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Risk factors for relapse among methamphetamine users receiving a joint legal–medical treatment program as a diversion intervention: A one-year follow-up study

  • Author Footnotes
    1 Dr. MCH and Dr. SCF contributed equally to this paper as co-first authors.
    Ming-Chyi Huang
    Footnotes
    1 Dr. MCH and Dr. SCF contributed equally to this paper as co-first authors.
    Affiliations
    Department of Addiction Sciences, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan

    Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

    Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan
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  • Author Footnotes
    1 Dr. MCH and Dr. SCF contributed equally to this paper as co-first authors.
    Su-Chen Fang
    Footnotes
    1 Dr. MCH and Dr. SCF contributed equally to this paper as co-first authors.
    Affiliations
    Department of Nursing, Mackay Medical College, New Taipei City, Taiwan
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  • Chun Lin
    Affiliations
    Department of Addiction Sciences, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan
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  • Ta Lin
    Affiliations
    Taiwan Taipei District Prosecutors' Office, Taiwan
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  • Hu-Ming Chang
    Affiliations
    Department of Addiction Sciences, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan
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  • Tien-Wei Yang
    Affiliations
    Department of Addiction Sciences, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan
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  • Lian-Yu Chen
    Correspondence
    Corresponding author at: Institute of Epidemiology and Preventive Medicine, National Taiwan University, Kunming Prevention and Control Center, Taipei City Hospital, No.100, Kunming St., Wanhua Dist., Taipei City 108, Taiwan.
    Affiliations
    Kunming Prevention and Control Center, Taipei City Hospital, Taipei, Taiwan

    Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan

    Department of Forensic Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan

    CTBC Center for Addiction Prevention and Policy Research, National Taiwan Normal University, Taipei, Taiwan
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  • Author Footnotes
    1 Dr. MCH and Dr. SCF contributed equally to this paper as co-first authors.
Published:January 13, 2023DOI:https://doi.org/10.1016/j.josat.2023.208955

      Highlights

      • A joint legal–medical intervention program provides 12-month addiction treatment.
      • We followed 449 individuals with methamphetamine offense receiving the treatment.
      • 37.8 % had methamphetamine relapse and 26.5 % discontinued the 12-month treatment.
      • Baseline urine positive of methamphetamine and high craving increased relapse risk.
      • Tailored treatment incorporating these findings is warranted in the joint program.

      Abstract

      Background

      Methamphetamine (METH) is a Schedule II illicit drug in Taiwan. A 12-month legal–medical joint intervention program has been developed for first-time METH offenders during deferred prosecution. Risk factors associated with METH relapse use among these individuals were unknown.

      Methods

      We enrolled a total of 449 METH offenders referred by the Taipei District Prosecutor's Office to Taipei City Psychiatric Center. The study defines relapse as having any positive urine toxicology result or self-report of METH use during 12-month treatment. We compared demographic and clinical variables between a relapse group and nonrelapse group and used a Cox proportional hazards model to determine variables associated with time to relapse.

      Results

      Of all participants, 37.8 % relapsed to use METH and 23.2 % were noncompleters in the one-year follow-up. Compared to the nonrelapse group, the relapse group had lower educational attainment, more severe psychological symptoms, longer duration of METH use, higher odds of polysubstance use, higher craving severity, and higher odds of positive baseline urine. The Cox analysis revealed individuals with positive urine results and higher craving severity at baseline were at higher risks of METH relapse (hazard ratio [95 % CI]: 3.85 [2.61–5.68] and 1.71 [1.19–2.46], respectively, p < 0.001). Baseline positive urine results and high craving could also predict a shorter length of time to relapse than their respective counterparts.

      Conclusions

      Positive urine screening for METH at baseline and high craving severity are two indicators of an increased risk of drug relapse. Tailored treatment plans incorporating these findings to prevent relapse are warranted in our joint intervention program.

