1 How Effective Is Alcoholism Treatment in the United States?* WILLIAM R. MILLER, PH.I)., SCOTT T WALTERS, M.A., AND MELANIE E. BENNETT, PH.D. Department of Psychology, University of New Mexico, Albuquerque, New Mexico, ABSTRACT. Objechtive: Following in the footsteps of several prior attempts, this review seeks a meaningful and data-based answer to the common question of how people fare, on average, after being treated for alcoholism (broadly defined as alcohol use disorders). Method: Findings from seven large multisite studies were combined to derive estimates of the average effectiveness of alcoholism treatment. To provide common outcome measures, conversion equations were used to compute variables not reported in the original studies. Results: During the year after treatment, I in 4 clients remained continuously abstinent on average, and an additional I in 10 used alcohol moderately and without problems. During this period, mortality averaged less than 2 B,. The remaining clients, as a group, showed substantial improvement, abstaining on 3 days out of 4 and reducing their overall alcohol consumption by 87%, on average. Alcohol-related problems also decreased by 60%, Conclusions: About one third of clients remain asymptomatic during the year following a single treatment event. The remaining two thirds show, on average, large and significant decreascs in drinking and related problems. This substantial level of improvement in 'unremitted" clients tends to be overlooked when outcomes are dichotomized as successful or relapsed. (I Stud. Alcohol 62: ,2001) How EFFECTTVE is alcoholism treatment? This is a simple pragmatic question that is asked by legislators, reporters, funding sources, concemed families and clients themselves. It is also a difficult question to answer in a straightforward manner. The questioner often expects a simple response (c.g., a percentage rate of success). Two decades ago there was an informal word-of-mouth "industry standard" of sorts in the United States. in that it was common for programs to claim success rates of 80% or higher (Miller and Hester, 1986). The introduction to the "big book" of Alcoholics Anonymous (1976) implies a similarly high rate of success. Those who conduct alcohol treatment outcome research know that claims of a greater than 80% success rate in any single treatment event do not converge well with carefully observed reality. There are, to be sure, many ways to inflate success statistics. Miller and Sanchez-Craig (1996) provided a tongue-in-cheek list of program evaluation strategies for improving success rates (e.g., exclude poor prognosis clients, disregard lost cases and dropouts, and keep followup very short). Outcomes look less rosy when all cases are included in analyses (e.g., intent to treat), follow-up extends for at least a year after treatment, a high proportion of cases is interviewed, there is a careful and detailed reconstruction of alcohol and other drug use, and self-report is confinmed against collateral interviews, biochemical measures or records (Miller et al., a). Researchers also know that a myriad of complexities forestalls any simple answer to the question of the general effectiveness of alcoholism treatment. * There is no general consensus as to what defines a good outcome, or the period of remission required in order to declare a success. * There are widely varying conceptions of "alcoholism," and people with alcohol-related problems are quite heterogeneous (institute of Medicine, 1990). (For purposes of this review, we have chosen a broad conception of the tem similar to Jellinek's , as encompassing a wide range of severity of alcohol problems and alcohol dependence.) * Although aggregate outcomes remain reasonably stable after 6-12 months, individual cases continue to shift quite a bit in outcome status over time. * It is unclear how much imperfection constitutes a "relapse" (Miller, a) and how much deviation from perfect abstinence defines a treatment failure. * What is described as "trcatment" is highly variable and no blanket endorsement of all forms of treatment can be given (Institute of Medicine, 1990). * Even within the same treatment approach or program, there are often wide differences in therapists' effectiveness, so that it also matters who is doing the treating (Najavits and Weiss, 1994; Project MATCH Research Group, 1998). Received: June 12, Revision: October23, *This research was supported in pan by National Institute on Alcohol Abuse and Alcoholism grants T32-AA07465 and K05-AAO Notwithstanding these complexities, however, there are reasons why the question of alcohol treatment's effectiveness deserves consideration and an answer that is based on more 211
