Chapter 20 Linking Organizational Factors to Substance Abuse Tre.docx

Chapter 20 Linking Organizational Factors to Substance Abuse
Treatment Outcomes: Multilevel Correlates of Treatment
Effectiveness
Toorjo Ghose While substance abuse treatment is a vast and
complex industry in the United States, few studies have
examined the characteristics of this industry or its institutional
processes (Kimberly & McLellan, 2006; Roman, Ducharme, &
Knudsen, 2006). Reflecting the dearth of scholarship on
organizational processes, research on substance abuse treatment
effectiveness has focused exclusively on individual-level
correlates of treatment outcomes (Etheridge, Hubbard,
Anderson, Craddock, & Flynn, 1997; Hubbard et al., 1989;
Schildhaus, Gerstein, Dugoni, Brittingham, & Cerbone, 2000;
Sells, 1975; Simpson & Sells, 1982). With a few exceptions
(Heinrich & Fournier, 2004; Hser, Anglin, & Fletcher, 1998;
Sosin, 2002), organizational correlates of posttreatment
substance use are unexamined. Orwin, Ellis, Williams, and
Maranda (2000) point out that substance abuse treatment
practice and policy will be better informed by understanding the
link between organizational variables and treatment outcomes.
Scholars have speculated that program policies, the quality of
therapeutic staff, and the breadth of services provided by a
facility substantially influence outcomes (Anglin & Hser, 1990;
Ball & Ross, 1991; McGlothlin & Anglin, 1981). Consequently,
researchers have emphasized the need to use multilevel models
in examining program-level and individual-level processes in
substance abuse treatment (Broome, Simpson, & Joe, 1999;
Heinrich & Lynn, 2002; Hser, Joshi, & Anglin, 1999; Orwin &
Ellis, 2000). This study answers the need to expand the universe
of correlates of posttreatment use to include organizational
variables, as well as factors associated with the organizational
field of substance abuse treatment. It uses data from the Alcohol

and Drug Services Study (ADSS) Survey, 1996–1999, a national
study on substance abuse treatment conducted by the U.S.
Department of Health and Human Services, Substance Abuse
and Mental Health Services Administration (U.S. DHHS
SAMHSA, 2002). Individual-level and organizational factors
are simultaneously examined in a multilevel analysis of
posttreatment illicit drug use. Addressing Research Gaps The
lack of research on the link between organizational processes
and individual-level treatment outcomes can be attributed to
limitations of data sets (Heinrich & Fournier, 2004) and
analytical methods (Yoo & Brooks, 2005). This research seeks
to address both shortcomings. It uses data from the ADSS
survey, which contains rich information on clients and their
posttreatment behaviors, as well as the treatment facilities they
attended. ADSS is the latest in a series of three national studies
on substance abuse treatment clients and facilities. It builds on
the previous two, the Drug Services Research Study conducted
in 1990 and the Services Research Outcome Study, 1989–1995,
by using a more complete sampling frame, incorporating an
enhanced sampling design and including more detailed measures
of facilities, treatment services, and clients in treatment (Office
of Applied Studies [OAS], 2005). The data set allows an
examination of a more comprehensive set of organizational and
individual-level variables than have been undertaken in
previous studies. Addressing the dearth of analytical methods to
examine the multiple levels of treatment, this study uses
hierarchical, logistical models that simultaneously examine
individual-level and organizational influences on posttreatment
substance use. These multilevel models account for the variance
within facilities, as well as between them, resulting in more
precise estimations of variance and accurate inferences
(Roudenbush & Bryk, 2002). Author's Note: I would like to
thank the U.S. Substance Abuse and Mental Health Services
Administration for making the ADSS data publicly available. I
would like to thank Drs. Yeheskel “Zeke” Hasenfeld, Kim
Blankenship, Rob Schilling, Oscar Grusky, Jeannette Ickovics,

Todd Franke, and the CIRA NIMH fellows at Yale University
for their helpful feedback. A version of this paper has been
published in 2008 in the Journal of Substance Abuse Treatment,
38, 49–62. Background Conceptual Framework Scholars have
argued that substance abuse service provision and treatment
outcomes are affected by factors in the external political and
economic environment of a facility, by internal program-level
variables, and by client characteristics (Etheridge & Hubbard,
2000; Ghose, 2007; Heinrich & Fournier, 2004). Drawing on
this work, this article uses a multilevel conceptual framework
comprising external and internal organizational as well as
individual-level factors to examine posttreatment substance use.
It is assumed that factors at each of these levels influence the
treatment process, thus ultimately affecting posttreatment
outcomes. In identifying correlates of post-treatment use,
factors examined include (a) those external to the organization
that influence treatment decisions, (b) aspects of the internal
program-level treatment technology, and (c) individual-level
treatment characteristics. The External Organizational Field of
Substance Abuse Treatment Pfeffer and Salancik (1978) argue
that an organization's policies, norms, and practices are
influenced by external agencies that it depends on for resources.
Moreover, institutional theorists point out that organizational
practices are shaped by an external community of agencies that
an organization seeks legitimacy in (DiMaggio & Powell, 1983;
Meyer & Rowan, 1977). Treatment-related decisions and
processes are influenced by three types of actors in a treatment
facility's external environment: funders, parental organizations,
and regulatory agencies. Managed Care and the Private Sector.
Managed-care organizations have significantly increased their
role as a funding resource for substance abuse treatment
facilities, through the years (Alexander, Lemak, & Campbell,
2003). Scholars have found that increased managed-care
regulation decreases treatment intensity (Lemak & Alexander,
2001a, 2001b), reduces the number of services (Corcoran &
Vandiver, 1996; Gold, Hurley, Lake, Ensor, & Berenson, 1995;

Iglehart, 1996; Lo Sasso & Lyons, 2004; Olmstead, White, &
Sindelar, 2004), limits autonomy of the provider (Alexander &
Lemak, 1997; Mechanic, Schlesinger, & McAlpine, 1995;
Schlesinger, Dorward, & Epstein, 1996; Schwartz & Wetzler,
1998), fails to increase technical efficiency in service provision
(Alexander, Wheeler, Nahra, & Lemak, 1998), and increases
relapse rates (Sosin, 2005). Scholars have found that service
provision and treatment are adversely affected when a facility
depends on the private sector rather than the government for its
resources. For example, compared with public nonprofit
facilities, private for-profit facilities were less likely to provide
social and medical services (Friedmann, Durkin, Lemon, &
D'Aunno, 2003). Given the constraints imposed on services and
treatment by managed-care regulation and for-profit ownership,
it is proposed that they will adversely affect posttreatment
outcomes. Accreditation. Monitoring agencies have been found
to increase the quality of treatment provided in treatment
centers. Facilities accredited by the Joint Commission on the
Accreditation of Healthcare Organizations (JCAHO) were more
likely to provide primary care and mental health services
(D'Aunno, 2006; Friedmann, Alexander, & D'Aunno, 1999), as
well as physical exams and routine medical care (Durkin, 2002).
Parent Organizations. An important element of an organization's
external environment is its relationship with its parent facility.
D'Aunno, Vaughn, and McElroy (1999) note that parent
facilities tend to provide more financial and resource support to
substance abuse facilities that adopt their methods,
technologies, and ideologies. Support from a parent facility has
been found to be associated with greater service provision to
specialized populations (Ghose, 2007), whereas a lack of links
with parental entities was associated with the provision of fewer
services (Lee, Reif, Ritter, Levine, & Horgan, 2001), and in
many cases, dissolution of the facility (Johnson & Roman,
2002). Given the supportive role JCAHO and parental
organizations play in the provision of services, it is proposed
that they will have beneficial effects on posttreatment

