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Journal of Dual Diagnosis Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/wjdd20
Differences Between Older and Younger Adults in Residential Treatment for Co-Occurring Disorders a
b
c
Siobhan A. Morse MHSA CRC CAI, MAC , Cayce Watson MSW , Samuel A. MacMaster PhD MSW & Brian E. Bride PhD MSW MPH
d
a
Foundations Recovery Network, Brentwood, Tennessee, USA
b
Department of Social Work and Sociology, Lipscomb University, Nashville, Tennessee, USA
c
University of Tennessee, College of Social Work-Knoxville, Nashville, Tennessee, USA
d
Georgia State University, School of Social Work, Atlanta, Georgia, USA Accepted author version posted online: 22 Dec 2014.
Click for updates To cite this article: Siobhan A. Morse MHSA CRC CAI, MAC, Cayce Watson MSW, Samuel A. MacMaster PhD MSW & Brian E. Bride PhD MSW MPH (2015) Differences Between Older and Younger Adults in Residential Treatment for Co-Occurring Disorders, Journal of Dual Diagnosis, 11:1, 75-82, DOI: 10.1080/15504263.2014.993263 To link to this article: http://dx.doi.org/10.1080/15504263.2014.993263
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JOURNAL OF DUAL DIAGNOSIS, 11(1), 75–82, 2015 C Foundations Recovery Network Copyright ISSN: 1550-4263 print / 1550-4271 online DOI: 10.1080/15504263.2014.993263
Differences Between Older and Younger Adults in Residential Treatment for Co-Occurring Disorders
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Siobhan A. Morse, MHSA, CRC, CAI, MAC,1 Cayce Watson, MSW,2 Samuel A. MacMaster, PhD, MSW,3 and Brian E. Bride, PhD, MSW, MPH4
Objective: The purpose of this study was to examine differences between older and younger adults who received integrated treatment for co-occurring substance use and mental disorders, including differences on demographic and baseline characteristics (e.g., substance use, readiness for change, mental health symptoms, and severity of problems associated with substance use), as well as predictors of retention in treatment. Methods: This study included 1400 adults who received integrated substance abuse and mental health treatment services at one of two private residential facilities offering residential and outpatient services. Initial analyses consisted of basic descriptive and bivariate analyses to examine differences between older (≥ 50 years old) and younger (< 50 years old) adults on baseline variables. Next, three ordinary least squares regression models were employed to examine the influence of baseline characteristics on length of stay. Results: Three main findings emerged. First, older adults differed from younger adults on pretreatment characteristics. Older adults used more alcohol and experienced greater problem severity in the medical and alcohol domains, while younger adults used more illicit drugs (e.g., heroin, marijuana, and cocaine) and experienced problems in the drug, legal, and family/social domains. Second, while readiness to change did not differ between groups at baseline, older adults remained enrolled in treatment for a shorter period of time (nearly four days on average) than younger adults. Third, the pattern of variables that influenced length of stay in treatment for older adults differed from that of younger adults. Treatment retention for older adults was most influenced by internal factors, like psychological symptoms and problems, while younger adults seemed influenced primarily by external factors, like drug use, employment difficulties, and readiness for change. Conclusions: The results of this study add to the limited knowledge base regarding older adults receiving integrated treatment for co-occurring substance use and mental health disorders by documenting that age-based differences exist in general and in the factors that are associated with the length of stay in residential treatment. (Journal of Dual Diagnosis, 11:75–82, 2015)
Keywords co-occurring disorders, residential treatment, older adults, retention, addiction severity, mental health, substance use