2 212 JOURNAL OF STUDIES ON ALCOHOL / MARCH 2001 than guesswork. Legislators, reporters and third-party payers are understandably dissatisfied with a litany of reasons why the question cannot be answered. We are not the first to attempt an answer to this question based on the treatment outcome literature. Although a comprehensive review of either efficacy or effectiveness research is well beyond the scope of this article, two classic reviews are illustrative. Emrick (1974), reviewing 265 uncontrolled and controlled outcome studies, concluded that about one third of clients abstain and another one third show substantial improvement after treatment, at least over short periods of follow-up. A less optimistic conclusion was reached by Costello et al. (1977), who confined themselves to 80 studies with at least 12 months of follow-up and counted as failures any cases lost to follow-up, arriving at an average alcoholism treatment success rate of 26%. The present review is a further attempt to provide a fair and comprehensible response to the question, "How effective is alcoholism treatment?" We offer the following principles that guided our work: * It is a reasonable question, despite the complexities. People with a life-threatening diagnosis often want to know their chances for survival and recovery. * What is being asked for is an average, a representative sense of treatment outcomes, rather than the best possible scenario. Though there may be substantial variability across populations, clients, programs, therapists and time, the question asks for a reasonable estimate of typical outcomes. * Answers should therefore be based on outcome data for a broad spectrum of populations and treatment approaches. That makes this a very different question from the effectiveness of a particular treatment or program, or the prognosis for a particular person. * The question is not primarily concemed with efficacy (the causal attribution of outcomes to specific aspects of treatment), but rather asks about people's average expected course after treatment, "all else being equal." * Answering the question in a careful, data-based manner can be of scientific as well as practical utility. In his widely cited review, Emrick (1974) sought to establish average alcoholism treatment outcome ratcs, with which specific observed outcomes might be compared. Such comparisons are not straightforward, because outcomes can vary for many different reasons. Nevertheless, we believe that Emrick's quest was worthwhile, to give some reasonably objective basis for an answer to this question. * Common metrics are needed in order to combine and compare outcome data sources. The lack of consensus outcome measures has been a persistent problem in this field, and a principal source of frustration for meta-analysts. * Because of the complexity of outcomes, and the fact that different dependent variables may portray outcomes quite differently (Miller, a; Westerberg et al., 1998), no single metric is sufficient. A fair answer is one that characterizes outcomes in several different ways. Outcome measures Method If there is any point on which most parties seem to agree with regard to outcomes, it is that alcohol consumption is of central concern in judging adlcoholism treatment effectiveness. Drinking, however, is not the only relevant aspect to consider in studying recovery. It is widely recognized in Alcoholics Anonymous, for example, that sobriety involves far more than abstinence, encompassing mental, emotional, physical and spiritual dimensions of outcome. Yet, it is fair to say that if alcoholism treatment does not change drinking behavior, it has not succeeded. For this reason, nearly every outcome study reports, in some form, changes in drinking behavior. It has also become reasonably clear that one should consider at least two dimensions of drinking outcomes: frequency and intensity (Project MATCH Research Group, 1993). Although more emphasis is often given to frequency (e.g., percent days abstinent), how often a person drinks is not the whole story, even with alcohol dependent clients. These two different aspects of alcohol use were evident in Cahalan's pioneering work on the quantity/frequency measures that are now widely used in survey research (Cahalan, 1970; Cahalan et al., 1969). Abstinence. Frequency of drinking is usually discussed as its inverse, abstinence. One of the crudest measures of outcome is the proportion of cases maintaining perfect continuous abstinence from alcohol during a specified period. An obvious shortcoming of this metric is that recurrence of addictive behaviors is exceedingly common (Brownell et al., 1986; Hunt et al., 1971), even among those who ultimately maintain stable abstinence. Definitions of abstinence differ, allowing for various levels of slippage before incurring a judgment of relapse or treatment failure. The definition, method and care taken in ascertaining abstinence are sometimes left unspecified. Over the past decade, with the widespread use of timeline