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© 2003 American Society for Clinical Oncology Smoking Among Participants in the Childhood Cancer Survivors Cohort: The Partnership for Health Study
From the Dana-Farber Cancer Institute/Harvard School of Public Health, Massachusetts General Hospital/Harvard Medical School, Boston, MA; University of Massachusetts at Amherst, Amherst, MA; and University of Minnesota, Minneapolis, MN; The University of Texas, M.D. Anderson Cancer Center, Houston, TX. Address reprint requests to Karen M. Emmons, PhD, Harvard School of Public Health and Dana-Farber Cancer Institute, Center for Community-Based Research, 44 Binney Street, Boston, MA 02115; email: Karen_Emmons{at}dfci.harvard.edu.
Purpose: This article describes baseline data collection and the intervention design of Partnership for Health, a smoking cessation intervention for smokers in the Childhood Cancer Survivors Study. The purpose of this article is to evaluate demographic, psychosocial, and cancer-related factors that are associated with smoking behavior and mediators of smoking cessation. Patients and Methods: This study includes 796 smokers from the Childhood Cancer Survivors Study database who were diagnosed with cancer before the age of 21, had survived at least 5 years, and were at least 18 years of age at the time of the baseline survey. Correlates of smoking behaviors included smoking rate, number of recent quit attempts, and nicotine dependence; two key mediators of smoking cessation, readiness to quit smoking and self-efficacy, were also assessed. Results: Participants smoked, on average, 14 cigarettes/day; 53.2% were nicotine dependent, and 58% had made at least one quit attempt in the past year. Smoking behaviors were primarily associated with demographic variables; mediators of cessation were primarily associated with age at cancer diagnosis and perceived vulnerability to smoking-related illnesses. Severity of psychologic symptoms was associated with increased smoking rate, high nicotine dependence, and low self-efficacy. Support for quitting was related to smoking rate, number of quit attempts, readiness to quit smoking, and self-efficacy. Conclusion: These findings indicate that many cancer survivors who smoke are receptive to smoking cessation interventions. Factors related to mediators of smoking cessation might be particularly good targets for intervention.
TREATMENT OF childhood cancers has become increasingly successful. Just three decades ago, most childhood cancers were fatal; today, more than 70% of children diagnosed with cancer survive 5 years or longer.1 Although childhood cancers are now largely curable, a variety of long-term medical effects, such as therapy-related second cancers and damage to vital organs, can develop and persist among survivors.2–4 Exposure to cancer-causing agents such as tobacco are known to influence an individual’s risk for disease5–10 and, thus, may further increase the risk of adverse late effects among cancer survivors.11 However, several studies have documented that smoking rates among childhood cancer survivors are similar to or only slightly lower than corresponding age-specific rates for the general population.12–14 The largest and most recent study of childhood cancer survivors, the Childhood Cancer Survivors Study (CCSS), revealed that 17% of survivors were active smokers and another 10% were former smokers.12 Persistence of smoking among survivors necessitates the development of effective smoking cessation interventions for this group. This article describes the baseline data collection and intervention design for Partnership for Health Study (PFH), a smoking cessation intervention trial for smokers in the CCSS. The intervention was guided by several health behavior theories that evaluate key mediating mechanisms by which health behavior change occurs. In particular, we draw on Social Cognitive Theory,15 which emphasizes self-efficacy and social support,16 and the Transtheoretical Model.17 The Transtheoretical Model recognizes the importance of matching the content and message of behavior change interventions to participants’ level of readiness to change. There are considerable data in the literature to suggest that self-efficacy and readiness to change are important mediating mechanisms for smoking cessation.18–22 In addition, nicotine dependence is a key factor in the failure of quit attempts.23 Data are scarce regarding how the experience of having had cancer during the developmental years affects these mediating variables. Our purpose is to evaluate the effect of demographic, psychosocial, and cancer-related factors on smoking behaviors, as well as self-efficacy and readiness to quit within a sample of adult survivors of childhood cancer.
