Journal of Addiction Research & Therapy
Open Access

Subjective ratings; Prognoses for abstention; Substance use disorders; Addiction; Methamphetamine; Psychiatric disorders; Post treatment abstention

Over 20 million Americans aged 12 and older meet the diagnostic criteria for a substance use disorder (SUD) [1]. While an estimated 7.8% of persons aged 12 and older in the United States possess a need for substance use treatment in 2019, only 1.5% of those individuals received any form of substance use treatment in the past year [1]. Approximately 1 million people received substance use treatment in an inpatient or residential treatment facility in 2019 [1]. In addition to the glaring treatment gap for persons with SUDs, persons discharging from residential or inpatient substance use treatment experience the highest level of vulnerability to relapse in first three months post treatment with relapse prevalence rates ranging from 37 to 75% [2-8]. To support the ongoing recovery efforts of persons discharging from residential and inpatient substance use treatment, it is important to identify clients’ likely prognosis for post treatment abstinence.

Given the limited predictive power of objective measures for post treatment abstinence, the subjective evaluations based on AOD counselors’ clinical impressions of the clients’ prognoses for abstention serve as an important predictor of post treatment outcomes and enhance the predictive power of objective factors [9]. While Schuckit and colleagues [9] established that the subjective ratings of prognoses by treatment staff were predictive of short-term outcomes among male inpatients with primary diagnosis of alcohol use disorder, the study did not examine the factors influencing these subjective ratings, nor did it include persons who use illicit drugs.

To date, only one known study has examined the predictive factors related to AOD counselor’s subjective ratings of prognoses for abstention from drugs and alcohol for clients who completed SUD treatment. In the 1997 study by Gutierres and Todd of men and women receiving residential SUD treatment in Phoenix, men were more likely to receive positive prognoses for abstention than their female counterparts. However, a positive history of Adverse Childhood Experiences (ACEs) for abuse experiences was not predictive of positive prognoses for abstention for males or females [10].

This study stands to provide greater insight into the factors related to AOD Counselors’ subjective ratings of clients’ prognoses subsequent to discharge from residential or inpatient substance use treatment facilities. In addition to the relationships between traumatic experiences, psychiatric illness and treatment, readiness for change, and self-reported history of self-medication for psychiatric disorders and prognoses for abstention, respectively, this study is also interested in the role of external coercion from the criminal justice and child welfare systems, housing, age, gender, and race/ethnicity on counselor ratings of prognoses. Additionally, the present study includes persons who use alcohol, stimulants, marijuana, or heroin or other opiates.

To address these gaps, this study examined a de-identified dataset provided by a local non- profit agency, which provides publicly funded residential SUD treatment services at the 3.1 and 3.5 ASAM LOC for clients enrolled in or eligible for Medicaid or a county-based no-cost health insurance plan for low-income individuals in a large urban county. This dataset included all of the information recorded in the ASAM Multidimensional Assessment and the treatment disposition, prognosis, and aftercare services listed in the Discharge/Transfer Form. The central research focus for this analysis was to determine the predictors of the clients’ assigned prognosis at the end of their residential SUD treatment episodes. This research was approved by the IRB.

Sample

Inclusion criteria: The sample includes all clients admitted into an abstinence-based residential treatment facility located in a large urban county between August 1, 2017, and March 1, 2018. These clients all possessed or were eligible for Medicaid, a county-based no-cost health insurance plan for low-income individuals, or participants in county funded programs for persons who are indigent or involved with the criminal justice or child welfare system; aged 18 and older; and were residents of the large urban county for at least the past 60 days prior to their assessment. Additionally all of the met the criteria for a DSM-5 SUD with a moderate or severe specifier. The sample size includes 200 clients (n = 200).

Exclusion criteria: As this residential SUD treatment facility is not authorized to provide services to minors, no minors were included in the sample. Similarly, no mono-lingual non-English speakers were included in the sample, as the treatment facility operates in English. Clients testing positive for opiates, alcohol, or benzodiazepines at the time of assessment were also excluded from this study, as they were referred to treatment at facilities licensed to provide withdrawal management (WM) services.

Measures

AOD Counselor Prognoses for Abstention: The dependent variable of interest in this study, an ordered categorical variable, was the clients’ prognosis rating assigned by their primary AOD Counselor when the participants’ treatment episode ended. The AOD Counselors had the option of assigning ratings based on a three-point Likert scale, lower scores representing better prognosis. These ratings were coded as the following three categories: 1 = good, 2 = fair, and 3 = poor.

Pretreatment substance use: Types of Polysubstance Use was constructed through performing Ward’s Method of Cluster Analysis. Four distinct classifications of combinations of types of substances used emerged: participants who reported predominantly using methamphetamine (MA) and alcohol, participants who reported predominantly using alcohol and marijuana, participants who reported predominantly using MA and marijuana, and participants who reported predominantly using MA, alcohol, heroin, and marijuana.

