Adherence to Scheduled Sessions in a Randomized Field
Trial of Case Management: The Criminal Justice–Drug
Abuse Treatment Studies Transitional Case
Michael Prendergast & Lisa Greenwell &
Jerome Cartier & JoAnn Sacks & Linda Frisman &
Eleni Rodis & Jennifer R. Havens
Published online: 4 July 2009
# The Author(s) 2009. This article is published with open access at Springerlink.com
Abstract The Transitional Case Management (TCM) study, one of the projects of
the Criminal Justice Drug Abuse Treatment Studies (CJ-DATS) cooperative, was a
multi-site randomized test of whether a strengths-based case management interven-
tion provided during an inmate’s transition from incarceration to the community
increases participation in community substance abuse treatment, enhances access to
needed social services, and improves drug use and crime outcomes. As in many
intervention studies, TCM experienced a relatively large percentage of treatment-
group participants who attended few or no scheduled sessions. The paper discusses
issues with regard to participation in community case management sessions,
examines patterns of session attendance among TCM participants, and analyzes
client and case manager characteristics that are associated with number of sessions
attended and with patterns of attendance. The average number of sessions (out of 12)
attended was 5.7. Few client or case manager characteristics were found to be
J Exp Criminol (2009) 5:273–297
M. Prendergast (*):L. Greenwell:J. Cartier
Integrated Substance Abuse Programs, Semel Institute for Neuroscience and Human Behavior,
University of California Los Angeles, 1640 S. Sepulveda Blvd., Suite 200,
Los Angeles, CA 20025, USA
Center for the Integration of Research & Practice, National Development & Research Institutes, Inc.,
New York, NY, USA
L. Frisman:E. Rodis
Research Division, Connecticut Department of Mental Health & Addiction Services,
Hartford, CT, USA
J. R. Havens
Center on Drug and Alcohol Research, University of Kentucky, Lexington, KY, USA
significantly related to session attendance. Clinical and research implications of the
findings and of adherence in case management generally are discussed.
A growing body of research indicates that prison-based substance-abuse treatment
needs to be followed by community treatment in order to obtain optimal outcomes
(Hall et al. 2004; Knight et al. 1999; Martin et al. 1999; Wexler et al. 1999). As a
result, many states have designed their correctional treatment system to include a
continuum of care: prison-based treatment, followed by community-based treatment.
To link these two phases of treatment, there is usually a planning and transition
process between prison and the community. Depending on the correctional system,
referral to community-based treatment consists of either encouraging inmates with
drug problems to volunteer for treatment or mandating them to treatment as a
condition of parole. Multiple agencies are usually involved in this process. Too
often, however, the transition process breaks down, for both individual and systemic
The promised improvements in offender behavior from continued treatment in the
community can be realized only if the transition plan is clear and if prison treatment
participants follow through on referrals to community treatment and remain in
treatment for a minimum period of time, generally considered to be approximately
3 months (Hubbard et al. 1988; National Institute on Drug Abuse 2006; Simpson et
al. 1997a). Failure of parolees to enter community treatment often results in a greater
likelihood of relapse to drug use or of rearrest and reincarceration. No-shows and
dropouts are particularly problematic in those states where participation in
community treatment by parolees is voluntary, since the incentives to enter and
remain in treatment are minimal. For example, an evaluation of in-prison drug
treatment programs in California indicated that only 34% of program graduates who
had a referral to community-based treatment actually entered treatment following
release to parole, and nearly 45% of those who did enter treatment dropped out
within the first 90 days (Prendergast et al. 2005). Even in jurisdictions where
community treatment is mandated, attendance may be lax, often because of high
parole caseloads, poor coordination between criminal justice and treatment
personnel, or low motivation and accountability on the part of the parolee. For
whatever reasons, low participation by parolees in community treatment is clearly a
barrier to the overall effectiveness and cost effectiveness of offender treatment
Improving the re-entry process from prison treatment to parole treatment was the
goal of the Transitional Case Management (TCM) study, which was conducted as
part of the Criminal Justice–Drug Abuse Treatment Studies (CJ-DATS) research
cooperative funded by the National Institute on Drug Abuse (NIDA). TCM utilized a
strengths model of case management (Hall et al. 1999; Siegal and Rapp 1996) to
attempt to improve the re-entry process at two levels: (1) by improving the
collaboration among correctional and treatment staff, community parole and
treatment staff, and other health, mental health, and social service providers, and
274M. Prendergast et al.
(2) by working directly with the client to develop specific goals and plans for
transition to the community and by assisting him/her during the crucial early months
in the community to obtain needed services. The rationale and description of the
TCM intervention are described in detail elsewhere (Prendergast and Cartier 2008).
As with most, if not all, field studies, TCM faced the problem of varying levels of
client participation in the intervention. Poor adherence is an issue in any treatment
that is intended to change behavior or is directed toward the management of a
chronic problem (McLellan et al. 2000). For example, clients do not take their
medications as directed, or they do not attend all of the expected treatment sessions,
even though there might be relatively few sessions to attend. Much research is
directed toward increasing adherence rates in psychosocial interventions, including
those for offenders (Siegal et al. 1993; Sung et al. 2004), either because optimal
adherence is necessary for the treatment to have the intended effect or because
greater adherence is associated with better outcomes. For case management
interventions specifically, regular interaction between the client and the case
manager is necessary if the client is to address his/her problems or achieve desired
goals. Clients who do not meet with their case manager, or do so irregularly, are less
likely to take the steps needed to alleviate their problems or to accomplish their
goals. From a research perspective, low participation raises issues of internal validity
of the study findings. This paper begins with a review of adherence within field
interventions generally, and case management interventions specifically, then
summarizes the study design and its implementation, describes the methods for
analyzing the data, presents results from the analyses, and discusses the results in
terms of their implications for research and practice.
1 Session adherence in behavioral treatment interventions
Many intervention protocols for substance abusers, including offenders, include
meetings with a counselor, case manager, or other service provider, and study
participants are expected to attend all, or at least some, of these meetings. In
addition, journals that use the Consolidated Standards of Reporting Trials
(CONSORT) guidelines (Moher et al. 2001) require researchers to identify the
number of subjects who did and did not complete the intervention, which involves a
definition of completion for a given study. At the most basic level, clients cannot
benefit from an intervention if they do not participate in its activities, and increasing
levels of participation are likely to be related to better outcomes. Many studies are
also concerned with a minimum ‘dosage’ needed to see a treatment effect. For all of
these reasons, with the understanding that participation in both the intervention and
the research activities is voluntary, researchers seek to keep clients in an intervention
as long as possible, track clients’ participation in intervention activities, and address
the issue of non-participation in analysis.