follow-back interviewing (Sobell and Sobell, 1995), abstinence has come to be more carefully quantified in terms of continuous variables: the percentage of days abstinent versus drinking, time to first drink or heavy drinking day, or longest duration of abstinence. Different metrics yield different answers. Intensity. The intensity of drinking (when it does occur) has been assessed in a wide variety of ways. Some have quantified the total amount of alcohol consumed within a certain assessment window of time, using such outcome vanables as average number of drinks per day or per week (e.g., Miller et al., 1992). Some have designated a threshold for "heavy drinking days" and have counted the number of these that occur (e.g., Project MATCH Research Group, 1993, 1997). Most studies now convert alcohol consumption into a standard drink unit, the size of which varies
3 MILLER, WALTERS AND BENNETT 213 considerably from one study or nation to another (Miller et al., 1991). Following Cahalan's lead, the total amount of alcohol consumed during a given month is often determined by multiplying the frequency of drinking (number of drinking days) by the average amount consumed on drinking days. Some investigators have used as their outcome metric "drinks per drinking day" (DDD; e.g., Project MATCH Research Group, 1997). A serious problem with DDD, however, is what to do with abstainers, who had no drinking days. DDD makes little sense for abstainers, in that a true zero value is logically impossible. Yet in order to have a numeric entry for each case, researchers often assign a zero DDD value to abstainers (e.g., Miller et al., 1996; Project MATCH Research Group, 1997). This creates a misleading impression of the intensity of alcohol use by drinkers. In a sample with 75% abstainers, for example, an average of 5 DDD would mean that the drinkers (excluding the abstainers) were actually consuming 20 drinks on a typical drinking day. As will be discussed shortly, however, intensity measures can be interchanged through relatively simple calculation procedures. Mortality. In cancer treatment, the rate of survival versus mortality during various lengths of time is a common outcome measure. Death rates are also often reported in alcoholism treatment research, and represent another way to characterize outcomes. Problems. Alcohol-related problems have been defined and measured in many ways, complicating comparisons across studies. Whatever the method, however, continuous measures of alcohol-related problems or dependence symptoms can be used to compute percent reductions at specific intervals after treatment. Other dimensions. There are many other ways in which outcomes can be characterized. For example, studies focused on alcoholism treatment may or may not reveal its impact on the use of other psychoactive substances. Changes in concomitant psychological or family problems are sometimes reported. There has been commendable attention, more recently, to general quality of life (the rest of sobriety). Yet there has been so little consistency in the measures used to report such ancillary outcomes, that summaries across studies are nearly impossible at present. Converting outcome measures As discussed above, a problem well known to any metaanalyst in the alcohol field is the inconsistency of outcome measures. In preparing this review, we assembled a spreadsheet of multisite studies, showing the outcome measures reported in each. The result looked rather like a low-budget chocolate chip cookie. Clusters of values (the "chocolate chips") dotted a grid of mostly empty space. Part of our challenge was to increase the density of chocolate chips, filling in missing measures wherever possible by estimation from other available data. Measures offrequency oqfdrinking. In order to yield common outcome indices, drinking measures can often be converted into other metrics so that variables not reported in a treatment study can, in some cases, be computed or estimated from values that are reported. For example, the proportion of drinking days (PDD) is simply l-pda (proportion days abstinent). Thus, if one knows the mean proportion of abstinent days for an entire sample (PDAJ, the sample size (N), and the number of individuals who were totally abstinent during an assessment period (n,absiaines), then one can estimate PDAd, the mean proportion of abstinent days among drinkers (who did not abstain; nd,inkcr.). -PDA for total abstainers is by definition 1.0 (100%), therefore: PDAd = (PDA, Days- N) -(nbstainer, * Days) ndrinkers Days where Days is the number of days in the assessment period. This reduces to the simpler formula: PDAd - (PDAt, N) - abtainer 0 drinkers which can also be solved for PDAt: PDA4 = ( PDA,. ndrinkers )_tb&tainers + N For the proportion of drinking days (PDD), the formulae for conversion are still simpler: and 5 PDDd = = PDD, / proportion of drinkers ndrinkcrs PDD = P-DDd N and of course drinkers = PDDd, proportion of drinkers PDD, = I - PD4, and PD14, = I - PDDd Measures of quantity of drinking. All of the above measures pertain to the frequency of drinking. The other traditional outcome dimension has to do with the quantity or intensity of drinking. Beginning with drinks per drinking day (DDD), it is possible to move back and forth between this metric for the total sample (DDD,) and for drinkers only (DDDd). For example: which reduces to: DDDd = (1DDD1* Days * PDD,, N) nd,i,k,,k, Days * PDDt DDDQ = DD'. N = DDD, / proportion of drinkers ndrinker,