Number of days of MA use in past 30 days was a continuous variable constructed for all participants who reported MA as either their primary or secondary DOC and the corresponding number of days they reported using MA out of the past 30 days.

Number of days of primary substance use in past 30 days was a continuous variable constructed for all participants based on the number of days they reported using their primary substance used out of the past 30 days.

Presence of active withdrawal symptoms was a dichotomous variable constructed based on participants’ responses to, “Are you currently experiencing withdrawal symptoms?” The variable was coded 1 for participants who responded “yes” and reported withdrawal symptoms. The variable was coded 0 for participants who responded “no” and did not report any active withdrawal symptoms.

Mental health: Number of Lifetime Inpatient Psychiatric Hospitalizations was a continuous variable constructed for all participants based on number of days they reported previous inpatient psychiatric hospitalizations in their lifetime.

Presence and Treatment of Psychiatric Conditions was a categorical variable constructed through performing Ward’s Method of Cluster Analysis. Four distinct classifications emerged regarding the presence and treatment of psychiatric conditions: participants who did not report any significant psychiatric symptoms or history of receipt of mental health services; participants who reported psychiatric symptoms, including psychotic symptoms, and had a history of receipt of mental health services; participants who reported psychiatric symptoms, excluding psychotic symptoms, and had a history of receipt of mental health services; and participants who reported psychiatric symptoms but denied a history of mental health services.

Score on Mood Symptoms was constructed by creating a composite score (sum) ranging from 0 to 8 based on the number of mood symptoms out of a list of eight symptoms the participant agreed in the affirmative that they had experienced in the past 30 days. These mood symptoms included “depression/sadness,” “loss of pleasure/interest,” “hopelessness,” “irritability/anger,” “impulsivity,” “pressured speech,” “grandiosity,” and “racing thoughts.”

Score on Anxiety Symptoms was constructed by creating a composite score (sum) ranging from 0 to 4 based on the number of anxiety symptoms out of a list of four symptoms the participant agreed in the affirmative that they had experienced in the past 30 days. These anxiety symptoms included “anxiety/excessive worry,” “obsessive thoughts,” “compulsive behaviors,” and “flashbacks.”

Score on Psychotic Symptoms was constructed by creating a composite score (sum) ranging from 0 to 3 based on the number of psychotic symptoms out of a list of three symptoms the participant agreed in the affirmative that they had experienced in the past 30 days. These psychotic symptoms included “paranoia,” “delusions,” and “hallucinations.”

Score on PTSD Symptoms was constructed by creating a composite score (sum) ranging from 0 to 7 based on the number of PTSD symptoms out of a list of seven symptoms the participant agreed in the affirmative that they had experienced in the past 30 days. These PTSD symptoms included “sleep problems,” “anxiety,” “problems with memory/concentration,” “irritability,” “obsessive thoughts,” “compulsive behaviors,” and “flashbacks.”

Trauma: Type of Trauma was categorical variable constructed through performing Ward’s Method of Cluster Analysis. Five distinct trauma classifications emerged: limited trauma exposure; experiences of intimate partner violence and sexual assault as an adult as well as loss/separation from a child (e.g. death, loss of child custody); deaths of family members; multiple instances and types of non-abuse trauma transpiring in adulthood, including death of family, death of close friends, and witnessing violence (watching others get stabbed or shot; discussed in terms of community violence as well as in the course of participating in criminal/gang activity or while incarcerated); experiences of childhood abuse, IPV and sexual assault in adulthood, and loss/separation from a child (e.g. death or loss of child custody).

History of Abuse was a dichotomous variable constructed based on participants’ responses to, “Have you ever experienced physical, emotional, or sexual abuse in your lifetime?” The variable was coded 0 = no, for participants who did not report a history of abuse, and 1 = yes, for participants who reported a history of abuse.

History of Other Significant Trauma was a dichotomous variable constructed based on participants’ responses to, “Have you ever experienced a traumatic event in your lifetime?” The variable was coded 0 = no, for participants who did not report a history of traumatic events, and 1 = yes, for participants who reported a history of traumatic events.

Self-medication for psychiatric distress: Triggers to Use - Mental Health Symptoms was a dichotomous variable constructed based on participants’ responses to, “Are you aware of your triggers to use alcohol or drugs?” One of the triggers listed was “Mental Health.” The variable was coded 0 = no, for participants who reported that mental health symptoms were not a trigger for substance, and 1 = yes, for participants who reported that mental health symptoms were a trigger for substance use.

Triggers to Use - Negative Intrapersonal Contexts was a dichotomous variable constructed based on participants’ responses to, “Are you aware of your triggers to use alcohol or drugs?” One of the triggers listed in was “Negative Emotions.” The variable was coded 0 = no, for participants who reported that negative emotions were not a trigger for substance, and 1 = yes, for participants who reported that negative emotions were a trigger for substance use.

