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Predictors of Residential Treatment Retention among Individuals with Co-Occurring Substance Abuse and Mental Health Disorders a
b
c
Sam Choi Ph.D. , Susie M. Adams Ph.D. R.N. , Samuel A. MacMaster Ph.D. & John Seiters B.A.
d
a
Assistant Professor, College of Social Work, University of Tennessee-Knoxville , Nashville , TN b
Professor, Vanderbilt University, School of Nursing , Nashville , TN
c
Associate Professor, College of Social Work, University of Tennessee-Knoxville , Nashville , TN d
Research Specialist, Foundations Recovery Network , Nashville , TN
To cite this article: Sam Choi Ph.D. , Susie M. Adams Ph.D. R.N. , Samuel A. MacMaster Ph.D. & John Seiters B.A. (2013) Predictors of Residential Treatment Retention among Individuals with Co-Occurring Substance Abuse and Mental Health Disorders, Journal of Psychoactive Drugs, 45:2, 122-131, DOI: 10.1080/02791072.2013.785817 To link to this article: http://dx.doi.org/10.1080/02791072.2013.785817
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Journal of Psychoactive Drugs, 45 (2), 122–131, 2013 Copyright © Taylor & Francis Group, LLC ISSN: 0279-1072 print / 2159-9777 online DOI: 10.1080/02791072.2013.785817
Predictors of Residential Treatment Retention among Individuals with Co-Occurring Substance Abuse and Mental Health Disorders Sam Choi, Ph.D.a ; Susie M. Adams, Ph.D., R.N.b ; Samuel A. MacMaster, Ph.D.c & John Seiters, B.A.d
Abstract — A significant number of individuals with co-occurring substance abuse and mental health disorders do not engage, stay, and/or complete residential treatment. The purpose of this study is to identify factors during the initial phase of treatment which predict retention in private residential treatment for individuals with co-occurring substance use and mental health disorders. The participants were 1,317 individuals with co-occurring substance abuse and mental health disorders receiving treatment at three residential treatment centers located in Memphis, TN, Malibu, CA, and Palm Springs, CA. Bivariate analysis and logistic regression were utilized to identify factors that predict treatment retention at 30 days. The findings indicate a variety of factors including age, gender, types of drug, Addiction Severity Index Medical and Psychiatric scores, and readiness to change. These identified factors could be incorporated into pretreatment assessments, so that programs can initiate preventive measures to decrease attrition and improve treatment outcomes. Keywords — co-occurring substance abuse and mental disorders, predictors, residential treatment, retention
INTRODUCTION
abuse and mental disorders in 2005 (SAMHSA, 2006). Among them, 53% never received any type of treatment, 34.3% received treatment for mental health problems, 4.1% received treatment for substance use problems, and only 8.5% received treatment for both mental health and substance abuse use problems. In addition, treatment dropouts are a well-known problem in the field of both substance abuse and mental health services. Substance abuse research indicates that length of stay in treatment of 90 days or longer generally predicts better treatment outcomes at follow-up (Simpson, Joe, Broome, Hiller, Knight & Rowan-Szal 1997; Simpson, Joe & Brown 1997). Patients who remain in treatment over a year were nearly five times more likely to have positive treatment outcomes for substance use disorders (Simpson, Joe & Rowan-Szal 1997). Longer retention in treatment predicted longer periods of
Although the efficacy of integrated treatment has been supported in prior research, a significant number of individuals with co-occurring substance and mental disorders do not access, engage, stay, and/or complete treatment. Over five million individuals had co-occurring substance a Assistant Professor, College of Social Work, University of Tennessee-Knoxville, Nashville, TN. b Professor, Vanderbilt University, School of Nursing, Nashville, TN. c Associate Professor, College of Social Work, University of Tennessee-Knoxville, Nashville, TN. d Research Specialist, Foundations Recovery Network, Nashville, TN. Please address correspondence to Sam Choi, College of Social Work, University of Tennessee-Knoxville, 193 E. Polk Ave., Nashville, TN 38210; email: schoi9@utk.edu
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remaining substance-free after leaving treatment. Yet retention studies indicate that the initial phase of treatment poses the greatest challenge for attrition (Carroll 1997; George, Joe, Simpson & Broome 1998; Stark 1992). Understanding the factors related to early treatment retention for this vulnerable population with both substance use and mental health disorders is a priority for both researchers and clinicians. The “self-medication” hypothesis of addictive disorders suggests that individuals with mental health disorders use alcohol and substances to mitigate painful affects and subjective states of distress (Khantzian 1997). Several epidemiologic studies have found correlations between alcohol and substance use disorders with anxiety, depression, bipolar disorders, attention-deficit hyperactivity disorders (ADHD), and schizophrenia, yet these studies could not impute causation or identify any linear relationship (Bolton, Cox, Clara & Sareen 2006; Bolton, Robinson & Sareen 2009; Harris & Edlund 2005; Robinson, Sareen, Cox & Bolton 2009). These studies collectively suggest that a more complex interaction among the emergence and progression of mental health