Of relevance to participation in an intervention such as case management is the
issue of dose. While ‘dose’ in a behavioral intervention cannot be specified with the
same precision as with medications, it is nonetheless important to have some
estimate of the appropriate dose, since too small a dose may not result in the desired
outcomes, while too large a dose may be an inefficient use of resources, or may even
Adherence in a randomized field trial275
be harmful. For case management specifically, the question is how many sessions or
contacts are needed to produce desired outcomes, given the characteristics and needs
of the population and the resources available? This is not to say that dose is the only
factor influencing outcomes; other important factors include the specific model of
case management used, case manager characteristics, client characteristics, the
nature of the alliance that develops between the client and the case manager, and the
density and quality of the local service ecology.
Findings from studies that report on session attendance indicate that the level of
attendance varies considerably across interventions for substance-abusing clients.
The problem is not a new one. Over 30 years ago, in a detailed review of treatment
participation in substance abuse and mental health interventions, Baekeland and
Lundwall (1975) reported that between 52% and 75% of clients in outpatient
treatment for alcoholism dropped out before attending four sessions. Nor was the
problem confined to substance abuse clients; 31% to 56% of patients who were
admitted to general psychiatric clinics attended fewer than four times. More recent
studies summarized below continue to show low attendance rates. These studies
include comparative studies of case management and other behavioral interventions
and reports of attendance based on program records.
With respect to formal evaluations, Lidz et al. (1992) reported that, of 287 clients
who agreed to participate in a four-session case management intervention to reduce
human immunodeficiency virus (HIV) risk behaviors, 36% had one session with the
case manager; of these, 38% completed all four sessions (14% of the total). In a
case management component of the South Florida AIDS Network provided to
clients over several months, 80% of clients attended only one session (McCoy et al.
1992). Cox et al. (1998) reported that 28% of long-term public inebriates in an
intensive case management intervention had no face-to-face contacts with the case
managers, either because they refused services or because they could not be located
for services. In comparing dosage of strengths case management across three
conditions (delivered within drug treatment programs, delivered by an outside
service agency, and delivered by telephone), Huber et al. (2003) reported that,
within the first 90 days of the intervention, the mean number of hours of contact
ranged from 3.0 to 3.2 and the mean number of sessions attended ranged from 3.5
to 5.7. Jansson et al. (2005) compared routine case management for drug-dependent
women and their newborns with intensive case management that involved
additional and more frequent contact with the mother and infant. Over the
4-month intervention, women in routine case management attended a mean of 1.6
sessions (range=0 to 4), whereas those in intensified case management attended a
mean of 5.8 visits (range=0 to 9). A quasi-experimental study of case management
for substance-abusing parolees in the San Francisco Bay area found that, over an
intervention period of 180 days, 40% of the parolees reported no more than one in-
person or telephone contact with their case manager, and 70% reported no more
than three contacts (Longshore et al. 2005). In a study testing the use of case
management to re-enroll discharged methadone clients, Coviello et al. (2006)
reported 4.4 contacts between the case managers and clients over a 6-week
intervention. A study of enhanced treatment for clients with co-occurring disorders
found that the mean [standard deviation (SD)] number of sessions attended out of
12 scheduled was 1.67 (0.95) (Sacks et al. 2008).
276 M. Prendergast et al.
Session attendance has also been examined from records of community agencies
or programs. Ventura et al. (1998), in examining attendance at case management
sessions among mentally ill persons released from jail, found that, in the first year
following release, 27% of subjects received some case management services,
typically fewer than 20 hours over the year. Using program data on admissions to an
outpatient substance abuse clinic in Chicago, King and Canada (2004) reported that
36% of clients attended fewer than five individual therapy sessions. Records data
from a multi-site treatment program in Auckland, New Zealand, indicated that 73%
of admissions attended four or fewer sessions and that 40% attended only a single
session (Pulford and Wheeler 2007). A naturalistic evaluation of three cognitive-
behavioral programs for parolees in England and Wales found that 46% of clients
attended no sessions, 25% completed some of the sessions, and 29% completed all
expected sessions (between 20 and 38, depending on the program) (McGuire et al.
Two observations can be made about these findings. First, the degree of
attendance at sessions is difficult to compare across studies, because of the different
ways in which attendance is defined and measured. Second, although there are
exceptions, clients participate in considerably fewer sessions than the number
planned by the researcher; the average number of sessions attended is five or fewer,
and a relatively large percentage of clients attend no sessions.
Arguing for a conceptualization of ‘dose’ that extends beyond session attendance,
Huber et al. (2001) present a model of dosage for case management (and, by
extension, for other interventions) that consists of four dimensions. Amount refers to
the quantity of the activity provided in a single episode (e.g., number of minutes for
a session between the client and the case manager). Frequency refers to the rate of
occurrence of the activity or the amount of time between activities (e.g., weekly in-
office sessions, monthly telephone calls). Duration refers to the length of time over
which the intervention is provided (e.g., 3 months of case management). Breadth
refers to the type and number of activities or components that are provided in the
intervention (e.g., assessment, counseling, service referrals, or telephone calls that
take place within an episode or over the course of the intervention). Within this
model, dose can be regarded either as normative, that is, the expected amount,
frequency, duration, and breadth (e.g., as specified in the intervention manual), or as
descriptive, that is, the actual amount, frequency, duration, and breadth of activities
actually received (e.g., as determined by service logs). In the evaluation of an
intervention, the actual dose received, averaged over study participants, is of
particular interest to determine the integrity or fidelity of intervention implementa-
tion, to examine the relationship of dose to study outcomes, and to estimate optimal
dosage levels for desired outcomes. In this paper we consider the amount, frequency,
and duration of case management sessions for clients in the TCM group (for an
analysis of the breadth of case management services received in TCM, see Coen et
al., manuscript submitted).
After the degree of compliance with treatment or case management sessions has
been considered, it is also useful to determine which client and program factors may
predict retention, in order to improve adherence to treatment protocols. Although
many studies have reported on a wide variety of client and program variables, a
number of researchers have noted that, within the literature on predictors of
Adherence in a randomized field trial277
retention, there is considerable inconsistency in the selection of variables and in the
variables found to be significant (Hawkins et al. 2008; Stotts et al. 2007). The
direction of effect also varies from one study to another for some variables. For
example, at the client level, age is commonly found to predict retention, but longer
retention is associated in some studies with younger clients (Justus et al. 2006), in
others with older clients (Brecht et al. 2005; Mateyoke-Scrivner et al. 2004).
