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Effectiveness of stopsmoking medications: findings from the international tobacco control (itc) four country survey

RESEARCH REPOR T
Effectiveness of stop-smoking medications: findings
from the International Tobacco Control (ITC)
Four Country Survey

Karin A. Kasza1, Andrew J. Hyland2, Ron Borland3, Ann D. McNeill4, Maansi Bansal-Travers2,
Brian V. Fix2, David Hammond5, Geoffrey T. Fong5,6,7 & K. Michael Cummings8

Division of Cancer Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, NY, USA,1 Department of Health Behavior, Roswell Park CancerInstitute, Buffalo, NY, USA,2 VicHealth Centre for Tobacco Control, The Cancer Council Victoria, Carlton, Victoria, Australia,3 Division of Epidemiology and PublicHealth, UK Centre for Tobacco Control Studies, University of Nottingham, Nottingham, UK,4 School of Public Health and Health Systems, Propel Centre forPopulation Health, University of Waterloo, Waterloo, Ontario, Canada,5 Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada,6 OntarioInstitute for Cancer Research, Toronto, Ontario, Canada7 and Department of Psychiatry and Behavioral Sciences, Hollings Cancer Center, Medical University ofSouth Carolina, Charleston, SC, USA8 ABSTRACT
To evaluate the population effectiveness of stop-smoking medications while accounting for potential recall bias by controlling for quit attempt recency. Design
Prospective cohort survey. Setting
Australia and the United States. Participants
A total of 7436 adult smokers (18+ years) selected via random digit dialling and interviewed as part of the International Tobacco Control Four Country Survey (ITC-4) between 2002 and 2009. Primary analyses utilized the subset of respondents who participated in 2006 or later (n = 2550).
Measurements
Continuous abstinence from smoking for 1 month/6 months. Findings
recalled making a quit attempt within 1 month of interview, those who reported using varenicline, bupropion or nicotine patch were more likely to maintain 6-month continuous abstinence from smoking compared to those who attempted to quit without medication [adjusted odds ratio (OR) 5.84, 95% confidence interval (CI) (2.12–16.12), 3.94 (0.87–17.80), 4.09 (1.72–9.74), respectively]; there were no clear effects for oral NRT use. Those who did not use any medication when attempting to quit tended to be younger, to be racial/ethnic minorities, to have lower incomes and to believe that medications do not make quitting easier. Conclusions
Consistent with evidence from randomized con- trolled trials, smokers in the United Kingdom, Canada, Australia and the United States are more likely to succeed in quit attempts if they use varenicline, bupropion or nicotine patch. Previous population studies that failed to find an effect failed to control adequately for important sources of bias.
Keywords
Nicotine replacement therapy, population effectiveness, recall bias, smoking cessation, stop-smoking Correspondence to: Karin A. Kasza, Division of Cancer Prevention and Population Sciences, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263, USA. E-mail: karin.kasza@roswellpark.org Submitted 22 February 2012; initial review completed 9 April 2012; final version accepted 29 June 2012 INTRODUCTION
stop-smoking medications (SSMs) [7–9], although the use of SSMs has been increasing over time [10–13].