Barriers to Recovery - Mental Health was a dichotomous variable constructed based on participants’ responses to, “What are potential barriers to your recovery?” in Dimension 4, Readiness for Change, in the ASAM Multidimensional Assessment Tool. The variable was coded 0 = no, for participants who did not verbalize mental health symptoms as a barrier to their recovery as well as for those participants who could not identify any barriers to recovery. The variable was coded 1 = yes, for participants who explicitly stated that mental health symptoms would be a barrier to their recovery.

Barriers to Recovery - Negative Intrapersonal Contexts was a dichotomous variable constructed based on participants’ responses to, “What are potential barriers to your recovery?” The variable was coded 0 = no, for participants who did not verbalize negative emotions as a barrier to their recovery as well as for those participants who could not identify any barriers to recovery. The variable was coded 1 = yes, for participants who explicitly stated that negative emotions would be a barrier to their recovery.

Readiness for change: Dimension 4 Severity Rating was a categorical variable coded as 0 = None-“Willing to engage in treatment,” 1 = Mild - “Willing to enter treatment but ambivalent to the need to change,” 2 = Moderate - “Reluctant to agree to treatment; low commitment to change substance use; passive engagement in treatment,” 3 = Severe - “Unaware of need to change; unwilling or partially able to follow through with recommendations for treatment,” and 4 = Very Severe - “Not willing to change; unwilling/unable to follow through with treatment recommendations.” Each participant was assigned one of the aforementioned ratings based on the clinician’s perception of one’s “Readiness to Change.” The variable was later collapsed into 1 = Low - Willingness to participate in treatment (e.g. those participants who had been rated none to Moderate) and 2 = High - Limited to no willingness to participate in treatment (e.g. those participants who had been rated Severe to Very Severe).

History of SUD Treatment was a dichotomous variable constructed based on participants’ responses to, “Have you received help for alcohol and/or drugs in the past?” The variable was coded 0 = no, for participants who never previously received any form of SUD treatment, and 1 = yes, for participants who previously received SUD treatment.

Importance of SUD Treatment was a categorical variable constructed based on participants’ responses to, “How important is it for you to receive treatment for alcohol and/or drug problems?” The variable was coded 0 = “Not at all important,” 1 = “Slightly important,” 2 = “Moderately important,” 3 = Considerably important,” and 4 = “Extremely important.” Categories with less than 10% of the sample size (n = 20) were collapsed. As a result, the following categories remained: “No to considerable importance,” and “Extreme importance.”

Sociodemographic variables: Gender was a dichotomous variable was coded male = 1, female = 2. Since only one transgender woman and no transgender males entered treatment during the course of the study, the transgender female was collapsed into the category “female.”

Race/ethnicity was a categorical variable constructed from participants’ responses to “How do you identify in terms of race or ethnicity?” The variable was coded as 1 = Caucasian, 2 = Black, 3 = Hispanic, 4 = Asian/Pacific Islander, 5 = Native American, 6 = Multiracial, and 7 = Other. Categories with less than 10% of the sample population were collapsed. As a result, the following four categories remained: 1 = Caucasian, 2 = Black, 3 = Hispanic, and 4 = Other.

Age was a continuous variable calculated by subtracting birth year, month, and day from the intake date to residential SUD treatment.

Living Arrangement was categorical variable coded as 1 = homeless, 2 = independent living, and 3 = other. As none of the participants reported “other,” the variable was collapsed into 1 = homeless and 2 = independent living.

Forensic Status was a categorical variable constructed based on participants’ responses to “Are you currently involved with social services or the legal system (e.g. child welfare, court mandated, probation, parole)?” The variable was coded as 1 for participants who responded that they were on probation or parole, had been court mandated to SUD treatment, or were participants in an in-custody release program. The variable was coded 0 for participants who denied any type of forensic involvement.

Child Welfare Status was dichotomous variable constructed based on participants’ responses to, “Are you currently involved with social services or the legal system (e.g. child welfare, court mandated, probation, parole)?” The variable was coded as 1 for participants who responded that they had an open child welfare case and 0 for participants who denied that they had an open child welfare case.

Analysis: Descriptive information including means, standard deviations and frequencies were generated for all variables in the dataset (Table 1). Correlations and/or associations were produced for all the variables in the study. Since the majority of the variables in the dataset were categorical, associations were presented (Table 2). For the continuous variables, correlations were provided.

Ordinal logistic regression was used to determine the predictors of the clients’ assigned prognosis at the end of the treatment episode (Table 3). This study examined the role of substance use, mental health trauma, readiness to change, and self- medication for psychiatric distress to predict clients’ assigned prognosis at the end of the treatment episode. The following control variables also were included in the analysis: gender, race/ethnicity, age, living arrangements, forensic status, and child welfare status. SPSS 25 was used to conduct the statistical analyses.