Similarly, while women are generally found to stay in treatment longer (Brecht et al.
2005; Hser et al. 2001), in other studies, men stay longer (McCaul et al. 2001).
Other client characteristics associated with retention include race/ethnicity (Brecht et
al. 2005; Hiller et al. 1998; Mateyoke-Scrivner et al. 2004), marital status (Hiller et
al. 1998; Mateyoke-Scrivner et al. 2004); education (Hser et al. 2001; Mattson et al.
1998), and employment (Lang and Belenko 2000; Mattson et al. 1998; Siqueland et
al. 2002). Findings on motivation as a predictor of retention are inconsistent
(Brocato and Wagner 2008; De Weert-Van Oene et al. 2001), but legal pressure
seems to result in longer stay in treatment (Brecht et al. 2005; Hiller et al. 1998; Hser
et al. 2001).
Fewer studies have examined program-level predictors of retention. In an analysis
of the effects of program factors on retention in drug treatment programs (residential,
outpatient, methadone), Hser et al. (2001) found that better retention (in at least one
of the modalities) was associated with programmatic focus, case load, an emphasis
on group therapy, and percentage of staff in recovery. Researchers in Project
MATCH (matching alcoholism treatments to client heterogeneity) reported that
clients who were promptly admitted to treatment (following study recruitment)
attended a greater proportion of sessions (Mattson et al. 1998). De Weert-Van Oene
et al. (2001) found that helping alliance was a strong predictor of treatment retention;
one component of helping alliance (cooperation with the therapist) was positively
associated with retention, while helpfulness of the therapist was negatively
associated with retention, possibly because clients with greater confidence in their
ability to deal with their drug problems left treatment early. Counselor or therapist
characteristics have also been found to be associated with treatment retention (Hser
et al. 2001; Sterling et al. 2002).
2 Research questions
This paper provides an examination of the pattern of attendance by TCM clients at
scheduled community case management sessions, with particular attention to the
following questions: How many scheduled sessions did clients attend? How many
hours of contact were there between the client and the case manager? Which client
and/or case manager characteristics were associated with attendance? What was the
pattern of attendance over the course of the intervention?
3 Study design and procedures
The TCM study was a multi-site randomized field trial of strengths-based case
management for use with drug-abusing parolees who were completing prison-based
278M. Prendergast et al.
treatment and transitioning to parole supervision in the community. The study
conditions were the TCM intervention (described below) or standard referral.
Participants in both groups received standard planning and referral services that were
available to parolees within their respective state correctional systems, and
participants in both groups received a referral to community-based treatment that
was at least partly funded by local or state dollars. In addition, during the month
prior to release from prison, participants in both groups viewed a professionally
produced motivational video that emphasized the importance of community
treatment for offenders. Four CJ-DATS research centers were involved, each of
which collaborated with criminal justice and treatment partners in four states.
3.1 Description of TCM intervention
As adapted for a parolee population, the TCM intervention included three phases,
the first two of which occurred in prison and the third in the community (Prendergast
and Cartier 2008):
Strengths assessment. At the first session, conducted approximately 2 months
prior to release, the case manager met with the client to complete a strengths
assessment that identified strengths, accomplishments, resources, and goals and
to develop plans for addressing immediate needs upon release, including
enrollment in treatment.
Conference call. Approximately 1 month prior to release, a conference call took
place that included the client and the client’s treatment, supervision, and family
support network. The conference call was intended to provide an opportunity for
the client and the other people involved in the transition from prison to
community to discuss the discharge plan, to provide encouragement to attend
community treatment, and to identify sources of community support.
Community sessions. In the community, the case manager met with clients
weekly for 3 months, followed by 3-monthly follow-up contacts for any clients
needing additional help. The focus of the weekly sessions was to encourage
clients to enter and remain in substance abuse treatment and to obtain other
services and assistance to address their goals and needs. The analyses presented
below focus on attendance at the 12 weekly community sessions.
3.2 Case managers: characteristics and training
Across the sites, 12 case managers delivered the intervention over the course of the
study. Of these, seven were women and five were men. The average age was
39.6 years (range 31–55 years). By ethnicity, six were African American and six
were White. Seven had a bachelor’s degree and five a master’s degree, with three
also having counseling certification. Seven had some experience as case managers
before becoming involved in the TCM study. All sites except one experienced
turnover in the case manager position during the study.
To facilitate fidelity to TCM, case managers received training and supervision
during the study. A comprehensive three-and-a-half day training was conducted prior
to study initiation by Dr. James Hall, whose Iowa Case Management Project model
Adherence in a randomized field trial279
of strengths case management was modified for this study. The training included
both clinical and research topics, including review of the TCM study and use of
study instruments; an introduction to strengths case management; details on
conducting the intervention; and clinical issues in client–case manager interaction.
Half way through implementation, Dr. Hall conducted 3-hour refresher training for
the case managers via a conference call. To train new case managers who were hired
later in the study, the sites used printed materials and a video of the initial training.
Once the study was underway, conference calls that focused on case managers’
questions and issues were conducted bi-weekly initially and then monthly.
Additionally, site-specific supervision of case managers was conducted regularly.
The caseload for each case manager was expected to be 15, although it varied over
time, being smaller when the study was beginning and winding down, and reaching
20 at other times.
Subjects for the study were recruited from prison-based treatment programs in four
states. The number of prisons for recruitment across states varied from 3 to 15.
Inmates (men and women) were eligible to participate in the study if they were aged
18 years or older, were enrolled in a treatment program within a correctional
institution, had a referral to a community-based drug abuse treatment program
following release to parole, were within approximately 3 months of release, and
were being released to city or county where TCM case managers were located.1
Even if inmates met these criteria, they were excluded from participation if they had
a referral to formal case management services not part of the TCM study (e.g.,
mental health services), were a registered sex offender, had parole requirements that
would prevent study participation (e.g., Immigration and Customs Enforcement hold
for deportation), or were unable to provide informed consent. In each of the
participating states participation in community treatment was mandated, although the
degree to which the mandate was enforced varied from state to state.