The CCSS was established by the National Cancer Institute as a large research resource for studies of childhood cancer.24,25 Coordinated by the Department of Pediatrics at the University of Minnesota, CCSS represents the largest and most comprehensively characterized research cohort of childhood cancer survivors ever assembled in the United States and Canada. Baseline data for CCSS were collected between 1994 and 1998 via a mailed survey; smokers were identified and invited to participate in another study, PFH, with a focus on smoking and health issues. Baseline data for PFH were collected between 1999 and 2000 via a telephone survey. Follow-up data collection was completed in December 2001.
PFH Study Design Self-help intervention. Participants in this condition received both a letter from the study investigators highlighting the importance of smoking cessation to reduce the risk of secondary cancers and the National Cancer Institute’s "Clearing the Air: How to Quit Smoking and Quit for Keeps" manual.26 Self-help was used rather than a no-intervention control group because of the ethical issues associated with not providing some smoking cessation assistance for this high-risk group of smokers. Peer-delivered telephone-counseling intervention. Participants in this condition received a telephone-delivered peer-counseling program and tailored and targeted printed intervention materials. With the consent of their primary care physicians, interested participants and their partners who wanted to quit smoking were mailed nicotine replacement therapy, which was donated to the study by SmithKline Beecham, Pittsburgh, PA. An 8-week supply of patches was provided to heavy smokers, and a 6-week supply was given to light smokers. The baseline survey was used to tailor the intervention materials. Each participant in the intervention group received a personalized health profile that was tailored to his or her readiness to quit smoking, feelings about the pros and cons of smoking, nicotine dependence, and cancer treatments. Intervention materials also included smoking cessation manuals that were targeted to each participant’s stage of change, a personalized health decision-making worksheet, and targeted tip sheets on topics related to smoking cessation, health, and survivorship. As part of the intervention calls, peer counselors assessed participants’ movement through the stages of change by reading a series of statements that "others have made about smoking"; participants indicated which statement best described how they felt. On the basis of these responses, counselors sent updated targeted materials to participants who moved through the stages. For example, if a participant moved from the precontemplation stage of readiness to the contemplation stage, the smoking cessation manual for contemplators was sent. The intervention was based on the principles of motivational interviewing,27 which emphasizes the smoker’s choice, discrepancies between goals and behavior, personal responsibility for change, and enhancement of self-efficacy. Because a strong survivorship identity has been documented,28 we chose childhood cancer survivors as counselors. Survivors may be more receptive to messages about behavior change advanced by peers rather than health professionals. Each participant received up to six calls within a 4-month period; the goal was to provide at least four calls, with the opportunity for two additional follow-up calls. Call content was tailored to the participant’s stage of readiness to quit smoking. Counselors focused on smoking cessation strategies with participants who expressed interest in quitting.
Sample
From the CCSS database, 1,769 subjects were identified as current smokers in the baseline survey. Of these, 1,391 were alive and had a known address at the beginning of PFH. Because relapse rates are high among former smokers, 890 subjects who reported the status of former smoker in the baseline survey were identified; 398 were alive, had a known address, and were screened for current smoking status. Of the 1,789 CCSS participants with known addresses, 528 (29%) were not eligible, and 1,261 individuals were potentially eligible. We contacted 959 individuals and determined them to be eligible but were unable to contact 302 individuals (17%); thus, their eligibility status is unknown. Seven hundred and ninety-six subjects were enrolled (83% of known eligible; 63% of potentially eligible), and 162 declined participation in PFH (17% of known eligible; 13% of potentially; Fig 1
After the consent was obtained and the baseline survey was completed, participants were randomly assigned to either the self-help condition or the peer counseling condition. Follow-ups were conducted 8 and 12 months after baseline. Data for this article are from the baseline survey only.