Table 3: Ordinal logistic regression of counselor prognoses for clients’ abstention Pseudo R-Square.

Variable selection approach: The number of variables in this study was considerable, let alone the number of parameter estimates. As a result, user determined hierarchical regression was conducted. Variables significant at p < .05 for each conceptual domain were included in the full model.

Descriptive statistics

The final sample of 200 participants consisted of mostly males (60%). The participants reported their race/ethnicity as White (29%), Black (28.5%), Hispanic (36%), and Other (6.5%). Ages ranged from 20 to 83 years, with an average age of 36.6 years. A majority of the participants (60.5%) did not have stable living arrangements and reported being homeless. Almost half of the participants (45%) identified that they were currently involved with the criminal justice system (e.g. probation, parole, and court ordered to treatment, or in- custody program participants). However, only 14% of the participants reported that they had an open child welfare case.

Most of the participants identified using MA in combination with other illicit drugs or alcohol (75%). For the 30 days prior to entering treatment, participants reported an average of 14.6 days in which they used their primary substance and an average of 9.5 days in which they used MA, respectively. Less than half of the participants (41.5%) reported that they were experiencing withdrawal symptoms at the time of intake to residential SUD treatment. Most participants (73.5%) had attempted SUD treatment in the past.

At time of intake, participants reported an average of 3.39 mood symptoms (SD = 2.439), 1.57 anxiety symptoms (SD = 1.351), .62 psychotic symptoms (SD = 1.031), and 3.08 PTSD symptoms (SD = 2.182). Participants reported an average of 1.04 (SD = 3.228) acute inpatient psychiatric hospitalization episodes. While 21.5% of participants reported history of few to no psychiatric symptoms, 20.5% reported symptoms of serious mental illness and were not receiving mental health services. The remainder of the participants (n = 98) reported symptoms of serious or any mental illness and had history of mental health services.

Almost half of the participants (46.5%) reported a history of abuse. Furthermore, the vast majority of the participants (77%) reported that they had experienced a significant trauma. In terms of types of traumatic experiences, 11.5% reported experiencing abuse in adulthood, 7.5% reported experiencing both abuse in adulthood and childhood, 17.5% reported the death of family members, 10.5% reported some type of non-abuse trauma in adulthood, and 53% reported limited trauma exposure.

A majority of the participants (56.5%) indicated that mental health issues were a trigger to use alcohol and/or drugs. Similarly, a vast majority of participants (81%) identified negative intrapersonal contexts as a trigger to use alcohol and/or drugs. However, only 10% of participants identified mental health issues as a barrier to their recovery, and only 16% identified negative intrapersonal contexts as a barrier to their recovery (Table 1).

Table 1: Demographic and descriptive characteristics of participants (N = 200).

All participants received a rating of “good,” “fair,” or “pair” related to their prognoses for abstention from their primary AOD Counselor at the termination of their treatment episode. Correspondingly, just under half of the participants (47%) received a rating of “poor” related to their prognosis for abstention. On the other hand, 39% received a rating of “good” and 14% received a rating of “fair,” respectively.

Associations

In relation to the variables in the full model for the ordinal logistic regression for AOD Counselor prognoses for client abstention, the results of Cramer’s V association revealed multiple significant positive associations (see Table 2). These significant positive associations included between: AOD Counselor prognosis for client abstention and Mental Health Treatment and Symptoms (V = .198), AOD Counselor prognosis for client abstention and Readiness for Change (V = .185), Gender and History of Abuse (V = .467), Living Arrangement and Race (.202), Living Arrangement and History of Abuse (V = .241), Living Arrangement and Mental Health Symptoms and Treatment (V = .209), Race and History of Abuse (V = .217), Race and Mental Health Symptoms and Treatment (V = .182), Race and Combination of Substances Used (V = .222), History of Abuse and Mental Health Treatment and Symptoms (V = .331), Mental Health Treatment and Symptoms and Readiness for Change (V = .193), Combination of Substances Used and Readiness for Change (V = .187), Number of Lifetime Inpatient Psychiatric Episodes and Gender (η = .307), Number of Lifetime Inpatient Psychiatric Episodes and History of Abuse (η = .347), Number of Lifetime Inpatient Psychiatric Episodes and Mental Health Treatment and Symptoms (.303), Past 30 Days of Use of Primary Substance Used and Gender (η = .458), and Past 30 Days of Use of Primary Substance Used and Combination of Substances Used (η = .417), respectively (Table 2).

Table 2: Associations.

