Predicting the effect of substance abuse treatment on probationer recidivism

Predicting the effect of substance abuse treatment

on probationer recidivism

PAMELA K. LATTIMORE*Department of Criminology and Criminal Justice, University of South Carolina, Columbia,

SC 29208, USA

Health, Social, and Economics Research, RTI International**, 3040 Cornwallis Road, Research

Triangle Park, NC 27709-2194, USA

*corresponding author: E-mail: [email protected]

CHRISTOPHER P. KREBS, WILLEM KOETSE, CHRISTINE LINDQUIST and

ALEX J. COWELLHealth, Social, and Economics Research, RTI International**, 3040 Cornwallis Road, Research

Triangle Park, NC 27709-2194, USA

Abstract. Support for the effectiveness of substance abuse treatment to reduce substance use and

recidivism among populations supervised by the criminal justice system continues to grow in substance

abuse and criminal justice literature. Recent studies show that a variety of programs including the

Breaking the Cycle program and drug courts appear to result in improved outcomes for offenders. In this

paper, we examine the effect of non-residential substance abuse treatment on arrest. Our data are for

almost 134,000 Fdrug-involved_ individuals sentenced to probation in Florida between July 1995 and

June 2000. Nearly 52,000 of these individuals received non-residential substance abuse treatment, while

81,797 did not. Our approach is a methodologically simple one that entails stratifying our data by

treatment status, estimating logit and negative binomial models of arrest for each of the two datasets,

and then applying each model to both datasets. This approach, which requires that both groups include

subjects for whom treatment is appropriate, is analogous to using regression models to predict outcomes

for new values of independent variables. For each observation in the dataset, we use the models to

predict the expected outcomes for each individual under two scenarios Y receiving non-residential

treatment and receiving no treatment. Summing over these individual estimates provides an estimate of

the total numbers of arrests that would be expected under different levels of population exposure to

treatment. Results suggest that non-residential treatment reduced both the expected numbers of

individuals who recidivated (i.e., were arrested) and the expected total numbers of arrests in the 12 and

24 months following placement on supervision.

Key words: drug-involved offender, drug treatment, non-residential substance abuse treatment,

probation, recidivism

Introduction

As of December 31, 2003, there were nearly 6.9 million offenders under

correctional supervision in the United States Y nearly 4.8 million of whom were

**RTI is an independent organization dedicated to conducting innovative, multidisciplinary research

that improves the human condition.

Journal of Experimental Criminology (2005) 1: 159–189 # Springer 2005

being supervised by state or Federal probation (Glaze 2004). A large proportion

of these offenders are drug-involved. For example, the Arrestee Drug Abuse

Monitoring (ADAM) Program reported that approximately 63% of arrestees

in 2001 tested positive for at least one illicit drug (ADAM 2001). Providing

substance abuse treatment to drug-involved offenders is one of the most com-

mon approaches to preventing both drug use and associated crime, and extant

literature suggests that treatment can reduce recidivism and a variety of other

social consequences (Lurigio 2000; Pelissier et al. 2001; Banks and Gottfredson

2003).

The high prevalence of drug use among offender populations and the increase

in the proportion of offenders who are drug-involved have been well-documented

Y 24% of the 4 million adults on probation have a drug law violation (BJS

2003a) and approximately 20% of state prisoners and 55% of Federal inmates are

being held for drug offenses (BJS 2003b). The criminal justice system has

responded to the large numbers of drug-involved offenders through institution-

based strategies such as self-help groups and therapeutic communities, as well as

community-based strategies such as providing offenders with treatment through

drug courts, TASC (Treatment Alternatives to Street Crimes, also known as

Treatment Approaches for Safer Communities) models, or specialized caseloads

for supervising drug-involved probationers. Forty-one percent of adults on

probation in 1995 had drug or alcohol treatment imposed as a condition of their

supervision, and 17% reported having participated in a drug treatment program

during their probation sentence (BJS 1995). Although drug treatment is intended

to result in a variety of positive outcomes for individual offenders and society,

recidivism is the primary outcome of interest to the criminal justice system because

treatment provided or coordinated by the criminal justice system is intended to

reduce substance use and, therefore, the number of offenders re-entering the

system with drug or drug-related charges. Therefore, methodologically rigorous

evaluations of the impact of drug treatment on offender populations constitute

important contributions to the field.