outcomes. Hypotheses. It is hypothesized that elements of the
external institutional environment will significantly influence
treatment outcomes. Specifically, it is proposed that 1.
managed-care regulation and for-profit ownership will increase
the risk of posttreatment substance use, while JCAHO
accreditation and parental organizational support will reduce the
risk of resumption of drug use. Internal Program-Level Factors
The modes of treatment, types of services, skill levels of service
providers, and the distribution of workload constitute the
treatment technology of addiction treatment programs.
Treatment technology is therefore manifested in treatment
modality, counselor expertise, scope of services provided, and
caseload size. Modality. Scholars have found that compared
with outpatient facilities, residential facilities were more
effective in reducing relapse (Hubbard et al., 1989) as well as
social problems and psychiatric symptoms (Guydish, Wardegar,
Sorenson, Clarke, & Acampora, 1998). Hser, Evans, Huang, and
Anglin (2001) found that in residential treatment, caseload size
decreased the chances of retention and programmatic focus
increased it, while in outpatient drug-free programs, group
therapy focus decreased the odds of retention. Given the
differential effects of factors in the two modalities, this
research hypothesizes that in addition to a direct effect of
modality on posttreatment use, modality will interact with other
program factors in influencing outcomes. Degreed Counselors.
D'Aunno et al. (1999) note that with the blending together of
the community mental health and substance abuse treatment
sectors, facilities are now influenced by two types of distinct
ideologies: the mental health perspective and the Alcoholics
Anonymous (AA) 12-step orientation to treatment. The mental
health orientation is embedded in a psychological perspective
with degreed professionals providing psychosocial treatment,
whereas the AA perspective places emphasis on the role of
recovering, often with nondegreed counselors providing the
majority of treatment. The distinction in the type of counselors
hired by a facility can have implications for client outcomes.

Hser, Evans, et al. (2001) found that a greater proportion of
counselors who were themselves recovering from addiction
resulted in lower retention rates within methadone maintenance
programs. It is proposed that a greater proportion of degreed
counselors will result in better treatment outcomes for clients.
Scope of Services. The breadth of services in a facility is a
measure of its ability to provide comprehensive wrap-around
care. Some researchers have found that a broader array of
services were associated with longer stays in treatment and
better treatment outcomes (Hoffman, Caudill, Koman, & Lucky,
1996; McLellan, Woody, & Metzger, 1996). Thus, treatment
outcomes should be better in organizations that provide more
services. Caseload and Facility Size. Staff time spent with
clients can be expected to diminish with higher ratios of clients
to staff members (D'Aunno et al., 1999), leading to lower rates
of retention in treatment (Hser, Joshi, Maglione, Chou, &
Anglin, 2001), decreased use of HIV testing and counseling
services (D'Aunno & Vaughn, 1995; D'Aunno et al., 1999), as
well as lower rates of medical services (Durkin, 2002;
Friedmann et al., 1999) and employment counseling (Durkin,
2002). Larger facilities were associated with greater
administrative and clerical hours worked by clinical staff
members and thus reduced clinical productivity (Lemak,
Alexander, & Campbell, 2003). Given their effect on time and
attention spent with clients, this paper argues that higher
caseloads and bigger facilities (i.e., greater likelihood of
administrative requirements) will lead to poorer treatment
outcomes. Hypotheses. It is proposed that the aspects of the
treatment technology reviewed above will influence
posttreatment outcomes. Specifically, it is hypothesized that 2.
a greater proportion of professional counselors, lower client to
staff ratio, smaller facility size, and a higher number of services
offered will reduce posttreatment risk of substance use.
Moreover, it is proposed that in addition to a direct effect on
risk of use, treatment modality will modify the effects of other
program-level factors. Individual-Level Correlates Length of

Stay in Treatment. Several national studies have found that the
time spent in treatment is the most consistent protective factor
for clients in treatment. The Drug Abuse Reporting Program
(DARP) 1969-1974 and the Service Research Outcomes Study
(SROS) 1990 found that the length of treatment was negatively
associated with the use of any drugs (Schildhaus et al., 2000;
Simpson, 1981), while heroin use dropped with longer treatment
stays in the Treatment Outcome Prospective Study (TOPS;
Hubbard et al., 1989) and the Drug Abuse Treatment Outcome
Study (DATOS) 1991-1993 (Simpson, Joe, & Broome, 2002).
Interactions of Organizational Factors With Time Spent in
Treatment. Simpson (2004) notes that length of treatment
effects constitutes the cumulative effect of patient-level,
therapeutic, and environmental factors. Attempting to unpack
the effects of length of stay in treatment by examining its
interaction with other variables, Simpson et al. (2002) found
that longer treatment episodes benefited clients with problems
of higher severity more than those with lower severity. In the
only study examining the interaction of treatment length with
organizational characteristics, a 5-year follow-up of DATOS
clients found that length of stay reduced posttreatment use
among residential but not outpatient clients (Hubbard,
Craddock, & Anderson, 2003). It is proposed that in addition to
its direct effect in preventing posttreatment substance abuse,
length of stay in treatment will interact with organizational-
level factors to influence treatment outcomes. Specifically, it is
hypothesized that the organizational-level factors reviewed
above, in influencing treatment processes, will modify the
effect of length of stay in treatment on post-treatment substance
use. Treatment Completion. While length of stay in treatment
has often been used as a proxy measure of treatment completion
(Wickizer et al., 1994), the two are related but different
concepts (Sayre et al., 2002). Messina, Wish, and Nemes (2000)
found that treatment completion significantly reduced drug use
and increased employment. Individual-Level Controls. Several
client-level factors have been found to be associated with

treatment outcomes and need to be controlled for in an
examination of posttreatment substance use. Drug Use. Several
studies have found that regular use of drugs before treatment
raised the risk of posttreatment substance use (Cushman, 1977;
Hser et al., 1998; Hubbard et al., 1989; McLellan, Luborsky, &
O'Brien, 1986; Schildhaus et al., 2000; Simpson et al., 2002).
Client Demographics. Some studies indicate that compared with
whites, African Americans entering treatment had lower
retention rates (Agosti, Nunes, & Ocepeck-Welikson, 1996;
McCaul, Svikis, & Moore, 2001; Milligan, Nich, & Carrol,
2004), more severe levels of addiction (Bernstein et al., 2005),
and were more likely to use posttreatment (Schildhaus et al.,
2000). Hispanics on the other hand were more likely to stay
abstinent from any drug use, posttreatment (Bernstein et al.,
2005). Researchers have found that women were more likely to
be involved in treatment (Hser, Huang, Teruya, & Anglin, 2004)
and less likely to use posttreatment (Hser et al., 2004; Hubbard
et al., 1989). Age has been found to be correlated with illicit
drug use (OAS, 2006), with older clients recording a greater
reduction in posttreatment substance use (Brennan, Nichol, &
Moos, 2003; Satre, Mertens, Arean, & Weisner, 2003). Criminal
and Employment History. Pre-treatment criminal history was
negatively associated with treatment completion (Knight,
Logan, & Simpson, 2001) and positively associated with
posttreatment substance use (Hubbard et al., 1989; Messina et
al., 2000). Researchers have found that employment is
associated with higher rates of treatment retention (Vendetti et
al., 2002) and lower rates of posttreatment use (Leukefeld,
McDonald, Staton, & Mateyoke-Scrivner, 2004; Platt, 1995;
Wolkstein & Spiller, 1998). Mental Illness. Risk of
posttreatment substance use was found to be higher for clients
who suffered from antisocial personality (Grella, Joshi, & Hser,
2003; Hesselbrock, 1991; Woody, McLellan, Luborsky, &
O'Brien, 1995) and depression (McKay et al., 2002; Ross,
Glaser, & Germanson, 1988; Roy, Dejong, Lamparski, George,
& Linnoila, 1991). Hypotheses. Given the individual-level