With the aging of the baby boom generation, older adults have become the fastest-growing segment of the U.S. population. The population aged 50 and older, in particular, is estimated to increase 52% by 2020 over 1999 to 2001 estimates (Neve, Lemmens, & Drop, 1999; Colliver, Compton, Gfroerer, & Condon, 2006). High rates of substance abuse have been identified among older adults and are expected to increase (Wetterling, Veltrup, John, & Dreiessen, 2003; Colliver et al., 2006; Han, Gfoerer, Colliver, & Penne, 2009; Moos, Schutte, Brennan, & Moos, 2011; Cooper, 2012; Wang & Andrade, 2013). Colliver et al. (2006) estimate the number of illicit drug users aged 50 and older will increase to over 3 million by 2020. Current estimates of illicit drug use appear to be 1Foundations
Recovery Network, Brentwood, Tennessee, USA of Social Work and Sociology, Lipscomb University, Nashville, Tennessee, USA 3University of Tennessee, College of Social Work-Knoxville, Nashville, Tennessee, USA 4Georgia State University, School of Social Work, Atlanta, Georgia, USA Address correspondence to Siobhan A. Morse, MHSA, CRC, CAI, MAC, Foundations Recovery Network, 5409 Maryland Way, Suite 320, Brentwood, TN 37027, USA. E-mail: siobhan.morse@frnmail.com 2Department
consistent with such predictions. Over the past decade, illicit drug use among adults aged 50 to 64 more than doubled from 3.4% to 7.2% (Substance Abuse and Mental Health Services Administration, 2013). From 2002 to 2012, non-medical use of prescriptions increased significantly among adults aged 50 to 59 and marijuana use significantly increased among adults aged 50 to 64 (Substance Abuse and Mental Health Services Administration, 2013). The higher substance use rates of this generation in tandem with the volume of the cohort suggest that special attention should be given to the treatment needs of this population. The most commonly abused substance is alcohol (Lin, Zhang, Leung, & Clark, 2011), and a number of studies have attempted to characterize problem alcohol use in older adults. Alcohol abuse in older adults is often referred to as either early-onset or late-onset, a distinction that accounts for differences in characteristics, comorbidity, and impairment associated with alcohol abuse in this population (Wetterling et al., 2003). Christie et al. (2013) have further identified a subgroup of problem drinkers aged 60 and older, referred to as “late-onset reactors,” who do not self-identify as having a drinking problem until their late 50s. Despite that alcohol
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intake tends to decrease with advancing age, a high proportion of older adults drink in excess of recommended guidelines (McEvoy, Kritz-Silverstein, BarrettConnor, Bergstrom, & Laughlin, 2013). In addition, older adult problem drinkers consume alcohol in comparable amounts to their younger counterparts (Christie, Bamber, Powell, Arrindell, & Pant, 2013). Older adults with substance use disorders are at risk for a range of emotional, functional, financial, social, and health problems (Blazer, 2013). They accrue risk of injury, death, and other adverse events at lower consumption levels than younger counterparts (Fink et al., 2002; Sorock, Chen, Gonzalgo, & Baker, 2006). In addition, problem drinkers who are older evidence declines in functional status particularly in cognitive ability, depression, and comorbidity (Fink et al., 2002). Older adults are also more sensitive to treatment barriers and less likely to recognize symptoms of addiction and therefore are less likely to receive treatment (Cooper, 2012). Older adults are also less likely to perceive the need for treatment or to be sensitive to barriers to treatment, such as stigma, lack of information and transportation, and cultural barriers (Eden, Maslow, Le, & Blazer, 2012). In addition to having high rates of substance use disorders, older adults often present with mental health issues that further complicate the diagnosis, treatment, and referral process. Twenty percent of older adults in the general population have a psychiatric disorder, most commonly depression or anxiety (Bartels, Blow, Brockmann, & Van Citters, 2005; Lin et al., 2011). Adults aged 50 and older account for 16% of individuals reporting mental illness (Substance Abuse and Mental Health Services Administration, 2013). The Centers for Disease Control and Prevention (CDC) estimate that seven million adults experience depression during later life (CDC, 2012). Further, the higher incidence of chronic health conditions among older adults population elevates