Journal of Pediatric Psychology Advance Access published online on June 21, 2008
Journal of Pediatric Psychology, doi:10.1093/jpepsy/jsn063
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Asthma-specific Predictors of Smoking Onset in Adolescents with Asthma: A Longitudinal Study
1Behavioural Science Institute, Radboud University Nijmegen, 2Centre for Adolescent Health, Royal Children's Hospital, 3Department of Paediatrics, the University of Melbourne, and 4Murdoch Childrens Research Institute
All correspondence concerning this article should be addressed to Monique Van De Ven, Behavioural Science Institute, Radboud University Nijmegen, PO Box 9104, 6500 HE Nijmegen, the Netherlands. E-mail: M.vandeven{at}pwo.ru.nl
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Abstract |
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Objective Despite even occasional smoking being more risky for adolescents with asthma, the smoking rate in this vulnerable population remains high. This is the first longitudinal study investigating asthma-specific predictors of smoking initiation. Methods A three-wave longitudinal survey study (22 months) among 257 adolescents with asthma was conducted. The effects of asthma-specific factors [symptom severity, medication adherence, coping, attitude towards asthma, and quality of life (QOL)] on smoking onset were tested with logistic regression models. Results Poorer self-reported adherence and the maladaptive coping strategy of hiding asthma predicted smoking onset. Poorer QOL predicted smoking in boys only. Conclusions Our findings underscore the importance of recognizing and addressing adherence problems during adolescence as low adherence is a risk factor for smoking initiation. Moreover, psychosocial factors, such as coping and QOL for boys, were associated with smoking initiation. This highlights the importance of attending to the psychosocial needs of youth with asthma.
Key words: asthma; adolescent; psychological adaptation; risk factors; tobacco use..
For adolescents with asthma, prevention of smoking onset is even more important than for healthy adolescents, as the health risks of smoking are more pronounced in people with asthma (Thomson, Chaudhuri, & Livingston, 2004
). Smoking in people with asthma may have both an immediate impact by triggering asthma, as well as long-term effects such as increased asthma severity and an increased frequency of attacks (Siroux, Pin, Oryszczyn, Le Moual, & Kauffmann, 2000), reduced efficacy of corticosteroid treatment (Chalmers et al., 2002), and increased risk of developing chronic obstructive pulmonary diseases (George, 1999).
Smoking is usually initiated during adolescence. Research has demonstrated that early smoking onset, including occasional smoking, is a strong predictor of later daily smoking (Patton et al., 1998), and smoking even one cigarette predicts later tobacco use (Fidler, Wardle, Brodersen, Jarvis, & West, 2006). Other predictors of smoking onset in adolescents include personality, genetics, peer influences, and cognitive factors (Conrad, Flay, & Hill, 1992).
Despite the increased risks of smoking for adolescents with asthma, studies have shown that smoking rates are equally high (Forero, Bauman, Young, & Larkin, 1992; Tercyak, 2003) or even higher (Forero, Bauman, Young, Booth, & Nutbeam, 1996; Precht, Keiding, & Madsen, 2003) among adolescents with asthma compared to their healthy peers. Adolescents with asthma are thus doubly disadvantaged: they have higher odds of smoking and experience more consequences from smoking (Sawyer, Drew, Yeo, & Britto, 2007). This emphasizes the importance of understanding the predictors of smoking onset in adolescence. However, there have been few prospective studies of smoking onset in adolescents with asthma (Tercyak, 2006; Van De Ven, Engels, Otten, & Van Den Eijnden, 2007), and to our knowledge, no study has concentrated on asthma-specific predictors of smoking in adolescents or adults with asthma.
Three categories of asthma-specific factors can be distinguished: illness characteristics, reactions to illness, and outcomes of the illness. These three categories are interrelated: outcomes of having asthma may be directly affected by the illness characteristics, as well as indirectly via reactions to having asthma (Adams, Wilson, Smith, & Ruffin, 2004; Van De Ven, Engels, Sawyer, Otten, & Van Den Eijnden, 2007).