      Keywords

      1. Introduction

      Methamphetamine is among the most widely used illicit drugs and poses a growing threat to public health worldwide (
      • Park-Lee E.
      • Lipari R.N.
      • Hedden S.L.
      • Kroutil L.A.
      • Porter J.D.
      Receipt of services for substance use and mental health issues among adults:Results from the 2016 National Survey on Drug Use and Health, CBHSQ data review.
      ). According to the 2020 National Survey on Drug Use and Health report, 0.9 % (about 2.5 million) of Americans aged 12 years or older reported the use of methamphetamine in the previous year. Methamphetamine is associated with severe consequences, such as cardiovascular diseases and stroke (
      • Huang M.C.
      • Yang S.Y.
      • Lin S.K.
      • Chen K.Y.
      • Chen Y.Y.
      • Kuo C.J.
      • Hung Y.-N.
      Risk of cardiovascular diseases and stroke events in methamphetamine users: A 10-year follow-up study.
      ), and an increased risk of suicide (
      • Darke S.
      • Kaye S.
      • Duflou J.
      • Lappin J.
      Completed suicide among methamphetamine users: A national study.
      ) and mortality (
      • Lee W.C.
      • Chang H.M.
      • Huang M.C.
      • Pan C.H.
      • Su S.S.
      • Tsai S.Y.
      • Chen C.C.
      • Kuo C.J.
      All-cause and suicide mortality among people with methamphetamine use disorder: A nation-wide cohort study in Taiwan.
      ). One systemic review revealed that methamphetamine use is a major contributor to serious mental illness and recidivism among people in the criminal justice system, indicating the urgent need to prioritize mental health treatment (
      • Cumming C.
      • Kinner S.A.
      • McKetin R.
      • Li I.
      • Preen D.
      Methamphetamine use, health and criminal justice system outcomes: A systematic review.
      ).
      The 1990s saw a surge of methamphetamine use in Taiwan, resulting in the promulgation of a new law “Narcotics Hazard Prevention Act” (
      • Shaw K.-P.
      Human methamphetamine-related fatalities in Taiwan during 1991–1996.
      ). By this law, strategies for managing illicit drug users shifted to treating an addict as a “diseased criminal” rather than simply a “criminal” in 1998. The recent two waves of National Survey of Substance Use respectively held in 2014 (
      • Chen W.J.
      • Wu S.-C.
      • Tsay W.-I.
      • Chen Y.-T.
      • Hsiao P.-C.
      • Yu Y.-H.
      • Ting T.-T.
      • Chen C.-Y.
      • Tu Y.-K.
      • Huang J.-H.
      Differences in prevalence, socio-behavioral correlates, and psychosocial distress between club drug and hard drug use in Taiwan: Results from the 2014 National Survey of substance use.
      ) and 2018 (
      Taiwan Food and Drug Administration
      National survey of substance use.
      ) reveal that methamphetamine remains the predominant illicit drug of use in Taiwan. With growing evidence supporting the efficacy of addiction treatment for substance use disorders (
      • Stizer M.L.
      • Owen P.L.
      • Hall S.M.
      • Rawson R.A.
      • Petry N.M.
      CPDD policy statement. Standards for drug abuse treatment providers.
      ), the government has gradually reconceptualized the role of drug offenders, regarding them as both criminals and patients. Detention centers started to provide 2 to 4 weeks of addiction treatment for drug offenders. However, this approach yielded very limited effect in counteracting re-offense as one study reported that approximately two thirds recidivated within 5 years, with most recidivism occurring in the first 18 months (
      • Chiang S.C.
      • Chan H.Y.
      • Chen C.H.
      • Sun H.J.
      • Chang H.J.
      • Chen W.J.
      • Lin S.K.
      • Chen C.K.
      Recidivism among male subjects incarcerated for illicit drug use in Taiwan.
      ). An amendment in the law allows for a diversion intervention for first-time drug offenders with deferred prosecution if they agree to receive addiction treatment as an alternative to imprisonment; collaborative intervention programs bridging the legal and medical systems were thus established (
      • Sirotich F.
      The criminal justice outcomes of jail diversion programs for persons with mental illness: A review of the evidence.
      ). As such, prosecutors can defer prosecution as conditional treatment for individuals with first-time drug offense convictions and refer them to appointed addiction treatment centers.
      Evidence has demonstrated that patients who receive short-term treatment (2–4 months) have an increased risk of relapse relative those who receive long-term treatment (>6 months;
      • Andersson H.W.
      • Wenaas M.
      • Nordfjaern T.
      Relapse after inpatient substance use treatment: A prospective cohort study among users of illicit substances.
      ), suggesting a need for long-term intervention programs. Lending support to it, a study using a computer simulation approach demonstrated that addiction treatment with deferred prosecution for 12 months is beneficial in reducing relapse for individuals convicted of amphetamine-type stimulants use (
      • Chen I.C.
      • Chen C.J.
      • Hsieh Y.C.
      • Tsai W.J.
      • Lan T.H.
      Boosting treatment stabilization in patients of amphetamine-type stimulant use disorder.
      ). In this context, the District Prosecutor's Office collaborated with addiction treatment medical institutions to design a 12-month joint legal–medical program for individuals with first-time methamphetamine use offenses. Completion of the 12-month treatment is considered a prerequisite for exemption from prosecution.
      Substance use disorder has long been described as a chronic mental illness with high rates of relapse (
      • McLellan A.T.
      • Lewis D.C.
      • O'Brien C.P.
      • Kleber H.D.
      Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation.
      ). Relapse, as the biggest problem in substance use treatment, is a common outcome measure in substance-related research (
      • Bradizza C.M.
      • Stasiewicz P.R.
      • Paas N.D.
      Relapse to alcohol and drug use among individuals diagnosed with co-occurring mental health and substance use disorders: A review.
      ). Identification of characteristics associated with relapse is critical for the development of adjusting and tailoring treatment programs for those at risk. Methamphetamine is highly addictive; 36 % and 61 % of individuals who use methamphetamine relapse within 6 months and 1 year following treatment, respectively (
      • Brecht M.L.
      • Herbeck D.
      Time to relapse following treatment for methamphetamine use: A long-term perspective on patterns and predictors.
      ). Research suggests that factors associated with methamphetamine relapse may include frequency of methamphetamine use (
      • Yen C.F.
      ), the intensity of craving (
      • Galloway G.P.
      • Singleton E.G.
      • Authors M.T.P.C.
      How long does craving predict use of methamphetamine?Assessment of use one to seven weeks after the assessment of craving.
      ;
      • Hartz D.T.
      • Frederick-Osborne S.L.
      • Galloway G.P.
      Craving predicts use during treatment for methamphetamine dependence: A prospective, repeated-measures, within-subject analysis.
      ), self-efficacy, and motivation to change (
      • Brecht M.L.
      • Herbeck D.
      Time to relapse following treatment for methamphetamine use: A long-term perspective on patterns and predictors.
      ). In addition, negative affect, such as depression and anxiety, is linked to increased relapse rates in patients with substance use disorder (
      • Hammerbacher M.
      • Lyvers M.
      Factors associated with relapse among clients in australian substance disorder treatment facilities.
      ;
      • McCarthy D.M.
      • Tomlinson K.L.
      • Anderson K.G.
      • Marlatt G.A.
      • Brown S.A.
      Relapse in alcohol-and drug-disordered adolescents with comorbid psychopathology: Changes in psychiatric symptoms.
      ). Given that relapse may vary with the type of study populations and the length of treatment duration (
      • Andersson H.W.
      • Wenaas M.
      • Nordfjaern T.
      Relapse after inpatient substance use treatment: A prospective cohort study among users of illicit substances.
      ), we do not know the risk factors associated with methamphetamine relapse in first-time offenders who receive a 12-month joint legal–medical program. In addition, only a few studies have provided information in temporal features such as duration of abstinence or specific timing of relapse, thus allowing researchers to explore pertinent predictors of time to relapse (
      • Brecht M.L.
      • Herbeck D.
      Time to relapse following treatment for methamphetamine use: A long-term perspective on patterns and predictors.
      ;
      • Hser Y.I.
      Predicting long-term stable recovery from heroin addiction: Findings from a 33-year follow-up study.
      ;
      • McKetin R.
      • Najman J.M.
      • Baker A.L.
      • Lubman D.I.
      • Dawe S.
      • Ali R.
      • Lee N.K.
      • Mattick R.P.
      • Mamun A.
      Evaluating the impact of community-based treatment options on methamphetamine use: Findings from the methamphetamine treatment evaluation study (MATES).
      ). With this knowledge, an appropriately timed treatment program could be developed to reduce relapse and curb drug re-offense (
      • Xie H.
      • McHugo G.J.
      • Fox M.B.
      • Drake R.E.
      Substance abuse relapse in a ten-year prospective follow-up of clients with mental and substance use disorders.
      ).
      In the current study, we aimed to use the cohort of methamphetamine offenders attending the 12-month joint legal–medical intervention program to examine the rate and timing of methamphetamine relapse and explainable factors associated with relapse (
      • Bradizza C.M.
      • Stasiewicz P.R.
      • Paas N.D.
      Relapse to alcohol and drug use among individuals diagnosed with co-occurring mental health and substance use disorders: A review.
      ). We divided the participants into two groups, the relapse group and the nonrelapse group, based on self-report or drug toxicology results of methamphetamine use. We compared participants' sociodemographic characteristics, psychological correlates, and substance use patterns. Furthermore, using the natural history follow-up data, the study applied the Cox regression approach to examine time to relapse to methamphetamine use and predictors.