4 214 JOURNAL OF STUDIES ON ALCOHOL / MARCH 2001 Solving for DDD,, this becomes: DDD, = DDDd proportion of drinkers DDDd = DDD, proportion of drinkers and As noted above, however, DDD, is an odd metric because it has no true zero value. We therefore recommend reporting DDD for drinkers only (DDDd). if mean quantity of consumption is to be represented for an entire sample, it is more meaningful to report an average amount of drinking per unit time, standard drinks per day (DPD,), which can also be computed just for drinkers (DPDd). When only DDD has been reported, DPD can be estimated. Some formulae for moving back and forth between DDD and DPD values are: DPD, = DDD,- PDD, and DPD PD and proportion of drinkers DPDd - (DDD, -PDD,) d proportion of drinkers It is noteworthy that these estimates closely approximate but may not equal the same variables as computed directly from timeline data. For example, when PDAd was computed from Project MATCH outpatient data, the actual value over 12 months was 69.25%, whereas when estimating from PDA,, we computed 68.0%. Whenever raw data are available, it is preferable to compute outcome variables directly. When only summary data are reported, however, the above procedures can be used to fill in missing metrics with quite reasonable estimates. Estimates from seven multisite studies The availability of a number of multisite trials provided an opportunity to develop estimates of the average outcomes of treatment. Following the principles described above, we identified longitudinal studies of alcoholism treatment that (I) were multisite, including treatment programs in at least three different geographic locations; (2) evaluated a treatment (not brief intervention) for alcohol use disorders; (3) reported some quantified measure of drinking outcomes; (4) followed clients for at least 12 months from intake; and (5) obtained follow-up data for at least 60% of the sample at 12 months. The seven studies that met these criteria were the Veterans Affairs (VA) cooperative trials of lithium (Dorus et al., 1989) and disulfiram (Fuller et al., 1986), the Relapse Replication and Extension Project (RREP; Lowman et al., 1996), the Project MATCH outpatient (opt) and aftercare (aft) studies (Project MATCH Research Group, 1993, 1997), the VA study of treatment for substance abuse (VAST; Ouimette et al., 1997) and the Rand report (Polich et al., 1981). All seven were conducted in the United States. Study and sample characteristics for the seven studies are summarized in Table 1. How representative are these seven studies of alcoholism treatment in the United States? Generalizability was one reason for limiting our analyses to multisite rather than single-site studies. Four of the studies were randomized clinical trials (LITHIUM, DISULFIRAM and the two MATCH studies) and three were uncontrolled studies of treatment-as-usual (RREP, VAST and RAND). Both the lithium and disulfiram trials, however, involved treatmentas-usual delivered to all participants, with only the medication controlled (which in both studies exerted no overall effect on outcomes). The MATCH treatments were carefully controlled, and in the aftercare study were delivered immediately after participants had completed intensive treatment-as-usual. The VA collaborative trial of lithium (LITHIUM). Dorus et al. (1989) assessed treatment with lithium carbonate for 457 male alcoholics from seven VA medical center inpatient programs. In addition to detoxification and unspecified inpatient treatment of at least 30 days, all were offered weekly outpatient visits for 13 weeks and biweekly visits for the remainder of a year, and were encouraged to seek additional treatment for alcoholism and to attend Alcoholics Anonymous. Lithium (vs placebo) was found to exert TAsBLE. Sample characteristics for the seven multisite studies Length of Alcohol follow-up Gender Treatment Minority dependent (months) (% male) seting (%) (%) LITHIUM IP+OP DISULFIRAM IP or OP 46 NR RREP IP or OP 33 NR MATCH opt OP MATCH aft IP/lO + OP VAST IP+OP RAND IP or OP 25 NR Notes: IP - inpatient; OP = outpatient; 10 = intensive outpatient treatment; NR = not reported; opt = outpatient; aft - aftercare. alength of follow-up in months after intake.