Illness Characteristics
A previous study, which drew from the same sample as the present study, showed that adolescents who experienced more symptoms indicative of asthma (e.g., wheezing) had a higher risk of being regular smokers at follow-up 22 months later (Van De Ven, Engels, Kerstjens, & Van Den Eijnden, 2007). No other asthma-specific factors were examined in that study. A limitation of the previous study was that there was no assessment of whether self-reported respiratory symptoms were in fact due to asthma; these same symptoms could reflect other diagnoses. In the current study, the effects of having more symptoms of asthma (i.e., asthma severity) as well as the effects of other asthma-specific factors on smoking initiation were only examined in those subjects who fulfilled criteria for current asthma.
Reactions to Illness
There is very little research about the relation between reactions to illness and tobacco use in adolescents with asthma. A cross-sectional study by Precht et al. (2003) among adolescents with asthma suggested a relationship between taking medication and smoking: symptomatic adolescents who were not taking medicine in the past 14 days were twice as likely to smoke compared to asymptomatic adolescents who were taking their medicine. In a more recent cross-sectional study in adolescents with asthma, Bush and colleagues (Bush et al., 2007) found that smokers were more likely to use rescue medication, but less likely to use controller medication, despite more symptoms of asthma. These cross-sectional results suggest a relation between poor adherence and smoking in adolescents with asthma. In the current study, this relationship was examined longitudinally.
Another factor that could potentially relate to smoking onset is how adolescents cope with asthma. Research in healthy populations showed that coping is related to substance use. Wills and colleagues (2001) tested the relation between coping and substance use (i.e., tobacco, alcohol, and marijuana) in healthy adolescents. "Behavioral coping" (i.e., making an action plan and following it) was related to lower substance use, whereas "disengagement coping" (i.e., anger coping, helpless coping, and hangout coping) was related to higher levels of use. To our knowledge, no studies have examined whether coping is also related to smoking onset in adolescents with asthma. In addition to adherence and coping, this study examined attitude towards asthma in relation to smoking onset of adolescents with asthma.
Outcomes of Illness
Studies in healthy as well as illness populations have demonstrated that illness outcomes such as QOL and smoking are related (Wilson, Parsons, & Wakefield, 1999). Although the majority of studies were of cross-sectional design, it is assumed that smoking causes a reduction in QOL. Support for this assumption was found in some longitudinal studies (Mitra, Chung, Wilber, & Klein Walker, 2004), but other longitudinal studies found that changes in smoking status did not predict changes in QOL (Quist-Paulsen, Bakke, & Gallefoss, 2006). The present study aimed to test whether the outcomes of asthma, as measured by QOL, predict smoking onset among adolescents with asthma.
Previous studies demonstrated gender differences in asthma-specific factors: girls reported more symptoms of asthma (Van De Ven, Van Den Eijnden, & Engels, 2006), had lower QOL (Warschburger et al., 2004), but seemed better adapted to living with asthma (Williams, 2000) than boys. Moreover, research in healthy adolescents found that gender could moderate the effect of smoking predictors on smoking onset (Van Den Bree, Whitmer, & Pickworth, 2004).
In sum, research has indicated that asthma-specific factors are potentially related to smoking initiation in adolescents with asthma (Wills et al., 2001; Wilson et al., 1999). The current prospective study of Dutch adolescents with asthma aims to identify which asthma-specific factors (i.e., symptom severity, medication adherence, coping strategies, attitude towards asthma, and QOL) are associated with smoking initiation. Moreover, it was tested whether any effect of asthma-specific factors might be moderated by gender. In line with the literature described earlier (Precht et al., 2003; Wills et al., 2001), we expected that smoking initiation would be more frequent in adolescents with higher symptom severity, lower adherence, more maladaptive coping, a more negative attitude towards smoking, and lower QOL. Because gender may influence the effects of smoking predictors on smoking behavior in complex ways (Van Den Bree et al., 2004), no hypotheses were formulated on the moderating role of gender in predicting smoking among adolescents with asthma.