This study examines the impact of non-residential substance abuse treatment on

the recidivism of drug-involved probationers under supervision in Florida.1 The

study focuses on nearly 134,000 individuals admitted to community supervision in

Florida between July 1, 1995 and June 30, 2000. Data describing the characteristics

of the offenders were obtained from the Florida Department of Corrections

(FLDC) and were augmented by up to six years of post-admission arrest data

provided by the FLDC and the Florida Department of Law Enforcement (FDLE).

Our analytic methods control for differences between those who did and those who

did not receive treatment and are used to predict the expected outcomes under

alternative treatment assumptions: the total numbers of individuals arrested and the

total numbers of arrests.

The next section provides a brief summary of the literature on the impact of

substance abuse treatment on recidivism, focusing on some important method-

ological issues. Subsequently, we provide a description of our sample, our study

methodology and our findings. In the concluding section, we discuss our findings

PAMELA K. LATTIMORE ET AL.160

and their policy relevance. Our results suggest that substance abuse treatment

reduces recidivism and that states might be able to dramatically reduce arrests

and, we presume, crime by providing treatment to a larger percentage of drug-

involved offenders who are under correctional supervision. The results also imply

a simple approach to costYbenefit estimation, allowing examination of the point(s)

at which investment in treatment may provide positive returns in terms of Farrest

savings._

Previous research

Several evaluations of drug abuse treatment have illustrated the effectiveness of

drug treatment in reducing crime. Much of this research has focused on the ef-

fectiveness of prison-based residential treatment programs, with several studies

finding reduced recidivism among treatment participants (Pelissier et al. 2001;

Rhodes et al. 2001), particularly when prison-based therapeutic communities are

combined with community-based aftercare (Martin et al. 1995; Wexler et al. 1995;

Prendergast et al. 1996). Because the current analysis considers the effectiveness of

community-based treatment among probationers, we have prioritized methodolog-

ically strong evaluations of treatment delivered to offenders under community su-

pervision in the review that follows. The treatment evaluated in these studies

includes a number of modalities and approaches; reviews of specific approaches to

delivering treatment to offenders under community supervision have been con-

ducted previously (Prendergast et al. 1995) and are beyond the scope of the current

paper.

Among the earliest evaluations of community-based drug treatment were

NIDA-funded studies that reported declines in recidivism rates as a result of

community-based drug treatment (representing a variety of treatment modalities),

including the Drug Abuse Reporting Program (DARP), the Treatment Outcome

Prospective Study (TOPS), and the Drug Abuse Treatment Outcome Study

(DATOS) (reviewed in Lurigio 2000).

One widely used approach for community-based treatment among offenders is

the TASC model, which establishes linkages between the criminal justice system

and community-based substance abuse treatment and emphasizes referral, moni-

toring, and case management. Although some studies classified by Chanhatasilpa,

Mackenzie, and Hickman in their 2000 review as methodologically weak have

found positive results for TASC-like programs when comparing completers to non-

completers (Van Stelle et al. 1994; Vito et al. 1993), a rigorous 1996 evaluation of

the effectiveness of TASC did not find evidence of reduced recidivism among

TASC participants compared to non-participants (Anglin et al. 1996). Other meth-

odologically strong evaluations of programs modeled after TASC have generated

similarly negative results (Rhodes and Gross 1997).

Given the increasing popularity of drug treatment courts as a strategy for

treating drug-involved offenders, much of the recent literature on the effectiveness

PREDICTING THE EFFECT OF SUBSTANCE ABUSE TREATMENT 161

of drug treatment in reducing recidivism focuses on treatment provided through

drug courts. However, because drug courts include components in addition to drug

treatment (e.g., increased monitoring, intensive case management and provision of

other social services, and judicial contact), it is not possible to isolate the

independent effects of treatment on recidivism. Although not all drug court

evaluations report reductions in recidivism for drug court participants (Miethe

et al. 2000; Wolfe et al. 2002), a recent summary of drug court evaluation studies

conducted between 1999 and April 2001 (Belenko 2001) identified several studies

reporting significantly lower recidivism rates among drug court participants than

comparison offenders (Brewster 2001; Deschenes et al. 2001; Truitt et al. 2000;

Gottfredson and Exum 2002). A study on the effectiveness of both graduated

sanctions and drug testing in the Washington, DC Adult Drug Court program

(Harrell 1998) found that the arrest rates among sanctions program participants

were substantially lower than arrest rates among defendants in a control group.

