Paths to tobacco abstinence: A repeated-measures latent class analysis.

Relapse is the most common outcome of a smoking cessation attempt. As many as 94% of smokers who cease smoking for at least one day resume smoking within months (Centers for Disease Control & Prevention, 2011). Even with the best available treatments, at most, one-third of smokers will achieve 6 or 12 months of continuous abstinence (Fiore et al. 2008; Stead & Lancaster, 2012). Although relapse rates vary in change efforts across drugs of abuse (e.g., Amato, Minozzi, Davoli, Vechhi, 2011; Bonn-Miller & Moos, 2009; Moos & Moos, 2006) and other health-relevant behaviors and conditions (Jeffery et al., 2000; Linke, Gallo, & Norman, 2011), difficulty in initiating and sustaining behavior change are problems common to all of these domains (McLellan, Lewis, O’Brien, & Kleber, 2000). Although most cessation studies focus on distal outcomes (typically continuous or prolonged abstinence for 6- or 12-months; Fiore et al., 2008; Stead & Lancaster, 2012), survival analyses suggest most returns to smoking begin within the first few days of a change attempt, and this is therefore a critical period in which to intervene (Piasecki, Fiore, McCarthy, & Baker, 2002; Shiffman et al., 2006). Early outcomes are crucial: individuals who smoke at all during the first weeks of their quit attempt are very likely to smoke in the long run (Kenford et al., 1994; Wileyto et al., 2005). Across many other domains (e.g., treatment for depression, anxiety, eating disorders, and alcohol use disorders), early response to treatment is an excellent indicator of later treatment response, in addition to being useful in the development of adaptive treatments (e.g., Ciraulo, Dong, Silverman, Gastfriend, & Pettinati, 2008; Steidmann et al., 2013). The focus on distal outcomes in smoking cessation research makes sense from public health and treatment efficacy perspectives; lasting abstinence will improve individual and community health (U.S. Department of Health and Human Services, 1990) and this is the outcome we want to influence through treatment. Yet, studies of distal outcomes do not inform our understanding of how smokers achieve distal success or failure or how treatment influences this process (Brandon, Vidrine, & Litvin, 2007). To advance our understanding of the processes that lead to distal outcomes, we may need to examine the process in new ways. Rather than adopting simple, binary, and ultimately arbitrary, definitions of relapse based on convention (e.g., smoking any amount 7 days in a row, Ossip-Klein et al. 1986; smoking at least 5 cigarettes per day in 3 consecutive days, Shiffman et al., 2006; smoking at least once a month, Herd & Borland, 2009), research might productively focus on objectively characterizing patterns of daily smoking and abstinence. In a similar spirit, Hughes and colleagues have recently tracked day-to-day intentions to smoke and smoking in community smokers hoping to quit, documenting remarkable instability in smoking intentions and behavior across days (Hughes et al., 2013, 2014; Peters & Hughes, 2009). Such natural history studies illustrate the complexity of smoking over time, and the degree to which simple classifications of smokers as smoking/abstinent long-term may mask this complexity, and thus perhaps keep us from understanding the processes we try to affect through treatment. The current project sought to identify latent classes of smokers based on patterns of smoking status (any smoking vs. no smoking each day) over the first month of a quit attempt. This secondary data analysis aimed to generate new information about the number, nature, and prevalence of smoking or abstinence patterns during the initial phase of an attempt to change. We sought to empirically identify underlying classes of smoking status trajectories, using repeated measures latent class analysis (RMLCA). RMLCA of daily smoking status has the potential to enhance our understanding of the varying paths smokers may take to ultimate abstinence or smoking. Other approaches to modeling daily smoking, such as generalized estimating equations (GEE) or mixed effect logistic regression modeling (cf. Li, Wileyto, & Heitjan, 2011), fit functional forms such as linear or quadratic trends. In contrast, RMLCA can capture and identify arbitrarily complex patterns of smoking (e.g., step patterns) that may be observed early in a quit attempt (Collins & Lanza, 2010). In addition, RMLCA allows investigators to estimate the size of subpopulations of individuals who are expected to follow similar patterns of change over time (i.e., share latent class membership), and to link latent class membership to both predictors and distal outcomes (Bray, Lanza, & Tan, in press; Collins & Lanza, 2010). Class membership may also be linked to treatment, and this may help explain treatment effects on distal outcomes, particularly