and substance use problems is operative in co-occurring disorders. The “self-medication” hypothesis cannot adequately describe this interaction. Most of the studies investigating treatment for individuals with co-occurring substance use and mental health disorders focus on those with severe mental illness (SMI). An early review of 36 studies with integrated treatment for co-occurring disorders identified treatment components associated with promising outcomes: assertive outreach, case management, and a longitudinal, stage-wise, motivational approach to substance abuse treatment (Drake, Mercer-McFadden, Mueser, McHugo & Bond 1998). One study described the need for integrated rather than sequential or separate treatment services for co-occurring disorders (Watkins, Burman, Kung & Paddock 2001). A review of 10 controlled studies for co-occurring disorders found that integrated services are more effective than less integrated services in engagement and retention in residential treatment (Brunett, Mueser & Drake 2004). An evaluation of co-occurring treatment services in Nevada found that neither mental health indicators nor substance abuse problem indicators assessed at treatment admission could singly predict treatment completion with significant improvement (33%) or treatment readmission after discharge (21%). However, the interaction of the two indicators did predict these outcomes. Both mental health and substance abuse indictors were highly associated with physical and sexual abuse, domestic violence, homelessness, out of labor force, and prior treatment. More recent studies have identified the need to provide trauma-informed care for women with co-occurring disorders to engage and retain them in treatment (Adams et al. 2011; Greenfield et al. 2007). Collectively these studies underscore the complexity of
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treating co-occurring disorders and the need for integrated and trauma-informed services. Previous substance abuse studies found that personal and substance use characteristics were inconsistent predictors of treatment outcomes (Hiller, Knight & Simpson 1999; Hser, Joshi, Maglione, Chou & Anglin 2001; Joe, Simpson & Broome 1998, 1999), while motivation to change and readiness to engage in treatment were found to be better predictors in studies where this variable was considered (Langan & Pelissier 2001; Joe, Simpson & Broome 1998, 1999; Pelissier 2004). In a two-year study of insured outpatients in a Health Maintenance Organization (HMO), fewer and less severe drug problems predicted retention in both Intensive Outpatient Programs and traditional drug and alcohol outpatient treatment for both men and women (Mertens & Weisner 2000). However, among women in this study, higher retention was associated with higher income, being married, unemployed, ethnicity other than African American, and having lower severity levels of psychiatric co-morbidities. Among men, higher retention was associated with being older, receiving employer suggestions to enter treatment, and having abstinence goals (Mertens & Weisner 2000). Age seems to consistently predict retention in substance abuse treatment, with older age predicting longer length of stay in treatment (Adams, Peden, Hall, Rayens, Staten & Leukefeld 2011; McCaul, Svikis & Moore 2001; Pelissier, Motivans & Rounds-Bryant 2005; Strauss & Falkin 2000, 2001; Wellisch, Prendergast & Anglin 1994). It has been clinically observed that young substance abusers often minimize the adverse impact of substance abuse. Clinicians have hypothesized that the lack of insight and maturity diminishes their ability to actively engage and use treatment services, resulting in higher personal and societal costs over time. However, early detection and treatment of both mental health and substance abuse problems among youths, adolescents, and young adults are critical to change the incidence, prevalence, morbidity, and mortality from co-occurring disorders. Since 1990, attention has focused on the role of motivation and readiness to change in substance abuse treatment and recovery (DeLeon, Melnick & Tims 2001). Prochaska, DiClemente and Norcross (1992) proposed the Transtheoretical Model of Change (TTM) to understand the role of motivation in the process of changing addictive behaviors. The stages of change identify the sequential steps and associated level of motivation in this process: precontemplation (individuals with little or no interest in change), contemplation (individuals considering behavioral change), action (individuals taking steps to implement behavioral change), and maintenance (individuals who are actively integrating these changes into their lifestyle). An early study found that pre-treatment motivation assessed by the treatment readiness scale predicted