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Methods |
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Sampling and Sampling Procedure
This three-wave longitudinal study was approved by the local medical ethics committee (CMO Arnhem-Nijmegen). Data collection for the first wave took place in January 2003 as part of the International Study of Asthma and Allergies in Childhood (ISAAC) phase III (Asher et al., 1995
). All seventh- and eighth-grade students (mean age 13.0 years) from 33 secondary schools that participated in this study (of 55 schools that were approached to take part following random selection from the phone book) were asked to complete a written questionnaire during school hours under the supervision of a teacher. A more detailed description of the first wave of this study, which screened for symptoms of asthma, can be found elsewhere (Van De Ven et al., 2006). The second wave was undertaken 4 months later, when different questionnaires were given to adolescents with asthma measuring several aspects about their illness and smoking behavior. Smoking was assessed again at follow-up in November 2004 (Fig. 1).
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Adolescents with asthma were identified using a Dutch translation of the extended version of the ISAAC asthma questionnaire (Asher et al., 1995
; Wieringa, Weyler, Van Bever, Nelen, & Vermeire, 1999). This questionnaire was designed for population-based research and showed good sensitivity and specificity when compared with a physician's assessment of asthma (Jenkins et al., 1996). Students who ever had asthma and who either reported they had suffered from asthma in the past 12 months or who had used asthma medication in the past 12 months were categorized as currently having asthma.
Participants
Figure 1 shows the flow diagram of the three measurement waves including information on the number of participants, their age, and their smoking status. Of the 6,769 adolescents who completed all three questionnaires (67% of first wave participants), those who had current asthma at the first two waves, who reported they had never smoked at the second wave, and who filled out the smoking question at the third wave were selected, resulting in a final sample of 257 adolescents. Asthma status was thus assessed before smoking initiation. Most students were of Dutch origin (84.4%); 125 (48.6%) were girls. At the first wave, the mean age of the respondents was 12.8 years (SD = 0.77). Most students (n = 230; 90.2%) reported they needed to use medication in the past 12 months. At the first wave, 71% of these students reported they had used rescue medication and 47% reported they had used controller medication in the past 12 months.
Measures
All measures were self-reported by adolescents.
Smoking onset was assessed by a single item on a nine-point scale, referring to smoking cigarettes (either ready made or hand rolled) (1: "I have never smoked, not even a puff," 2: "I have tried smoking once in a while, but don't smoke anymore," 3: "I have quit smoking, I have always smoked less than once a week," 4: "I have quit smoking, after having smoked at least once a week," 5: "I try smoking once in a while," 6: "I smoke less than once a month," 7: "I do not smoke weekly, but at least once a month," 8: "I do not smoke daily, but at least once a week," and 9: "I smoke at least once a day") (Kremers, Mudde, & De Vries, 2001). This variable was dichotomized into 1 (never smoked) versus 2 (ever smoked: smoked once or more) because of the skewness of the distribution of this variable, and because even smoking one cigarette is an important predictor of later tobacco use (Fidler et al., 2006). Self-report of smoking in adolescents is recognized as a valid method (Barnea, Rahav, & Teichman, 1987; Forastiere et al., 1993) and this instrument has been used in other health studies in Europe (De Vries, Candel, Engels, & Mercken, 2006; Harakeh, Scholte, De Vries, & Engels, 2006).
Severity of asthma symptoms was assessed using a Dutch translation of the student questionnaire of the American College of Allergy, Asthma and Immunology (ACAAI) (Redline et al., 2004). Respondents were asked to indicate on a three-point scale how often they suffered from seven symptoms (e.g., "I wake up at night because I have trouble breathing"). Responses were averaged to form a severity score (Cronbach's 
= .68), with higher scores indicating more severe asthma. The validation study of this questionnaire showed high internal consistency ( = .85) and results of the student questionnaire were comparable with a standardized clinical evaluation of asthma (Redline et al., 2004).