Trained research staff from the participating research centers contacted the
inmates individually, met with them in a private room, confirmed eligibility,
described the study and informed consent procedures, and answered any questions.
The baseline interview occurred at the time of consent or soon after, usually within
1 week. After completing the baseline interview, clients were randomly allocated to
the TCM group or to the standard referral group. Study procedures and informed
consent forms received approval from the CJ-DATS Steering Committee and from
the Institutional Review Board at each of the participating research centers. A data
and safety monitoring board conducted a quarterly review of study implementation
and adverse events. In accordance with human subject requirements, participation in
research interviews and attendance at TCM sessions was voluntary.
The four research centers recruited 812 clients, with each center contributing
approximately 200 clients. By design, 25% of the sample consisted of women.
1In one of the states a small number of the study participants were under probation supervision upon
release from the institution. For convenience, the criminal justice status of the sample is referred to as
‘parolee’ throughout the paper.
280M. Prendergast et al.
Initially, 411 study participants were assigned to the TCM group and 401 to the
standard referral group. One client assigned to the standard referral group mistakenly
ended up in the TCM group. At the time of recruitment, all clients assigned to the
TCM group were scheduled for parole within approximately 3 months to a county
where they could participate in the TCM study, but subsequently 25 of them either
had their release date changed such that they would not be paroled in time to receive
TCM services or had their parole location changed to a county or state where TCM
services were not provided. These clients, who did not have the opportunity to
participate in case management sessions in the community, were excluded from
analyses, leaving a baseline sample of 386 in the TCM condition (some analyses
include a somewhat smaller sample due to missing data).
To document session attendance, case managers completed forms for each scheduled
session with a client, whether it happened or not, consisting of closed-ended
responses to questions about which of the various activities of the case management
protocol were completed for each session. For purposes of this paper, the forms
provided information on which sessions were attended and the duration of each
session. These variables are presented as descriptive statistics.
The next stage of analysis consisted of attempting to identify possible predictors
of attendance at case management sessions. Preliminary analysis indicated that there
was substantial variation among case managers in the average number of sessions
that their clients completed. Therefore, to adjust for a ‘case manager effect,’ which
would lead to correlated unobserved factors common to all the clients of a particular
case manager, we used mixed Poisson regression models to analyze predictors of the
number of sessions attended. The Poisson distribution is appropriate for count
variables (Greene 1993). In this analysis, clients were the level-1 units and case
managers the level-2 units.
In the analysis the intercept was modeled as a random effect. Thus, case manager
intercepts were allowed to vary randomly around the overall intercept. Predictors
(discussed below) were modeled as fixed effects. The estimation method was
residual pseudo-likelihood, with a Taylor series expansion that was specific to
subject (i.e., to case manager). The estimator chosen for the covariance matrix of the
fixed-effects parameters was asymptotically consistent and corrected for bias; all test
statistics and standard errors were adjusted accordingly. The relatively stable
Cholesky root algorithm was chosen for creating and solving the mixed model
equations. An unstructured variance–covariance matrix using the Cholesky root was
specified (SAS Institute 2006).
Predictors consisted of client characteristics from the baseline interview and case
manager characteristics coded by project staff. Client characteristics included
sociodemographic measures (age, ethnicity, gender, marital status), measures of
socioeconomic position (number of years of schooling completed and recent
employment status), number of lifetime arrests, the Texas Christian University
(TCU) drug screen (dichotomized at the clinical cut-off point of 3 or above) to
indicate severity of substance use (Simpson et al. 1997b), and the desire for help
subscale of the TCU client evaluation of self at intake (Simpson and Joe 1993),
Adherence in a randomized field trial281
dichotomized at the median, as a measure of treatment motivation. Case manager
characteristics included gender, ethnicity, age, and whether the case manager held a
credential or other certification specific to substance abuse counseling. Continuous
predictors were centered at the mean number of sessions attended by the clients of
each case manager. This centering made these into level-2 (case manager level)
variables, whose effects were interpreted as effects on within-case manager variation
in the number of sessions (Singer 1998). Sample size for the mixed Poisson
regression was 370. Of the 382 clients available for analysis, eight had missing case
manager information, and an additional four had missing values on other analysis
variables, leaving 370 cases for the mixed Poison regression analysis. Analyses were
conducted using SAS/STAT® version 9.1.3 PROC GLIMMIX (SAS Institute 2006).
Finally, to identify patterns of participation in case management sessions, we
analyzed the attendance data using Nagin’s semiparametric group-based method for
defining trajectories of behavior over time (Nagin 1999; Nagin and Land 1993). The
12 dependent variables in the model were binary indicators of whether or not a TCM
client attended a given session, in sequence. The data were analyzed using a logistic
distribution that allowed for quadratic parameters to form nonlinear curves. The
Bayes Information Criterion (BIC) statistic and substantive criteria were used to
select the number of trajectories for the final model. Once the trajectories had been
identified and described, we compared client and case manager characteristics across
trajectories using chi-square for categorical variables and analysis of variance
(ANOVA) for continuous variables.
Table 1 lists the characteristics of clients in the TCM condition. The reference point
for variables such as employment and education was the time before the arrest that
resulted in the current incarceration. (Clients in the control condition did not differ
significantly from those in the treatment condition in baseline characteristics, except
for race/ethnicity, where TCM clients were more likely than control clients to be
White.) The client profile was typical of that found in research samples of substance-
abusing parolees, except that, by design, approximately 25% were women.
The results for session attendance, overall and for each site, are shown in Table 2.
Overall, nearly 97% of the clients completed the strengths assessment. In the large
majority of cases, the strengths assessment occurred in prison shortly after the
participant’s recruitment into the study, but, in a few cases, where the client was
released from prison earlier than expected, the case manager completed the strengths
assessment with the client at the initial community session.
Across all sites, approximately 72% of the clients participated in the conference
call. Scheduling the calls often proved difficult. In some cases, inmates were paroled
before the call could take place. In other cases, institutional staff or parole officers
declined to participate in the conference call. Equipment malfunction prevented
some calls from occurring.
The intervention protocol called for 12 weekly sessions between the case manager
and the client, although additional sessions could occur if needed. When it became
clear that attendance at the weekly sessions was a problem, we set four sessions as
282M. Prendergast et al.
the minimum desired level of participation. Four community sessions was selected
after discussion with experts in case management and after a review of studies of
brief interventions, which indicated that four sessions could be expected to have a
positive effect on outcomes. Overall, 13% of the clients attended no sessions, 18%
attended between one and three sessions, and 69% attended four or more community
sessions. The average number of community sessions attended was 5.7 (SD=3.).