as treatments often seem to have their greatest effects early in the quit attempt (McCarthy et al., 2008a; Piasecki et al., 2002). Importantly, RMLCA class membership is a categorical latent variable; latent variable approaches help to separate error variance from class-relevant variance. Latent class modeling is a person-centered approach that may be particularly useful in identifying smokers at high-risk of relapse (Collins & Lanza, 2010), which could aid treatment planning. Variable-centered approaches, such as GEE and mixed-effects regression models (e.g., Li et al., 2011), are very useful in testing specific hypotheses about independent variables, including treatment, but do not help identify smokers displaying different patterns of smoking over time. Although latent class analyses have examined tobacco use before, these analyses have mostly focused on tobacco use and other risk behavior initiation in adolescents and young adults over long time-frames (e.g., Chen et al., 2004; Rose et al., 2007; Sutfin, Reboussin, McCoy, & Wolfson, 2009), rather than on cessation over shorter time-frames. Other latent class analyses have focused on subtypes of tobacco dependence (e.g., Furberg et al., 2005; Storr, Zhou, Liang, & Anthony, 2004; Timberlake, 2008) or stage of change (Guo, Aveyard, Fielding, & Sutton, 2009; Harell, Trenz, Scherer, Martins, & Latimer, 2013). The only latent class analyses related to smoking cessation identified latent classes of first lapse contexts (Deiches, Baker, Lanza, & Piper, 2013) and latent classes of retrospectively assessed withdrawal severity (Xian et al., 2005). To our knowledge, no repeated measures latent class analyses of smoking during the course of a quit attempt have been conducted. The current project analyzed data from a clinical trial in which adult, daily smokers were randomized to one of five pharmacotherapy regimens or placebo (Piper et al., 2009), plus brief individual smoking cessation counseling. This efficacy trial evaluated the following smoking cessation treatments: nicotine patch, nicotine lozenge, bupropion, the combination of nicotine patch and lozenge, and the combination of bupropion and nicotine lozenge, relative to one another and to placebo (Piper et al., 2009). Based on this and other recent trials, it appears as though combination nicotine replacement therapy yields the highest short- and long-term (6-month) abstinence rates and is superior to monotherapies and placebo (Cahill, Stevens; Perera, & Lancaster, 2013; Piper et al., 2009; Smith et al., 2009). The original efficacy trial (Piper et al., 2009) focused on binary abstinence outcomes at various time points (i.e., cessation in the first week of the quit attempt, 1 week, 8 weeks, or 6-months post-quit). A subsequent analysis focused on progression through cessation milestones (Shiffman et al., 2006) and found treatment effects on initial abstinence, lapse occurrence and latency, and relapse occurrence and latency (Japuntich, Piper, Leventhal, Bolt, & Baker, 2011). The current study extends these analyses by examining treatment effects on complex patterns of smoking during the first month of a quit attempt, when most lapses and relapses have occurred and treatments have their largest effects (McCarthy et al., 2008a; Piasecki et al., 2002; Piper et al., 2009). We anticipated that we would find a class of smokers who never quit (i.e., whose probabilities of abstinence remained near 0% across the first month of the quit attempt), a class of smokers who quit immediately and stayed quit (i.e., whose probabilities of abstinence remained near 100%), and some intermediate groups of smokers of particular interest who were engaged in efforts to change their smoking status, but struggling to do so. We also examined the extent to which membership in latent classes based on first-month smoking was predictive of later (six-month) biochemically verified abstinence. We expected that groups who struggled to achieve or maintain abstinence in the first month would be less likely to be abstinent five months later. The project also examined how treatment and theoretically- and clinically-relevant individual differences were related to the smoking pattern classes identified by RMLCA. In terms of covariates, we focused on variables identified in previous research as risk or protective factors for relapse (e.g., nicotine dependence, quitting self-efficacy). This analysis served simultaneously to validate the class solution, by demonstrating associations between class membership and known relapse risk factors, and to determine the extent to which these variables might be useful in identifying smokers at risk for smoking in the first weeks of a cessation attempt. Thus, the study aimed to identify distinct patterns of smoking early in a quit attempt, and to identify treatment effects on and individual differences associated with those patterns.