5 MILLER, WALTERS AND BENNETT 215 no specific effect on outcomes. At 12-month follow-up, 280 participants (63%) were reassessed, completed breath tests and had collateral interviews. Several drinking outcome variables were assessed, including the number of clients who were abstinent, the number of drinking days in the preceding 4-week period, and days until first drink. Abstinence was defined as no drinking, based on reports from both the participant and the collateral, as well as no positive results on a breath test. No definition of moderation was included. Alcohol problem severity was evaluated via the Addiction Severity Index (ASI; McLellan et al., 1992) and dependence was measured using the Diagnostic Interview Schedule (DIS; Robins et al., 1981). The VA collaborative study of disuljiram (DISULFIRAM). As in the lithium study, participants in this trial received standard (primarily group) alcoholism treatment in nine VA alcoholism programs (seven inpatient and two outpatient) and, in addition, were randomized to receive disulfiram, placebo or no medication (Fuller et al., 1986). Relative to placebo, disulfiram was found to exert no overall effect on treatment outcomes, with some difference observed when analyses were limited to cases showing high medication compliance. Weekly aftercare was encouraged for 6 months and biweekly visits for an additional 6 months. Follow-up interviews (including blood and urine samples) were completed every 8 weeks throughout the 12 months following intake, and collateral interviews were also conducted. Outcome data were obtained for 90% of the sample at 12 months. Outcomes were judged from three drinking measures (complete abstinence, time to first drink and percent drinking days) and two psychosocial measures (employment status and social stability). Any indication of drinking (from self-report, collateral report, or blood or urine tests) excluded a client from the complete abstinence outcome status. Moderate drinking was not evaluated in this study. The Relapse Replication and Extension Project (RREP). The RREP study (Lowman et al., 1996) followed 563 clients receiving treatment-as-usual at three sites. The study included multiple follow-up points and high follow-up rates: 544 participants (97%) were interviewed at 2 months, 539 (96%) at 4 months, 518 (92%) at 6 months, 514 (91%) at 8 months, 507 (90%) at 10 months and 469 (83%) at 12 months posttreatment. Breath tests were conducted at all assessments. In addition, blood and urine tests were performed at the 6- and 12-month intervals. Quantity and frequency were measured via the Form-90 interview (Miller, 1996b). Drinking-related dependent variables included time to first drink, time to first heavy drinking day, percent days abstinent and drinks per drinking day. Abstinence was defined as no alcohol use during each follow-up interval, but no definition of moderation was specified. The Alcohol Dependence Scale (ADS; Skinner and Allen, 1982) and DIS were completed to assess symptoms of dependence. Project MATCH (MA TCH). Project MATCH (1997) included outpatient (opt) and aflercare (aft) samples that were defined and analyzed as two separate studies: 952 individuals participating in outpatient treatment for alcohol problems at five outpatient treatment centers, and 774 individuals who had completed inpatient or intensive outpatient treatment in community programs just prior to entering additional MATCH treatment at one of five aftercare sites. Consistent with the approach of the Project MATCH Research Group, we treated the two study arms as separate studies because (1) all aftercare clients had just received intensive treatment-as-usual, whereas outpatient clients received only the MATCH treatments, (2) the aftercare sample reported substantially more severe problems on many dimensions and (3) overall outcomes and matching effects were different for the two samples. MATCH clients were assessed at multiple time points, with the major outcome assessment coming at 12 months after treatment termination (15 months from intake) for both samples. At that assessment, 92% of the outpatient sample and 93% of the aftercare sample were reassessed. Both arms of the study included collateral interviews and objective breath, blood and urine tests that were completed by over 75% of each sample. Quantity and frequency of use were measured in both arms of the study using Form 90, the Alcohol Use Inventory (AUI; Wanberg et al., 1977) and the ASI. Two drinking-related dependent variables were assessed: percent days abstinent and drinks per drinking day. Abstinence was defined as no use of alcohol, and a discrete outcome variable provided a composite measure of outcome with the following options: (I) no drinking, (2) moderate drinking without problems, (3) heavy drinking or recurrent problems and (4) both heavy drinking and recurrent problems. The Drinker Inventory of Consequences (DrInC; Miller et al., 1995b) was used to assess alcohol-related problems and the Structured Clinical Interview for DSM-1II-R (SCID; Spitzer et al., 1990) to assess alcohol dependence. VA study of treatment Jbr substance abuse (VAST). Ouimette et al. (1997) compared the effectiveness of 12- step and cognitive-behavioral treatment for 3,698 clients from 15 substance abuse treatment programs at United States VA medical centers. At the 12-month follow-up interval, 3,018 (81.6%) were reassessed and a subsample (230) provided breath, blood and urine tests. Drinking-related dependent variables were absence of alcohol dependence syndrome, absence of substance-related problems, percent improved, percent deteriorated, percent abstinent and average ounces of alcohol consumed per day. A conservative definition of abstinence was used, which was no alcohol consumption, no illicit drug use and no problems resulting from alcohol or drug use. A definition of moderation was also included: consumption of 3 ounces or less on a typical drinking day, no illicit drug use and no problems from al-