Inferential statistics

As a consequence of the relatively large number of predictors, ordinal logistic regression was conducted for each of the six conceptual blocks (e.g. sociodemographic, substance use, mental health, trauma, self-medication for psychiatric distress, and readiness for change variables, respectively). Within each of the blocks, those predictor variables that significantly predicted counselors’ prognoses for abstention (good, fair, or poor) at the p < .05 level were entered into the corresponding ordinal logistic regression analysis. From the sociodemographic conceptual block, only living arrangement (p < .009) and age (p < .037) were significant. In terms of the substance use conceptual block, past 30 days of use of primary substance used (p < .004) and combination of substances used - alcohol and marijuana (p < .022) were both significant. From the mental health conceptual block, mental health symptoms and treatment - serious mental illness with treatment (p < .03) was significant. In the readiness for change conceptual block, Dimension 4 Severity rating - None (p < .003) and Dimension 4 Severity rating - Mild to Moderate (p < .006) were significant. However, none of the variables in the traumatic exposure block were significant. Similarly, none of the variables in the selfmedication for psychiatric distress block were significant.

A test of the model using all of the aforementioned predictor variables as well as race, gender, number of inpatient psychiatric episodes, and history of abuse was significant (p < .001) with a R2 value of .299 [11] as seen in Table 3 (Table 3).

Significant predictor variables of counselor prognoses included gender (p < .028), past 30 days of use of primary substance used reported at intake (p < .001), number of lifetime inpatient psychiatric episodes (p < .026), use of alcohol and marijuana in combination (p < .026), readiness to change - no severity (p < .046), and readiness for change - mild to moderate severity (p < .025). Being female was significantly associated with a greater likelihood of being rated “good” or “fair” as compared to being rated “poor” (OR = 2.30).

Participants who reported polysubstance use of alcohol and marijuana in comparison to those participants who reported polysubstance use of heroin, alcohol, methamphetamine, and marijuana were significantly more likely to be rated “good” or “fair” compared to being rated “poor” (OR = 2.933). For past 30 days of use of primary substance used, the odds ratio of .952 reveals that for each additional day the participant used one’s primary substance in the past 30 days, one’s odds of receiving a rating of “good” or “fair” decreased by 4.8% for each day of use. Consequently, a 10-day increase in primary substance use out of the past 30 days would decrease an individual’s chances of receiving a rating of “good” or “fair” by 61.6%.

Participants who received a severity rating of “None - Willing to Engage in Treatment” in comparison to those participants who received a severity rating of “Severe to Very Severe - Unaware of Need for Change or Unwilling to Change” for the Readiness to Change Dimension of the ASAM Assessment were significantly more likely to be rated “good” or “fair” compared to “poor” (OR = 2.634). Similarly, participants who received a severity rating of “Mild to Moderate - Ambivalent to Change or Reluctant to Enter Treatment” in comparison to those participants who received a severity rating of “Severe to Very Severe - Unaware of Need for Change or Unwilling to Change” for the Readiness to Change Dimension of the ASAM Assessment were significantly more likely to be rated “good” or “fair” compared to “poor” (OR = 2.221).

In regards to number of inpatient psychiatric episodes, the odds ratio of .88 indicates that for each additional inpatient psychiatric episode, the individual’s odds of receiving a rating of “good” or “fair” decreased by 12%.

Contrary to the findings by Gutierres and Todd [10], women were significantly less likely to receive ratings for prognoses for abstention of “fair” or “poor” than “good” compared to their male counterparts. In this sample, the female participants tended to be more likely to be consumers of mental health services in their lifetime than their male counterparts. Furthermore, male participants were more likely to be involved with the criminal justice system and required to participate in SUD treatment than their female counterparts. On the other hand, female participants were more likely to be involved with the child welfare system and required to participate in SUD treatment as a component of their parental reunification plan than their male counterparts. These differences related to historical utilization of mental health services and external pressure from the criminal justice system as opposed to the child welfare system in this sample may have affected participants’ treatment engagement and progress, which in turn affected their AOD Counselors’ prognoses for their ongoing abstention from drugs and/or alcohol. However, no other sociodemographic variables were significant predictors of AOD Counselor prognoses for clients’ abstention.

Both measures related to substance use were significantly predictive of AOD Counselor prognoses for clients’ abstention. For each additional day of use of primary substance used, participants were 4.8% less likely to be rated “good” or “fair.” While no other studies have previously addressed past 30 days of use of primary substance used related to AOD Counselor prognoses for clients’ abstention, this measure has been found to be significantly predictive of SUD treatment outcomes [12-20]. As higher rates of pretreatment substance use, especially among persons who use MA, cause greater neurocognitive impairment and psychiatric symptoms such as depression, anxiety, hallucinations, paranoia, irritability, anhedonia, hypersomnia, and aggression [21], it is not surprising that pretreatment substance use in the 30 days preceding residential SUD treatment affects participants’ treatment engagement and progress, which in turns affects the prognoses for abstention that they receive from their primary AOD Counselor. Given the importance of pretreatment substance use on clients’ treatment engagement and progress in residential SUD treatment settings, the present findings suggest that clients who indicate a high level of pretreatment substance use and positive UA or breathalyzer test at time of assessment for SUD treatment may benefit from receiving 3.2 WM LOC services prior to engaging in a clinically managed residential SUD treatment setting at the 3.1 or 3.5 LOC to enhance their physiological and psychological functioning.