      2. Methods

      2.1 Study participants

      The Research Ethics Committee of Taipei City Hospital approved this cohort study (IRB No: TCHIRB-10810018). Individuals who are convicted of a first-time drug offense of methamphetamine use were referred by the Taiwan Taipei District Prosecutors Office (TTDPO) to undergo 12-month mandatory addiction treatment in Taipei City Psychiatric Center (TCPC) of Taipei City Hospital as a condition for deferred prosecution. The study inclusion criteria for participants were as follows: (a) age ≥ 18 years; (b) fulfilment of the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) criteria for methamphetamine use disorder, as verified by at least one board-certified psychiatrist; (c) first-time drug offense with no other criminal history; (d) methamphetamine as the main drug of abuse in the past; (e) no current involvement in any other intervention program for this offense; and (f) ability to read Chinese and willingness to provide informed consent. Study staff informed all participants that their legal status would not be influenced by their participation in this study. We recruited all participants between January 1, 2016, and August 30, 2018, and they provided written informed consent.

      2.2 Intervention

      TCPC provided a 12-month addiction treatment program in their Department of Addiction Sciences for all individuals referred by the TTDPO. Participants were required to visit an addiction psychiatrist in an outpatient setting for scheduled sessions comprising four weekly sessions, followed by four biweekly sessions, and then nine monthly sessions (i.e., 17 sessions in total). We adopted contingency management for the treatment program, which offers systematic reinforcement of desired behaviors and the withholding of reinforcement of undesired behaviors, which has been proven to be an effective strategy in addiction treatment (
      • Higgins S.T.
      • Petry N.M.
      Contingency management. Incentives for sobriety.
      ). During each session, we offered treatment based on a standardized protocol that adopts a multicomponent approach integrating motivational interviewing, psychotherapy aimed at relapse prevention, and routine urine toxicology tests for methamphetamine and other illicit drugs including heroin, 3,4-methylenedioxymethamphetamine (MDMA), marijuana, cocaine, and ketamine. The study considered those who failed to attend >13 sessions (about 80 % of required sessions) or stay fewer than 12 months as noncompleters.
      Trained assistants interviewed the participants to collect information on their sociodemographic characteristics (e.g., age, sex, education level, employment status, and marital status) and methamphetamine use patterns (e.g., age at first use, duration of methamphetamine use, and history of polysubstance abuse). The participants were administered hard-copy questionnaires and underwent a urine toxicology test for methamphetamine at each session.
      • 1.
        The study assessed depressive symptoms using the Chinese version of the 21-item Beck Depression Inventory (BDI) (
        • Lu M.L.
        • Che H.H.
        • Chang S.W.
        • Shen W.
        Reliability and validity of the chinese version of the Beck depression inventory-II.
        ). A score of 20 or more indicates moderate-to-severe depression.
      • 2.
        Anxiety symptoms were evaluated using the Chinese version of the 21-item Beck Anxiety Inventory (BAI) (
        • Che H.H.
        • Lu M.L.
        • Chen H.C.
        • Chang S.W.
        • Lee Y.J.
        Validation of the Chinese version of the Beck anxiety inventory.
        ). A score of 16 or more indicates moderate-to-severe anxiety symptoms.
      • 3.
        We measured craving severity by using a visual analog scale (VAS), on which the participants, after obtaining a careful explanation from research assistants, self-rated their craving for methamphetamine by marking a position on a continuous line ranging from 0 (no craving) to 100 mm (severe craving, such that an individual cannot resist methamphetamine if available).
      • 4.
        The study measured the severity of methamphetamine dependence by using the four-item Severity of Dependence Scale (SDS), which has been validated previously for the Chinese version (
        • Chen V.C.
        • Chen H.
        • Lin T.Y.
        • Chou H.H.
        • Lai T.J.
        • Ferri C.P.
        • Gossop M.
        Severity of heroin dependence in Taiwan: Reliability and validity of the Chinese version of the severity of dependence scale (SDS[Ch]).
        ). A score of 4 or more indicates that individuals may have methamphetamine dependence (
        • Lindblad B.E.
        • Hakansson N.
        • Wolk A.
        Smoking cessation and the risk of cataract: A prospective cohort study of cataract extraction among men.
        ).
      • 5.
        We assessed life satisfaction by using the five-item Satisfaction with Life Scale (SWLS), the Chinese version of which has been tested and determined to be a reliable measure of life satisfaction (
        • Li R.-H.
        • Yu M.-N.
        Reliability, validity, and measurement invariance of the brief Chinese version of Psychological Well-Being Scale among college students.
        ). Higher scores indicate a higher level of life satisfaction (
        • Diener E.
        • Emmons R.
        • Larsen R.
        • Griffin S.
        The satisfaction eith life scale.
        ).