Adherence to asthma medication was measured with the Morisky Adherence Scale (Morisky, Green, & Levine, 1986), which was composed of four yes/no response items (e.g., "Do you ever forget to take your medication?"). The answers were recoded so that higher scores represented better self-reported adherence. Internal consistency of this questionnaire was assessed by calculating the Kuder–Richardson 20 (KR20) reliability coefficient, because of the dichotomous nature of the responses. The KR20 was .55, which was somewhat lower than the results found in the validation study (.61; Morisky et al., 1986). This instrument showed concurrent and predictive validity (Morisky et al., 1986).
Coping strategies were assessed with the Asthma-Specific Coping Scale (Aalto, Harkapaa, Aro, & Rissanen, 2002). The scale comprised six subscales: restricted lifestyle (four items e.g., "I avoid exertion"), hiding asthma (four items, e.g., "I avoid talking about my asthma"), positive reappraisal (four items, e.g., "I try to learn something positive about my falling ill and related experiences"), information seeking (four items, e.g., "I try to find out more about my asthma"), ignoring asthma (four items, e.g., "I avoid thinking about my asthma"), and worrying about asthma (three items, e.g., "I am afraid that my asthma will get worse"). All items were measured on a four-point Likert scale. Responses were averaged to yield a score for each domain; higher scores indicated more use of that specific coping strategy. Cronbach's s in the present study ranged from .61 to .73. The psychometric characteristics of this questionnaire were tested in a validation study among a large general asthma sample of 3,459 adults and in two smaller intervention samples of adults, supporting construct, concurrent and discriminant validity, with Cronbach's s ranging from .63 to .84 (Aalto et al., 2002). Concurrent validity for adolescent samples was shown in an earlier Dutch study (Van De Ven, Engels, Sawyer et al., 2007).
Attitude towards asthma was measured using the Child Attitude Toward Illness Scale (CATIS) (Austin & Huberty, 1993). The scale consisted of 13 items with a five-point Likert scale ( = .79). Higher scores represented a more positive attitude. This scale showed good internal consistency and test–retest reliability, and the validation study showed good construct validity (Austin & Huberty, 1993).
Overall QOL and positive effects QOL were measured using a Dutch translation of the Adolescent Asthma Quality of Life Questionnaire (AAQOL) (Rutishauser, Sawyer, Bond, Coffey, & Bowes, 2001). The AAQOL questionnaire consists of 32 items and covers six dimensions: symptoms (e.g., "How bothered have you been by coughing"), medication (e.g., "How bothered did you feel about having to carry your inhaler with you"), physical activities (e.g., "How often have you been restricted in sports, hobbies or other recreational activities because of your asthma"), emotions (e.g., "How often did you feel frustrated because of your asthma"), social interaction (e.g., "How often have you been annoyed by your asthma when going to a party"), and positive effects (e.g., "How often has your asthma brought you closer as a family"), with Cronbach's s ranging from .74 to .90. All responses were measured on a seven-point Likert scale. As recommended by the authors (Rutishauser et al., 2001), the domains are added together to create one overall QOL scale ( =.95), with the exception of the positive effects domain which was scored separately. Higher scores represented better QOL. This instrument was validated among Australian and European adolescents and showed good construct validity and high test–retest reliability (Rutishauser et al., 2001; Somerville, Knopfli, & Rutishauser, 2004). A translation of this questionnaire was used in a previous study in the Netherlands (Van De Ven, Engels, Sawyer et al., 2007).
Social–demographic information and parental smoking status were also assessed by adolescent self-report. Adolescents were asked to fill out items about their gender, age, education, country of birth, parental country of birth, and parental smoking status. Adolescents who were born in the Netherlands and whose parents were also born in the Netherlands were categorized as Dutch; other adolescents were categorized as non-Dutch. Adolescents were categorized in one of three educational placement categories: lower vocational training, intermediate vocational training, and high school/pre-university education.