Table 3 presents the percentage of clients who attended each of the 12 community
sessions. Overall, 70% attended the first session, with the percentage steadily
declining to just under one-half by the 12th session. The pattern of session
attendance was similar across the individual sites.
Session attendance as defined above means only that the client and case manager
met, either in person or by telephone. Within the ‘dosage’ model of Huber et al.
(2001), the amount or quantity of case management is also important. Calculations
VariableTCM Group (N=382)
Legally or living as married
Education (highest grade completed)
Employment (worked in past 6 months)
Times in jail or prison (lifetime)
Months incarcerated (lifetime)
Drug-related arrests (lifetime)
Any arrests (lifetime)
Other (excluding tobacco)
Desire for help
Table 1 Baseline characteristics
of clients in the TCM group
[percentage or mean (SD)]
Adherence in a randomized field trial283
from the case manager forms indicated that the average session lasted 35 minutes
and that, across all sessions, the average amount of time spent between the client and
the case manager was 181 minutes; if clients who attended no sessions were
removed from the calculation, the average increased to 184 minutes. The TCM
protocol did not specify a minimum amount of contact time per session.
Half way into the study, case managers were asked to indicate reasons why clients
attended fewer than four sessions. Since case managers responded only for clients
who had attended fewer than four sessions and some case managers had left the
project and could not complete the forms, data were available on 91 clients in the
TCM group. Their responses, accumulated through to the end of the study, are
shown in Table 4. The most common reason for limited attendance (46%) was that
the case manager was unable to contact the client to schedule the first session or to
follow-up on missed sessions. Although contact information was obtained at the
strength assessment session, upon release parolees may have inadvertently or
deliberately failed to inform the case manager about a change in telephone number
Table 2 Attendance at scheduled TCM sessions (in percent) (excludes 25 inmates originally assigned to
the TCM group but who were not released in time to participate in community case manager sessions or
who were paroled to a county or state where TCM services were lot available)
Table 3 Percentage of TCM clients attending each community session
284M. Prendergast et al.
or address. Parolees who absconded would be reluctant to maintain contact with the
case manager, despite assurances that the case manager would not provide any
information to criminal justice staff. The second most common reason for attending
fewer than four sessions (24%) was incarceration. One in six of the clients (16%)
indicated that they no longer wanted to participate in case management services,
either because they had no further need of such services or because they had no time
Mixed Poisson regression analysis was used to determine which client or case
manager characteristics predicted the number of sessions attended (Table 5). Case
manager characteristics were more important than client characteristics in predicting
the session attendance. African American case managers had clients who attended
more sessions than case managers who were White. Clients of case managers who
were older attended more sessions than clients whose case managers who were
younger. At the client level, clients who were older attended significantly more
sessions than did younger clients.
The final question examined had to do with identifying patterns (trajectories) of
session attendance over time and determining whether any client or case manager
characteristics were associated with the identified trajectories. Nagin’s semipara-
metric group-based method for defining behavior over time (Nagin 1999; Nagin and
Land 1993) was used to identify trajectories of session attendance (see Addendum
for statistical model). In the selection of the number of trajectory groups, lower BIC
values generally indicate a better model fit. For the attendance trajectories, although
the three-group model did not fit quite as well as the four-group model (BIC=−2,448
vs. −2,434), the four-group model produced a group with only ten people,
suggesting that it reflected idiosyncrasies in the data. The three-group model is
shown in Fig. 1. Clients in the low attendance group, comprising one-fourth (24.8%)
of the sample, exhibited low levels of attendance from the beginning and, by the
fourth session, were unlikely to attend any sessions, for a mean of 0.64 (SD=0.74)
Table 4 Reasons (from case managers) for TCM clients attending fewer than four community sessions
ReasonsNumber (n=91) Percentage
No need for services
No time to participate
Client known to have relapsed, no further participation
Client had prolonged hospitalization
Client had died
Case manager unable to contact
Moved, no forwarding address
Adherence in a randomized field trial285
Table 5 Mixed Poisson regression of number of sessions on predictors (n=370) (intercept modeled as a
Fixed Effects and Covariance Parameter EstimateEstimate Standard Error
Male (vs. female)
White (vs. all other)
Highest grade completeda
Worked in past 6 months (yes/no)
Number of lifetime arrestsa(‘Winsorized’ at 100)
Desire for help (high vs. low)
Case manager is male (vs. female)
Case manager is Black/African American (vs. White)
Case manager age
Case manager has credential (yes/no)
Covariance parameter estimate (for the intercept)
aCentered around case manager mean
*P<0.05; **P<0.01; ***P<0.001
Group 1 = Low Attendance Group 2 = Moderate Attendance Group 3 = High Attendance
Fig. 1 Results of trajectory analysis of number of community sessions attended. Group 1 low attendance;
group 2 moderate attendance; group 3 high attendance
286M. Prendergast et al.
sessions. Clients in the moderate attendance group (43.1%) were highly likely to
attend the first session, but then steadily declined in their attendance at subsequent
sessions, with a slight increase for the last two sessions, for a mean of 5.34 (SD=
1.54) sessions. The high attendance group (32.1%) consisted of clients who attended
nearly all the sessions, for a mean of 10.01 (SD=1.33) sessions.
Table 6 presents the results of the analysis to examine any differences across
trajectory groups in terms of client and case manager characteristics. Few
characteristics emerged as being significant. Among client characteristics, only age
was significant, with older clients being more likely to be in the high attendance
group. Also, only one of the case manager characteristics was significant. Contrary
to what might be expected, clients whose case managers had credentials were less
likely to be in the high attendance group.
Clients in the TCM group attended fewer than half of the 12 scheduled case
management sessions, an average of 5.7 sessions. Approximately 13% attended no
session, and another 18% attended one to three sessions. Still, the 69% who attended
four or more sessions was higher than the level of attendance reported in many of the
behavioral interventions reviewed above, although strict comparison across studies
and programs is not possible. TCM clients received, on average, approximately
3 hours of case management across all sessions. Using a similar strengths case
management model with substance abuse clients, Huber et al. (2001) also found that,
over the first 90 days of the intervention, the average contact time was 3 hours. Most
clients attended the first community session, but then participation gradually
declined, to fewer than one-half attending the 12th session.