treatment-related correlates of posttreatment use identified in
the literature reviewed above, it is hypothesized that 3a. after
controlling for pretreatment drug-use history, criminal activity,
mental health, and client demographics, the time spent in
treatment and treatment completion lower posttreatment risk of
use. 3b. Moreover, it is proposed that the effect of length of
stay will be modified by program-level factors, such as
treatment modality, proportion of professional counselors,
client-to-staff ratio, facility size, and scope of services offered
as well as external organizational factors, such as managed-care
funding, parent organizational support, for-profit ownership,
and JCAHO accreditation (Figure 20.1). Methods Data and
Sample ADSS 1996–1999, commissioned by the U.S. DHHS
SAMHSA (2002), was conducted by the Schneider Institute for
Health Policy, Brandeis University. It is a national longitudinal
survey of substance abuse treatment facilities and clients. The
sample for the study was constructed using a multistage
sampling strategy. In Phase 1, substance abuse treatment
facilities were selected using Stratified Random Sampling
(SRS) procedures. Program-level data were collected from
facility directors in 1996. Directors were mailed the
questionnaire and requested to complete it in advance of a
follow-up telephone interview using the same instrument. In
Phase 2, SRS procedures were used to select a nationally
representative subsample of 155 residential and outpatient
nonmethadone treatment facilities. Records of 2,670 clients,
who were eligible to be followed up with, were abstracted from
these facilities to gather information on clients and their
treatment. In Phase 3, 1,184 of these clients (48.5% response
rate) from 128 facilities were interviewed in 1999 (for a full
discussion of the sample, see also SAMHSA, 2003). Adjusting
for Attrition Bias. Longitudinal studies with substance users
usually suffer from significant attrition. Through 3 years, the
attrition rate for this sample was 51.5%, which is similar to
other large national longitudinal samples (Simpson et al., 2002).
The advantage of a multistage sample such as the ADSS is that

information on nonresponders collected at prior stages can be
used to adjust weights. Using raking procedures that incorporate
Phase 1 facility-level, Phase 2 abstract-level, and Phase 3
management-level data on nonresponsive clients, weights were
adjusted in the ADSS to ensure a nationally representative
sample (see also Ritter et al., 2003, for details). Figure 20.1
Conceptual Framework Variables and Measures Dependent
Variable. Several past studies examining posttreatment
outcomes use the dichotomous measure of any use post-
treatment versus staying abstinent (Gossop, Stewart, Browne, &
Marsden, 2002; Hser et al., 1999; Hubbard, Flyn, Craddock, &
Fletcher, 2001; Walton, Castro, & Barrington, 1994). McLellan,
Lewis, O'Brien, and Kleber (2000) point out that addiction is a
chronic medical condition and question the appropriateness of
measuring treatment success in terms of a “cure,” such as
complete cessation of use. While this study uses a measure of
“any use posttreatment” as the dependent variable, it seeks to
make the measure more appropriate by interpreting it strictly as
the risk of use, thereby highlighting the fact that it is a measure
of vulnerability rather than “failure.” It is operationalized as the
posttreatment risk of using any of the following drugs in the
period from the end of treatment in 1996 to the 3-year follow-up
interview in 1999: crack, cocaine, heroin, marijuana,
amphetamines, hallucinogens, nonprescribed methadone,
opiates, sedatives, tranquilizers, or nonmedical use of over-the-
counter drugs. Organizational-Level Variables. All
organizational data are taken from Phase 1 of the study.
Regulation by managed-care organizations is operationalized as
proportion of managed-care funding, calculated by dividing the
amount of private managed-care funding by total revenue.
Accreditation was operationalized as being accredited by the
JCAHO. A dichotomous variable measured whether or not a
facility operated for-profit and was an outpatient facility (with
the reference category being residential). Facility size was
calculated as the total number of staff members and clients in a
program. Support by a parent organization was measured as an

additive scale summing the number of the following dimensions
of support provided by a parent organization: (1) personnel, (2)
funding, (3) treatment protocols, (4) pricing of services, (5)
waiting lists, (6) client intakes and assessments, and (7) quality
assurance and utilization review. The presence of professional
degreed counselors was operationalized as the proportion of
counselors with doctorates. Number of services offered was the
total number of services offered out of a menu of 18 services.
Caseload was operationalized as the number of clients per full-
time staff member. Treatment-Level Variables. Clinical
information is taken from client records abstracted in Phase 2 of
the study. Completion of treatment is operationalized as a
dichotomous measure, while time spent in treatment is measured
as a continuous variable. Individual-Level Controls. Data on
client history and characteristics are taken from the Phase 3
follow-up interview. Pretreatment drug use is operationalized as
the use of cocaine, heroin, marijuana, or intravenous drugs prior
to treatment. Being African American, Hispanic, employed, or
female, as well as the diagnosis of a mental illness and arrest
for an illegal offense while in treatment are operationalized as
dichotomous measures. Age is a continuous measure. All
program-level variables are grand-mean centered, which
controls for between-program differences in the centered
variables (Roudenbush & Bryk, 2002, p. 142). Dichotomous
variables were not centered, allowing regression coefficients to
represent the difference in the odds of post-treatment use
between the dichotomized groups. Models The Hierarchical
Linear Modeling (HLM 6) software was used to analyze
correlates of posttreatment use. The multilevel logistic models
use the following three equations: Equation 1 regresses the
likelihood of posttreatment substance use on individual-level
predictors. Yij represents the log odds of posttreatment use,
while βij represents the effect of i individual-level correlates
within j organizations. Equation 2 represents the association
between organizational correlates and the risk of use. It models
the direct effect of organizational factors on the likelihood of