risk of depression (CDC, 2012). The relationship between substance abuse and mental illness in older adults is such that a history of one is associated with increased risk of the other (Bartels et al., 2005; Pennay et al., 2011; Salmon & Forester, 2012). Most commonly, substance abuse co-occurs with depression and anxiety (Devanand, 2002). Older adults who are depressed are up to four times more likely to have alcohol-related problems than their counterparts who are not depressed (Devandad, 2002). Rates of lifetime substance abuse are also high among individuals with bipolar disorder (Cassidy, Ahearn, & Carroll, 2001), and bipolar depressive episodes may intensify with age (Coryell, Fiedorowicz, Solomon, & Endicott, 200). Among patients hospitalized with bipolar disorder, Cassidy et al. (2001) found that 43.9% had problems with drug abuse. Given the likelihood of comorbidity, it is important that older adults who present for mental health treatment be assessed for substance abuse (Salmon & Forester, 2011). Growing demographics, gaps in service delivery, and increasing demands for mental health services have resulted Journal of Dual Diagnosis
in depression often going untreated in older adults (Bartels & Smyer, 2002). Further, the treatment of depression is often complicated by multiple presenting problems including comorbid mental health disorders and psychosocial stressors (Proctor, Hasche, Morrow-Howell, Shumway, & Snell, 2008). Older adults presenting with co-occurring mental health diagnoses are at risk for poorer physical and behavioral health outcomes and are less likely to have positive responses to therapy and medication management (Wuthrich & Rapee, 2013). In addition to these risks, older adults with depression may experience declines in memory (Deluca et al., 2005). Wuthrich and Rapee (2013) suggest that higher rates of comorbidity, risk of worsening health-related outcomes, and competing diagnostic symptoms in older adults prompt the need for special consideration during treatment planning, specifically for older adults presenting with co-occurring disorders. Further, engaging the older adult population in mental health treatment is challenging due to their reticence to prioritize treatment among multiple overwhelming life stressors including chronic health conditions and other cognitive, emotional, or social issues (Proctor et al., 2008). As such, it is increasingly important that older adults be assessed and treated for co-occurring substance use and mental health disorders. Research has consistently demonstrated that retention in addiction treatment is positively associated with a range of post-treatment outcomes, including increased abstinence, greater participation in continuing care, higher levels of employment, and lower rates of relapse and readmission (Chi, Sartre, & Weisner, 2006; Claus et al., 2007; Greenfield et al., 2004; Luchansky, Brown, Loghi, Stark, & Krupski, 2000; Wallace & Weeks, 2004; Zarkin, Dunlap, Bray, & Wechsburg, 2002). Unfortunately, research also indicates that clients with co-occurring disorders have lower rates of treatment completion, shorter stays in treatment, and higher rates of relapse and readmission (Compton, Cottler, Jacobs, Ben-Abdallah, & Spitznagel, 2003; Weisner, Matzger, & Kaskutas, 2003). Consequently, a central goal in treating clients with co-occurring disorders is to increase treatment retention. Successful development or implementation of strategies to increase retention requires more knowledge regarding factors that influence patient length of stay in treatment, as well as differences in said influences across subgroups. Unfortunately, little is known regarding predictors of treatment retention among individuals seeking treatment for co-occurring disorders. The purpose of this study was to examine differences between older and younger adults who received integrated treatment for co-occurring substance use and mental disorders. In particular, we examined differences on demographic and baseline characteristics including recent substance use, readiness for change, mental health symptoms, and severity of problems associated with substance use. In addition, we examined predictors of retention in treatment and differences between older and younger adults in patterns of said predictors.