Measures Psychosocial variables. To determine the social norms related to the smoking habits of the participants, we assessed the number of family and friends of the respondent who smoke. We assessed support for quitting by asking how much they had been encouraged by friends and family to quit during the last 12 months. Social support was measured with the emotional/informational support subscale of the Medical Outcomes Study.29 In addition, we assessed perceived vulnerability specific to cancer and smoking using Tyc’s Perceived Importance of Health Protection scale, developed for adolescent cancer survivors;30 this measure was adapted to include an older age range. We also assessed knowledge of the risks of smoking for cancer survivors using the Tobacco Knowledge questionnaire.30 Severity of psychological symptoms was assessed using the global severity index (GSI) of the Brief Symptom Inventory 18.31 These 18 items ask participants to rate the severity of depression, anxiety, and somatic symptoms over the last several days. Responses are given on a scale of 1 (not at all) to 5 (extremely). Finally, perceived health status was assessed with one item from the 12-item Short-Form Health Survey.32 Smoking behaviors. Smoking rate was assessed using self-reported number of cigarettes smoked per day. However, seven of the 796 participants were not daily smokers, and thus we calculated number of cigarettes smoked per week for all participants as the measure of smoking rate. Self-reported number of quit attempts in the past 12 months was ascertained. Nicotine dependence was determined by assessing time to first cigarette.33 Smoking within the first 30 minutes after waking is considered nicotine dependent. Motivation and self-efficacy. Readiness to quit smoking was assessed using the stages of change algorithm.34 Participants were placed into one of three stages of readiness for change: (1) precontemplators, not thinking about quitting in the next 6 months; (2) contemplators, thinking about quitting in the next 6 months; or (3) in preparation, thinking of quitting in the next month and having made recent attempts to quit. Because all the participants in this study were active smokers, no one was placed in the action stage. Self-efficacy19 was assessed by asking participants, "How confident are you that you will have quit smoking one month from now?" Responses were given on a scale of 1 (not at all confident) to 5 (extremely confident).
Data Analysis
Response Bias Response bias was assessed by testing for differences between the participants in PFH and the smokers in the overall CCSS database. For the most part, differences, although statistically significant, are quite small. For example, participants in the current analyses were approximately 10 months older when they were diagnosed than nonparticipants (P  .01); participants in the current study also smoked 1.23 more cigarettes per day (P .01). Two demographic differences are more meaningful: participants in this study were more likely to be female (46.6% v 37.1%, P = .0001) and more likely to be white (88.8% v 81.1%, P < .0001) compared with smokers in the CCSS database.
Descriptive Analyses
Psychosocial variables. Table 2  provides descriptive statistics for the psychosocial variables. Only a small number of participants had no smokers in their social networks; more than half of participants reported that half or most members in their social networks were smokers. Most of the respondents felt that they would receive support to quit smoking from their families and friends. On average, the level of social support reported by participants was high. The perceived vulnerability to health problems resulting from smoking because of previous treatment for cancer was high; only 12% participants perceived a low or slightly increased risk for smoking-related illnesses. In addition, the participants had high levels of knowledge about tobacco and health. This sample did have a slightly increased rate of psychological symptoms. Compared with community norms in which 10% of the population has elevated scores on the GSI,31 14.19% in the present study had elevated scores. However, this rate of psychological symptoms was lower than in adult cancer patients, in which 23.8% of the population has elevated scores.35 Most of the participants rated their health as good, very good, or excellent.
Smoking behaviors. On average, participants smoked approximately 102 cigarettes per week (SD = 84, range = 1 to 700). (Note: in the modeling reported below, smoking rate was transformed to the log scale so as to meet the assumption of normality for the linear modeling. All reported P values and coefficients are on the log scale.) More than half (n = 458, 58%) had made at least one 24-hour quit attempt in the past year; 423 participants (53.2%) were nicotine dependent. Motivation and self-efficacy. Most of the respondents were thinking about quitting; only 143 (18.1%) were in precontemplation, 337 (42.7%) were in the contemplation stage, and 310 (39.2%) were in preparation. However, self-efficacy levels about quitting smoking within the next month were low (mean = 2.21, SD = 1.19).
Multivariate Analyses
Quit attempts. The response variable for quit attempts was ordinal, and therefore, we used the proportional odds model36 and compared the one to two and three to four quit attempts groups to the no-quit attempts group. The final polytomous logistic regression model is presented in Table 4 . Younger participants made more quit attempts; participants who reported a lot of encouragement from family and friends for quitting made more quit attempts than those who reported no support; participants who saw themselves as more vulnerable to smoking-related illnesses made more quit attempts; and participants whose social networks were at least half nonsmokers made more quit attempts than those whose networks were comprised of mostly smokers. However, participants who reported greater levels of emotional and informational social support made fewer quit attempts.