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therapeutic engagement across different treatment modalities (long-term residential, outpatient methadone treatment, and outpatient drug-free treatment) (George, Joe, Simpson & Broome 1998). This study informed subsequent investigations focusing on readiness for change and interventions that motivate change across a variety of health-related areas. A review of 72 clinical trials of motivational interviewing (MI) found variable effectiveness on treatment outcomes across a variety of target health problems and attributed much of the positive effects of MI to treatment adherence (Hettema, Steele & Miller 2005). Proponents of MI argue the importance of understanding the role of personal motivation in addiction treatment that focuses on readiness to change rather than denial of addiction (DiClemente, Schlundt & Gemmell 2004). While several instruments have been developed to assess motivation to change in individuals with alcohol and drug problems, the most widely used selfreport measure is the University of Rhode Island Change Assessments (URICA, DiClemente & Hughes 1990). The URICA assesses an individual’s motivation to change their substance use across the four stages of change (precontemplation, contemplation, action, and maintenance) and produces a single readiness to change score calculated by subtracting the precontemplation score from the sum of the contemplation, action, and maintenance scores. One recent study demonstrated the psychometric validity and reliability of several measures associated with the TTM, including the URICA for individuals with co-occurring substance use and mental health disorders (Nidecker, DiClemente, Bennett & Bellack 2008). However, a study by Kinnaman, Bellack, Brown & Yang (2007) found the URICA-M (shorter 24-item version) less psychometrically stable than the Cartoon Stage of Change Measure (C-SOC) assessing readiness to change at baseline and six-months follow-up in dually diagnosed schizophrenia patients. This study suggests that the C-SOC that was developed primarily to provide a non-written measure of stage of change may be more suitable to assess motivation to change substance use with cognitively impaired patients. Other studies using the URICA to evaluate readiness to change among individuals with co-occurring disorders produced variable results. Bellack, Bennett, Gearon, Brown & Yang (2006) did not find baseline URICA scores predictive of treatment participation, attrition, or outcomes in serious mentally ill patients with drug dependence. Pantalon & Swanson (2003) found that individuals with cooccurring disorders who had high motivational readiness on the URICA attended fewer clinical appointments than those with low motivational readiness. Zeidonis & Trudeau (1997) found that individuals with schizophrenia and substance use disorders who had lower motivation to change at baseline had higher rates of seeking treatment. These findings contradict expectations that higher motivation or Journal of Psychoactive Drugs
readiness to change would predict retention in treatment, adherence to treatment, and better treatment outcomes. It is unclear whether these mixed results are a function of inadequate assessment measures, study limitations such as small sample size and diverse acuity of mental health disorders, or different motivational processes among individuals with co-occurring disorders. While there is a growing body of literature, few studies have focused on identifying factors related to the early treatment retention of individuals with co-occurring substance use and mental health disorders. Similarly, there is a need clinically to identify factors during the initial phase of treatment services, as this is a time when service users are most likely to drop out. Finally, most studies of treatment retention have been conducted with individuals in public services who may have additional resource issues affecting treatment retention. The purpose of this study, therefore, is to identify factors during the initial phase of treatment which predict retention in private residential treatment for individuals with co-occurring substance use and mental health disorders. METHOD Setting Data was collected at three private residential facilities that provide integrated substance abuse and mental health treatment services in Memphis, Tennessee, Malibu, California, and Palm Springs, California. All three programs are operated by Foundations Recovery Network (FRN), a private for-profit substance abuse treatment provider offering residential and outpatient substance abuse treatment services. Service recipients at all three facilities are drawn from across the United States and Canada. Treatment services are based on an integrated model of mental health and substance abuse services consisting of both individual and group evidence-based interventions (FRN 2010). Participants All participants who enter residential services are offered an opportunity to participate in an ongoing evaluation study during the initial phase of treatment. A trained intake person located at each facility describes the evaluation, reviews and obtains informed consent, and collects the locator information for post-discharge interviews. The Addiction Severity Index (ASI) (McClellan et al. 1992) from the initial clinical assessment is used as the baseline assessment if informed consent is obtained. Masters’-level clinicians complete the initial clinical assessment within the first four days following admission to FRN’s residential programs. Data is collected at three additional time points: at discharge, and one, six, and twelve months postdischarge. A community-based Institutional Review Board reviewed study protocols. 124
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Treatment retention. This study focuses on the initial phase of treatment retention because prior studies indicated that retention losses are highest during the first four weeks (Carroll 1997; Stark 1992). Retention status was observed at 30 days calculated by days between a program start date and a discharge date. Data was collected on all admissions between February 1, 2008, and July 31, 2010, with final observation of discharge data on August 31, 2010.