      2.3 Definition of relapse of methamphetamine use

      We defined methamphetamine relapse by the following criteria: a positive urine toxicology result for methamphetamine or self-report of methamphetamine use during any treatment session. The study measured time to first relapse in days from the date of treatment initiation to the date of the first methamphetamine-positive urine result or date of self-report of methamphetamine use in treatment sessions, whichever occurred first.

      2.4 Statistical analysis

      We compared the sociodemographic characteristics, baseline methamphetamine use patterns, mental health variables, and life satisfaction between the relapse and nonrelapse groups. A t-test and chi-squared test assessed continuous variables and categorical variables, respectively. We applied Cox proportional hazards model to examine the hazard ratio (HR) of related factors including sociodemographic characteristics, baseline mental health variables, and methamphetamine use patterns on relapse into methamphetamine use. Multivariate Cox proportional hazards analysis estimated the adjusted HR for the related factors for relapse (also called the “full model”) after adjustment for potential confounders. In the Cox model, the study followed-up all participants from the date of treatment initiation to the date of the first methamphetamine-positive urine result, date of self-report of methamphetamine use in treatment sessions, date of loss to follow-up, or end of study (full 12-month), whichever occurred first. The date of loss to follow-up was coded as censoring in the model. The study assessed collinearity by using the statistical factor of tolerance and variance inflation factor. We estimated the relapse-free survival by Kaplan–Meier method to differences in survival stratified by baseline urine result for methamphetamine use or craving severity (high or low craving based on the median VAS scores) and analyzed them using the log-rank test. We used SAS 9.4 (SAS Institute, Cary, NC, USA) for the analyses and set statistical significance at a level of p < 0.05.

      3. Results

      3.1 Clinical characteristics of participants

      Table 1 shows the socio-demographic data of the 449 individuals who used methamphetamine. Their mean age was 35.3 ± 9.3 years. The participants were predominantly male (94.4 %), employed (95 %), had education ≥ high school (53.1 %). One hundred and seventy (37.8 %) participants experienced relapse into methamphetamine use. The average time to relapse was 7 months (217.5 ± 148.0 days). Compared with the nonrelapse group, participants in the relapse group were more likely to have a lower education level, lower level of life satisfaction, more serious depressive and anxiety symptoms, earlier onset and longer duration of methamphetamine use, higher odds of having polysubstance use, higher craving severity, and higher percentage of positive baseline urine results (32.4 % vs. 6.8 %). Of the entire sample, 23.2 % (N = 104) were noncompleters. Among them 50 % (N = 52) relapsed to use methamphetamine, and 13 %, 16 %, 43 %, and 28 % dropped out within 3, 4–6, 7–9, and 10–12 months, respectively (data not shown).
      Table 1Baseline demographic and clinical characteristics of study participants stratified by relapse or not during the one-year follow-up (N = 449).
      Overall

      (N = 449)
      Relapse group

      (N = 170)
      Non-relapse group

      (N = 279)
      p
      Demographic
      Age, mean ± SD35.3 ± 9.335.3 ± 9.835.3 ± 9.00.999
       Age ≤ the median 35 y/o, n (%)240 (53.5)90 (52.9)150 (53.8)0.866
      Male, n (%)424 (94.4)162 (95.3)262 (93.9)0.534
      Married, n (%)58 (13.2)22 (13)36 (13.2)0.680
      Did not complete high school, n (%)207 (46.9)92 (55.1)115 (42)0.007
      Employed, n (%)400 (95.0)152 (96.2)248 (94.3)0.384
      Psychological symptoms
      BDI score, mean ± SD11.1 ± 10.413.5 ± 12.29.7 ± 8.9<0.001
       ≥20 (moderate-to-severe), n (%)82 (18.3)44 (25.9)38 (13.6)0.003
       <20 (minimal-to-mild), n (%)367 (81.7)126 (74.1)241 (86.4)
      BAI score, mean ± SD6.1 ± 8.27.4 ± 9.25.3 ± 7.40.012
       ≥16 (moderate-to-severe), n (%)48 (10.7)27 (16.2)21 (7.6)0.005
       <16 (minimal-to-mild), n (%)401 (89.3)143 (84.1)258 (92.4)
      SWLS score, mean ± SD20.9 ± 7.419.9 ± 7.421.5 ± 7.30.027
      METH use-related variables
      Age of first use substance, mean ± SD29.9 ± 10.328.1 ± 9.931 ± 10.40.004
      Months of substance use, mean ± SD61.7 ± 96.179.1 ± 11651 ± 80.10.006
      Polysubstance use101 (22.5)48 (28.2)53 (19)0.023
      SDS score, mean ± SD4 ± 2.54.2 ± 2.63.9 ± 2.50.182
       ≥4 score241 (53.7)97 (57.1)141 (51.6)0.262
      VAS for craving, mean ± SD13.8 ± 19.3919.7 ± 22.510.2 ± 16.7<0.001
      Baseline urine positive for METH, n (%)74 (16.5)55 (32.4)19 (6.8)<0.001
      Non-completers
      Non-completers were defined as those who failed to attend >13 sessions (about 80 % of required sessions) or stay <12 months.
      , n (%)
      104 (23.2)52(30.6)52(18.6)0.0036
      Abbreviations: BDI: Beck Depression Inventory; BAI: Beck anxiety inventory; SWLS: Satisfaction with Life Scale; SDS: Severity of Dependence Scale; METH: methamphetamine; VAS: Visual Analogue Scale.
      a Non-completers were defined as those who failed to attend >13 sessions (about 80 % of required sessions) or stay <12 months.