Nicotine dependence. The final logistic regression model is presented in Table 4  . Each year of age increased the odds of being nicotine dependent by 4%. Lower levels of education ranged from doubling to nearly tripling the odds of being nicotine dependent compared with college graduates. Those with severe psychological symptoms were 1.74 times more likely to be dependent compared with those without severe symptoms. Those with greater proportions of smokers in their social network were between 1.72 and 2.16 times as likely to be nicotine dependent as those with none or few smokers in their social group.
Motivation to quit.
Readiness to change was created as a three-category variable, and therefore, analyses also used proportional odds logistic models to compare contemplation and preparation stages to the precontemplation stage. The final polytomous model is presented in Table 5
Self-efficacy. Multivariate models of self-efficacy used the proportional odds assumption comparing both the high- and medium- self-efficacy groups to the low-self-efficacy group. The final logistic model is presented in Table 5 . Men were 1.51 times more likely to have higher self-efficacy than were women. In addition, compared with those who received little or no support for cessation, those who received a lot of support had higher levels of self-efficacy. Being diagnosed at a younger age was associated with higher levels of self-efficacy (continuous increasing age variable odds ratio = 0.97, P .02). Increases in perceived vulnerability were also associated with greater self-efficacy. Finally, compared with participants who did not have elevated scores on the GSI, those participants with severe psychological symptoms had lower levels of self-efficacy.
PFH is a randomized smoking-cessation intervention targeting childhood cancer survivors. In this article, we describe the associations between demographic, psychosocial, and cancer-related variables and smoking and known mediators of smoking cessation in this population. We found that more than half of childhood cancer survivors who smoked were nicotine dependent, defined as smoking within 30 minutes of waking. Although use of a one-item measure of nicotine dependence is limited, this variable has been shown to be highly related to biochemical markers of nicotine37,38 and to smoking cessation.39 More than half of the participants had made at least one 24-hour quit attempt in the past year, and most were thinking about quitting. The high rate of nicotine dependence and unsuccessful quit attempts indicate that pharmacotherapies might be helpful for this group. Nicotine replacement therapy and bupropion have both been shown in other studies to be effective in increasing quit rates.40–42 A major focus of this study was to examine variables that are related to known mediators of smoking cessation, particularly motivation to quit and self-efficacy for quitting. It is noteworthy that such a large percentage of the study participants (82%) were considering quitting. In a survey of representative smokers in California, about 64% were at least thinking about quitting.43 In other states that have fewer resources devoted to tobacco control, interest in quitting may be even lower. Childhood cancer survivors might be more motivated to quit smoking than the general population, and thus particularly receptive to smoking cessation interventions. An alternative explanation is that CCSS smokers most interested in quitting agreed to participate in this study. Although the participation rate was similar to other studies that have proactively recruited participants rather than recruiting smokers who express interest in quitting, 17% of eligible participants refused. Despite the higher rate of readiness to quit among childhood cancer survivors compared with the general population, our participants were not very confident in their ability to quit smoking. More than half of the participants reported making at least one unsuccessful quit attempt during the previous year. Thus, interventions that capitalize on motivation while building skills and self-efficacy are needed. In multivariate analyses, social support for quitting smoking was consistently related to both smoking and mediators of smoking cessation. Participants who reportedly had support for smoking cessation smoked more than participants who reported no support. Although this finding does seem counterintuitive, it may be that smoking more heavily elicits recommendations and encouragement to quit from family and friends.44 Heavier smokers might also experience more smoking-related health concerns, and thus get more encouragement to quit. It is possible that lighter smokers are observed smoking less often as a function of their lower smoking rate, and, thus, provide fewer cues to prompt support from others. In contrast, participants who received more encouragement from family and friends to quit smoking made more quit attempts, were in higher stages of readiness to change, and felt more confident in their ability to quit smoking. With the exception of social support for quitting, we found that there were different correlates of smoking behaviors and mediators of cessation. In multivariate analyses, demographic variables were the most consistent correlates of smoking behaviors