Numerous randomized, placebo-controlled clinical trials It is important to assess the ‘real-world’ effectiveness have demonstrated that nicotine replacement therapy of SSMs in the contexts in which they are being used, [1,2], bupropion [3] and varenicline [4] are efficacious because compliance with medication use instructions in increasing the odds of smoking cessation, and clinical in the controlled trial setting is probably higher than it practice guidelines recommend the use of pharmaco- is in the population setting, and because subjects who therapy as a first-line agent for treating nicotine depend- are selected to participate in clinical trials may not be ence [5,6]. Despite the recommendations, the majority representative of the self-selected smokers who ultimately of smokers who attempt to quit do so without the aid of use the medications [14]. The real-world effectiveness of Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
varenicline and bupropion as quitting smoking treat- decreases with increasing time since the quit attempt ments has not yet been assessed widely in population [35]. In addition, Borland et al. found that, compared studies, and studies of the effectiveness of nicotine to those who attempted to quit with medication, those replacement therapies (NRT) have produced mixed who attempted to quit without medication recalled results [15–26]. For example, Pierce & Gilpin reported their last unsuccessful quit attempts as starting more that NRT is ineffective since becoming available over-the- recently, with a significantly greater proportion of counter (OTC) [17], Hyland et al. found nicotine patch unaided attempts being reported in the previous month quit rates to be lower after becoming available OTC [18], [36]. Adjusted for nicotine dependence to equate groups but Thorndike et al. found quit rates among those using on the likelihood of making a recent quit attempt, this NRT to be nearly identical during the period before and association demonstrates that failed quit attempts occur- after the medication became available OTC [19]. Studies ring longer ago are more likely to be forgotten by those evaluating the effectiveness of NRT without focusing on the impact of OTC availability have also produced mixed results [20–26]. For example, Shiffman et al. reported that population-based evaluations of SSM effectiveness that use of NRT was associated with decreased rates of control for systematic differences between self-selected smoking cessation, and pointed to bias inherent in retro- medication users and non-users, as is carried out rou- spective surveys to account for this finding [20]. Similarly, tinely in population studies, and be limited to respondents Alberg et al. found that NRT users were less likely to for whom systematic recall bias is minimal (i.e. those quit smoking than were those who never used NRT [21].
who recalled their last quit attempts as occurring recently Recently, Alpert et al. concluded, surprisingly, that NRT is relative to survey date), which has not yet been carried not effective for long-term smoking cessation because out methodically in population studies. The purpose they found relapse rates between those who quit with and of this study was to evaluate the real-world effective- without NRT to be equivalent in a period of a year or ness of SSMs while accounting for previously considered more after use ceased [22], which does not relate to the confounders, as well as recall bias. We also describe question of medication effectiveness. Others have found the characteristics of medication users, such that the positive effects [23–26]. West & Zhou, using data col- comparability of our sample to previous population- lected every 3 months, reported that cessation rates were two to three times higher among NRT users compared to nonusers [23]. Similarly, a prospective evaluation of the New York State Smokers’ Quitline program to give away free nicotine patches showed quit rates among those Participants
who received the patches to be nearly two times higher than rates observed prior to the implementation of the Participants were adults (aged 18 years or older) who program [24,25]. Additionally, Gilpin et al. found a ces- were interviewed as part of the International Tobacco sation advantage among NRT users living in smoke-free Control Four Country Survey (ITC-4). The ITC-4 is an homes, and suggested that medication may be more effec- ongoing prospective cohort survey designed initially to tive among those who are more motivated to quit [26].
evaluate the psychosocial and behavioral impacts of Within the context of the widespread observation various national-level tobacco control policies. Beginning that population level quit rates have not increased over in 2002, nationally representative samples of smokers time despite increases in usage of stop-smoking medica- from the United Kingdom, Canada, Australia and the tions [10,11,27,28], some have taken the failure to United States were surveyed using standardized data col- consistently find positive effects of NRT as evidence lection methods and measurements. Detailed descrip- that it is not effective in the real world [17,29]. How- tions of the survey procedures can be found elsewhere ever, others have pointed to confounders inherent in [37–40]. Random digit dialing was used to recruit population-based survey designs that might explain the smokers within strata defined by geographic region lack of compelling real-world evidence for effectiveness and community size to complete the 45-minute survey.
[15,16,20,30–36]. First, medication users in the general Response rates ranged from 26% in the United States population are systematically different from non-users in to 50% in Canada, which are comparable with other important characteristics, such as being more heavily telephone surveys in these countries. Previous analyses addicted to nicotine, which predisposes medication users have demonstrated good correspondence between the to be unsuccessful in quitting [7,20,30–32]. Secondly, demographic characteristics of those who responded retrospective survey designs may be subject to biased to this survey and the characteristics of respondents recall of failed quit attempts [20,33–36]. It has been from national benchmark surveys, indicating that non- shown that the likelihood of recalling a quit attempt response is not a source of systematic bias in this study Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Effectiveness of stop-smoking medications [38]. Participants were re-contacted approximately in waves 5–7. Quitters were asked: ‘When did your most annually to complete follow-up surveys, and those lost recent quit attempt start?’, while smokers were asked: to attrition (~30% on average) were replenished each ‘How long ago did your most recent quit attempt end?’ year to maintain a sample size of ~2000 participants per and ‘How long were you quit for, on your most recent quit country [39]. Previous analyses of attrition rates have attempt?’. The number of days elapsed since the start of indicated that age, gender and racial/ethnic groups vary the most recent quit attempt (calculated as the sum of with respect to retention [40]; thus, we compared those days since the end of the most recent quit attempt and lost to follow-up with those who were retained in the length of the most recent quit attempt for smokers) was sample, and we performed sensitivity analyses of medica- used to indicate quit attempt recency.