Data for this study is drawn only from the baseline interview and length of stay (LOS) in treatment days. The participants were 1,317 individuals who voluntarily sought residential treatment at one of the three treatment centers. All participants received an intake assessment by a multidisciplinary team which provides the basis for an individual treatment plan to address substance use, psychiatric disorder, and medical and social service needs. At this time the evaluation data was collected for those individuals who consented to participate in the evaluation process. Co-occurring disorders were assessed over the course of treatment, starting with initial screening, assessment, and psychiatric evaluation. A master’s-level clinician conducted a complete psychiatric evaluation with one of the programs psychiatrists within 72 hours after arriving at the residential facility. Each patient was assigned to a licensed clinician who used the information gathered through initial screening and assessment to develop the initial treatment plan with the patient during an initial individual session within the first week of treatment. Ongoing psychiatric and individual therapy sessions were utilized along with weekly treatment team meetings to update each patient’s treatment plan, including updates/confirmation of specific co-occurring substance use and psychiatric disorders.
Data Analysis Descriptive statistics and bivariate analysis are used to examine differences between those who were retained in treatment and those who were not. Logistic regression is used to investigate the predictors of treatment retention. The dependent variable is dichotomous (whether a client stayed in treatment at least 30 days or not). Logistic regression is employed here because the predictors are a mix of continuous and categorical variables and many of the predictor variables are not symmetrically distributed. That is, logistic regression makes no assumptions about the distribution of the predictor variables. Logistic regression models in this current study are developed in three separate steps. In the first step, clients’ sociodemographic information is entered. In the second step, ASI measures are entered. In the last step, the readiness to change measure is entered.
Instruments Addiction severity. The scalable questions that make up the composite scores of the Addiction Severity Index (ASI) (McClellan et al. 1992) were utilized to measure addiction severity. The ASI was developed to measure problem severity in each of seven potential problem areas: medical, employment, alcohol, drug, legal, family/social, and psychiatric problems. To ensure that each question within a given problem area is given the same weight in calculation of the composite score, each item in a subscale is divided by its maximum value and by the total number of questions in a composite. This scoring yields a score from 0-1 in each composite. Readiness for change. The University of Rhode Island Change Assessment (URICA) (DiClemente & Hughes 1990) consists of 32 statements that subjects endorse on a five-point scale from strongly agree to strongly disagree. The URICA yields scores on each of four scales: Precontemplation, Contemplation, Action, and Maintenance, or each of the stages of change described by Prochaska, DiClemente & Norcross (1992). Additionally, the scores from these scales are used to create a Readiness to Change composite score. The Readiness to Change score was derived for this study in the same manner used in Project MATCH (1997). The average Contemplation, Action, and Maintenance scores were added and the Precontemplation score was subtracted from the sum. The Readiness to Change score was used as a predictor variable in subsequent analysis. Journal of Psychoactive Drugs