Given the population being served by TCM, the reasons for limited attendance
should not be surprising. Nearly one-quarter (24%) of the TCM clients were
reincarcerated at some time during the intervention. For clients who attended fewer
than four sessions, in 46% of the cases the case manager had no working telephone
number or address to contact the client. Case management services were ended by 16%
of clients, who said that they had no need of further services or that they did not have
time to participate. Since attendance at case management sessions was voluntary, the
case manager had little leverage with those clients who had no need or desire to attend.
In a regression analysis to determine which client and case manager character-
istics were associated with session attendance, characteristics of the case managers
were somewhat more important than client characteristics. Clients who attended
more sessions were more likely to have case managers who were older or who were
African American than clients who attended fewer sessions. It might be expected
that the case manager characteristics had an effect on attendance, as has been found
in other studies (Hser et al. 2001; Sterling et al. 2002), but, for the purpose of
guiding hiring decisions in the future, the demographic and experience variables that
we included in the analysis might not be all that useful. More clinically relevant
variables were not available for analysis. The only client-level characteristic that
predicted session attendance was age, with older clients attending more sessions than
Adherence in a randomized field trial 287
Table 6 Client and case manager characteristics by trajectory group [percentage or mean and (SD)]
Variable Group 1
Number of community sessions completed**
Legally or living as married
Education (highest grade completed)
Employment (worked in past 6 months)
Times in jail or prison (lifetime)
Months incarcerated (lifetime)
Drug-related arrests (lifetime)
Number of arrests (lifetime; ‘Winsorized’ at 100)
Other (excluding tobacco)
Desire for help
Case Manager Characteristicsa
aBased on 12 case managers who had more than one client and complete data. These 12 case managers
had a total of 374 clients (90 in group 1, 160 in group 2, and 124 in group 3)
288M. Prendergast et al.
The pattern of attendance across time (trajectories) is probably what would be
expected. One group of clients consistently met with the case manager at all or
nearly all of the sessions. Another group steadily declined in attendance. The third
group attended few or no sessions. In examining client and case manager
characteristics associated with these three trajectory groups, we found that, again,
only age was a significant client characteristic. Unexpectedly, a lower percentage of
clients whose case managers had credentials were in the high attendance group than
in the other two trajectory groups. The possession of credentials might have been
confounded with some other variable(s) that we did not measure. For both analyses,
it was surprising that, besides age, client-level characteristics that were identified in
previous studies were not associated with attendance based on this sample. However,
as noted earlier, there is considerable inconsistency in whether certain client
characteristics are found to be significant or not and even in the direction of the effect.
5.1 Clinical implications
Clients do not participate in case management in a straightforward manner. They
miss sessions, they reschedule sessions, they ask for more sessions. This suggests
that there is a need for flexibility both in how ‘adherence’ is measured in case
management studies and in how case management interventions are designed and
implemented. We may be too rigid in our expectations of how clients would actually
utilize case management services, especially those in the criminal justice system.
Parolees face numerous challengeswhen they enter thecommunity in order to avoid
being returned to prison. Depending on the jurisdiction, level of supervision, and
personal circumstances, these may include meeting regularly with the parole officer,
finding housing, securing employment, paying restitution, completing court-mandated
anger management or domestic violence classes, attending substance abuse treatment,
re-establishing family relationships, avoiding criminal associates, engaging in
prosocial leisure or recreational activities, attending to medical problems, and
maintainingprescribedpsychiatricmedications.Ifattendingcase management sessions
is not required by parole conditions, the incentives for meeting with the case manager
are low, particularly for parolees who are still involved in a criminal lifestyle.
Given the minimal pressures on parolees to participate in case management, and
the competing demands on parolees’ time, the case manager or the agency providing
case management needs to consider steps to increase attendance at case management
sessions. Lefforge et al. (2007) reviewed research-based interventions for improving
session attendance in mental health and substance abuse settings, with an emphasis
on attendance at the initial session. The methods included scheduling appointments
as soon as possible after initial contact with the client, reminding clients of
appointments through letters and telephone calls, establishing commitment contracts
with clients, providing clients with incentives for attendance, and addressing
obstacles faced by clients in attending sessions. Obstacles to attendance might
include the location of the case manager’s office, the convenience of office hours for
clients, the availability of the case manager by telephone or email, and the ability
and willingness of the case manager to meet with clients outside of the office.
When implementation monitoring indicated that attendance at community case
management sessions was a problem, the research centers participating in the TCM
Adherence in a randomized field trial 289
study identified specific activities that case managers could take to improve
attendance and developed procedures to document activities. Case managers were
expected to undertake and document the following activities to re-engage clients
who had missed two consecutive sessions: write letters; make telephone calls during
work hours and during evenings and weekends; attempt to locate clients in the
community (home, parole office, etc.); and conduct database searches for contact
information. The case manager documented each activity for each client and
reported aggregate results monthly to the lead center of the TCM study. How
effective were these efforts to re-engage clients? When this heightened activity
began (in June 2006), 67% of TCM clients had attended four or more sessions. At
the end of the intervention (December 2007), the percentage was 69%, with the
percentage for the intervening months never exceeding 70%. It appears that the
activities undertaken to improve session attendance did little to increase attendance
above what it was at the beginning of these efforts, although they may have
prevented a decline in attendance over time. It should be recalled that participation in
case management was voluntary and that 24% of the clients were reincarcerated
before the end of the 12-week intervention.
Could additionalefforts,beyond what thecasemanagers did, haveproduced an ever
greater increase in attendance? The main issue becomes whether efforts to increase
attendance, such as those conducted within the context of a research study, could
their parolees. For TCM, we developed what we believed was a reasonable balance
between the time spent maximizing attendance and the time spent addressing client
needs. In research terms, we believed that the study should be carried out with as much
attention to generalizability as to internal validity. In clinical terms, practitioners would
likely adopt another intervention that could be implemented with more reasonable cost
and effort. An alternative strategy, based on the assumption that most clients would
attend only a few sessions, would be to concentrate case management services in the
early sessions, possibly increasing the length and/or frequency of sessions during the
first few weeks, with later sessions tapering to a less intense schedule.