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METHODS
University of Rhode Island Change Assessment (URICA)
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Sample and Procedure This study utilized data from 1400 adults who received integrated substance abuse and mental health treatment services between 2009 and 2011 at either of two private residential facilities operated by Foundations Recovery Network (FRN), a private for-profit treatment provider offering residential and outpatient services. Though the facilities are located in Tennessee and California, service recipients are drawn from across the United States and Canada. Treatment was delivered within an integrated model of evidence-based mental health and substance abuse services consisting of individual and group interventions (FRN, 2010). The study was conducted in accordance with the Declaration of Helsinki and was approved and monitored by the Foundations Recovery Network Humans Subjects Committee. Trained intake professionals located at each facility described data collection to and obtained written informed consent from study participants following a complete discussion of the study. Baseline data on recent substance use, readiness for change, mental health symptoms, and substance use severity was collected within 72 hours following admission by a masterâ&#x20AC;&#x2122;s-level clinician. Retention data were collected through a retrospective review of discharge records. Instruments Addiction Severity Index (ASI) Recent substance use, addiction severity, and mental health indicators were measured with the ASI (McLellan, Luborsky, Woody, Oâ&#x20AC;&#x2122;Brien, & Druley, 1983; McLellan et al., 1992). For recent substance use, participants indicated how many days in the past month they used a range of specific substances. Addiction severity was measured with the ASIâ&#x20AC;&#x2122;s composite severity indices in each of seven potential problem areas: medical, employment, alcohol, drug, legal, family/social, and psychiatric problems. In order to ensure that each question within a given problem area is given the same weight in calculation of the composite index, each item is divided by its maximum value and by the total number of questions assigned to each composite problem area. This scoring yields a score from 0 to1 for each composite index, with higher scores indicating greater severity. As mental health indicators, we used 10 individual items from the psychiatric status section of the ASI. Eight of these items indicate whether a respondent has had a significant period of time in which they have experienced symptoms not as a result of substance use in the following domains: depression, anxiety, hallucinations, cognitive difficulties (trouble understanding, concentrating, or remembering), violence (trouble controlling violent behavior), suicidal ideation, attempted suicide, and medication prescribed for psychological or emotional problems.
The URICA (DiClemente & Hughes, 1990) is a measure of readiness to change that has been studied with a variety of populations. It consists of 32 statements that participants endorse on a 5-point scale from strongly agree to strongly disagree. The URICA yields scores on each of four subscales corresponding with the stages of change (precontemplation, contemplation, action, and maintenance) described by Prochaska, DiClemente, and Norcross (1992). A readiness to change composite score can be derived by adding the contemplation, action, and maintenance subscales and subtracting the precontemplation subscore (Project MATCH Research Group, 1997). The readiness to change composite score was used as the measure for readiness to change.
Treatment Retention Treatment retention was operationalized as length of stay and calculated by the total number of days between program start date and discharge date.
Data Analysis Initial analyses consisted of basic descriptive statistics and bivariate analyses to examine differences between older (50 + years of age) and younger (less than 50 years of age) adults on pre-treatment demographic, substance use, addiction severity, and mental health variables. Next, three ordinary least squares regression models were employed to examine the influence of baseline characteristics on length of stay. Model 1 employed the entire sample and included group membership (older versus younger adult) as a variable to determine whether there was a difference between groups on length of stay after controlling for demographic and baseline characteristics including recent substance use, readiness for change, mental health symptoms, and addiction severity. Models 2 and 3 were run on the subsamples of younger and older adults respectively in order to determine whether the predictors of retention were the same for older and younger adults. These models regressed length of stay on the same demographic and baseline characteristics included in model 1.