tion effectiveness supposing that those lost to attrition This study used data collected during the first eight survey waves (2002–09). Analyses were restricted to During each survey wave, respondents were asked to respondents who participated in at least two consecutive recall their use of medications since the last survey, and survey waves and reported making a quit attempt those who reported using medications were asked a series between waves, yielding a total sample eligible for analy- of questions regarding the medications indicated, includ- sis of 7436 individuals. The primary medication effec- ing: ‘What was the main reason you used [the medica- tiveness analyses utilized the subset of respondents who tion]?’. Only those who reported using medication in an participated in 2006 (i.e. wave 5) or later (n = 2550).
attempt to stop smoking completely were considered to be medication users for the purpose of these analyses. The actual wording of all questions used in the different ITC Measures
survey waves can be found at: http://www.ITCproject.
A separate medication use variable was created for To be eligible for enrolment in the ITC survey, respondents each of four types of medication: nicotine gum/other had to report smoking at least 100 cigarettes during oral forms of NRT (i.e. lozenges and sublingual tablets), their life-times and had to smoke at least once in the 30 nicotine patch, bupropion (i.e. Zyban or Wellbutrin) and days preceding the baseline survey. Smoking status was varenicline (waves 5–7 only). Respondents who indi- assessed at first follow-up interview among those who cated that they used more than one type of medication had the opportunity to reach the cessation end-points, at the same time were excluded from analyses used to which were defined as 1 month/6 months continuous evaluate the effectiveness of individual medications (but abstinence from smoking. The smoking status of those were included in analyses evaluating the effectiveness who attempted to quit less than 1 month/6 months prior of any medication use), and were included in analyses to the first follow-up interview, and thus did not have the used to describe the characteristics of medication users opportunity to reach these end-points at interview, was (where they contributed to analyses predicting use of determined using the following question asked at the next each of the four most common types of medication follow-up interview: ‘How long were you quit for, on your quit attempt that had started on [Quit Date from Last The order in which medication use questions were Survey Date (LSD)]?’. Respondents who attempted to quit asked differed between waves 2–4 and waves 5–7; there- within the end-point window and were not contacted fore, criteria for inclusion in analyses differed between during the following wave were excluded from primary these sets of waves as follows: for waves 2–4, medication analyses, even if they were known to have relapsed at first users were defined as those respondents who: (i) reported having made an attempt to quit smoking since LSD; (ii) reported that [type of medication] was the most recent medication used (since LSD); and (iii) reported that [type During each follow-up wave, participants who were of medication] was used to stop smoking completely. For smokers in the previous wave were asked: ‘Have you waves 5–7, medication users were defined as those who: made any attempts to stop smoking since we last talked (i) reported having made an attempt to quit smoking with you?’. All analyses were restricted to respondents since LSD; and (ii) reported using [type of medication] who reported making a quit attempt between waves.
the last time a medication was used to quit (since LSD).