RESULTS Sample Description The sample description is displayed in the first column of Table 1. The mean age in this study sample was 36 years (SD = 12.1) with 40.9% being female. The majority of study participants (90.2%) were Caucasian, 8.1% were African American, and 1.7% were Latino. Nearly 56% were employed in the last 30 days. Approximately 67% had alcohol-related disorders abuse or dependence, 18.8% had opioids abuse or dependence, and 18.1% had cocaine abuse or dependence. In terms of identified mental health disorders, 82.9% had a diagnosis of anxiety disorders, followed by major depression (74.9%) and mood disorder (26.6%). Most (80%) were diagnosed with more than one mental health disorder. Among all participants, 43.7% stayed in treatment at least 30 days. The average length of their stay in treatment was 32.08 days (SD = 19.29). Bivariate Analysis Bivariate analyses were conducted to determine the relationship between various independent variables and retention rates using chi-square and t tests. T-tests were used to compare study participants who stayed in the treatment less than 30 days and those who stayed more 125
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TABLE 1
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Sample Description
Age∗∗ ASI: Medical∗∗∗ ASI: Employment/Support∗∗∗ ASI: Alcohol ASI: Drug ASI: Legal ASI: Family/Social Relationships∗∗∗ ASI: Psychiatric∗∗∗∗ Readiness for Change∗∗∗∗ Precontemplation∗∗ Contemplation∗∗∗ Action∗∗ Maintenance∗∗∗∗ Days in Treatment Gender ∗∗∗ Male Female Race/Ethnicity∗∗∗ African American Caucasian Latino Employment in last 30 days: Yes No Type of Substance Use Disorders Alcohol ∗ Cocaine Cannabis ∗∗ Opioids∗∗∗ Poly Substance Others Type of Mental Health Disorders Major Depression Anxiety Disorder∗∗∗ Mood Disorder Bi-Polar Disorder Eating Disorder∗∗ ADHD Dementia Missing Locations A (Michaels)∗∗∗∗ B (Canyon)∗∗∗∗ C (Paloma)∗∗∗ ∗p
Total Sample N = 1,317 Mean (SD) 36 (12.11) .26 (.36) .40 (.27) .38 (.34) .17 (.16) .11 (.21) .30 (.26) .49 (.20) 7.94 (4.93) 1.23 (.86) 3.25 (1.98) 3.15 (1.92) 2.77 (1.74) 32.08 (19.29) N (%)
Retention N = 575 Mean (SD) 35.9 (12.17) .30 (.37) .43 (.28) .39 (.33) .18 (.15) .12 (.21) .33 (.25)
Not Retained N = 742 Mean (SD) 37.82 (12.01) .23 (.35) .38 (.26) .37 (.35) .17 (.16) .11 (.20) .29 (.26)
.52 (.19) 11.02 (1.54) 1.62 (.51) 4.48 (.42) 4.32 (.49) 3.84 (.62) 45.57 (21.3) N (%)
.46 (.21) 10.6 (1.56) 1.70 (.52) 4.36 (.44) 4.24 (.45) 3.69 (.64) 21.67 (7.6) N (%)
778 (59.1) 539 (40.9)
318 (40.9) 257 (47.7)
450 (59.1) 282 (52.3)
107 (8.1) 1188 (90.2) 22 (1.7) 733 (55.7) 584 (44.3)
34 (31.8) 534 (44.9) 7 (31.8) 302 (41.2) 273 (46.7)
73 (68.2) 654 (55.1) 15 (68.2) 431 (58.8) 311 (53.3)
885 (67.2) 238 (18.1) 88 (6.7) 248 (18.8) 145 (11.0) 29 (2.2)
402 (69.9) 115 (20.0) 29 (5.0) 75 (13.0) 79 (13.7) 9 (1.6)
483 (65.1) 123 (16.6) 59 (8.0) 173 (23.3) 66 (8.9) 20 (2.7)
986 (74.9) 1090 (82.8) 350 (26.6) 16 (1.2) 9 (0.7) 10 (0.8) 19 (1.4) 89 (6.8) 291 (22.1) 64 (4.9) 962 (73.0)
434 (75.5) 496 (86.3) 163 (28.3) 9 (1.6) 7 (1.2) 5 (0.9) 5 (0.9) 32 (5.6) 87 (15.1) 49 (8.5) 439 (76.3)
552 (74.4) 594 (80.1) 187 (25.2) 7 (0.9) 2 (0.3) 5 (0.7) 14 (1.9) 57 (7.7) 204 (27.5) 15 (2.0) 523 (70.5)
<.10. ∗∗ p <.05. ∗∗∗ p <.01. ∗∗∗∗ p <.001.
than 30 days on medical, employment, alcohol, drug, legal, family/social relationships, and psychiatric severity scores as measured by the ASI. The differences were significant in the areas of medical, employment, family/social
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relationship, and psychiatric measures. The results suggest that study participants with higher medical, employment, family/social relationships, and psychiatric scores were more likely to stay in treatment more than 30 days.