Participants who reported using alcohol and marijuana in combination were significantly more likely to receive ratings of “good” or “fair” as opposed to “poor” compared to participants who used any combination of substances, all of which included MA. Although no previous known studies have examined the effect of pretreatment polysubstance use and AOD Counselor prognoses for clients’ abstention, the literature has consistently established that clients who report alcohol as their primary substance used are more likely to complete SUD treatment compared to participants who reported use of any other type of substance [1, 15, 22-31]. These findings further support the negative and highly disruptive effects of pretreatment MA use on SUD treatment engagement and progress as noted in previous research, which is reflected in clients’ prognoses for ongoing abstention from drugs and/or alcohol. As prolonged MA causes significant neurocognitive deficits and neurotoxic effects [21] as well as physiological decline [32] and has been linked to high rates of relapse [33], additional clinical trials to explore MAT options for MA users warrant ongoing funding and support.

For each additional episode of acute inpatient psychiatric hospitalization, participants were 12% less likely to receive a rating of “good” or “fair.” As previously discussed, repeated need for acute psychiatric hospitalization indicates a high severity of mental illness as well as ongoing difficulty in stabilizing psychiatric symptoms. Based on the findings from the present study, clients with high levels of psychiatric distress and difficulty managing their psychiatric symptoms at time of assessment may struggle to adjust and function in a clinically managed residential setting, such as 3.1 and 3.5 LOCs, which, in turn, influences the AOD Counselors’ prognoses for their ongoing abstention from drugs and/or alcohol. Expanding the ASAM LOC Continuum in large urban communities throughout the United States to include 3.7 and 4.0 LOCs would enable clients with co-occurring SUDs and SMIs with high levels of psychiatric distress to receive medically managed and monitored residential care and adequately stabilize their psychiatric symptoms prior to transitioning to a clinically residential SUD treatment setting, such as 3.1 or 3.5 LOCs, which may improve their subsequent treatment engagement and progress and corresponding prognoses for abstention from their AOD Counselors. However, presence and treatment of mental health symptoms was not a significant predictive factor of AOD Counselor prognoses for clients’ abstention.

In keeping with the findings by Gutierres and Todd [10], history of abuse was not significantly predictive of AOD Counselor prognoses for clients’ abstention. As history of traumatic exposure can significantly influence mental health [34], participants’ current level of psychiatric distress and lifetime history related to mental illness and treatment more significantly predict their ability to cope and function in a clinically managed residential SUD treatment facility and serve as a more relevant predictive factor of AOD Counselors’ prognoses for abstention than participants’ history of abuse alone without information related to their current psychological well-being and functioning. As a recent SAMHSA [1] publication addresses the role of trauma as a social determinant of SMI, this finding supports the imperative for early childhood intervention and treatment for children who experience abuse in order to enhance their ability to cope with emotional dysregulation and cognitive impacts of trauma in order to promote long-term mental health stability and mitigate risk for the onset of SMI.

Participants who received an overall rating of “None” or “Mild to Moderate” for Readiness to Change were significantly more likely to receive ratings for prognoses for abstention of “good” or “fair” as compared to “poor” as opposed to those participants who received an overall rating of “Severe to Very Severe” for Readiness for Change. While no previous studies have examined the role of clients’ internal motivation related to AOD Counselor prognoses for abstention, this study reveals the importance of assessing clients’ motivational levels for participating in residential SUD treatment, as addressed by the ASAM Multidimensional Assessment in Dimension 4 - Readiness for Change. Participants’ level of internal motivation significantly affects participants’ treatment engagement and progress during their residential SUD treatment episodes, which informs their AOD Counselors’ prognoses for their continued abstention from drugs and/ or alcohol. Participants determined to have “Severe” or “Very Severe” ratings based on ASAM Multidimensional Assessment criteria for Dimension 4 should be targeted for increased levels of contact and individual counseling sessions with AOD Counselors and clinicians through motivational interviewing, which has been found to be an effective evidence-based treatment for SUDs [35].