In order to address confounding of the association For all waves, non-users were defined as those who between medication use and smoking cessation due to reported having made an attempt to quit smoking since differential recall of failed quit attempts, a recency of last LSD, and had not used any SSM (for any reason) since quit attempt variable was derived using questions asked Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Demographic and smoking-related characteristics confounding due to systematic recall bias were removed (i.e. those who recalled their most recent quit attempts The following baseline variables were included in the as occurring within the last 3 months, 2 months and 1 analyses: country (i.e. United Kingdom, Canada, Aus- month of interview), and to identify the characteristics of tralia and United States), gender and identified majority/ those who used each of the four most common types of minority group (based on the primary means of medication when attempting to quit smoking.
identifying minorities in each country, i.e. racial/ethnic Specifically, repeated longitudinal logistic regression group in the United Kingdom, Canada and the United analyses were performed such that medication effective- States, and English language spoken at home in ness analyses were adjusted for smoking behavior characteristics measured prior to the quit attempt, and The following time-varying demographic variables predictors of medication use were measured prior to the were also included in the analyses: age group (i.e. 18–24, assessment of use. Repeated analyses via GEEs allowed for 25–39, 40–54 and 55+ years), level of education (defined individuals who were present in multiple pairs of waves to as ‘low’ if respondent completed high school or less contribute multiple observations (if applicable), while in Canada, United States and Australia, or secondary/ accounting for the inherent correlated nature of data vocational or less in the United Kingdom, ‘moderate’ within individuals over time [44,45]. All models included respondent completed community college/trade/ a specification for the binomial distribution of the technical school/some university (no degree) in Canada dichotomous dependent variables, a specification for the and the United States, college/university (no degree) in unstructured within-person correlation matrix, and all the United Kingdom or technical/trade/some university confidence intervals were computed using the robust (no degree) in Australia, or ‘high’ if respondent com- Huber (‘sandwich’) estimator of variance. Model covari- pleted university or postgraduate in all countries), and ance parameters were set at a maximum of 100 itera- annual household income (defined as ‘low’ if it was less tions and convergence tolerance for the coefficient vector than US$30 000 (United States, Canada and Australia) or less than £30 000 (United Kingdom), ‘moderate’ if it Analyses were adjusted for country, gender, age was between US$30 000 and US$59 999 (or £30 000 group, majority/minority group, education, income, HSI and £44 999 in the United Kingdom) or ‘high’ if it was and self-efficacy. Analyses utilizing data from the entire equal to or greater than US$60 000 (or £45 000 in the study period were adjusted additionally for the change United Kingdom); respondents who did not provide this in instrument that occurred between waves 4 and 5, information (~5%) were included in adjusted analyses as and analyses assessing medication effectiveness were restricted to daily smokers of 10+ cigarettes (i.e. the The following time-varying smoking-related charac- typical criterion for medication use). Medication effective- teristics and beliefs, assessed while respondents were ness analyses were performed using both unweighted still smoking, were also examined: nicotine dependence data and weighted data, and both sets of analyses pro- (measured with the heaviness of smoking index (HSI), duced the same conclusions. As the structure of our data- a short form of the Fagerström tolerance question- base renders no single set of longitudinal weights to be naire [42]), self-efficacy [assessed with the item: ‘If you suitable for all respondents, necessitating multiple sets decided to give up smoking completely in the next 6 of weighted analyses, we present only results produced months, how sure are you that you would succeed?’.
Response options were collapsed to: not at all sure +slightly sure (low self-efficacy), moderately sure (moder- ate), very sure + extremely sure (high)], and belief about Ethics approval
medication effectiveness [assessed with the item ‘Stop The study protocol was approved by the institutional smoking medications make it easier to quit’. Response review boards or research ethics boards of the University options were collapsed to indicate agreement (i.e.
of Waterloo (Canada), Roswell Park Cancer Institute strongly agree/agree) versus other (i.e. strongly disagree/ disagree/neither agree nor disagree)].
Kingdom), University of Stirling (United Kingdom), The Open University (United Kingdom) and The Cancer Statistical analyses
All analyses were conducted using STATA version 11 [43]. The generalized estimating equations (GEE) approach was used to evaluate the association between medication use and smoking cessation overall, as well as The odds of 1-month and 6-month continuous abstin- specifically among those for whom increasing amounts of ence from smoking as a function of medication use are Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Effectiveness of stop-smoking medications presented in Table 1, both overall (i.e. since the previous particularly true of younger smokers, minorities, those wave—around 1 year), and stratified by quit attempt with low incomes and those, understandably, who did not recency (i.e. within 3, 2 and 1 month of interview). In the believe medications make quitting easier.