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Regarding readiness to change, the composite URICA score indicated that study participants who stayed in treatment more than 30 days had a higher mean compared to those who stayed less than 30 days. In the subscale comparisons, the contemplation, action, maintenance subscale scores showed significance with greater values for those who stayed in more than 30 days. In the subscale of precontemplation, those who stayed in their treatment more than 30 days had a lower mean compared to those who stayed in treatment less than 30 days. Differences in the type of substance and mental health disorder were also related to treatment retention. Individuals with alcohol-related disorders (69.9%) had a higher rate of retention at 30 days than individuals with non-alcohol-related disorders (65.1%). Individuals with cannabis-related disorders (5.0%) were less likely to be retained in treatment than individuals with non-cannabisrelated disorders (8.0%). Individuals with opioid-related disorders (13.0%) were less likely to remain in treatment than individuals with non-opioid-related disorders (23.3%). In terms of differences in participantsâ&#x20AC;&#x2122; demographic information, age, gender, and race/ethnicity were significantly associated with treatment retention at 30 days. Younger adults were more likely than older adults to be retained in treatment at 30 days. The results from chisquare indicated that women (47.7%) were more likely to be retained in treatment than men (40.9%). Caucasians (44.9%) were more likely to stay in treatment more than 30 days than African Americans (31.8%) and Latinos (31.8%).
decreases by approximately 2%. Caucasians in this study were about 16% more likely than African Americans or Latinos to remain in treatment at 30 days (OR = 1.645, CI = .978-2.768, p<.1). Individuals with opiate abuse or dependency were about 47% less likely than individuals with other substance abuse disorders to remain in treatment at 30 days (OR = .528, CI = .352-.790, p<.01). Location of facilities was significant. Compared to individuals in other facilities, individuals in the Memphis (La Paloma) location were 1.8 times more likely to remain in treatment (OR = 1.800, CI = 1.259-2.574, p<.001). The increasing ASI-medical, employment, and psychiatric scores were positively associated with the treatment retention at 30 days. As one standard deviation of composite scores increases, the likelihood of treatment retention at 30 days increases 1.77 times for the medical needs ASI score and 1.65 times for the employment needs ASI score (OR = 1.655, CI = .942-2.907, p<.1). Individuals with higher psychiatric score were three times more likely to remain in treatment (OR = 3.025, CI = 1.153-7.934, p<. 05). The likelihood of treatment retention at 30 days was 1.1 times greater as the standard deviation for the readiness to change composite score increased (OR = 1.155, CI = 1.038-1.285, p<. 01). DISCUSSION The purpose of the current study was to identify pre-treatment factors during the initial phase of treatment that predict 30-day retention in private residential treatment for individuals with co-occurring substance abuse and mental health disorders. The bivariate and multivariate models indicate that a wide range of factors, including age, race/ethnicity, types of substance abuse disorders, ASI-medical, employment, and psychiatric measures, and readiness to change measured by URICA scores, are significantly associated with of treatment retention. The final model indicates that those who stayed more than 30 days are more likely to be younger, Caucasian, and non-opiate abusers or dependent. Somewhat unexpectedly, the results indicate that younger individuals with co-occurring substance abuse and mental health disorders were more likely to stay in treatment for 30 days. Younger age in most prior studies predicted shorter retention (Greenfield, Burgdorf, Chen, Porowski, Roberts & Herrell 2003; McClellan 1983). For example, in resident treatment settings, clients who were older were more likely to remain in treatment for at least 90 days (Simpson, Joe & Brown 1997). Our sample is based on privately funded residential treatment compared to most prior studies conducted in publically funded settings. Further investigation is needed to determine whether this finding is consistent in other privately funded treatment programs. This study also found race/ethnicity effects on treatment retention. Compared to African Americans and Latinos, Caucasians
Logistic Regression The logistic regressions (Table 2) only include variables significant in the bivariate analyses. Model 1 contains demographic characteristics, type of co-occurring disorders, and locations of facilities. Model 1 suggests that age, gender, race/ethnicity, types of substance abuse disorders (opiate related abuse and dependency vs. others) and mental health disorders (anxiety disorders vs. others) have a significant effect on treatment retention at 30 days. The location of facilities (Memphis vs. others) was also significantly related to retention. Model 2 also includes the various scalable ASI measures. Adding the ASI measures also improves the overall strength of the model. Gender effect and type of mental health disorders (anxiety disorders vs. others) become non-significant whereas the other effects remained. Model 3 includes the readiness to change composite score from the URICA. This final model indicates that age, race/ethnicity, type of substance abuse disorder, location, ASI-medical and psychiatric subscales, and readiness to change have significant effects on retention at 30 days. Increasing age was negatively related to the retention at 30 days. The odds ratio of 0.98 indicates that for each year of age increase, the likelihood of treatment retention Journal of Psychoactive Drugs
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<.10. ∗∗ p <.05. ∗∗∗ p <.01. ∗∗∗∗ p <.001.