RESULTS Table 1 displays results of bivariate analysis of study variables comparing younger and older adults. There were no differences between groups on gender, with 60% (n = 844) of the sample being male. The most frequently used substance in the past 30 days was alcohol (M = 12.21 days, SD = 11.74) followed by opiates other than heroin and methadone (M = 5.84 days, 2015, Volume 11, Number 1
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TABLE 1 Bivariate Analyses of Study Variables Comparing Younger and Older Adults (N = 1400) Younger Adults (n = 1168)
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Variable Gender (male) Substance use1 Alcohol Cocaine Heroin Non-Rx Methadone Other opiates Marijuana Barbiturates Sedatives Amphetamines Addiction Severity Index1 Medical Employment/Support Alcohol Drug Legal Family/Social Psychiatric Mental health indicators1 Depression Anxiety Hallucinations Cognition Violence Suicidal ideation Suicide attempt Psych medication Problem frequency Bothered by problems Readiness for change Length of stay
n, M (SD)
Older Adults (n = 232)
n (%)
n, M (SD)
713 (61.0%)
n (%) 131 (56.5%)
Statistic χ 2 = 1.65
1166, 11.63 (11.58) 1148, 3.28 (7.48) 1144, 2.71 (7.83) 1139, 0.85 (4.37) 1143, 6.16 (10.69) 1143, 5.12 (9.77) 1138, 0.65 (3.87) 1140, 4.15 (8.76) 1144, 1.22 (5.06)
229,15.07 (12.08) 219, 1.90 (5.95) 217, 0.27 (2.78) 216, 0.72 (4.10) 216, 4.00 (9.15) 217, 2.10 (6.89) 215, 0.77 (4.35) 215, 2.92 (8.09) 216, 0.51 (3.36)
t = −4.07∗∗∗ t = 2.57∗∗ t = 4.53∗∗∗ t = 0.39 t = 2.79∗∗ t = 4.35∗∗∗ t = −0.41 t = 1.92 t = 1.97∗
1153, 0.264 (0.361) 1149, 0.421 (0.277) 1063, 0.365 (0.344) 1042, 0.191 (0.168) 1145, 0.136 (0.232) 1117, 0.326 (0.264) 1113, 0.497 (0.215)
227, 0.402 (0.398) 225, 0.393 (0.261) 196, 0.456 (0.353) 203, 0.114 (0.146) 226, 0.067 (0.170) 221, 0.270 (0.276) 225, 0.474 (0.218)
t = −5.15∗∗∗ t = 1.43 t = −3.40∗∗∗ t = 6.13∗∗∗ t = 4.27∗∗∗ t = 2.86∗∗ t = 1.43
840/1155 (72.7%) 953/1157 (82.4%) 84/1158 (7.3%) 640/1159 (55.2%) 232/1158 (20.0%) 211/1161 (18.2%) 63/1161 (5.4%) 702/1151 (61.0%) 1143, 21.95 (11.26) 1149, 2.85 (1.38) 1168, 10.94 (1.59) 1168, 31.26 (17.62)
168/229 (73.4%) 186/231 (80.5%) 18/230 (7.8%) 118/229 (51.5%) 25/229 (10.9%) 43/230 (18.7%) 15/230 (6.5%) 141/231 (61.0%) 230, 21.53 (11.47) 231, 2.67 (1.42) 232, 10.77 (1.85) 230, 27.50 (13.79)
χ 2 = .040 χ 2 = 0.45 χ 2 = 0.09 χ 2 = 1.05 χ 2 = 10.53∗∗∗ χ 2 = 0.04 χ 2 = 0.44 χ 2 = 0.00 t = 0.52 t = 1.76 t = 1.40 t = 3.06∗∗
Note. 1Time frame is past month. < .05; ∗∗ p < .01; ∗∗∗ p < .001.
∗p
SD = 10.50), marijuana (M = 4.65 days, SD = 9.43), sedatives (M = 3.99 days, SD = 8.70), and cocaine (M = 3.09 days, SD = 7.32). Older adults used alcohol more frequently than younger adults, while younger adults used cocaine, heroin, other opiates, marijuana, and amphetamines more frequently than older adults. Indicators of baseline addiction severity, mental health, and readiness for change were also compared for younger and older adults. Statistically significant differences were found between younger and older adults on five of the seven ASI composite indices. Older adults had higher composite scores on the medical and alcohol indices, while younger adults had higher composite scores in the drug, legal, and family indices. No differences were found between groups on the employment and psychological indices. In addition, younger and older adults did not differ on the measure of readiness for change. Most participants reported experiencing anxiety (82%, 1139/1388) and/or depression (73%, 1008/1384) during the Journal of Dual Diagnosis