posttreatment use. γ00 represents the mean likelihood of
posttreatment use across all j organizations. U0j is the residual
in the mean likelihood of use between the organizations. γ0j is
the association between odds of posttreatment use and
organizational-level correlates. Equation 3 models the
association between organizational factors and the individual-
level slopes. It models the extent to which organizational
factors modify the association between individual-level factors
(in this case, length of stay) and the likelihood of posttreatment
use. γio represents the mean value of the slope of length of stay
across all j organizations. Uij is the residual in the slope of
length of stay between the organizations. γij is the effect of
organizational predictors on the slope of length of stay. Five
models were used to test the hypotheses of this research. The
first model was an unconditional model with no independent
variables that allowed the intercept to vary randomly across
organizations. The second, a random slope and intercept model,
included all hypothesized individual-level correlates and
allowed the intercept as well as the slope of the length of stay
to vary randomly across organizations in order to examine the
between-organization variance in the odds of posttreatment use
and the length of stay slope. In the third and fourth models, all
hypothesized correlates at the individual and organizational
level were included. The third model fixed the slope and the
intercept, while the fourth model allowed the intercept to vary
randomly across organizations. This allowed for a comparison
of results between a true multilevel model and one that is
essentially non-hierarchical. Finally, the fifth model retained all
significant correlates. Results Table 20.1 presents the means of
the variables included in the model. Among clients followed up,
47% reported that they had used illicit drugs, posttreatment.
Table 20.2 presents the results of the unconditional random
intercept model. The mean odds of posttreatment substance use
is 0.86 and varies significantly across facilities (U0 = 0.361, p
< .01). The next model examined the extent to which odds of
posttreatment use and the effects of length of stay varied across

facilities after all individual-level correlates were included.
Table 20.2 presents the results of this random intercept and
slope model. The residuals of the intercept (U0) and slope (U1)
are significantly greater than 0 (p < .001 for both), thus
indicating that the odds of posttreatment use and effect of
length of stay fluctuate significantly from one facility to the
next. These results provide support for the hypothesis that
organizational context plays a significant role in clients using
substances posttreatment and in modifying the effect of length
of stay on posttreatment substance use. The variance of the odds
of posttreatment substance use in this model (variance
component = 0.227) is 37% less than the variance in the
unconditional model, indicating that this model is better
specified, with more of the variance accounted for, than the
unconditional model. Table 20.3 presents the results of two
models. Model 1 regressed posttreatment use onto all the
correlates, without accounting for variance between facilities.
In other words, this is a single-level regression model where
organizational- and individual-level correlates are treated
similarly. Model 2 allowed the intercept (or the mean odds of
posttreatment use) to vary across organizations. It is a true
multilevel model and takes into account the variance between
and within organizations. All individual-level and
organizational correlates were simultaneously entered into both
models. The length of stay slope was regressed on each
organizational factor sequentially, and variables with significant
associations were retained in the model. Table 20.1 Means and
Proportions Note: DOC, drug of choice; IV, intravenous;
JCAHO, Joint Commission on the Accreditation of Healthcare
Organizations; SEM, standard error of mean. Comparing Models
1 and 2. A comparison of the standard errors in Models 1 and 2
indicates that the errors in Model 1 are uniformly lower. This
follows from the fact that unlike Model 2, this model does not
account for the variance between organizations.
Underestimating the standard error leads to inflated t values and
incorrect inferences in Model 1. Thus, several factors in Model

1 emerge as significantly associated with the odds of use,
although they are not significant correlates in Model 2. Being
employed, Hispanic, as well as attending facilities with higher
levels of parent monitoring are all significantly associated with
posttreatment use in Model 1, but not in Model 2. Conclusions
drawn from Model 1 would lead to a Type 1 error where the null
hypothesis (that there is no significant association) is rejected
incorrectly. Table 20.2 Unconditional and Random Intercept and
Slope Models Results from Model 2 indicate that controlling for
client demographics and pre-treatment client history, length of
stay in treatment was significantly associated with the odds of
posttreatment use. At the program level, managed care and
JCAHO accreditation were associated with use, while parental
facility monitoring, caseload size, managed-care funding, and
proportion of counselors with a doctorate degree modified the
effect of treatment episode length. Moreover, treatment
modality significantly modified the effects of managed care and
JCAHO accreditation. To build the most parsimonious model
with the highest power, only significant correlates from Model
2 were retained in the final model. The results are presented in
Table 20.4. The residual of the intercept continued to be
significantly greater than 0 (χ2 = 184.08, p < .001), while the
residual of the slope of length of stay was not (χ2 = 144.45, p =
.09). Consequently, the intercept was allowed to vary randomly,
while the slope was fixed. The inclusion of organizational
variables explains the 27% more of the variance in the
dependent variable than the model that included only
individual-level variables and the 54% more of the variance
than the unconditional model. With more of the variance in the
odds of use accounted for, the estimate of the mean odds of
posttreatment use (OR = 0.52, 95% CI = 0.39, 0.73) is more
precise than the unconditional model and is significant at the
.01 level. Thus, inclusion of organizational factors leads to a
better specified model, indicating that they play a salient role in
explaining posttreatment substance use. The relative strength of
association in this model is indicated by the semistandardized

coefficient. Managed care emerged as the strongest correlate,
followed by age, preference for cocaine, JCAHO accreditation,
preference for marijuana, commission of an arrestable offence,
prior intravenous drug use, time spent in treatment, preference
for heroin, and treatment completion, in that order. Risk factors
included behaviors and personal characteristics. Intravenous
drug use multiplied the risk of posttreatment use by more than a
factor of two; committing an arrestable offence during treatment
increased risk almost three times; while a preference for
marijuana, cocaine, and heroin increased risk by factors of
approximately 4.5, 3.8, and 3.6, respectively (all significant at
the .01 level). Older clients were less likely to use
posttreatment, with every standard unit increase in age reducing
the risk by 47%(p < .01). Table 20.3 Multilevel Correlates of
Posttreatment Substance Use Note: JCAHO, Joint Commission
on the Accreditation of Healthcare Organizations; MCO,
managed-care organization. a. Controlling for drug of choice,
drug-use and criminal history, age, and being black and female.
Table 20.4 Significant Correlates of Posttreatment Substance
Use Note: DOC, drug of choice; IV, intravenous; JCAHO, Joint
Commission on the Accreditation of Healthcare Organizations.
a. Adjusted odds. b. Interaction effects reported after
controlling for main effects of interacting variables. *p < .05;
**p = < .01. Measures of the treatment process were found to be
significantly associated with posttreatment use. Providing
support for previous research findings, a standard unit increase
in time spent in treatment decreased the risk by 27%, while
completing treatment reduced it by 36% (both significant at the
.01 level). Managed care emerged as the strongest direct
correlate of posttreatment use. A standard unit increase in
proportion of managed-care funding more than doubled the risk
of posttreatment use (p < .01). Clients who were treated at a
facility accredited by JCAHO were 46% less likely to use
posttreatment compared with clients from non-JCAHO
facilities. The effects of managed care and JCAHO accreditation
were modified by treatment modality. Managed-care funding

was less of a risk for clients in outpatient facilities—perhaps
because there was less intervention at that level. For every
standard unit increase in managed-care funding, the odds of
posttreatment use for residential clients was 30% more than for
the average outpatient client. On the other hand, JCAHO
accreditation had less of a beneficial effect in outpatient
programs. Whereas accredited residential facilities reduced the
risk of posttreatment use by 65% compared with nonaccredited
facilities, accreditation reduced the risk of use by 15% in
outpatient facilities. Finally, organizational factors significantly
modified the effect of length of stay in treatment. The
ameliorative effect of longer length of stay was significantly
lowered by some program-level factors. For example, a 10%
increase in the level of parental organizational monitoring,
caseload size, proportion of managed-care funding, and
proportion of counselors with doctorates reduced the beneficial
effect of length of stay by 7.5%, 2.5%, 5.7% (all significant at
the .01 level), and 4.4% (p < .05), respectively. The results are
consistent with the hypothesized effects of external
organizational factors, such as managed-care funding and
JCAHO accreditation, as well as individual-level factors, such
as treatment length and treatment completion on posttreatment
use. Moreover, the findings support the hypothesized
interactions between modality and organizational variables,
such as managed-care funding and JCAHO accreditation.
Among the hypothesized organizational modifiers of the effects
of length of stay, managed care, parent organizational
monitoring, caseload size, and proportion of degreed counselors
were significantly associated with the slope of length of stay.
However, the reduction in the benefits of length of stay
associated with higher proportions of counselors with a
doctorate degree is contrary to the hypothesis that the presence
of degreed counselors leads to better treatment outcomes.
Moreover, contrary to the hypotheses, for-profit ownership and
most aspects of the treatment technology at the program level
had no direct effects on the risk of use. Discussion The findings