Odds Ratio .987 1.407 1.775 .604 2.208 .501
CI (.976−1.000) (1.061−1.910) (1.065−2.932) (.408−.817) (1.421−3.399) (1.126−2.135)
1055.623 47.653,6∗∗∗∗ 47.653,6∗∗∗∗
∗∗∗∗
∗∗∗∗
∗∗
∗∗
∗∗
∗∗
Model 1 Odds Ratio .988 1.146 1.688 .525 1.370 1.629 1.775 1.594 .966 1.241 1.137 1.572 3.132 ∗
∗∗
∗∗∗
∗∗∗
∗∗
∗∗∗
∗
Odds Ratio .989 1.130 1.645 .528 1.287 1.800 1.772 1.655 .869 1.126 1.151 1.416 3.025 1.155
CI (.975−1.002) (.826−1.546) (.978−2.768) (.352−.790) (.750−2.207) (1.259−2.574) (1.167−2.691) (.942−2.907) (.524−1.441) (.390−3.252) (.554−2.391) (.770−2.602) (1.153−7.934) (1.038−1.285)
1022.953 80.323, 14∗∗∗∗ 7.041, 1∗∗∗
∗∗
∗∗∗
∗
∗∗∗
∗∗∗∗
∗
∗∗∗
∗
Model 3
the reference group. missing were the reference group.
1029.995 73.282,13∗∗∗∗ 25.628, 7∗∗∗∗
CI (.975−1.002) (.838−1.566) (1.005−2.833) (.351−.785) (.804−2.334) (1.150−2.307) (1.170−2.694) (.911−2.790) (.588−1.587) (.434−3.547) (.549−2.355) (.861−2.869) (1.202−8.157)
Model 2
1 African American and Latino were the reference group. 2 All non-opiate abuse/dependences including alcohol, cocaine, cannabis, poly substance, and other abuse and dependence were 3 All non-anxiety disorders including major depression, mood disorder, bi-polar disorder, eating disorder, ADHD, dementia, and 4 ichaels and Canyon were the reference group.
∗p
−2 Log Likelihood χ 2 , df Model Contribution
Variables Age Female Caucasian1 Opiate Abuse/Dependence2 Anxiety Disorder3 Location (La Paloma)4 ASI: Medical ASI: Employment ASI: Alcohol ASI: Drug ASI: Legal ASI: Family/Support ASI: Psychiatric Readiness to Change
Logistic Regression Models for Treatment Retention
TABLE 2
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were more likely to remain in treatment at 30 days. This finding should be interpreted with caution because of the homogeneity of the study sample (90.2% Caucasian). Regarding the type of substance abuse and mental health disorders, individuals with opiate abuse or dependence were less likely to stay in the initial period of treatment compared to other substance-related abuse or dependence. Prior studies focused on the severity of drug use and pretreatment factors as predictors of treatment retention. Those with less severe drug use were more likely to stay longer in treatment (Miller 1995; Chou, Hser & Anglin 1998). The majority of prior studies focused on a single type of substance abuse/dependence; the few studies that compare various types of drug use report inconclusive findings. Accordingly, it is critical for clinicians to tailor treatment plans according to types of substance use disorders. This study also found a significant relationship between types of mental health disorders and retention in 30 days. Many in this study (80%) had concurrent mental health disorders. Accordingly, it is difficult to conclude that one is worse than others but bivariate analysis indicated that individuals with anxiety disorders had significantly higher rates of treatment retention compared to other types of mental health disorders. This study also found that the location of facilities had a significant impact on the early treatment retention. Controlling for pre-treatment demographic factors, individuals in Memphis were more likely to stay in their initial treatment at 30 days. Although those three different facilities have similar therapeutic philosophies and operation systems, the findings of this study indicate the needs of recognizing the uniqueness of each program. According to Simpson, Joe & Brown (1997), treatment facilities differ in staff skills, resources, service intensity, environmental setting, and client demands, which may impact their retention and effectiveness. Accordingly, treatment evaluation studies must recognize the multi-level factorsâ&#x20AC;&#x201D;client level and program levelâ&#x20AC;&#x201D; on treatment retention (Simpson et al. 1997; Simpson, Joe & Brown, 1997). The findings also indicate that ASI-medical, employment, and psychiatric scores are strong predictors of treatment. Individuals with co-occurring substance abuse and mental disorders with higher medical, employment, and psychiatric problems were more likely to remain in treatment more than 30 days. These findings are consistent with other studies that have found that the residential treatment may be especially suited to tackling problems of more severely disturbed individuals with co-occurring substance abuse and mental disorders (Nuttbrock, Rahav, Rivera, Ng-Mak & Link 1998; Etheridge, Anderson, Vraddock & Flynn 1997). Findings from this study also highlight the importance of readiness to change as a predictor of treatment retention. Bivariate analysis indicates that patients in the Journal of Psychoactive Drugs