among childhood cancer survivors. Consistent with findings from the general population, older participants smoked more, had made fewer recent quit attempts, and were more likely to be nicotine dependent. Less-educated participants smoked more and were more likely be nicotine dependent. Participants with a higher percentage of smokers in their social networks smoked more, had made fewer quit attempts, and were more likely to be nicotine dependent. In contrast, perceived vulnerability, age at cancer diagnosis, and sex emerged as the most consistent predictors of both readiness to quit and self-efficacy. Men reported more confidence in their ability to quit, as did those who were diagnosed with cancer at older ages. Participants who believed that their cancer history put them at increased risk for smoking-related illnesses were in higher stages of readiness to change and felt more confident in their ability to quit. Age and age at diagnosis were associated with the outcome variables, except readiness to quit, in the bivariate analyses; therefore, both variables were considered in the multivariate analyses. Because these two variables are so highly correlated (r = .75), colinearity problems were averted by choosing only one for the final models. Older participants are likely to have smoked for a longer period of time, which could account for their higher rates of smoking and nicotine dependence. Most of the participants in this study started smoking during their teenage years, suggesting that prevention and cessation interventions should begin in childhood and adolescence.45 In addition, a significant portion (18%) of the smokers in this study had started smoking before they were diagnosed with and treated for cancer.
Limitations of the present study should be noted. The large number of potential participants who could not be located or contacted lowered the response rate. Despite availability of a previously accrued cohort, recruitment for this study proved difficult (Fig 1 There are several important strengths of this study. First, our sample was drawn from the CCSS cohort, which is the largest and most comprehensively characterized research cohort of childhood cancer survivors ever assembled in North America.12 Second, we evaluated the associations between psychosocial variables that are amenable to intervention and smoking and known mediators of smoking cessation. Results demonstrate that certain demographic characteristics (age, education, and number of smokers in the social network) are related to smoking. Although these variables are helpful in identifying risk subgroups, they are less useful in designing interventions. In contrast, psychosocial variables (eg, support for cessation, perceived vulnerability) were related to mediators of cessation and are amenable to intervention. Third, we specifically focused on mediators of behavior change. It has been argued that the effectiveness of behavior change interventions has been limited because interventions do not substantially effect change in mediating variables; thus, research that examines the relationships between theoretical mediators and outcomes should be given high priority.46,47 PFH was designed to emphasize these mediators of smoking cessation. The results of this study indicate that childhood cancer survivors may be especially receptive to smoking cessation interventions. Because of the high rate of nicotine dependence, pharmacotherapy should be considered as a key intervention strategy for this population. The high rate of smoking in these survivors’ social networks indicates that interventions should focus on increasing support for quitting and addressing the large percentage of network members who also smoke, increasing accurate perceptions of disease risk related to smoking in survivors, and, interventions beginning in childhood and adolescence.
Supported by grants U24-CA55727 and RO1-CA77780 from the National Institutes of Health; support provided to the University of Minnesota by the Children’s Cancer Research Fund, and support to the Dana-Farber Cancer Institute by Liberty Mutual and the Harry and Elsa Jiler American Cancer Society Research Professorship (F. Li).
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47. Hansen WB, McNeal RB Jr: The law of maximum expected potential effect: Constraints placed on program effectiveness by mediator relationships. Health Educ Res Theory Pract 11:501–507, 1996 Submitted June 21, 2002; accepted October 3, 2002. This article has been cited by other articles:
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Copyright © 2003 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
2 tests for categorical variables and general linear models for continuous variables) were used to evaluate the relationships among the five dependent variables and the independent variables. Multivariate models of the dependent variables were constructed using the independent variables that were significant at the P = .1 level in the bivariate analyses. Logistic regression (dichotomous and polytomous) was used to test models of number of quit attempts, nicotine dependence, readiness to quit smoking, and self-efficacy; linear regression was used to test models of smoking rate. In all multivariable models, confounders and interactions were tested. 