5.2 Research implications
The CONSORT guidelines (Moher et al. 2001) are largely based on the medical
model in which a minimal dose of medication or treatment is expected, and the
researcher is asked to indicate the percentage of clients who complied with the
treatment protocol. Does case management fit this model? Too much appears to be
happening in case management (at least in TCM) to set rigid and arbitrary
definitions/criteria of what constitutes ‘adherence.’ While some minimal level of
service may be necessary, it greatly simplifies what happens in case management. A
full analysis needs to take into account both scheduled and unscheduled sessions; in-
office, out of office, and telephone contacts with the client; contacts with persons
involved in the client’s goals plan; the pattern of utilization of case management
services (e.g., full vs. sporadic engagement, early vs. late engagement); and the
degree to which clients ‘need’ case management services beyond a certain point. It is
doubtful that a single definition (session attendance cut point) could capture the
complexity of a case management study in real world settings.
290M. Prendergast et al.
To monitor the implementation of a field study and assess the therapeutic integrity
of the intervention, the researcher establishes a level of participation that defines
‘adherence.’ As the study proceeds, monitoring bodies will expect the researcher to
meet the adherence goals and urge or require efforts to increase adherence if the
goals are not being met. In the extreme case, where adherence continues to fall far
short of the goals, the study might be stopped. In addition, in terms of the internal
validity of the study, the adherence goals determine whether the intervention has
integrity with respect to client participation. An assumption about an adherence goal
is that clients who do not attend the minimum number of sessions will not benefit
from the intervention. In other words, following the medical model, a minimum
dosage of treatment is assumed to be needed in order for the benefits of the
intervention to be realized. With respect to interventions for offenders, in particular a
case management intervention, this assumption might lead to the conclusion that
clients who do not meet adherence goals are ‘treatment failures.’ An alternative
assumption is that some clients attend the intervention until their needs are met and
then stop attending or attend more sporadically. On this assumption, those who
attend fewer than the defined minimum number of sessions may be considered
‘treatment successes.’ In explaining the decline in attendance in their case
management intervention (also called transitional case management) for high-risk
clients, Lidz et al. (1992: 129) write:
Many clients came to TCM for help of a specific kind, such as a letter of
referral to an agency distributing free food, placement in a shelter or
detoxification program, or an appointment at a medical clinic. . . . If their
specific needs were addressed in one or two sessions, they often did not return.
In many cases, dropping out reflected a judgment, even if unwise, that urgent
needs had been met and TCM was no longer necessary. For other clients, the
records indicate the case manager’s agreement that reasonable goals set in the
initial needs assessment had been achieved after two or three sessions. In these
cases, early termination was made or the case manager informally agreed that
the client had completed TCM unless an unexpected need arose.
From this perspective, specifying a particular ‘dose’ of case management in terms
of a minimum number of sessions fails to capture the complex nature of case
management by applying a medical model where a needs-based model may be more
relevant. Indeed, the philosophy of strengths case management is opposite to that of
the medical or disease model of substance abuse disorders, with its emphasis on
pathology, illness, and clinician focus. The application of a needs-based model
requires careful assessment of needs upon the client’s entrance to case management
and a system for documenting the type, frequency, and outcome of services for
identified needs. Further research would be useful in clarifying the appropriate
criterion for establishing an appropriate dose for case management for a particular
population of clients.
The fact that 25 offenders recruited into the TCM group were not released to
parole and never had the opportunity to participate in the community case
management sessions and that, of those who were released to parole, 13% attended
no sessions and 18% attended between one and three sessions, presents problems for
an intent-to-treat (ITT) analysis. In the analyses reported here, because our focus was
Adherence in a randomized field trial 291
oncommunitycasemanagementsessions, weexcludedthose whowereneverreleased
from prison, but we included all others. ITT analysis is a conservative approach to
estimating causal effects in that it provides an unbiased estimate of the effect of
assignment rather than treatment. However, even well-designed studies often suffer
from treatment dilution (i.e., clients assigned to treatment do not receive treatment) or
treatment migration (i.e., clients in the control group receive the treatment) (Gartin
1995). Under these conditions, an ITTapproach does not answer the clinical question
about treatment: What outcomes can one expect from clients who actually received
at least some amount of treatment? The ITT analysis will provide an unbiased
estimate of the effect of assignment, but it will generally be an underestimate of the
effect of receiving treatment, since treatment effects (as opposed to assignment
effects) are diluted by non-attendance among clients in the treatment group and
crossovers among clients in the control group. The issue of non-compliance is often
addressed by comparing treatment compliers with the full comparison group, but this
provides a biased estimate of the treatment effect because of selection bias.
Techniques to estimate unbiased treatment effects in the face of noncompliance is
beyond the scope of this paper, but, to address this problem, intervention researchers
should consider supplementary approaches to ITT analyses, such as the use of
instrumental variables (Angrist 2006), principal stratification (Barnard et al. 2003),
and complier average causal effects (CACE) (Little and Yau 1998).
In summary, field studies of correctional treatment interventions usually
experience lower rates of client participation in scheduled sessions than are
planned—often much lower. This study provided detailed information on session
attendance by parolees in a randomized study of case management for parolees re-
entering the community. Out of the scheduled 12 community sessions, 13% of
parolees in the treatment condition attended no sessions and 69% attended four or
more. These percentages are not atypical of other case management studies.
higher levels of attendance, since doing so increases internal validity, it is not clear how
much effort in time and resources should be devoted to increasing attendance.
Extraordinary efforts are likely to decrease external validity. Given the priority of
clinical trials on internal validity, it makes sense for clinically oriented researchers to
design and implement experiments in such a way as to ensure a maximum ‘dose’ of
treatment. To determine whether a clinically validated intervention would continue to
produce positive outcomes in real world settings, however, it would seem that the focus
needs to shift in the direction of external validity, and efforts to ensure high compliance
may need to be relaxed, not because high attendance is not important and desirable, but
because most community programs or systems of care do not have the resources to
implement research-based interventions on an on-going basis with the same degree of
attention to control, fidelity, and compliance as occurs in experimental studies. Taking
into account the trade offs between internal validity and external validity in designing
field studies calls for a delicate balance of competing objectives.
Drug Abuse, National Institutes of Health (NIDA/NIH), with support from the Center for Substance Abuse
Treatment of the Substance Abuse and Mental Health Services Administration, the Centers for Disease
Control and Prevention (CDC), the National Institute on Alcohol Abuse and Alcoholism (all part of the U.S.
This study was funded under a cooperative agreement from the National Institute on
292 M. Prendergast et al.
Department of Health and Human Services); and from the Bureau of Justice Assistance of the U.S.