precontemplation stage are more likely to drop out before they reach 30 days than patients in the contemplation, action, or maintenance stages. This finding is theoretically congruent with the transtheoretical model (TTM) stages of change. Several studies had similar findings regarding precontemplation stage predicting lower retention rates in substance abuse treatment (Callaghan, Hathaway, Cunningham, Vettesse, Wyatt & Taylor 2005) and psychotherapy samples (Brogan, Prochaska, & Prochaska 1999; Dozois, Westra, Collins, Fung & Garry 2004). However, a number of studies did not find readiness to change to be a reliable predictor of treatment retention. Ziedonis & Trudeau (1997) found that high motivation to change substance use was related with less treatment involvement. Pantalon & Swanson (2003) found that psychiatric outpatients with low URICA score were more likely to adhere to treatment recommendations than patients with high URICA scores in a sample of 120 psychiatric and dually diagnosed inpatient participants. Pantalon, Nich, Frankforter & Carroll (2002) found no relationship between the URICA score and treatment use among participants with co-occurring substance abuse and mental disorders in inpatient settings. Kinnaman and colleagues (2007) did not find a significant relationship between the URICA score and days in treatment in a sample of 120 individuals with co-occurring schizophrenia and cocaine dependence. Collectively, these mixed findings indicate that readiness to change alone cannot predict treatment retention and suggest that other factors influence retention in treatment. Finally, it is important to note that individuals with co-occurring substance abuse and mental health disorders have difficulty staying in treatment. Even in an integrated service model designed to address co-occurring substance abuse and mental health disorders in private residential treatment, only 43.7% stayed in treatment at least 30 days and the average length of stay is about a month. This is problematic because length of time in treatment predicts positive post-treatment outcomes and sustained recovery. This finding highlights the importance of early engagement efforts and developing tailored strategies to improve treatment retention. The current study makes a unique contribution to the literature for individuals with substance abuse and mental disorders, despite some limitations. First, the results of this study may be unique to private residential treatment programs. The predictors of treatment retention in publicly funded residential treatment programs may be different. Second, our analyses are limited to whether individuals stayed 30 days (or longer) and did not address censored observations. In addition, this study assumes that three treatment programs operate identically, although there is some variability in staff skills, resources, service intensity, environmental setting, and client demands that may impact their effectiveness (Simpson, Joe & Brown 1997). 129
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This study was limited to client-level variables and did not examine the impact of program-level variables on treatment retention. In summary, the current study investigated sociodemographic, addiction severity, and readiness to change factors that may explain treatment retention at 30 days or longer for individuals with co-occurring substance abuse and mental health disorders in private residential
settings. Since positive treatment outcomes are associated with greater treatment duration, developing strategies to improve treatment retention is critical to enhance the effectiveness of treatments for individuals with co-occurring substance abuse and mental disorders. The findings indicate a variety of factors that might be incorporated into pretreatment assessments, so that clinicians can initiate measures to decrease dropout and improve treatment outcomes.
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