Statistical Analysis
Data were analyzed using the Statistical Package for the Social Sciences (SPSS) for Windows, version 14.0. First, Pearson correlations were calculated to examine the associations between all variables in this study (Table I). Next, logistic regression analyses were conducted to investigate which asthma-specific factors predicted smoking onset, and whether the effects of these predictors were moderated by gender. All analyses were corrected for gender, age, ethnicity, education, and parental smoking behavior because these are known predictors of smoking onset in adolescents (De Vries et al., 2006). First, the effect of each asthma-specific factor was analyzed by a set of separate hierarchical logistic regression analyses, with the possible confounders entered first, one of the asthma-specific factors in the second step, and the moderation with gender in the last step (Table II, adjusted models). Moderation was tested by examining the interaction term, which was generated by multiplying the asthma-specific predictor by the moderator gender, while controlling for the main effects of gender and the asthma-specific factor (Baron & Kenny, 1986; Holmbeck, 2002). The predictor variables were centered before generating the interaction terms. Next, to control for correlations between the asthma-specific factors, all asthma-specific factors were tested in one hierarchical logistic regression model, again entering the possible confounders first, all asthma-specific factors second, and all interactions with gender third (Table II, complete model).
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Results |
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Fifty-one (19.8%) adolescents within the study sample started smoking between the second and third wave, compared to 25.5% of healthy adolescents in the larger sample [Pearson
2 (1, N = 4126) = 4.07, p = .04] (not in Table). Table I shows the correlations between all study variables. Symptom severity was related to overall QOL (r = –.50), attitudes toward asthma (r = –.38), and several coping strategies (correlations ranging from .15 to .30). Adherence was positively but weakly related to some coping strategies with correlations between .14 and .18. The QOL variables were negatively interrelated (r = –.39), supporting the integration of the first five subscales (and not the positive effects domain) into one overall QOL score. The QOL variables were related to most coping strategies, with correlations ranging from .16 to –.48, and with attitude towards asthma (r = .63 for overall QOL; r = –.19 for positive QOL). Furthermore, most coping strategies were interrelated with correlations ranging from .16 to –.49, and correlated with attitude towards asthma with significant correlations ranging from – .19 to –.49. Being female was positively associated with symptom severity (r = .19), the coping strategy of leading a restricted lifestyle (r = .15), and the coping strategy of worrying about asthma (r = .17). Being female was negatively associated with attitude towards asthma (r = –.13). Table II describes the results of the logistic regression models predicting smoking onset, for both the adjusted models and the complete model. Gender, age, ethnicity, education, and parental smoking did not predict smoking initiation in adolescents with asthma. The adjusted models showed that smoking onset was significantly predicted by self-reported medication adherence [odds ratio (OR) = 0.21; 95% confidence interval (CI): 0.06–0.75) and the coping strategy of hiding asthma (OR = 1.72; 95% CI: 1.06–2.78), with the odds of smoking initiation higher for adolescents with lower adherence and for those hiding their asthma more. Gender moderated the effect of overall QOL on smoking onset (OR = 2.53; 95% CI: 1.27–5.07). Separate analyses for boys and girls showed that better overall QOL significantly predicted lower odds of smoking onset in boys (OR = 0.45; 95% CI: 0.25–0.81), while the effect of overall QOL was not significant but appeared to be increasing the odds of smoking initiation in girls (OR = 1.18; 95% CI: 0.76–1.82).
In the complete model, low adherence was the only variable significantly predicting smoking onset in adolescents with asthma (OR = 0.24; 95% CI: 0.06–0.99). Moreover, the interaction between gender and overall QOL was significant (OR = 5.74; 95% CI: 1.43–23.0). Separate analyses for boys and girls showed that better overall QOL was significantly related to lower odds of smoking onset in boys (OR = 0.35; 95% CI: 0.13–1.00), while the effect of overall QOL was not significant but appeared to be reversed in girls (OR = 2.04; 95% CI: 0.74–5.61). The complete model without interactions predicted 15% of the variance in smoking onset, the model with interactions predicted 23% of the variance in smoking onset, as measured with Nagelkerke R2. The logistic regression's receiver operating characteristic (ROC) area was 0.77, indicating an acceptable predictive power of the complete model with interactions (Hanley & McNeil, 1982).