month prior to entry, and more than half (55%, 758/1388) reported experiencing cognitive difficulties such as trouble understanding, concentrating, or remembering. Psychiatric medications were prescribed to 61% of patients (843/1388) during the baseline period. About one-fifth of patients reported difficulty controlling violent behavior (19%, 257/1387) and/or suicidal ideation (18%, 254/1391) at baseline. Seven percent of patients (102/1388) reported hallucinations and 6% (78/1391) reported suicide attempts during the baseline period. On average, patients reported experiencing these psychological or emotional problems on 22 of the past 30 days (SD = 11.29). Only 1 of the 10 mental health indicators was found to differ significantly between groups. Nearly twice as many (20% versus 11%) younger adults than older adults experienced trouble controlling violent behavior at baseline. For the sample as a whole, the mean length of stay was 30.7 days (SD = 17.22). In comparing younger and older adults on length of stay, a statistically significant difference
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TABLE 2 Ordinary Least Squares Regression of Length of Stay on Study Variables (n = 10851)
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Variable Gender (male) Substance use2 Alcohol Cocaine Heroin Non-Rx methadone Other opiates Marijuana Barbiturates Sedatives Amphetamines Addiction Severity Index2 Medical Employment/support Alcohol Drug Legal Family/social Psychiatric Mental health indicators2 Depression Anxiety Hallucinations Cognition Violence Suicidal ideation Suicide attempt Psych medication Problem frequency Bothered by problems Readiness for change Older adult
Full Sample (n = 1085)
Younger Adults (n = 912)
β (SE)
β (SE)
p Value
β (SE)
p Value
p Value
Older Adults (n = 173)
−3.56 (1.08)
.001
−4.02 (1.22)
.001
−0.67 (2.28)
.770
0.07 (0.10) 0.18 (0.09) 0.07 (0.09) −0.25 (0.13) −0.16 (0.07) 0.02 (0.07) −0.22 (0.13) −0.02 (0.07) 0.14 (0.12)
.490 .055 .411 .054 .022 .828 .096 .762 .257
0.07 (0.12) 0.15 (0.10) 0.09 (0.09) −0.35 (0.15) −0.17 (0.07) −0.04 (0.07) −0.32 (0.15) 0.01 (0.08) 0.15 (0.13)
.559 .140 .335 .015 .026 .625 .031 .931 .265
0.21 (0.24) 0.39 (0.26) 0.05 (0.20) −0.24 (0.38) −0.04 (0.30) −0.24 (0.20) 0.19 (0.28) −0.09 (0.18) −0.08 (0.37)
.371 .133 .796 .524 .906 .226 .501 .605 .825
2.53 (1.69) 7.02 (2.19) −2.79 (4.00) 5.80 (7.68) 3.03 (2.67) 2.42 (2.38) −0.44 (31.33)
.135 .001 .484 .451 .258 .310 .989
−3.33 (2.76) 6.32 (4.11) −4.87 (8.53) −1.51 (18.00) 12.12 (6.54) 2.62 (4.20) 131.73 (48.94)
.230 .127 .569 .933 .066 .534 .008
−1.40 (3.45) 3.85 (3.74) 3.77 (3.71) 2.64 (3.19) −2.07 (3.22) 4.03 (3.25) 6.60 (4.01) 0.49 (3.24) −0.52 (0.12) −1.04 (1.30) 1.06 (0.39) —
.685 .303 .310 .407 .520 .215 .101 .881 .674 .423 .007
−14.18 (5.82) −6.88 (5.82) −17.38 (6.65) −10.94 (5.08) −17.29 (5.73) −8.43 (5.60) −18.80 (7.57) −10.03 (5.12) −0.40 (0.20) −5.00 (2.18) 0.77 (0.67) —
.016 .239 .010 .033 .003 .135 .014 .052 .051 .024 .255
1.59 (1.47) 7.19 (1.94) −2.66 (3.60) 3.09 (6.99) 4.84 (2.43) 2.31 (2.08) 22.79 (27.02) −3.42 (3.00) 1.10 (3.20) −0.06 (3.25) 0.09 (2.76) −4.96 (2.81) 1.82 (2.83) 1.86 (3.54) −1.30 (2.80) −0.12 (0.11) −1.58 (1.14) 0.96 (0.34) −3.12 (1.42)
.165 <.001 .460 .659 .047 .268 .399 .255 .730 .987 .974 .077 .521 .599 .643 .278 .165 .005 .028
Note. β = standardized coefficient; SE = standard error. 1Sample size is reduced as a result of listwise deletion of cases due to missing data. 2Time frame is past month.