      3.2 Factors associated with relapse

      Table 2 shows results from Cox proportional hazards model analysis. The univariate analysis revealed the factors associated with methamphetamine relapse, including education level, severity of depression or anxiety, age of first methamphetamine use, duration of methamphetamine use, history of polysubstance use, craving level for methamphetamine, and methamphetamine-positive baseline urine result. A higher level of life satisfaction and later onset of methamphetamine use appeared to be protective factors against relapse (HR = 0.98, 95 % confidence interval [CI] [0.96, 0.99]; HR = 0.97, 95 % CI [0.96, 0.99]). In multivariate analysis, we identified two risk factors, namely a methamphetamine-positive baseline urine result (HR = 3.85, 95 % CI [2.61, 5.68], p < 0.001) and high craving severity (HR = 1.01, 95 % CI [1.00, 1.02], p = 0.008 (by vas score); high craving group: HR = 1.71, 95 % CI [1.19, 2.46], p < 0.001).
      Table 2Univariate and multivariate Cox proportional hazards model to examine factors associated with time to relapse into METH use (N = 449).
      Univariate modelMultivariate model
      HR (95 % CI)paHR
      Multivariate model that included all variables listed in the table, with VAS scores as the craving indicators.
      (95 % CI)
      paHR
      Multivariate model that included all variables listed in the table, with craving groups (high vs. low craving) as the craving indicators.
      (95 % CI)
      p
      Demographic factors
      Age ≤ median 35 y/o (ref: >35 y/o)1.03 (0.76–1.4)0.8330.82 (0.52–1.29)0.3870.81 (0.52–1.29)0.377
      Male (ref: female)1.21 (0.6–2.46)0.5971.42 (0.57–3.55)0.4551.35 (0.54–3.4)0.522
      Marital status (ref: married)
       Unmarried1.00 (0.63–1.57)0.9941.02 (0.53–1.96)0.9571.07 (0.56–2.06)0.835
       Other1.27 (0.7–2.29)0.4321.06 (0.63–1.8)0.8161.11 (0.66–1.86)0.707
      Number of years of education ≤ 12 (ref: >12)1.61 (1.18–2.18)0.0021.28 (0.87–1.88)0.2041.34 (0.91–1.98)0.133
      Employed (ref: unemployment)0.66 (0.29–1.49)0.3160.92 (0.39–2.12)0.8350.85 (0.37–1.97)0.7
      Psychological symptoms factors
      Moderate-to-severe depression (BDI score ≥ 20) (ref: BDI < 20)
      BDI score ≥ 20 indicates moderate-to-severe depression.
      1.95 (1.38–2.78)<0.0011.18 (0.72–1.92)0.5151.19 (0.73–1.92)0.488
      Moderate-to-severe anxiety (BAI score ≥ 16) (ref: <16)
      BAI score ≥ 16 indicates moderate-to-severe anxiety.
      1.86 (1.23–2.8)0.0030.72 (0.39–1.32)0.2870.81 (0.45–1.46)0.486
      SWLS score0.97 (0.96–0.99)0.0120.99 (0.97–1.02)0.5230.99 (0.97–1.02)0.553
      Substance use-related factors
      Age of first METH use0.98 (0.96–0.99)0.0041 (0.97–1.02)0.6900.99 (0.97–1.02)0.618
      Months of METH use1.002 (1.001–1.003)0.0031 (1–1)0.3981 (1–1)0.384
      Polysubstance use (ref: no polysubstance use)1.45 (1.04–2.03)0.0291.36 (0.91–2.03)0.1331.35 (0.91–2.01)0.137
      SDS score ≥ 4 (ref: <4)
      SDS score ≥ 4 indicates severity of METH use potentially reaching dependence.
      1.18 (0.87–1.6)0.2790.93 (0.65–1.33)0.6770.92 (0.65–1.32)0.664
      VAS for Craving (score)1.19 (1.12–1.27)<0.0011.01 (1–1.02)0.008
       High craving group (ref: low craving group)
      Craving severity group was divided by medium value (5 mm) of craving into high and low craving group.
      2.00 (1.47–2.73)<0.0011.71 (1.19–2.46)0.004
      Baseline urine positive for METH (ref: negative for METH)4.17 (3–5.79)<0.0013.85 (2.61–5.68)<0.00185 (35.74–202.2)<0.001
      Abbreviations: BDI: Beck Depression Inventory; BAI: Beck anxiety inventory; SWLS: Satisfaction with Life Scale; SDS: Severity of Dependence Scale; VAS: Visual Analogue Scale.
      a BDI score ≥ 20 indicates moderate-to-severe depression.
      b BAI score ≥ 16 indicates moderate-to-severe anxiety.
      c SDS score ≥ 4 indicates severity of METH use potentially reaching dependence.
      d Craving severity group was divided by medium value (5 mm) of craving into high and low craving group.
      e Multivariate model that included all variables listed in the table, with VAS scores as the craving indicators.
      f Multivariate model that included all variables listed in the table, with craving groups (high vs. low craving) as the craving indicators.

      3.3 Survival analysis for relapse into methamphetamine use

      Overall, 37.8 % of our participants were assigned to the relapse group. One hundred and nineteen (70 %), 26 (15 %), 19 (11 %), and 6 (4 %) experienced relapses within 3, 4–6, 7–9, and 10–12 months (shown in Supplementary Table 1). The 1-year relapse rate was 74.3 % and 30.6 % for those with a baseline positive (i.e., UP group) and negative (i.e., UN group) urine result, respectively. The relapse rate was 61.2 % and 38.8 % for those with high and low craving group, respectively.
      The Kaplan-Meier analysis (Fig. 1) revealed that individuals in the UP group and those with high craving had a shorter length of time to relapse compared with their counterparts (66.8 vs. 309.0 days and 187.9 vs. 245.6 days, respectively) (p < 0.001). As illustrated in Fig. 1, the UP group had lower relapse-free survival than the UN group (p < 0.001; Fig. 1a) and individuals with high craving exhibited a lower relapse-free survival than those with low craving (p < 0.001; Fig. 1b).
      Fig. 1
      Fig. 1Comparison of relapse of methamphetamine use based on baseline urine test result (Fig. 1a) and craving severity (Fig. 1b) for METH using Kaplan-Meier analysis. UP group indicates those with baseline urine positive for METH whereas UN group indicates those with negative result.