(1) add to our understanding of how organizational factors,
especially those originating in the external institutional
environment, influence posttreatment use, (2) shed new light on
how the same length of stay in treatment can have differential
benefits in facilities with different organizational
characteristics, and (3) highlight how hierarchical methods, by
taking variance between organizations into account, avoid
errors of inference, even for individual-level associations. The
Influence of Organizational-Level Factors The significant
variance of the intercept and slope across organizations, as well
as results of the multilevel analysis, indicate that organizational
factors in treatment design and delivery play a salient role in
treatment outcomes. Specifically, external organizational
factors appear to significantly influence posttreatment risk of
use. The Salience of the External Institutional Environment.
Monitoring and regulation by external institutions directly
affect posttreatment risk of use and interact with other
predictors. Managed-care regulation (as measured by greater
levels of managed-care funding) increases the risk of use, while
JCAHO accreditation decreases it. Moreover, the ameliorative
effect of greater length of stay in treatment is reduced
significantly when those treatment episodes are regulated and
monitored through managed-care processes and/or by a parent
organization. Results of this research indicate that substance
abuse treatment facilities respond to three sources of
institutional pressure in their external environment: (1)
managed care, (2) parent organizations, and (3) accreditation
agencies. The Influence of Managed Care. Researchers have
found that the procedural algorithms used by managed-care
organizations to approve treatment entry and duration, along
with decreasing dollars per case or episode, have reduced
services, treatment sessions, and completion rates (Alexander &
Lemak, 1997; Durkin, 2002; Sederer & Bennett, 1996; Sosin,
2002). DiMaggio and Powell (1983) refer to these powerful
institutional rules, which are enforced through the threat of
sanctions, as comprising an “iron cage” that severely restricts

the autonomy of a facility. Results of this research also suggest
that the loss of autonomy associated with managed-care control
may have adverse results on a client's ability to stay sober. The
Influence of Parental Organization. Research shows that health
care organizations do in fact respond to the demands of the
economic environment by vertically integrating into networks
under the umbrella of parental organizations (Longest, Rakich,
& Darr, 2005; Walsh, 1998). Longest et al. (2005) note that in
an integrated delivery system of this kind, each facility provides
specialized care and services with the aim of providing
seamless, wrap-around services to clients. However, such
specialization can have negative outcomes for clients, as
suggested by the results of this research. Specifically, vertical
integration, as manifested through parent organizational
administration, might undermine the effects of time in treatment
insofar as facilities that provide a specialized (and therefore
narrow) spectrum of care may exhaust their menu of treatment
options for clients who stay longer. Moreover, heightened
monitoring by parental entities may compromise an
organization's ability to respond to its clients' needs over time.
The Influence of JCAHO Accreditation. This is the first study to
establish a direct association between accreditation and
treatment outcome: Risk of posttreatment use is reduced for
clients being treated in JCAHO-accredited facilities. Scholars
have found that JCAHO accreditation, which is based on a
triennial evaluation of facilities (JCAHO, 2006), is correlated
with increased staff productivity (Lemak et al., 2003) and a
greater breadth of service provision (Durkin, 2002; Friedmann
et al., 1999). These organizational outcomes of accreditation
translate into substantial benefits for clients, given that
accreditation is associated with a significant reduction in the
risk of posttreatment use. DiMaggio and Powell (1983) note that
organizational processes become uniform and standardized in a
given occupation as a result of processes that are coercive,
normative, and mimetic. Whereas managed care and parental
organizations influence facilities through coercive processes,

JCAHO's vector of influence cannot follow the same route, as
accreditation is voluntary in the field of substance abuse
treatment. With only a minority of facilities (31%) choosing to
become accredited, JCAHO accreditation has yet to become a
norm in the substance abuse treatment field. The results of this
research indicate that establishing accreditation as a
professional standard would loosen some of the bars of the iron
cage imposed by managed-care facilities and parent
organizations and would lead to beneficial outcomes for clients.
Interactions With Modality. JCAHO accreditation was more
beneficial for clients in residential programs than it was for
clients in outpatient facilities, while managed-care funding was
more detrimental for those residential programs. These results
indicate that on the policy front, bringing residential facilities
under the purview of JCAHO accreditation and limiting the role
of managed-care regulation might have benefits for clients and
might be a more feasible step than doing so for the larger
universe of treatment facilities. However, it is not known what
effects such a policy would have on the costs of care. Merging
the Public and Private Sectors. Gronbjerg (1993, 1998) notes
that privatization efforts and the penetration of managed care
have blurred the line between the public and private economies
in the human services industry. The contradictory mandates
introduced by the merger of economies have implications for
treatment outcomes. The profit maximization mandate of
managed care, for instance, has detrimental effects on
posttreatment use, whereas JCAHO, charged with the public
mandate to ensure quality services, ensures beneficial outcomes.
The results indicate that client outcomes might be compromised
if the private sector continues to take over the management of
substance abuse treatment. Treatment-Level Factors The
Modification of the Benefits of Extended Treatment Duration.
Length of stay in treatment has been found to be one of the most
consistent protective factors in posttreatment outcomes
(Simpson, 2004). The results of this research also indicate that
time spent in treatment is an effective deterrent to posttreatment

substance use. However, its ameliorative effect appears to be
modified by organizational factors. For example, there appears
to be less benefit from longer treatment episode length in the
presence of increased managed-care regulation and/or parental
organizational administration. The mechanism by which this
might occur is unknown, but it is possible that increased
treatment dosage loses its effect when treatment regimens are
rendered inflexible through external institutional control.
Proportion of counselors with a doctorate and caseload size,
both aspects of the treatment technology of facilities, also
appear to modify the influence of length of stay in treatment.
Higher caseloads seem to reduce the time that treatment
providers can spend with clients (D'Aunno et al., 1999), thus
reducing the beneficial effects of increased dosage, as
manifested in longer treatment episodes. The interaction
between length of stay and proportion of degreed counselors is
in the opposite direction of the hypothesized association: The
presence of counselors with doctorate degrees appears to reduce
the benefits of length of stay. The finding is probably an artifact
of anomalous data in this sample. It is possible, however, that
the results indicate the differential manner in which
professional and nonprofessional counselors work with clients.
Counselors without professional counseling degrees may be
more likely to be recovering individuals who favor a 12-step
total abstinence perspective (D'Aunno et al., 1999). Prolonged
treatment episodes, where more time is spent with clients, may
increase the effectiveness of such modeling and might allow the
12-step ideology to be modeled effectively by recovering
counselors and take root in clients. In contrast, professional
counselors with doctorates tend to be embedded in a mental
health perspective, delivering treatment within a more
traditional, psychological framework where face- to-face
contact is limited to sessions. Given this difference, time spent
around counselors oriented to a 12-step abstinence-only
approach might be more effective in keeping clients abstinent
than time spent in a program with professional, degreed