was found. Older adults remained in treatment for a shorter period of time, 3.76 fewer days on average, than younger adults. The results of the three ordinary least squares regression models are displayed in Table 2. The first model includes the entire sample, though the sample size is reduced as a result of listwise deletion of cases due to missing data. Of primary interest is the finding that group membership was significant, indicating that, holding all other variables constant, older adults had a shorter length of stay. Five other variables were significant in this model: gender, readiness for change, recent use of other opiates, and the ASI employment and legal composite scores. Women had longer lengths of stay after controlling for all other variables, and readiness for change was positively associated with length of stay. Recent use of opiates other than heroin and methadone was negatively associated with length of stay. Higher severity of substance use–related employment
and legal problems were positively associated with length of stay. In model 2, which included only younger adults, six variables were found to be statistically significant. Females had a longer length of stay, controlling for all other variables. More frequent recent use of several substances predicted shorter lengths of stay, specifically non-prescription methadone, other opiates, and barbiturates. Last, readiness for change and higher severity on the ASI employment composite index were associated with increased length of stay. Model 3, which included only older adults, revealed a different pattern of seven significant predictors, six of which were baseline mental health indicators. Specifically, having experienced depression, hallucinations, cognitive difficulties, trouble controlling violent behaviors, attempted suicide, and higher distress related to these problems were each predictive of shorter length of stay. Further, increased severity on
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the ASI psychological composite index was associated with longer length of stay.
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DISCUSSION The results of this study add to the limited knowledge base regarding older adults receiving integrated treatment for cooccurring substance use and mental health disorders. Three main findings emerged. First, older adults differ from younger adults on pre-treatment characteristics. Second, older adults remain enrolled in integrated treatment for a shorter period of time than do younger adults. Third, the pattern of variables that influence length of stay in treatment for older adults differs from those that influence length of stay for younger adults. The pre-treatment characteristics on which older and younger adults differed were primarily associated with recent substance use and addiction severity. In terms of recent substance use, older adults more frequently consumed alcohol and younger adults more frequently used cocaine, heroin and other opiates, marijuana, and amphetamines in the month prior to intake. Regarding addiction severity, older adults reported more severity relative to medical issues and alcohol-related problems and less severity in the areas of legal, family/social relationship, and drug-related problems compared to younger adults. Older adults were similar to younger adults in the severity of employment and psychiatric problems. Given the latter, it should not be surprising that older and younger adults differed very little on mental health indicators. The only statistically significant difference was in the domain of violence. Older adults reported difficulty controlling violent behavior at a rate almost half that of younger adults. This is consistent with findings reported by Morse & MacMaster (in press) that found older adults had a tendency to internalize, experiencing depression and suicide ideation more than younger adults, while adults aged 18 to 25 had a tendency to externalize, reporting more episodes of violent behavior. While older and younger adults did not differ in their readiness to change, older adults remained in treatment for a significantly shorter duration, nearly four days on average, than did younger adults even after controlling for pretreatment characteristics. This suggests that factors other than treatment motivation influence retention. To explore this issue, we examined predictors of retention for older adults and younger adults in separate models and found different patterns of variables accounted for length of stay for each group. Among younger adults, retention is influenced primarily by recent drug use, employment difficulties, and readiness for change. With regard to motivation, younger adults may perceive themselves as having more at stake socially and economically than older adults perceive in their lives. Therefore, younger adults might be more likely to remain in treatment those extra few days. Journal of Dual Diagnosis