      4. Discussion

      To our knowledge, this is the first study aiming to identify risk factors for methamphetamine relapse using the cohort from the legal–medical joint program with one-year follow-up. In this study, we found two major risk factors, craving severity and baseline methamphetamine-positive urine result, were associated with time to relapse to methamphetamine use even after adjustment for possible confounders. Overall, 37.8 % of all participants had relapse of methamphetamine use, in particular within the first 3 months. Those with baseline methamphetamine-positive urine results and high craving severity had significantly shorter time to relapse. These findings offer critical insight into the development of a methamphetamine intervention program. For example, an efficient strategy targeting craving severity could be incorporated into treatment programs. Also, baseline positive toxicology results may help in predicting future drug relapse; thus, treatment centers should provide a higher intensity of treatment sessions to these individuals.
      In our study, 37.8 % (170 out of 449) of the studied population relapsed into methamphetamine use. Across studies reporting the relapse rates of individuals who use methamphetamine following treatment, the data vary substantially. These inconsistent results may be attributable to different study designs, including different recruitment methods or participants' characteristics (e.g., whether they voluntarily sought or were involuntarily referred to treatment), treatment settings, and varying definitions of relapse among these studies (
      • Calabria B.
      • Degenhardt L.
      • Briegleb C.
      • Vos T.
      • Hall W.
      • Lynskey M.
      • Callaghan B.
      • Rana U.
      • McLaren J.
      Systematic review of prospective studies investigating "remission" from amphetamine, cannabis, cocaine or opioid dependence.
      ). For example, for methamphetamine users who sought treatment voluntarily, the posttreatment relapse rate has ranged from 61 % (
      • Brecht M.L.
      • Herbeck D.
      Time to relapse following treatment for methamphetamine use: A long-term perspective on patterns and predictors.
      ) to 69 % (
      • McKetin R.
      • Najman J.M.
      • Baker A.L.
      • Lubman D.I.
      • Dawe S.
      • Ali R.
      • Lee N.K.
      • Mattick R.P.
      • Mamun A.
      Evaluating the impact of community-based treatment options on methamphetamine use: Findings from the methamphetamine treatment evaluation study (MATES).
      ) at 12-month follow-up. These studies could not be compared to our study directly as their relapse rates should be treated as posttreatment relapse rates based on self-report data (as opposed to within-treatment relapse rates based on urine test and self-report data in our study). Also, our participants were first-time drug offenders with deferred prosecution. Failing to complete the treatment could lead to imprisonment because of their drug crimes, which might greatly increase their motivation to complete such treatment. Last, different treatment protocols may result in the dissimilar outcomes. The results of another study on participants recruited through a similar process (i.e., deferred prosecution) revealed that the relapse rate during outpatient services providing cognitive behavioral therapy (CBT) once per week for 12 weeks was 69 % (
      • Chen Y.C.
      • Chen C.K.
      • Wang L.J.
      Predictors of relapse and dropout during a 12-week relapse prevention program for methamphetamine users.
      ), much higher than that in our treatment program (26.7 % in the first 12 weeks). Overall, the lower relapse rate in our study might be attributable to the design of joint legal–medical intervention and our multi-component approach (integrating psychiatric evaluation and medical treatment, brief motivational intervention, and cognitive behavioral therapy). Future studies with similar design should investigate the factors for lower relapse rates.
      One major finding in our study is that baseline urine positive was the strongest risk factor to predict later methamphetamine relapse. Previous studies have suggested that urine toxicology screening should be applied in routine assessment as a standard of care in addiction treatment to provide objective support for clinical observations (
      • Tenore P.L.
      Advanced urine toxicology testing.
      ). A positive result was an indicator that our participants had used methamphetamine 2 to 5 days (
      • Huestis M.A.
      • Cone E.J.
      Methamphetamine disposition in oral fluid, plasma, and urine.
      ) prior to their first treatment session, despite having been informed that they would undergo urine toxicology testing on this scheduled day. Therefore, a positive urine result at baseline may indicate that an individual has less motivation to change, or a high addiction severity, and thus has high odds for relapse. In this study, the participants in the UP group had higher SDS scores and craving severity than those in the UN group (data not shown). Also, their time to relapse was significantly shorter than that of those in the UN group (66.8 vs. 309 days). Methamphetamine users who received substance use treatment and had a low motivation to change were more susceptible to relapse (
      • Tuliao A.P.
      • Liwag M.E.C.D.
      Predictors of relapse in Filipino male methamphetamine users: A mixed methods approach.
      ); treatment effectiveness was also likely to be lower in this group (
      • Ryan R.M.
      • Plant R.W.
      • O'Malley S.
      Initial motivations for alcohol treatment: Relations with patient characteristics, treatment involvement, and dropout.
      ;
      • Zeldman A.
      • Ryan R.M.
      • Fiscella K.
      Motivation, autonomy support, and entity beliefs: Their role in methadone maintenance treatment.
      ). Therefore, the baseline urine screening results of the joint program can offer crucial information to predict the likelihood of relapse and to point to the need for a more intense treatment program.
      Our study also found that participants with high craving severity had significantly shorter time to relapse. Craving is a prominent feature of addictive disorders, as evidenced by its classification as one of the six features of substance dependence in the diagnostic criteria of the DSM-5 (
      American Psychiatric Association
      Diagnostic and statistical manual of mental disorders (DSM-5®).
      ). Craving operates as the major motivational substrate for drug-seeking behavior (
      • Tiffany S.T.
      • Wray J.M.
      The clinical significance of drug craving.
      ) and is commonly used as a measure or predictor of outcomes in addiction treatment research (
      • Chen L.Y.
      • Chen C.K.
      • Chen C.H.
      • Chang H.M.
      • Huang M.C.
      • Xu K.
      Association of craving and depressive symptoms in ketamine-dependent patients undergoing withdrawal treatment.
      ;
      • Tziortzis D.
      • Mahoney III, J.J.
      • Kalechstein III, A.D.
      • Newton III, T.F.
      • De La Garza II, R.
      The relationship between impulsivity and craving in cocaine-and methamphetamine-dependent volunteers.
      ). Evidence has indicated that craving is a salient predictor of within-treatment methamphetamine relapse (
      • Hartz D.T.
      • Frederick-Osborne S.L.
      • Galloway G.P.
      Craving predicts use during treatment for methamphetamine dependence: A prospective, repeated-measures, within-subject analysis.
      ;
      • Tuliao A.P.
      • Liwag M.E.C.D.
      Predictors of relapse in Filipino male methamphetamine users: A mixed methods approach.
      ). In a study similarly dichotomizing craving scores on a VAS,
      • Hartz D.T.
      • Frederick-Osborne S.L.
      • Galloway G.P.
      Craving predicts use during treatment for methamphetamine dependence: A prospective, repeated-measures, within-subject analysis.
      demonstrated that a higher baseline craving for methamphetamine is associated with a 2.5 times greater likelihood of relapse relative to lower baseline craving in a 12-week treatment program. The magnitude of craving significantly predicts the probability of methamphetamine use during treatment, with this effect largely independent of recent methamphetamine use or time spent in treatment. Our results not only indicate that a high intensity of craving for methamphetamine is associated with an increased risk of relapse, but also link it to a shorter time to relapse. Our finding offers a clinical clue that treatment directly targeting cravings, such as craving management or craving-suppressant medication, would benefit patients with substance use disorders.
      One meta-analysis indicated that the dropout rate in substance use treatment adopting an in-person psychosocial approach was 30.4 %, but it could be as high as 53.5 % for studies targeting individuals with methamphetamine addiction (
      • Lappan S.N.
      • Brown A.W.
      • Hendricks P.S.
      Dropout rates of in-person psychosocial substance use disorder treatments: A systematic review and meta-analysis.