analyses using the full interwave interval, there was only These findings should be interpreted in light of the a small and inconsistent positive effect for NRT, effectively following study limitations: reliance on self-reported replicating previous findings of no or smaller effects.
smoking status (although it is unlikely that successful However, among those who recalled their last quit quitters in the real world, who were neither compelled attempts as occurring within 1 month of interview (i.e.
nor compensated to use medication, would misrepresent the stratum that excluded the most recall bias), vareni- how they achieved cessation), no control over potential cline users were nearly six times more likely to be quit for differences in motivation to quit or differences in relevant 6 months (adjusted OR = 5.84, 95% CI = 2.12–16.12), policy changes (e.g. increases in cigarette prices), the pos- bupropion users were nearly four times more likely to be sibility that some subgroups of the population may have quit for 6 months (adjusted OR = 3.94, (95% CI = 0.87– been under-represented, absence of an assessment of 17.80) and nicotine patch users were four times more medication side effects and reduced sample sizes when likely to be quit for 6 months (adjusted OR = 4.09, 95% analyses were restricted to recent quit attempters (which CI = 1.72–9.74), compared to those who attempted to left insufficient power to detect cross-country differences quit without medication. Indeed, as increasing amounts in effectiveness, P > 0.05 for all country interaction of recall bias were removed, the odds ratios for these medi- cations increased to be higher than those found from Also, prior to wave 6, we could not ascertain whether meta-analyses of randomized controlled trials. However, medication was used specifically during a respondent’s there were only non-significant associations for oral NRT last quit attempt, meaning that results presented in users, regardless of the recall time-frame.
Table 1 indicate estimated effect sizes for those known to As shown in Table 2, those who attempted to quit have used medication at some point during the preced- without medication were generally more likely to be male, ing year. However, beginning in wave 6, an additional to be younger, to be minorities, to have lower incomes, to item was added to the survey allowing for smoking ces- be less heavily addicted to nicotine and to have higher sation to be assessed as a direct function of medication self-efficacy compared to those who attempted to quit use/non-use during the last quit attempt in particular, with medication. Those who agreed that SSMs make it and analyses based on this subset of respondents easier to quit were approximately two to three times more (n = 1731) indicate that all recall bias-reduced estimates of medication effectiveness are higher when assessed as a direct function of respondents’ last quit attempts.
We also further restricted these analyses to respondents DISCUSSION
whose quit attempt lasted for at least 1 day, in an effort to Generally consistent with results from clinical trials, find- exclude short quit attempts that some might not con- ings from this study show that use of varenicline, bupro- sider to be serious, and found that although effectiveness pion or the nicotine patch is associated with increased estimates were somewhat attenuated, the conclusions quit rates compared to quit rates among those attempting drawn from these results were the same as those drawn to quit without medication. Among those for whom sys- tematic recall bias was largely minimized, those who used We carried out several additional analyses to address any of these medications exhibited a threefold or greater the representativeness of our effectiveness findings, increase in 6-month continuous abstinence, with vareni- including: (i) performing analyses using longitudinally cline users experiencing a nearly sixfold increase. Given weighted data, which produced the same conclusions as the limited power, no clear conclusions can be drawn those drawn from Table 1; (ii) statistically comparing about oral NRT use, but any effects appear smaller than those who were lost to follow-up (~30%) with those who those found for the other products. Results also suggest were retained in the sample in terms of demographic, that failure to control for differential recall of unsuccess- smoking-related and medication usage variables, and ful quit attempts between medication users and non- found these groups to be statistically indistinguishable on users may explain the inconsistent results of previous all variables; and (iii) performing sensitivity analyses population-based studies; as we tightened control over in which we supposed that all those who were lost to recall effects, the size of the positive effects for medica- follow-up did not quit smoking, and though the effect tions increased and the effect for NRT patches became sizes for nicotine patch effectiveness and varenicline significant. Lastly, our sample resembles samples from effectiveness were somewhat attenuated, the conclusions previous population studies, in that many smokers did drawn from these results were the same as those drawn not use medication when attempting to quit, and this was Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Effectiveness of stop-smoking medications Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Balanced against the above study limitations are CONCLUSIONS
several strengths, including: (i) the large sample of Consistent with the findings of clinical trials, results from smokers compared to some other studies; (ii) the breadth this study indicate that smoking cessation rates are of the sample (representative from four countries); (iii) higher among those using varenicline, bupropion or the the cohort design, which allowed for longer term out- nicotine patch compared to those attempting to quit comes to be evaluated at subsequent survey waves; (iv) use without medication; however, no clear effects for oral of GEEs, which allowed for repeat longitudinal analyses to NRT use were found. Despite the cessation advantage be performed while accounting for repeated measure- gained by using varenicline, bupropion or the nicotine ments within individuals over time; and (v) measurement patch, however, many of those making quit attempts do of time to recalled events and adjustment for numerous so without the aid of any medication. Thus, in theory, potential confounders of medication effectiveness.