The results should be interpreted in light of several considerations. The dataset included clients from one large urban county, so the results cannot be generalized to all persons participating in residential or inpatient substance use treatment programs. Additionally, the participants in this sample overwhelmingly reported MA as their primary substance used. Furthermore, persons with stimulant use disorders may have been overrepresented at this facility as WM services were not available at this treatment facility. As the other residential SUD treatment facilities operated by this non-profit agency had onsite WM units, these facilities may have been more likely to admit higher percentage of patients with severe opioid; sedative, hypnotic, and anxiolytic; and alcohol use disorders, respectively, to residential SUD treatment after they successfully completed WM episode. Finally, there may have been biases in the self-reported information related to pretreatment substance use, psychiatric history, and trauma history included in the ASAM Multidimensional Assessment Tool due to social desirability and recall.

To date, only one other study has examined factors influencing AOD Counselor prognoses for clients’ abstention. Therefore, this study’s findings significantly add to the literature and illuminate how AOD Counselors perceive their clients’ treatment engagement and progress and their likelihood for long term abstinence from drugs and/ or alcohol. In turn, the results may be useful in informing interventions to enhance treatment engagement as well as outreach efforts for clients with poor prognoses with abstention. Finally, this study highlights the importance of expanding stabilizing withdrawal symptoms from MA through 3.2 WM LOC treatment episodes as well as symptoms of severe psychiatric distress through medically managed 3.7 and 4.0 LOC prior to admitting clients to clinically managed inpatient or residential substance use treatment facilities.

Role of Funding Source:

Nothing declared.

Conflict of Interest:

No conflict declared.

1. Substance Abuse and Mental Health Services Administration (2019) Serious mental illness and trauma: A literature review and issue brief. Rockville, USA.
2. Alterman AI, McKay JR, Mulvaney FD, Cnaan A, Cacciola JS, et al. (2000) Baseline prediction of 7-month cocaine abstinence for cocaine dependency patients. Drug Alcohol Depend 59: 215-221. 

Indexed at, Google Scholar, Crossref

3. Andersson HW, Wenaas M, Nordfjærn T (2019) Relapse after inpatient substance use treatment: A prospective cohort study among users of illicit substances. Addict Behav 90: 222-228.

Indexed at, Gdoogle Scholar, Crossref

4. Gil-Rivas V, Prause J, Grella CE (2009) Substance use after residential treatment among individuals with co-occurring disorders: the role of anxiety/depressive symptoms and trauma exposure. Psychol Addict Behav 23:303-314.

Indexed at, Google Scholar, Crossref

5. Gossop M, Stewart D, Browne N, Marsden J (2002) Factors associated with abstinence, lapse or relapse to heroin use after residential treatment: Protective effect of coping responses. Addiction 97:1259-1267.

Indexed at, Google Scholar, Crossref

6. McKetin R, Kothe A, Baker AL, Lee NK, Ross J, et al. (2018) Predicting abstinence from methamphetamine use after residential rehabilitation: Findings from the Methamphetamine Treatment Evaluation Study. Drug Alcohol Rev 37: 70-78.

Indexed at, Google Scholar, Crossref

7. Pasareanu AR, Vederhus JK, Opsal A, Kristensen O, Clausen T (2016) Improved drug-use patterns at 6 months post-discharge from inpatient substance use disorder treatment: Results from compulsorily and voluntarily admitted patients. BMC Health Serv Res 16:291.

Indexed at, Google Scholar, Crossref

8. Suter M, Strik W, Moggi F (2011) Depressive symptoms as a predictor of alcohol relapse after residential treatment programs for alcohol use disorder. J Subst Abuse Treat 41: 225-232. 

Indexed at, Google Scholar, Crossref

9. Schuckit MA, Smithm TL, Irwin M (1993) Subjective predictions of outcome among alcoholics. Addiction 88: 1361-1367.

       Google Scholar, Crossref

10. Gutierres SE, Todd M (1997) The impact of childhood abuse on treatment outcomes of substance users. Professional Psychology: Res Prac 28: 348-354.

Google Scholar, Crossref

11. Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78: 691-692.

Google Scholar, Crossref

12. Brecht ML, Greenwell L, Anglin MD (2005) Methamphetamine treatment: Trends and predictors of retention and completion in a large state treatment system (1992-2002). J Subst Abuse Treat 29: 295-306.

Indexed at, Google Scholar, Crossref

13. Brecht ML, Greenwell L, von Mayrhauser C, Anglin MD (2006) Two-year outcomes of treatment for methamphetamine use. J Psychoactive Drugs 3: 415-426.

Indexed at, Google Scholar, Crossref

14. Evans E, Li L, Hser Y (2009) Client and program factors associated with dropout from court mandated drug treatment. Eval Program Plann 32: 204-212.

Indexed at, Google Scholar, Crossref

15. Guerrero E, Marsh J, Duan L, Oh C, Perron B, et al. (2013) Disparities in completion of substance abuse treatment between and within racial and ethnic groups. Health Serv Res 48: 1450-1467.