      ). For example, a prospective longitudinal study involving treatment-seeking methamphetamine users from 32 community-based outpatient and residential programs reported that 65.6 % of the residential sample remained in treatment for at least 3 months, as did 58.8 % of the outpatient sample (
      • Hser Y.I.
      • Evans E.
      • Huang Y.C.
      Treatment outcomes among women and men methamphetamine abusers in California.
      ). The noncompletion rate in our observational cohort was lower at 23.2 %, suggesting that about 3/4 of all participants were completed. A study in which admissions and dropout rates among methamphetamine users in the California criminal justice system were examined showed that the completion rates were one-third (
      • Anglin M.D.
      • Urada D.
      • Brecht M.L.
      • Hawken A.
      • Rawson R.
      • Longshore D.
      Criminal justice itreatment admissions for methamphetamine use in California: a focus on proposition 36.
      ). Several explanations may account for the higher rates of treatment completion in our study. One explanation is that our sample participants were all first-time drug offenders without any other criminal history, which might suggest their lower addiction severity compared to the participants in the California study. Another possibility is that treatment completion is a prerequisite for exemption from prosecution for our participants and the probation officers are involved in reinforcing the compliance of individuals with unexpected absence. Treatment completion has been considered one of the most consistent factors related to favorable outcomes, such as a low relapse rate (
      • Brewer D.D.
      • Catalano R.F.
      • Haggerty K.
      • Gainey R.R.
      • Fleming C.B.
      A meta-analysis of predictors of continued drug use during and after treatment for opiate addiction.
      ;
      • Brorson H.H.
      • Ajo Arnevik E.
      • Rand-Hendriksen K.
      • Duckert F.
      Drop-out from addiction treatment: A systematic review of risk factors.
      ;
      • Ciraulo D.A.
      • Piechniczek-Buczek J.
      • Iscan E.N.
      Outcome predictors in substance use disorders.
      ), during substance use treatment.
      • Hillhouse M.P.
      • Marinelli-Casey P.
      • Gonzales R.
      • Ang A.
      • Rawson R.A.
      • Methamphetamine Treatment Project Corporate A.</c.>
      Predicting in-treatment performance and post-treatment outcomes in methamphetamine users.
      study on methamphetamine users demonstrated that those who successfully completed the treatment were less likely to use methamphetamine after treatment. As such, strategies for promoting treatment adherence are crucial in the provision of substance use treatment (
      • Cheng W.J.
      • Chen L.Y.
      • Fang S.C.
      • Chang H.M.
      • Yang T.W.
      • Chang R.C.
      • Hsing T.C.
      • Huang M.C.
      Examining factors associated with postintervention recidivism in DUI repeat offenders after alcohol treatment: One-year follow-up study.
      ). Although our study design (legal–medical joint program) may have increased treatment adherence, future studies for people who seek treatment voluntarily could focus on removing treatment barriers that play a significant role of treatment adherence (
      • Chen L.Y.
      • Crum R.M.
      • Martins S.S.
      • Kaufmann C.N.
      • Strain E.C.
      • Mojtabai R.
      Service use and barriers to mental health care among adults with major depression and comorbid substance dependence.
      ). Our low dropout rates also support the expansion of such joint programs in which drug users are considered diseased criminals instead of criminals. A similar program in the United States, the pilot program Law Enforcement Assisted Diversion, had a 58 % reduced likelihood of recidivism (
      • Collins S.E.
      • Lonczak H.S.
      • Clifasefi S.L.
      Seattle's law enforcement assisted diversion (LEAD): Program effects on recidivism outcomes.
      ).
      Among our participants, relapse occurred most often in the first 3 months but declined over time. The highest probability of relapse in the early course of treatment is consistent with findings in the literature (
      • Brecht M.L.
      • Herbeck D.
      Time to relapse following treatment for methamphetamine use: A long-term perspective on patterns and predictors.
      ;
      • McKetin R.
      • Najman J.M.
      • Baker A.L.
      • Lubman D.I.
      • Dawe S.
      • Ali R.
      • Lee N.K.
      • Mattick R.P.
      • Mamun A.
      Evaluating the impact of community-based treatment options on methamphetamine use: Findings from the methamphetamine treatment evaluation study (MATES).
      ). Early relapse has been associated with a poor prognosis in maintaining long-term abstinence (
      • Charney D.A.
      • Zikos E.
      • Gill K.J.
      Early recovery from alcohol dependence: Factors that promote or impede abstinence.
      ). In the early stage of abstinence, the length of abstinence duration was associated with increased volumes in the brain regions crucial for cognitive function and recovery behavior during the course of treatment (
      • Nie L.
      • Ghahremani D.G.
      • Mandelkern M.A.
      • Dean A.C.
      • Luo W.
      • Ren A.
      • Li J.
      • London E.D.
      The relationship between duration of abstinence and gray-matter brain structure in chronic methamphetamine users.
      ). Evidence has supported the idea that earlier periods of abstinence are predictive of long-term abstinence (
      • Dennis M.L.
      • Foss M.A.
      • Scott C.K.
      An eight-year perspective on the relationship between the duration of abstinence and other aspects of recovery.
      ;
      • Hser Y.I.
      • Stark M.E.
      • Paredes A.
      • Huang D.
      • Anglin M.D.
      • Rawson R.
      A 12-year follow-up of a treated cocaine-dependent sample.
      ). Therefore, for individuals with a high risk of relapse, an intervention incorporating more medical resources in the early stage of abstinence might be crucial to achieve sustained abstinence and promote recovery.
      This study has several limitations. First, we targeted relapse as the outcome measure and examined the factors associated with it. While relapse is the cardinal feature of substance use disorder and generally the primary target for interventions, it is not the only outcome of interest (
      • Donovan D.M.
      • Bigelow G.E.
      • Brigham G.S.
      • Carroll K.M.
      • Cohen A.J.
      • Gardin J.G.
      • Lindblad R.
      Primary outcome indices in illicit drug dependence treatment research: Systematic approach to selection and measurement of drug use end-points in clinical trials.
      ). Other outcome domains, including employment,/self-support, and family function, should also be explored in future work (
      • Donovan D.M.
      • Bigelow G.E.
      • Brigham G.S.
      • Carroll K.M.
      • Cohen A.J.
      • Gardin J.G.
      • Lindblad R.
      Primary outcome indices in illicit drug dependence treatment research: Systematic approach to selection and measurement of drug use end-points in clinical trials.
      ). Second, relapse over a year-long period might be complicated with various factors such as social functioning, potential for polysubstance, emotional disturbance, motivation level, personality traits, and family support (
      • Moeeni M.
      • Razaghi E.M.
      • Ponnet K.
      • Torabi F.
      • Shafiee S.A.
      • Pashaei T.
      Predictors of time to relapse in amphetamine-type substance users in the matrix treatment program in Iran: A cox proportional hazard model application.
      ;
      • Tuliao A.P.
      • Liwag M.E.C.D.
      Predictors of relapse in Filipino male methamphetamine users: A mixed methods approach.
      ). Furthermore, the pattern of use (i.e. frequency/quantity) may vary among relapsers or different relapse episodes. Therefore, a simple bifurcation of the study sample into relapse/nonrelapse groups may preclude us from elucidating other risk factors associated with relapse than just baseline urine screening results or craving severity. Second, drug use relapse was recorded by urine screening or self-report, which might be vulnerable to recall bias. Third, this study used a cohort of first-time offenders for methamphetamine use under deferred prosecution in Taiwan, thus limiting its generalizability to other methamphetamine use populations, individuals with other types of illicit drug, or individuals in different criminal justice situations outside of Taiwan. Fourth, this study was not able to validate the effectiveness of the joint legal–medical treatment program. A randomized controlled trial including a control group for comparison could be conducted to investigate the efficacy of such a treatment program. Last, a longer follow-up period is required to examine whether predictors of relapse are sustained or differ longer term (
      • Scott C.K.
      • Foss M.A.
      • Dennis M.L.
      Pathways in the relapse–treatment–recovery cycle over 3 years.
      ).