Among older adults, psychological factors appear to be the primary influence for retention in treatment. Paradoxically, among older adults length of stay increases as a function of increased psychiatric severity, while it decreases in the presence of individual indicators of mental health problems. These seemingly paradoxical results make sense in that individual symptoms may lower motivation for treatment, but as the negative impact of mental health issues increases through the interaction of multiple problems, treatment motivation increases. When viewed together, older adults appear to be driven by internal factors while younger adults appear to be driven by external factors, which would render, for example, contingency management programming less effective for older adults than younger adults, who appear more likely to respond to external stimuli in treatment seeking and retention decisions. Intrinsic motivation is a strong predictor of engagement and retention (Joe, Simpson, & Broome, 1998). However, we found that the reverse was true and that external motivators, such as those associated with the younger adults, were more likely predictive of longer treatment stays. Data collected in this study suggest that there are some differences in the two populations upon entering treatment. However, the more important findings appear to be the differences in predictors of length of stay in treatment. Despite all participants being identified as having co-occurring mental health and substance use disorders, psychological issues appear to impact older adult participantsâ&#x20AC;&#x2122; length of stay more than young adultsâ&#x20AC;&#x2122; stay, whose main predictors appear to be more related to substance use issues. These results support othersâ&#x20AC;&#x2122; findings that older age has been found to be associated with higher levels of mental health service satisfaction as well as perceived benefit of mental health services (Ford, Bryant, & Giyeon, 2012). Psychological concerns may be of greater perceived importance to older adult participants and this needs to be considered in treatment planning and intervention. The relevance of tailored motivational interviewing and enhancement techniques to enhance engagement and support treatment retention is demonstrated, similar to results found by other researchers (Carroll et al., 2006; Shumway, Bradshaw, Harris, & Baker, 2013). It is important to note that the study population was drawn from private for-profit residential treatment centers. This is a population of individuals who do not typically enroll in substance abuse treatment research. Historically, research with individuals with co-occurring mental health and substance use disorders is usually conducted among clients in public treatment facilities, who tend to have lower incomes and possibly lower educational levels. Additionally, there may be differences in severity and duration of substance use, treatment settings, attitudes toward treatment, and motivational factors that could limit the generalizability of our findings to public mental health settings. Each of these variables that may differ between the populations could also account for differences in access to treatment, the decision to receive treatment, and the length of treatment retention, which would in turn impact treatment
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Older and Younger Adults in Residential Treatment
outcomes. Further, this study reports findings from residential treatment, whereas most of the literature on substance abuse treatment reports findings from outpatient settings. Thus, the application of these findings to dissimilar populations or types of settings should be viewed with caution. However, although this aspect of the study is a limitation, it is also a strength in that it broadens our understanding of treatment to a population underrepresented in the literature. It is also important to note that while retention has been determined to be substantially predictive of treatment outcomes, it is not a substitute for the measurement of outcomes themselves. Clearly, further research on the differences in outcomes by age groups is warranted. Despite the above limitations, these findings have important implications for the provision of treatment services to individuals with co-occurring disorders. The substance abuse treatment industry in general will need to widen the scope of services as the population needing treatment ages while simultaneously improving specialized programming to deal with the differences between older and younger adults seeking treatment. Given differences found between older and younger adults at baseline in our analysis, and then the disparity between length of stay ultimately, it might be wise to allow for some separation of programming by age. Creating tracks for older adults that focus on key areas of difference could allow both age groups more peer-to-peer support as well as improving patient comfort during group therapy. Similarly, older adult tracks that include specialized groups on issues associated with alcohol use, the physical and psychological effects of aging, or the interaction among substance abuse, psychiatric symptoms, and medical issues may support increasing the length of stay in older adults. Additionally, aging implies the greater likelihood of chronic medical disorders that will need to be included in discharge and continuing care planning. Targeting specific psychiatric symptoms both during treatment and in follow-up planning could be useful not only in creating longer periods of treatment engagement but also in providing needed support to maintain progress made during treatment. As with any dual diagnosis program, considerations need to be made for cognitive impairment. However, it has been well demonstrated that long-term, heavy alcohol use is associated with declines in cognitive functioning. Treating populations with greater severity of alcohol use and longer lengths of time using alcohol, such as an aging population of heavy drinkers, would suggest that specific programming must address the potential impairments of this population both during treatment and through continuing case management following discharge. DISCLOSURES There are no disclosures to report for any authors regarding the subject matter of this manuscript.
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