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Discussion |
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This is the first study to explore asthma-specific predictors of smoking onset in adolescents with asthma, a group that is especially vulnerable to the risks of smoking (Thomson et al., 2004
). The prevalence rates of smoking in this study are consistent with Dutch national youth surveys, where 35% of 13-year-olds and 49% of 15-year-olds reported they have smoked at least once in their lifetime (Stivoro, 2006). The smoking rates are also consistent with the results reviewed by Tyc and Throckmorton-Belzer (2006), who compared smoking rates in different paediatric chronic illness groups. They found higher rates of smoking in adolescents with asthma (11–21 years) than in other illness groups.
The rate of smoking onset in adolescents with asthma (19.8%) was lower than in the healthy adolescents in the larger sample (25.5%). Most of these adolescents were occasional smokers (Fig. 1), which is consistent with our previous work that showed that adolescents with asthma were less likely to experiment with smoking (Van De Ven, Engels, Kerstjens et al., 2007). However, in that study, it was of concern that once adolescents with asthma started to smoke, they were more likely to become regular smokers than healthy adolescents. While the current study showed a lower onset of smoking in adolescents with asthma, the increased health risks faced by young people with asthma means that a smoking onset rate of 20% is disturbing.
In line with previous cross-sectional results that showed an association between adherence with medication and adolescent smoking (Bush et al., 2007; Precht et al., 2003), poor self-reported adherence in early adolescence consistently predicted the onset of smoking 18 months later. Rather than considering this as a causal link, it is most likely explained by various shared underlying factors, such as a low value place on health (Ritt-Olson et al., 2004) or personality traits like conscientiousness (Christensen & Smith, 1995; Harakeh et al., 2006). Poor self-reported adherence to asthma medication during early adolescence should serve as an important warning to healthcare professionals of a higher risk for smoking initiation. Thus, in addition to the usual concerns that poor adherence leads to worse asthma control, low adherence also signals an increased risk of future smoking. Improving young people's capacity to take on greater self-management of asthma as they mature would hopefully also empower adolescents to make healthier choices in relation to choosing not to smoke. In addition, improved self-management may also reduce smoking risk by increasing general self-esteem, which is negatively related to smoking initiation (Conrad et al., 1992).
There was a trend for coping strategies to predict the onset of smoking, although the results were not statistically significant, except for hiding asthma in the adjusted model. The coping strategies hiding asthma and ignoring asthma seemed to be related to higher odds of smoking initiation, while positive reappraisal seemed to reduce the odds of onset of smoking in adolescents with asthma. As in a previous study (Van De Ven, Engels, Sawyer et al., 2007) the current study showed that more subjective psychological factors are important constructs in predicting health behavior and well-being in adolescents with asthma. However, a significant proportion of asthma consultation time is spent on the transfer of knowledge (Partridge, 2005). Within health consultations with adolescents, the challenge for health professionals is how to spend more time on these more subjective experiences of asthma. For instance, research on adolescents with diabetes showed that coping skills training helped adolescents cope better with their illness, which improved both their QOL and long-term adaptation (Grey et al., 1998).
Poorer overall QOL predicted smoking onset in boys. Most research on the relation between QOL and smoking is based on the assumption that smoking causes a reduction in QOL (Mitra et al., 2004). The present study extended this work by showing the importance of QOL as a predictor of smoking onset in boys. In addition to treating QOL as an outcome measure, future studies in chronic illness populations are encouraged to treat QOL as a predictor variable for several other health behaviors, such as medication adherence. Improving the QOL of chronically ill patients could have additional positive effects on illness outcomes, for example, via an increased self-management of illness. That poorer QOL predicted smoking onset in boys and not in girls indicates that boys and girls react differently to the various stressors related to asthma. This is consistent with research showing more externalizing problem behaviors (including aggression and delinquency) in boys, and more internalizing problem behaviors (including depression, anxiety, and withdrawal) in girls (Leadbeater, Kuperminc, Blatt, & Hertzog, 1999). It may be more difficult for boys with low QOL to resist peer pressure. Clinicians working with young people with asthma should be aware of these gender differences.