      5. Conclusion

      The criminal justice system has gradually integrated medical approaches to prevent the recidivism of illicit drug use (
      • Lowder E.M.
      • Rade C.B.
      • Desmarais S.L.
      Effectiveness of mental health courts in reducing recidivism: A meta-analysis.
      ). By following a cohort of first-time methamphetamine offenders who were involved in a joint legal–medical program for substance use treatment for one year as an alternative to imprisonment, we found that a methamphetamine-positive baseline urine result or high craving severity are associated with higher relapse rates and a shorter period of methamphetamine relapse among this population. Therefore, a pragmatic applied program could incorporate urine screening tests as a way of monitoring drug use and targeting craving as a major substrate for the joint intervention program. To increase treatment completion rates, efforts to remove stigma for substance use patients and to increases treatment availability are also imperative. Future studies should assess outcome measures other than relapse, including more detailed temporal characteristics concurrent with or proximal to methamphetamine use over time, and test the results in other populations of substance use disorders.
      The following is the supplementary data related to this article.

      Funding

      This study was supported by grants from Ministry of Science and Technology, Taiwan (106-2314-B-532-010-MY2 & 110-2628-B-532-001 [Lian-Yu Chen]; 109-2314-B-532-0041 & 110-2314-B-532-005-MY3 [Ming-Chy Huang]); Taipei City Government, Taiwan (TPECH 11001-62-003 & 11101-62-029 [Ming-Chy Huang]). The funders have no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

      Role of the sponsor

      The funders have no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in publication of this study.

      CRediT authorship contribution statement

      Ming-Chyi Huang conceptualized the study and wrote the study protocol. Ming-Chyi Huang, Lian-Yu Chen and Su-Chen Fang reviewed the bulk of literature and wrote the first draft of the manuscript. Su-Chen Fang analyzed the data. Chun Lin recruited and evaluated the participants. Ta Lin contributed to data interpretation and discussion. Lian-Yu Chen supervised the study and final revision of the manuscript and incorporated all the edits from all coauthors. All authors have read and approved the final manuscript.

      Uncited references

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      Declaration of competing interest

      The authors have no conflict of interest to declare in this study.

      Acknowledgment

      This study was supported by grants from Ministry of Science and Technology, Taiwan (106-2314-B-532-010-MY2; 110-2628-B-532-001 PI: Dr. Lian-Yu Chen) (109-2314-B-532-0041; 110-2314-B-532-005-MY3; PI: Dr. Ming-Chy Huang); Taipei City Hospital (TPECH 11001-62-003 and 11101-62-029). The funders have no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

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