population quit rates could be increased by promoting The association between medication use and recall use of demonstrably effective stop-smoking medications.
of failed quit attempts requires that population-based However, even among those using these medications to evaluations of medication effectiveness account for help them stop smoking, relapse to smoking remains the quit attempt recency [36]. Indeed, results reported in the norm, thus reinforcing the need for efforts to develop and present study show that the estimated magnitude of deliver more effective treatments to help smokers to quit.
effectiveness decreases with decreasing quit attempt recency. Reduction of recall bias can be achieved by using Declarations of interest
prospective cohorts and timely assessments, or by con- trolling, statistically, for time elapsed between events K. Michael Cummings has served as a paid consultant on and measurement of events. Failure to address this bias smoking cessation to Pfizer and Novartis, has received may account for some of the previous inconsistencies payment from Pfizer and GlaxoSmithKline for lectures on observed in the literature; retrospective studies evaluat- smoking cessation to health professionals, and has served ing quit attempts occurring within 1 year of interview as a paid expert witness in litigation against the tobacco generally found NRT to be ineffective [17–21], while a industry. All other authors declare no conflicts of interest.
study using assessments occurring every 3 months and a Acknowledgements
fully prospective study found NRT to be effective [23,24].
Gilpin et al., using a retrospective design, did find a cessa- We would like to thank Timea Partos and Hua-Hie Yong tion benefit of NRT for smokers living in smoke-free of The Cancer Council Victoria for their input regarding homes, and suggested that NRT is more effective among evaluation of quit attempt recall. We are also grateful to those who are more motivated to quit [26].
the anonymous reviewers who provided insightful feed- Although there was a suggestion that oral NRT users back on a previous version of this paper. The major may experience higher continuous abstinence rates than funders of the ITC Four Country Survey are: US National non-users, these rates were statistically indistinguishable Cancer Institute (P50 CA111326, P01 CA138389, R01 from those of non-users. Although our power to detect a CA100362), Canadian Institutes of Health Research significant effect was limited, it remains possible that (57897, 79551, and 115016), National Health and there is no long-term benefit of oral NRT when used in the population setting. We found that more than 80% of 450110, and 1005922), Cancer Research UK (C312/ nicotine gum users reported using fewer than the recom- A3726, C312/A6465 and C312/A11039), the Robert mended eight pieces per day [46], as have other studies Wood Johnson Foundation (045734) and the Canadian [47,48], and it remains plausible that insufficient use Tobacco Control Research Initiative (014578), with addi- contributed to reduced effectiveness.
tional support from the Propel Centre for Population The bias-reduced estimates of varenicline, bupropion Health Impact, the Ontario Institute for Cancer Research and nicotine patch effectiveness shown in our study are and the Canadian Cancer Society Research Institute.
somewhat higher than the clinical trial estimates of None of the sponsors played any direct role in the design medication efficacy [1–4]. This could be due to chance or conduct of the study, in the collection, management, effects but could, plausibly, be real; in real-life settings analysis or interpretation of the data, in the preparation we are testing the combined effect of the drug and non- of the manuscript, or in the decision to submit the manu- specific effects. To the extent that non-specific effects accompany the drug (e.g. the belief that it will help), success rates should be greater than those estimated from References
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