counselors. While retention of clients and expansion of the
length of their treatment episodes have become major objectives
for substance abuse treatment agencies, these results indicate
that such decisions need to factor in organizational
characteristics. Expanding treatment length may have
diminishing value in the presence of increased levels of
managed-care regulation and funding constraints; and/or greater
administrative monitoring and control by parent organizations;
and perhaps greater proportion of staff with doctorates.
Treatment Completion. The results indicate that even after
controlling for time spent in treatment, a favorable treatment
discharge (completion) reduced risk of use, post-treatment.
Researchers have found that self-efficacy is boosted by
treatment, with abstainers reporting higher self-efficacy scores
than users (Burling, Reilly, Molten, & Ziff, 1989). The role of
treatment completion in posttreatment use points to the fact that
after controlling for time in treatment, completion of treatment
appears to decrease posttreatment use by possibly increasing
self-efficacy among users. The Utility of Hierarchical Models
The use of multilevel models to examine posttreatment use adds
to the repertoire of analytical tools to examine posttreatment
outcomes and has two major benefits. First, both program-level
as well as individual-level correlates can be examined in one
model. Second, the results indicate that the high level of
intraclass correlation between subjects in treatment facilities
has to be accounted for, even when examining individual-level
correlates of posttreatment use. As a comparison of the two
models in Table 20.3 indicates, if the differences between
programs are not accounted for through hierarchical methods,
the significance of individual-level associations is inflated,
leading to Type I error. In a non-hierarchical model (Table 20.3,
Model 1), employment and race (Hispanic) on the individual
level and parental organizational monitoring at the program
level emerge as significant correlates. However, the
significance of these associations disappear in a multilevel
model. Previous studies, especially those focusing exclusively

on individual-level associations, may suffer from such errors of
inference. Practice, Policy, and Future Research Implications
The identification of correlates of post-treatment risk of use can
inform treatment and policies that seek to lower the chronicity
of addiction and possibly lengthen the time between treatment
admissions. The results indicate that facilities may be best
served by implementing feasible treatment plans that raise the
likelihood of treatment completion and that retain clients for
longer periods of time. However, efforts to lengthen treatment
episode may have to take into account the constraints on a
facility's autonomy exerted by external agencies, as well as the
nature of a facility's treatment technology. More is not
necessarily better in the case of facilities that need to adhere to
managed care and parent organizational rules and are relatively
understaffed. The same is true for facilities that depend on
counselors with doctorates for treatment provision, if the
counterintuitive association between length of stay in treatment
and levels of professional expertise among counselors can be
confirmed in future research and is not attributable to
anomalous data in this research. Decision-making processes in
facilities need to factor in organizational effects on treatment
outcomes. The results strike a cautionary note on allowing
managed-care funding to become a part of organizational
budgets. Given the new evidence that this research presents on
JCAHO accreditation's association with lower risk of
posttreatment use, making accreditation mandatory might result
in significant benefits for clients in treatment, especially in
residential facilities. Given the significant differences between
facilities, highlighted by the results of this research, future
studies on treatment efficacy need to factor in the variance
between treatment sites, even when examining individual-level
correlates of outcomes of treatment being administered across
facilities. The results of this research highlight the importance
of collecting data on the individual and program level as well as
using multilevel methods to examine the effects of substance
abuse treatment. Limitations of the Study Attrition. Similar to

the DARP and DATOS studies (Simpson et al., 2002), there was
significant attrition at the 3-year follow-up. While results have
been adjusted for nonresponse using data collected at prior
stages, generalizations of the results should be made with
caution. Despite the attrition (51.5%), the sample remains large
at follow-up, ensuring robustness of the results. Datedness of
the Data. Considerable changes have occurred in the substance
abuse treatment landscape since the data for the study were
collected. Managed care, for instance, has continued to expand
its presence in substance abuse treatment facilities' budgets
(Alexander & Lemak, 1997; D'Aunno, 2006; Drug and Alcohol
Service Information System [DASIS], 2001), and it is possible
that its adverse effects on treatment outcomes has intensified.
Current studies need to replicate the ADSS study and examine
the changes that have occurred since 1999. Conclusion This
research highlights the link between macro- and microlevel
processes in substance abuse treatment by examining program-
level and individual-level correlates of posttreatment substance
use. The findings highlight the role of the organization in
influencing posttreatment use. The multilevel analytical
framework provides the field of substance abuse research with a
useful analytical tool to study treatment outcomes. The results
of this research lay the foundation for further examination of
multilevel predictors of posttreatment outcomes. References
Agosti, V., Nunes, E., & Ocepeck-Welikson, K. (1996). Patient
factors related to early attrition from an outpatient cocaine
research clinic. American Journal of Drug and Alcohol Abuse,
22, 29–39. Alexander, J. A., & Lemak, C. H. (1997). The effects
of managed-care administrative burden in outpatient substance
abuse treatment organizations. Medical Care Research and
Review, 54, 490–507. Alexander, J. A., Lemak, C. H., &
Campbell, C. I. (2003). Changes in managed-care activity in
outpatient substance abuse treatment organizations, 1995–2000.
Journal of Behavioral Health Services Research, 30, 369–381.
Alexander, J. A., Wheeler, J. A., Nahra, T. A., & Lemak, C. H.
(1998). Managed care and technical efficiency in outpatient

substance abuse treatment units. Journal of Behavioral Health
Services and Research, 25, 377–396. Anglin, M. D., & Hser, Y.
(1990). Treatment of drug abuse. In M. Tonry & J. Q. Wilson
(Eds.), Drugs and crime (pp. 393–458). Chicago: University of
Chicago Press. Ball, J. C., & Ross, A. (1991). The effectiveness
of methadone maintenance treatment: Patients, programs,
services and outcome. New York: Springer-Verlag. Bernstein,
E., Bernstein, J., Tassiopoulos, K., Valentine, A., Heeren, T.,
Levenson, S., et al. (2005). Racial and ethnic diversity among a
heroin and cocaine using population: Treatment system
utilization. Journal of Addictive Diseases, 24 (4), 44–63.
Brennan, P. L., Nichol, A. C., & Moos, R. H. (2003). Older and
younger patients with substance use disorders: Outpatient
mental health service use and functioning over a 12-month
interval. Psychology of Addictive Behaviors, 17 (1), 42–48.
Broome, K. M., Simpson, D. D., & Joe, G. W. (1999). Patient
and program attributes related to treatment process indicators in
DATOS. Drug and Alcohol Dependence, 7, 127–135. Burling,
T. A., Reilly, P. M., Molten, J. O., & Ziff, D. (1989). Self-
efficacy and relapse among inpatient drug and alcohol abusers:
A predictor of outcome. Journal of Studies on Alcohol and
Drugs, 50 (4), 354–360. Corcoran, K. J., & Vandiver, V. (1996).
Maneuvering the maze of managed care: A manual for mental
health practitioners. New York: Free Press. Cushman, P. (1977).
Ten years of methadone maintenance treatment: Some clinical
observations. American Journal of Drug and Alcohol Abuse, 4,
543–553. D'Aunno, T. (2006). The role of organization and
management in substance abuse treatment: Review and
roadmap. Journal of Substance Abuse Treatment, 31, 221–233.
D'Aunno, T., & Vaughn, T. E. (1995). An organizational
analysis of service patterns in outpatient drug abuse treatment
units. Journal of Substance Abuse, 7, 27–42. D'Aunno, T.,
Vaughn, T. E., & McElroy, P. (1999). An institutional analysis
of HIV prevention efforts by the nation's outpatient drug abuse
treatment units. Journal of Health and Social Behavior, 40, 175–
192. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage

revisited: Institutional isomorphism and collective rationality in
organizational fields. American Sociological Review, 48, 147–
160. Drug and Alcohol Service Information System. (2001).
Growth of managed care in substance abuse treatment.
Arlington, VA: Substance Abuse and Mental Health Services
Administration. Retrieved June 30, 2006, from
http://oas.samhsa.gov/2k1/managedcare/managedcare.htmDurki
n, E. M. (2002). An organizational analysis of psychosocial and
medical services in outpatient drug abuse treatment programs.
Social Service Review, 76, 406–429. Etheridge, R. M., &
Hubbard, R. L. (2000). Conceptualizing and assessing treatment
structure and process in community-based drug dependency
treatment programs. Substance Use and Misuse, 55, 1757–1795.
Etheridge, R. M., Hubbard, R. L., Anderson, J., Craddock, S.
G., & Flynn, P. M. (1997). Treatment structure and program
services in the Drug Abuse Treatment Outcome Study.
Psychology of Addictive Behaviors, 11, 244–260. Friedmann, P.
D., Alexander, J. A., & D'Aunno, T. (1999). Organizational
correlates of access to primary care and mental health services
in drug abuse treatment units. Journal of Substance Abuse
Treatment, 16, 71–80. Friedmann, P. D., Durkin, E., Lemon, S.
C., & D'Aunno, T. A. (2003). Delivery of ancillary services in
outpatient substance abuse treatment programs. Journal of
Substance Abuse Treatment, 24, 81–88. Ghose, T. (2007).
Organizational correlates of specialized substance abuse
treatment provision to seropositive clients: A political economy
perspective. Journal of HIV/AIDS & Social Services, 5, 41–59.
Gold, M. R., Hurley, R., Lake, T., Ensor, T., & Berenson, R.
(1995). A national survey of the arrangements managed-care
plans make with physicians. Journal of Medicine, 333(25),
1678–1683. 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. Grella, C. E.,
Joshi, V., & Hser, Y. (2003). Followup of cocaine-dependent
men and women with antisocial personality disorder. Journal of

Substance Abuse Treatment, 25, 155–164. Gronbjerg, K. A.
(1993). Understanding nonprofit funding: Managing revenues in
social services and community development organizations. San
Francisco: Jossey-Bass. Gronbjerg, K. A. (1998). Markets,
politics and charity: Nonprofits in the political economy. In W.
W. Powell & E. S. Clemens (Eds.), Private action and the public
good (pp. 137–149). New Haven, CT: Yale University Press.
Guydish, J., Wardegar, D., Sorenson, J. L. Clarke, W., &
Acampora, A. (1998). Drug abuse day treatment: A randomized
clinical trial comparing day and residential treatment programs.
Journal of Consulting and Clinical Psychology, 66, 280–289.
Heinrich, C. J., & Fournier, E. (2004). Dimensions of
publicness and performance in substance abuse treatment
organizations. Journal of Policy Analysis and Management, 23,
49–63. Heinrich, C. J., & Lynn, L. E. (2002). Improving the
organization, management, and outcomes of substance abuse
treatment programs. American Journal of Drug and Alcohol
Abuse, 28, 601–622. Hesselbrock, M. (1991). Gender
comparison of antisocial personality disorder and depression in
alchoholism. Journal of Substance Abuse, 3, 205–219. Hoffman,
J. A., Caudill, B. D., Koman, J. J., & Lucky, J. W. (1996).
Psychosocial treatments for cocaine abuse: 12-month treatment
outcomes. Journal of Substance Abuse Treatment, 13, 3–11.
Hser, Y., Anglin, M. D., & Fletcher, B. (1998). Comparative
treatment effectiveness: Effects of program modality and client
drug dependence history on drug use reduction. Journal of
Substance Abuse Treatment, 15, 513–523. Hser, Y., Evans, E.,
Huang, D., & Anglin, M. D. (2001). Relationship between drug
treatment services, retention and outcomes. Psychiatric
Services, 55, 767–774. Hser, Y., Huang, Y., Teruya, C., &
Anglin, M. D. (2004). Gender differences in treatment outcomes
over a three-year period. Journal of Drug Issues, 34, 419–439.
Hser, Y., Joshi, V., & Anglin, D. M. (1999). Predicting
posttreatment cocaine abstinence for first-time admissions and
treatment repeaters. American Journal of Public Health, 89,
666–671. Hser, Y., Joshi, V., Maglione, M., Chou, C., &

Anglin, M. D. (2001). Effects of program and patient
characteristics on retention of drug treatment patients.
Evaluation and Program Planning, 24, 331–341. Hubbard, R. L.,
Craddock, S. G., & Anderson, J. (2003). Overview of 5-year
followup outcomes in the drug abuse treatment outcome studies.
Journal of Substance Abuse Treatment, 23, 125–134. Hubbard,
R. L., Flyn, P. M., Craddock, S. G., & Fletcher, B. W. (2001).
Relapse after drug abuse treatment. In M. Tims, C. G.
Leukefeld, & J. J. Platt (Eds.), Relapse and recovery in
addictions (pp. 109–121). New Haven, CT: Yale University
Press. Hubbard, R. L., Marsden, M. E., Rachal, J. V., Harwood,
H. J., Cavanaugh, E. R., & Ginzburg, H. M. (1989). Drug abuse
treatment: A national study of effectiveness. Chapel Hill:
University of North Carolina Press. Iglehart, J. K. (1996).
Managed care and mental health. Health Policy Report, 334,
131–135. Johnson, J. A., & Roman, P. M. (2002). Predicting
closure of private substance abuse treatment facilities. Journal
of Behavioral Health Services and Research, 29, 115–125. Joint
Commission on the Accreditation of Healthcare Organizations.
(2006). Comprehensive accreditation manual for behavioral
health care. Oak Brook, IL: Joint Commission Resources.
Retrieved July 31, 2006, from www.joincommission.org
Kimberly, J. R., & McLellan, T. A. (2006). The business of
addiction treatment: A research agenda. Journal of Substance
Abuse Treatment, 31, 213–219. Knight, D. K., Logan, S. M., &
Simpson, D. D. (2001). Predictors of program completion for
women in residential substance abuse treatment. American
Journal of Drug and Alcohol Abuse, 27, 1–18. Lee, M. T., Reif,
S., Ritter, G. A., Levine, H., & Horgan, C. M. (2001). Access to
services in the substance abuse treatment system: Variations by
facility characteristics. In M. Galanter (Ed.), Recent
developments in alcoholism (Vol. 15, pp. 137–156). New York:
Kluwer Academic. Lemak, C. L., & Alexander, J. A. (2001a).
Managed care and drug treatment practices: A model of
organizational response to external influence. Advances in
Health Care Management, 2, 131–159. Lemak, C. L., &

