Health Education Research, 18, 611–626.
DiFranza, J.R., Rigotti, N.A., McNeill, A.D., Ockene, J.K., Savageau, J.A., St Cyr, D. and Coleman, M. (2000) Initial symptoms of nicotine dependence in adolescents. Tobacco Control, 9, 313–319.
Dijkstra, M., Mesters, I., De Vries, H., Van Breukelen, G. and Parcel, G.S. (1999) Effectiveness of a social influence approach and boosters to smoking prevention. Health Education Research, 14, 791–802.
Department of Health, Medical Research Council and Measure D. (2002) South Africa Demographic and Health Survey 1998. Full Report. Department of Health, Medical Research Council. Measure DHS, Pretoria.
Dolcini, M.M., Adler, N.E. and Ginsberg, D. (1996) Factors influencing agreement between self-reports and biological measures of smoking among adolescents. Journal of Research on Adolescence, 6, 515–542.
Epstein, J.A., Botvin, G.J. and Diaz, T. (1998) Ethnic and gender differences in smoking prevalence among a longitudinal sample of inner-city adolescents. Journal of Adolescent Health, 23, 160–166.[CrossRef][Web of Science][Medline]
Ershler, J., Leventhal, H., Fleming, R. and Glynn, K. (1989) The quitting experience for smokers in sixth through twelfth grades. Addictive Behaviors, 14, 365–378.[CrossRef][Web of Science][Medline]
Fishbein, M. and Ajzen, I. (1975) Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, MA.
Flay, B. (1993) Youth tobacco use: risks, patterns and control. In Slade, J. and Orleans, C.T. (eds), Nicotine Addiction: Principles and Management. Oxford University Press, New York, pp. 365–384.
Holm, K., Kremers, S.P. and De Vries, H. (2003) Why do Danish adolescents take up smoking? European Journal of Public Health, 13, 67–74.
Kandel, D.B. and Davies, M. (1982) Epidemiology of depressive mood in adolescents. Archives of Pediatrics and Adolescent Medicine, 39, 1205–1212.
King, G., Flisher, A.J., Mallett, R., Graham, J., Lombard, C., Rawson, T., Morojele, N.K. and Muller, M. (2003) Smoking in Cape Town: community influences on adolescent tobacco use. Preventive Medicine, 36, 114–123.[CrossRef][Web of Science][Medline]
Kremers, K., Mudde, A. and De Vries, H. (2004) Development and longitudinal test of an instrument to measure behavioral stages of smoking initiation. Substance Use and Misuse, 39, 225–252.[CrossRef]
Kremers, S.P., Mudde, A.N. and De Vries, H. (2001a) ‘Kicking the initiation’: do adolescent ex-smokers differ from other groups within the initiation continuum? Preventive Medicine, 33, 392–401.[CrossRef][Web of Science][Medline]
Kremers, S.P., Mudde, A.N. and De Vries, H. (2001b) Subtypes within the precontemplation stage of adolescent smoking acquisition. Addictive Behaviors, 26, 237–251.[CrossRef][Web of Science][Medline]
Lancaster, T., Stead, L., Silagy, C. and Sowden, A. (2000) Effectiveness of interventions to help people stop smoking: findings from the Cochrane Library. British Medical Journal, 321, 355–358.
Lawrance, L. (1998) Validation of a self-efficacy scale to predict adolescent smoking. Health Education Research, 4, 351–360.
Markham, W.A., Aveyard, P., Thomas, H., Charlton, A., Lopez, M.L. and De Vries, H. (2004) What determines future smoking intentions of 12- to 13-year-old UK African-Caribbean, Indian, Pakistani and White young people? Health Education Research, 19, 15–28.
Panday, S., Reddy, S.P. and Bergstrom, E. (2003) A qualitative study on the determinants of smoking behaviour among adolescents in South Africa. Scandinavian Journal of Public Health, 31, 204–210.[CrossRef][Web of Science][Medline]
Panday, S., Reddy, S.P., Ruiter, R.A.C., Bergstrom, E. and De Vries, H. (2005) Nicotine dependence and withdrawal symptoms among adolescents in South Africa. Submitted for publication.
Peto, R., Lopez, A.D., Boreham, J., Thun, M. and Heath, C. (1994) Mortality from Smoking in Developed Countries 1950–2000. Oxford University Press, Oxford.
Prochaska, J.O. and DiClemente, C.C. (1983) Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51, 390–395.[CrossRef][Web of Science][Medline]
Redmond, W.H. (2002) Smoking reduction among high school seniors: a test of selected indicators. Journal of Adolescent Health, 31, 417–424.[CrossRef][Web of Science][Medline]
Riedel, B.W., Robinson, L.A., Klesges, R.C. and McLain Allen, B. (2003) Ethnic differences in smoking withdrawal effects among adolescents. Addictive Behaviors, 28, 129–140.[CrossRef][Web of Science][Medline]
Siqueira, L.M., Rolnitzky, L.M. and Rickert, V.I. (2001) Smoking cessation in adolescents: the role of nicotine dependence, stress and coping methods. Archives of Pediatrics and Adolescent Medicine, 155, 489–495.
Swart, D., Reddy, P., Panday, S., Philip, J., Naidoo, N. and Ngobeni, N. (2004) The 2002 Global Youth Tobacco Survey (GYTS): The 2nd GYTS in South Africa (SA)—A comparison Between GYTS (SA) 1999 and GYTS (SA) 2002. South African Medical Research Council, Cape Town.
Swart, D., Reddy, P., Ruiter, R.A.C. and de Vries, H. (2003) Cigarette use among male and female grade 8–10 students of different ethnicity in South African schools. Tobacco Control, 12, E1.
Tyas, S.L. and Pederson, L.L. (1998) Psychosocial factors related to adolescent smoking: a critical review of the literature. Tobacco Control, 7, 409–420.
UN Population Development South Africa (2003) South Africa Human Development Report 2003. The Challenge of Sustainable Development in South Africa: Unlocking People's Creativity. Oxford University Press, Cape Town.
US Department of Health and Human Services (1994) Preventing Tobacco Use among Young People: A Report of the Surgeon General. USDHHS, Atlanta, GA.
US Department of Health and Human Services (1998) Tobacco Use among US Racial/Ethnic Minority Groups. A Report of the Surgeon General. USDHHS, Atlanta, GA.
US Department of Health and Human Services (2004) The Health Consequences of Smoking. A Report of the Surgeon General. USDHHS, Atlanta, GA.
World Bank (1999) Curbing the Epidemic. Governments and the Economies of Tobacco Control. World Bank, Washington, DC.
Received on August 5, 2004; accepted on January 7, 2005
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