Indexed at, Google Scholar, Crossref

16. Hillhouse MP, Marinelli-Casey P, Gonzales R, Ang A, Rawson RA (2007) Predicting in-treatment performance and post-treatment outcomes in methamphetamine users. Addiction 102: 84-95.

Indexed at, Google Scholar, Crossref

17. Hohman MM, McGaffigan RP, Segars L (2000) Predictors of successful completion of a post-incarceration drug treatment program. J Addictions Offender Counseling 21: 12-22.

Indexed at, Google Scholar, Crossref

18. Maglione M, Chao B, Anglin D (2000) Correlates of outpatient drug treatment drop-out among methamphetamine users. J Psychoactive Drugs 32: 221-228.

Indexed at, Google Scholar, Crossref

19. Maglione M, Chao B, Anglin D (2000b) Residential treatment of methamphetamine users: Correlates of drop-out from the California Alcohol and Drug Data System (CADDS) Addict Res 8:  65-79.

Google Scholar, Crossref

20. Shoptaw S, Heinzerling KG, Rotheram-Fuller E, Steward T, Wang J, et al. (2008) Randomized, placebo-controlled trial of bupropion for the treatment of methamphetamine dependence. Drug Alcohol Depend 96: 222-232.

Indexed at, Google Scholar, Crossref

21. Courtney KE, Ray LA (2014) Methamphetamine: An updated on epidemiology, pharmacology, clinical phenomenology, and treatment literature. Drug Alcohol Depend 0: 11-21.

Indexed at, Google Scholar, Crossref

22. Bluthenthal RN, Jacobson JO, Robinson PL (2007) Are Racial Disparities
    in Alcohol Treatment Completion Associated with Racial Differences in Treatment Modality Entry? Comparison of Outpatient Treatment and Residential Treatment in Los Angeles County, 1998 to 2000. Alcohol Clin Exp Res 31: 1920-1926.

Indexed at, Google Scholar, Crossref

23. Callaghan RC (2003) Risk factors associated with dropout and readmission among First Nations individuals admitted to an inpatient alcohol and drug detoxification program. CMAJ 169: 23-27.

Indexed at

24. Fishman J, Reynolds T, Riedel E (1999) A retrospective investigation of an intensive outpatient substance abuse treatment program. Am J Drug Alcohol Abuse 25: 185-196.

Indexed at, Google Scholar, Crossref

25. King AC, Canada SA (2004) Client-related predictors of early treatment drop-out in a substance abuse clinic exclusively employing individual therapy. J Subst Abuse Treat 26: 189-195.

Indexed at, Google Scholar, Crossref

26. Longinaker N, Terplan M (2014) Effect of criminal justice mandate on drug treatment completion in women. The Am J Drug Alcohol Abuse 40:192-199.

Indexed at, Google Scholar, Crossref

27. McKellar J, Kelly J, Harris A, Moos R (2006) Pretreatment and during treatment risk factors for dropout among patients with substance use disorders. Addict
    Behav 31: 450-460.

Indexed at  Google Scholar, Crossref

28. Mutter R, Ali MM, Smith K, Strashny A (2015) Factors associated with substance use treatment completion in residential facilities. Drug Alcohol Depend 154: 291-295.

Indexed at, Google Scholar, Crossref

29. Scott-Lennox J, Rose R, Bohlig A, Lennox R (2000) The impact of women's family status on completion of substance abuse treatment. J Behavioral Health Serv Res 27:366-379.

Google Scholar, Crossref

30. Siqueland L, Crits-Christoph P, Frank A, Daley D, Weiss R, et al. (1998) Predictors of dropout from psychosocial treatment of cocaine dependence. Drug Alcohol Depend 52: 1-13.

Indexed at, Google Scholar, Crossref

31. Veach L, Remley T, Kippers S, Sorg J (2000) Retention predictors related to intensive outpatient programs for substance use disorders. Am J Drug Alcohol Abuse 26:417-428.

Indexed at, Google Scholar, Crossref

32. Darke S, Kaye S, McKetin R, Duflou J (2008) Major physical and psychological harms of methamphetamine use. Drug Alcohol Rev 27: 253-262.

Indexed at, Google Scholar, Crossref

33. Brecht ML, Herbeck D (2014) Time to relapse following treatment for methamphetamine use: A long-term perspective on patterns and predictors. Drug Alcohol Depend 139:18-25.

Indexed at, Google Scholar, Crossref

34. Center for Substance Abuse Treatment (2014) Trauma-Informed Care in Behavioral Health Services. Rockville (MD): Substance Abuse and Mental Health Services Administration (US). USA.

Indexed at, Google Scholar

35. Lundahl BW, Kunz C, Brownwell C, Tollefson D, Burke BL (2010) A meta-analysis of motivational interviewing: Twenty-five years of empirical studies. Res Social Work Practice 20:137-160.

Google Scholar, Crossref