Alexander, J. A. (2001b). Managed care and outpatient
substance abuse treatment intensity. Journal of Behavioral
Health Services and Research, 28, 12–29. Lemak, C. H.,
Alexander, J. A., & Campbell, C. (2003). Administrative burden
and its implications for outpatient substance abuse treatment
organizations. Psychiatric Services, 54, 705–711. Leukefeld, C.,
McDonald, S. H., Staton, M., & Mateyoke-Scrivner, A. (2004).
Employment, employment-related problems, and drug use at
drug court entry. Substance Use and Misuse, 39, 2559–2579.
Longest, B., Rakich, J. S., & Darr, K. (2005). Managing health
service organizations. Baltimore: Health Professionals Press. Lo
Sasso, A., & Lyons, J. S. (2004). The sensitivity of managed
care treatment intensity to co-payment levels. Journal of
Behavioral Health Services and Research, 31, 50–65. McCaul,
M. E., Svikis, D., & Moore, R. D. (2001). Predictors of
outpatient treatment retention: Patient versus substance use
characteristics. Drug and Alcohol Dependence, 62, 9–17.
McGlothlin, W. H., & Anglin, D. M. (1981). Long-term
followup of clients of high- and low-dose methadone programs.
Archives of General Psychiatry, 38 (9), 1055–1063. McKay, J.
R., Pettinati, H. M., Morrison, R., Feeley, M., Mulvaney, F. D.,
& Gallop, R. (2002). Relation of depression diagnoses to 2-year
outcomes in cocaine-dependent patients in a randomized
continuing care study. Psychology of Addictive Behaviors, 16,
225–235. McLellan, T. A., Lewis, D., O'Brien, C., & Kleber, H.
(2000). Drug dependence, a chronic mental illness: Implications
for treatment, insurance, and outcomes evaluation. Journal of
the American Medical Association, 284, 1689–1695. McLellan,
T. A., Luborsky, L., & O'Brien, C. P. (1986). Alcohol and drug
abuse treatment in three different populations: Is there
improvement and is it predictable? American Journal of Drug
and Alcohol Abuse, 12, 101–120. McLellan, T. A., Woody, G.
E., & Metzger, D. (1996). Evaluating the effectiveness of
addiction treatments: Reasonable expectations, appropriate
comparisons. Milbank Quarterly, 74, 51–85. Mechanic, D.,
Schlesinger, M., & McAlpine, D. (1995). Management of mental

health and substance abuse services: State of the art and early
results. Milbank Quarterly, 73, 19–55. Messina, N., Wish, E., &
Nemes, S. (2000). Predictors of treatment outcomes for men and
women admitted to a therapeutic community. American Journal
of Drug and Alcohol Abuse, 26 (2), 207–227. Meyer, J. W., &
Rowan, B. (1977). Institutionalized organizations: Formal
structure as myth and ceremony. American Journal of
Sociology, 83, 340–363. Milligan, C. O., Nich, C., & Carroll,
K. M. (2004). Ethnic differences in substance abuse treatment
retention, compliance, and outcome from two clinical trials.
Psychiatric Services, 55, 167–173. Office of Applied Studies.
(2005). Alcohol and drug services study. Retrieved July 31,
2006, from http://oas.samhsa.gov/adss.htm# Methods Office of
Applied Studies. (2006). Prevalence of substance abuse among
racial and ethnic subgroups in the U.S. Washington, DC: U.S.
Department of Health and Human Services, Substance Abuse
and Mental Health Service Administration. Olmstead, T., White,
W. D., & Sindelar, J. (2004). The impact of managed care on
substance abuse treatment services. Health Services Research,
39, 319–343. Orwin, R., & Ellis, B. (2000). The national
treatment improvement evaluation study: Multilevel reanalysis
of treatment outcomes. Fairfax, VA: Center for Substance
Abuse Treatment, National Evaluation Data and Technical
Assistance Center. Orwin, R. G., Ellis, B., Williams, V., &
Maranda, M. (2000). Relationships between treatment
components, client-level factors and positive treatment
outcomes. Journal of Psychopathology and Behavioral
Assessment, 22, 383–397. Pfeffer, J., & Salancik, G. R. (1978).
The external control of organizations: A resource dependence
perspective. New York: Harper &Row. Platt, J. J. (1995).
Vocational rehabilitation of drug abusers. Psychological
Bulletin, 117, 416–433. Ritter, G. A., Carusi, C., Krenzke, T.,
Legum, S. E., Mohadjer, L., Reif, S., et al. (2003). Phase III
methodology: Client follow-up study. In Alcohol and drug
services study (ADSS) methodology report: Phases I, II, and III.
Rockville, MD: Substance Abuse and Mental Health Services

Administration, Office of Applied Studies. Retrieved July 31,
2006, from www.oas.samhsa.gov/ADSS/ADSSmethods.pdf
Roman, P. M., Ducharme, L. J., & Knudsen, H. K. (2006).
Patterns of organization and management in private and public
substance abuse treatment programs. Journal of Substance
Abuse Treatment, 31, 235–243. Ross, H. E., Glaser, F. B., &
Germanson, T. (1988). The prevalence of psychiatric disorders
in patients with alcohol and other drug problems. Archives of
General Psychiatry, 45, 1023–1031. Roudenbush, S. W., &
Bryk, A. S. (2002). Hierarchical linear models: Applications
and data analysis methods. Thousand Oaks, CA: Sage. Roy, A.,
DeJong, J., Lamparski, D., George, T., & Linnoila, M. (1991).
Depression among alcoholics: Relationship to clinical and
cerebrospinal fluid variables. Archives of Psychiatry, 48, 28–
32. Satre, D. D., Mertens, J., Arean, P. A., & Weisner, C.
(2003). Contrasting outcomes of older versus middle-aged and
younger adult chemical dependency patients in a managed care
program. Quarterly Journal of Studies on Alcohol, 64, 520–530.
Sayre, S. L., Schmitz, J. M., Stotts, A. L., Averill, P. M.,
Rhoades, H. M., & Grabowski, J. J. (2002). Determining
predictors of attrition in an outpatient substance abuse program.
American Journal of Drug and Alcohol Abuse, 28, 55–72.
Schildhaus, S., Gerstein, D., Dugoni, B., Brittingham, A., &
Cerbone, F. (2000). Service research outcome study:
Explanation of treatment effectiveness, using individual-level
and programmatic variables. Substance Use and Misuse, 35,
1879–1910. Schlesinger, M., Dorward, R., & Epstein, S. (1996).
Managed care constraints in psychiatrists' hospital practices:
Bargaining power and professional autonomy. American Journal
of Psychiatry, 153, 256–260. Schwartz, B. J., & Wetzler, S.
(1998). A new approach to managed care: The provider-run
organization. Psychiatric Quarterly, 69, 345–354. Sederer, L. I.,
& Bennett, M. J. (1996). Managed mental health care in the
United States: A status report. Administration and Policy in
Mental Health and Mental Health Services Research, 23, 289–
307. Sells, S. B. (1975). The DARP research program and data

system. American Journal of Drug and Alcohol Abuse, 2 (1), 1–
14. Simpson, D. D. (1981). Treatment for drug abuse: Follow-up
outcomes and length of time spent. Archives of General
Psychiatry, 38 (8), 875–880. Simpson,

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