6 216 JOUJRNAL OF STUDIES ON ALCOHOL / MARCH 2001 cohol or drug use. Alcohol-related problems were assessed using 18 items sampling several domains including health, financial, occupational, intra- and interpersonal and residential difficulties. Symptoms of dependence were measured using nine items reflecting DSM-II1-R (American Psychiatric Association, 1987) criteria. The RAND reports (RAND). One of the earliest studies of alcohol treatment outcomes was reported by the Rand Corporation (Armor et al., 1978; Polich et al., 1981). Although these reports stirred public controversy over "controlled drinking," the study was large and well conducted, encompassing 1,340 clients from eight programs. Followup assessments through 4 years included collateral interviews and breath tests for some clients. Quantity-frequency measures included ounces of alcohol consumed daily; number of days of use in the last month of beer, wine and distilled spirits; and the amount of each beverage consumed on a typical drinking day. The drinking outcome variables in the study were the average ounces of alcohol consumed per day, the number of days of use in the past month, the percentage of clients abstinent at each follow-up interval and time to first drink. The study included an assessment of behavioral impairment, defined as the frequency of experiencing 12 alcohol-related problems on a 0-3 scale. These problems were used to create a behavioral impaimnent index, changes in which were examined at each follow-up interval. The study provided detailed operational definitions of both abstinence and moderation. Other multisite studies were considered but did not meet review criteria with regard to collection of follow-up data. In a multisite Midwestern-state study (Hartmann and Wolk, 1996), for example, only 45% of clients were retained at 12-month follow-up. A European multisite trial of acamprosate similarly retained only 44% of cases at 12 months (Paille et al., 1995). A Schick study (Smith et al., 1991) reported an 83% follow-up rate at 12 months for Schick clients and 82% follow-up with a multisite comparison sample. The number actually interviewed, however, comprised only 27% and 2% of the source samples from which they were drawn. Even when the number of clients contacted at 6-month follow-up is used as the denominator, only 45% and 3% were completed at 12 months. Review procedure For each of the seven multisite studies we began with the dependent variables reported by the authors. When sufficient information was provided, we applied the above transformation formulas to estimate outcome variables that were not directly reported. For the MATCH and RREP samples, access to the original data sets allowed direct computation of some variables not initially reported. Whenever possible, direct computation was preferred to estimation via transformation formulas. When different treatment approaches or programs were compared, we pooled them. Then we averaged across studies the available values for each variable, giving equal weight to all studies (rather than weighting by sample size). Weighted averages differed little from simple averages, and in no case would significantly alter conclusions. In two cases, one study yielded findings markedly different from all others, and these values are noted below as outliers (in Table 4) and excluded to avoid misleadingly skewing mean values. Results The seven studies together comprise 8,389 clients seeking treatment for alcoholism, a truly broad clinical sample. The percentage of clients with known outcomes (including deceased) at I-year follow-up ranged from 61% to 94%, with a mean (83%) high enough to provide reasonable confidence in the representativeness of follow-up data. Abstinence Table 2 reports outcome data from the seven multisite studies. The first of these is the percentage of cases with continuous abstention for at least 12 months at follow-up TABLF month drinking outcomes in multisite treatment trials Study N %FU %Abs %Mod 0 PDA, PDAd ODD, DEDD DPD8 DPDA LITHIUM NR NIR NR NiR NR DISUL.FIRAM NR NR NR NR NR RREP NR , * 1.12* MATCH opt * 1.66* MATCH aft * 1.45* VAST 3, NR NR NR ,99* Ni NR RAND 1, "' 11.6 NR NR NR NR Total 8,389 Mean Notes: Figures with asterisks (*) were computed from other values available in this table. All other values were obtained directly from published reports or from study authors. N = total number of clients; %FU = percentage of cases completed at 12-month follow-up, including documented deaths in numerator; %Abs = percentage of cases continuously abstinent for 12 months; %Mod = percentage of cases classified as moderate drinkers at 12 months; PDA = percent days abstinent; DDD = drinks per drinking day; DPD = drinks per day;, total sample; d drinkers only; NR = not reported: opt = outpatient; aft = aftercare. "Moderate drinking with no adverse consequences; />At least 12 months of continuous abstinence during 18-month follow-up period.