This study has several strengths. Due to the longitudinal design which included only baseline nonsmokers, it was possible to investigate predictors of smoking initiation instead of more simple cross-sectional associations with smoking behavior. Moreover, these results were adjusted for social–demographics and parental smoking, which are strong predictors of smoking onset (De Vries et al., 2006). The results of the complete model were also adjusted for asthma-specific factors, which also corrected for a possible overlap in constructs. In terms of limitations, while the sample size was acceptable, due to the young age of participants the rate of smoking onset was still relatively low. This reduces the power of the analyses, especially when examining interactions. The onset of regular smoking was even lower, with only seven adolescents becoming weekly or daily smokers. It was therefore only possible to study smoking initiation; it was not possible to distinguish between those who only experimented or have only tried one cigarette, and those who developed a more regular smoking pattern. Future studies would benefit from studying these asthma-specific factors (particularly predictive factors such as adherence, coping, and QOL) in larger cohorts, as well as in studies of older adolescents. Longer follow-up of an older sample of adolescents would identify more adolescents who smoke regularly in whom it would be possible to study how asthma-specific factors affect the transition into regular smoking in adolescents with asthma.
In this study, no differentiation was made with regard to adherence to different kinds of medication. Yet, as shown in a recent cross-sectional study (Bush et al., 2007), smoking may be related to taking more rescue medication and less controller medication. Moreover, as is common in population studies such as this, the results were based on self-report which could have affected the results. For example, certain personality traits such as agreeableness are associated with social desirability, which in this study could have resulted in underreporting of smoking and over-reporting of adherence, thereby inflating the predictive value of early adolescent adherence on smoking onset (Graziano & Tobin, 2002). Population studies have shown that self-report of smoking is reliable and valid if anonymity is guaranteed (Barnea et al., 1987; Forastiere et al., 1993) with prevalence estimates of smoking similar when using either self-report measures or a biological marker for salivary cotinine (Dolcini, Adler, Lee, & Bauman, 2003). Self-reported smoking in chronically ill populations has been shown to be less reliable (Holl, Grabert, Heinze, & Debatin, 1998), although this has not been tested in adolescents with asthma. It is not known whether self-reported smoking in a population-recruited rather than clinically recruited chronic illness sample would equally underestimate smoking.
The model including the interactions predicted 23% of the variance in smoking initiation. The remaining part of the variance may be explained by more general factors that are also predictive of smoking in healthy adolescents, such as personality, genetic susceptibility, peer influences, and social cognitive factors (e.g. attitude towards smoking). For future research, it would be interesting to study how the asthma-specific factors relate to other health models of substance use. Asthma-specific factors could be distal factors predicting, for instance, attitudes towards smoking (Theory of Planned Behaviour; Ajzen, 1991) or perceived susceptibility (Health Belief Model; Becker, Maiman, Kirscht, Haefner, & Drachman, 1977).
In sum, the results showed that several asthma-specific factors were predictive of smoking onset in adolescents with asthma. Healthcare professionals should be concerned by poor adherence to medication in young adolescents with asthma, since this predicted the later onset of smoking, even after adjusting for the known predictors of smoking. They are encouraged to pay more attention to the subjective experiences of adolescents with asthma, such as their coping strategies and QOL, since these subjective factors were also predictive of smoking initiation.
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This research was funded by a travel grant from the Netherlands Organisation for Scientific Research (NWO) and a grant from the Netherlands Asthma Foundation. The contribution of R.E. was supported by the Netherlands Organisation for Scientific Research (NWO).
Conflicts of interest: None declared.
Received July 18, 2007; revision received May 27, 2008; accepted May 28, 2008
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