Department of Justice. The authors gratefully acknowledge the collaborative contributions of NIDA, the
Coordinating Center (George Mason University/University of Maryland at College Park), and the Research
Centersparticipating inCJ-DATS [BrownUniversity, LifespanHospital;ConnecticutDepartment of Mental
Health and Addiction Services; National Development and Research Institutes (NDRI), Inc., Center for
Therapeutic Community Research; the NDRI Center for the Integration of Research and Practice; Texas
ChristianUniversity, Instituteof BehavioralResearch;Universityof Delaware, Centerfor Drug and Alcohol
Studies; University of Kentucky, Center on Drug and Alcohol Research; University of California at Los
Adolescent Drug Abuse]. The contents are solely the responsibility of the authors and do not necessarily
represent the views of the Department of Health and Human Services, the Department of Justice, NIDA, or
other CJ-DATS participants. In addition, we thank the following people for their contributions to this paper:
the TCM case managers, staff of ISAP’s Data Management Centers, Elizabeth Hall, Tania Beaudoin,
Elizabeth Nelson, CJ-DATS reviewers, and journal reviewers.
Noncommercial License which permits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
This article is distributed under the terms of the Creative Commons Attribution
Technical note on trajectory group analysis
Consider a longitudinal set of variables yitthat take on the value 0 if person i did not
attend session t, and 1 if he/she attended. The model assumes that each person is a
member of an unobserved trajectory group in the population. Each group has a unique
pattern of session attendance over time. The model estimates both these trajectories
and a person’s probability of membership in them. The following more detailed
description is largely taken from the discussion of Jones and Nagin (2007: 543–544).
First, the model assumes that the probability of attending any given session is
represented by the equation
with πj representing the probability of membership in group j, and Pj(Yi)
representing the probability of yit (session attendance) across all time points t,
conditional on membership in group j. Next, the model assumes that, for each
person, the variables yitare independent of each other, so Pj(Yi) can therefore be
estimated as the product of the individual pjt(yit) from 1 to t. The group membership
probabilities πjare estimated by multinomial logit. Finally, because the yitare binary
variables, pjt(yit) was equated to a binary logit model, ebjX?
represents their estimated coefficients for group j. In our quadratic model there were
two variables representing time, ‘session number’ and ‘session number squared,’ in
addition to the intercept estimated in the binary logit model.
ð Þ ¼
1 þ ebjX
??, where X is
the vector of variables representing time that were used in the model, and βj
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Michael Prendergast Ph.D., is Director of the Criminal Justice Research Group at the UCLA Integrated
Substance Abuse Programs (ISAP). He has been Principal Investigator of evaluations of various treatment
programs in correctional settings in California, including a five-year follow-up study of participants in a
prison-based therapeutic community, an evaluation of the use of vouchers within a drug court treatment
program, and an evaluation of the relative effectiveness of mixed-gender and women-only community
treatment. He is Principal Investigator of the Pacific Coast Research Center of the NIDA-funded Criminal
Justice Drug Abuse Treatment Studies (CJ-DATS).
Lisa Greenwell Ph.D. is a Senior Statistician at the UCLA Integrated Substance Abuse Programs. Her
statistical expertise includes advanced regression analysis (e.g., mixed models), meta-analysis, generalized
estimating equations, longitudinal analyses (e.g., survival analysis, growth modeling), and sample
selection models (e.g., inverse Mills ratio, propensity scores). Her research interests include the influence
of sociodemographic disparities and social networks on drug users’ well-being and the public health
implications of incarceration. She earned a Ph.D. in Sociology from Indiana University and was a Post-
doctoral Fellow in Population Studies at RAND.
Jerry Cartier M.A. has considerable experience in the drug and alcohol treatment field as a practitioner,
administrator, and researcher. After joining UCLA-ISAP in 1998, he became Study Director for an
evaluation of the California Substance Abuse Treatment Facility/State Prison (SATF) treatment programs.
He was Study Director for the Transitional Case Management Study (TCM), one of the NIDA-funded
studies in the Criminal Justice Drug Abuse Treatment Studies initiative. Mr. Cartier currently serves as
Study Coordinator for two NIDA-funded studies on correctional drug treatment.
Joann Y. Sacks Ph.D., is the Executive Director of National Development and Research Institutes, Inc.
(NDRI) in New York City. Dr. Sacks has been involved in the design, implementation and evaluation of
treatment models for special populations (individuals with co-occurring disorders (COD) and women) for
the past 20 years. She is an expert in training, technical assistance and implementation of evidence-based
practices for specialized populations. Dr. Sacks’ NIDA- and SAMHSA-funded research has focused on the
use of research data to improve treatment practices for single adults and homeless families within
community agencies as well as state correctional treatment systems, and the use of treatment outcome and
cost information to inform policy and planning on a national and state level.
Linda Frisman Ph.D., is Research Professor for the University of Connecticut School of Social Work,
and serves as the Director of Research at the Connecticut Department of Mental Health and Addiction
Services. Dr. Frisman holds a Ph.D. in Social Policy from the Heller School of Brandeis University, where
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she was a National Institute of Mental Health trainee in the economics of mental health care. She was also Download full-text
an NIMH post-doctoral fellow in mental health services research at Yale University. Dr. Frisman
specializes in cost-effectiveness studies. She has been the Principal Investigator of several federally funded
studies testing interventions that address homelessness, co-occurring mental health and substance use
disorders, and criminal justice populations with behavioral health disorders. Currently, she is the Principal
Investigator of the Connecticut Criminal Justice Drug Abuse Treatment Studies (CJDATS) Center funded
by the National Institute on Drug Abuse.
Eleni Rodis MS, is a project director for the Research Division of the Connecticut Department of Mental
Health and Addiction Services, and is on the staff of the University of Connecticut School of Social Work.
With a background in clinical psychology, she has worked on several federally funded studies testing
interventions that address homelessness, co-occurring mental health and substance use disorders, and
criminal justice populations with behavioral health disorders, as well as several evaluations of state-run
programs for people with serious mental health and/or substance use disorders.
Jennifer R. Havens has a PhD in epidemiology as well as a Masters in Public Health (MPH) from the
Johns Hopkins School of Public Health. Her research interests include the epidemiology of prescription
drug abuse, rural health disparities specific to substance use, and the infectious complications of injection
drug use, namely HIV and hepatitis C. Dr. Havens is currently funded through the National Institutes of
Health to examine social networks and HIV risk among rural drug users.
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