Lapses following alcohol treatment: modeling the falls from the wagon.

The world health organization (2002) has estimated that approximately 76.3 million people worldwide have diagnosable alcohol-use disorders. In the United States, roughly 17.6 million people meet criteria for an alcohol-use disorder (Grant et al., 2004), and only 12.5% of those who meet criteria receive treatment for an alcohol problem (Stinson et al., 1998). Among individuals who receive treatment, 65% to 90% have at least one drink, or a lapse, in the first year following treatment (Maisto et al., 2003; Sutton, 1979). The high rates of lapses following treatment have led researchers to question the effectiveness of treatments (Miller and Wilbourne, 2002), to develop alternative definitions of outcomes (Maisto et al., 2003), and to prescribe aftercare following treatment (McKay et al., 2005). Several studies have demonstrated the importance of early abstinence immediately following treatment (Booth, 2006; Carroll et al., 2000; Elal-Lawrence et al., 1987; Moos and Moos, 2006; Stout, 2003). Stout (2000) concluded that 1, 2, or 4 weeks of continuous abstinence predicted greater long-term success. Booth (2006) reported that early abstinence of 15 weeks or more played an important role in those with successful outcomes (e.g., sustained reduction in drinking quantity, frequency, and related consequences). One question that remains is whether continuous abstinence immediately following treatment is necessary for a successful outcome or whether an individual may experience a lapse following treatment and then return to abstinence or moderate drinking. Based on the cognitive–behavioral model of relapse, it has been hypothesized that a lapse often is a learning opportunity that may help a person cope more effectively with high-risk situations in the future (Carroll, 1996). Consistent with this idea, recent conceptualizations of the relapse process have defined posttreatment alcohol use as a dynamic process, whereby individuals cycle and recycle through drinking and nondrinking states (Hufford et al., 2003; Witkiewitz and Marlatt, 2004). This dynamic often had been observed clinically but had not been tested empirically. Likewise, the question of whether early lapsing still can lead to long-term successful outcomes (moderate drinking or abstinence) had not been tested. Furthermore, the question of whether moderate drinking is even a viable goal for those who are severely alcohol dependent is hotly debated (see Sobell and Sobell, 1995). Some studies concluded that individuals who are severely dependent are able to maintain moderate drinking (Heather et al., 2000; Miller et al., 1992). Other studies, however, showed that alcohol dependence predicts more rapid reinstatement of alcohol use after a period of abstention (Babor et al., 1987) and worse outcomes (increased drinking quantity and frequency) following the initial lapse (Witkiewitz and Masyn, in press). The goal of the current study was to gain a better understanding of posttreatment drinking by studying transitions between drinking and nondrinking states, as well as the relationship between alcohol dependence and drinking states during the first year following treatment. Quantifying patterns of alcohol use and predicting post-treatment drinking has been a goal of addictive behaviors researchers for nearly 30 years (Sutton, 1979). Over the past decade, methodology has been developed that provides opportunities for applying advanced statistical methods to identify variability in substance-use patterns across individuals as a function of time and covariates (Hser et al., 2001; Maisto and Connors, 2006; McKay et al., 2006; Muthen and Muthen, 2000; Stout, 2007). One method, latent growth mixture modeling (LGMM), is a longitudinal-data analytic technique that combines finite mixture modeling with a latent growth-curve model. It has increasingly been employed by addictive behaviors researchers (Jackson et al., 2006; Muthen and Muthen, 2000; Windle et al., 2005). A previous application of LGMM to the study of post-treatment drinking in the Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) data (Project MATCH Research Group, 1993) identified three common drinking trajectories, described as (1) continuously heavy drinkers, (2) drinkers who initially drank heavily and then returned to light drinking or nondrinking, and (3) light drinkers or nondrinkers. The results from that study supported theoretical descriptions of the relapse process. Erratic drinking patterns in the sample, however, were not explained by the model (see Witkiewitz et al. [2007], Figure 1b, p. 379). LGMM estimates variability based on continuous drinking patterns and cannot account for discontinuous changes in behavior. Thus, when individuals transition from frequent to infrequent drinking (i.e., have discontinuous trajectories), the change in drinking is considered error (or misclassification) by the model. Figure 1 Figure 1a. Conditional item probability plots for the three-class models for immediately after treatment and at 6 and 12 months after treatment in the outpatient sample. Class proportions are presented in the legend. LCA = latent class analysis; DrInC ... Latent transition analysis (LTA) has the potential to provide an analysis of discontinuous drinking trajectories by estimating initial drinking status and the probability of jumping between drinking states (Bockenholt, 2005; Dolan et al., 2005; Velicer et al., 1996). By assuming a latent transition process, the shifts from one drinking state (or class) to another can be described in probabilistic terms using latent transition probabilities (Bockenholt, 2005). LTA, which is based on latent class theory (Goodman, 1974; Lazarsfeld and Henry, 1968), already has been shown to be useful in estimating transitions to heavy drinking among young adults (Auerbach and Collins, 2006); in estimating the likelihood of developing alcohol dependence in adulthood (Guo et al., 2000); and in assessing the effectiveness of a substance-misuse prevention program (Graham et al., 1991). By constraining certain parameters, LTA can be used to test several aspects of the data. The current study was designed to determine whether the heterogeneity in drinking behavior following treatment can be explained by discrete drinking states and transitions between drinking states. Specifically, analyses were conducted to address four research questions: Is drinking behavior, as indicated by drinking frequency, quantity, and drinking-related consequences, homogeneous across participants? Based on previous studies of treatment outcomes, it is hypothesized that individuals will display significant heterogeneity in their drinking behavior across time (e.g., Russell et al., 2004). Assuming heterogeneity in drinking behavior at each time point, is there movement between latent drinking classes? Based on previous conceptualizations of relapse and empirical data (Miller, 1996; Witkiewitz et al., 2007), it is hypothesized that drinking behavior will be discontinuous across time, such that individuals may be 100% abstinent one month and 0% abstinent the next month (the “fall from the wagon”) or 0% abstinent one month and 100% abstinent the next month (e.g., “quantum change”; Miller and C’de Baca, 2001). Are drinking states and transitions between drinking states invariant across treatment settings or type of treatments received? Considering previous research on the relationship between alcohol dependence and posttreatment drinking outcomes (Babor et al., 1987; Moos and Moos, 2005), does alcohol-dependence severity relate to drinking states and movement between drinking states across time? It is hypothesized that participants with more alcohol-dependence symptoms will be unlikely to maintain moderate drinking and will be more likely to transition from nondrinking to heavier or more frequent drinking (Babor et al., 1987).