Several more recently published studies have also reported significantly lower

recidivism rates for drug court participants (Fielding et al. 2002; Turner et al. 2002;

Banks and Gottfredson 2003).

Among previous evaluations of substance abuse treatment, numerous predictors

of treatment success have been considered, with duration of, or retention in,

treatment being identified as an important factor (Simpson et al. 1997a, b, c; Flynn

et al. 1997; Hubbard et al. 1997). Several studies have reported that outpatient

treatment participants who remain in treatment for at least 90 days have

significantly lower arrest rates than those who terminate treatment earlier (Falkin

et al. 1999; Hubbard et al. 1984). However, the utility of retention indicators is

qualified by potential selection bias if those participants who stay in treatment

longer are those less likely to recidivate whether or not they received treatment.

Although many of the evaluations discussed above are methodologically

rigorous, design limitations in many offender-based drug treatment evaluations

limit the conclusions one is able to draw from the findings. These limitations

include short follow-up periods, small samples, limited populations, and in-

appropriate comparison groups. We briefly discuss each of these.

First, many drug treatment evaluations are characterized by short follow-up

periods, including, in many cases, follow up that does not extend beyond treat-

ment participation. Although it is necessary to assess whether treatment has

positive effects during or shortly after provision, studies that examine effec-

tiveness over multiple years are needed to determine whether the investment in

treatment provides benefits at the individual and societal level over an extended

period of time.

Second, many evaluations include small samples Y often for pragmatic reasons

of limited program capacity or evaluation funds. Although smaller studies may

have an advantage in monitoring treatment fidelity and minimizing variability

(Weisburd et al. 1993), the cost of small samples is limited statistical power to

detect small effects (e.g., a 0.1 or 0.2 reduction). Small effects may be the best

that current treatment approaches can obtain with criminal justice populations in

which substance abuse is often co-occurring with other problems. With small


sample sizes, we reduce the likelihood that we will find treatment effects if they

exist, leading to false conclusions of treatment ineffectiveness. From a policy

perspective, however, even small effects may be substantial Y and worth the

investment of public resources Y if they can be extended to large populations, such

as supervised offenders.

Third, studies often focus on programs provided in a single jurisdiction (e.g.,

one city or county). Extrapolating results from these single-population and/or

single-program studies requires confidence that there is no unobserved factor

associated with success that is unique to the population or program.

Finally, many quasi-experimental evaluations use inappropriate comparison

groups and/or methods. Of particular concern are evaluations that use treatment

dropouts as comparison subjects. Since those who drop out of treatment are likely

to be Fself selecting_ not only into the dropout group but the outcome failure group,

selection bias poses a threat to the validity of findings.

Although many studies of the impact of treatment on recidivism suffer from

methodological limitations, several studies have addressed selection bias issues by

using a variety of strategies, including the Heckman approach (Pelissier et al.

2001; Rhodes et al. 2001), instrumental variable approach (Rhodes et al. 2001),

and propensity modeling (Banks and Gottfredson 2003). These studies constitute

improvements in methodological rigor, but additional studies that address the

issues that complicate research of this kind are needed to create a better un-

derstanding of the potential impact of treatment on the likelihood of offenders to

recidivate.

The current analysis builds on the analytic approach of Linster (2000) and

overcomes several of the methodological limitations already discussed. Specifi-

cally, the analyses use a very large dataset, thus assuring substantial statistical

power to find treatment effects if they exist. As the data include cases from across

Florida, the generalizability of the findings are greater than those based, e.g., on a

single program in one location. We examine results at both 12 and 24 months

following assignment to supervision, incorporating the period in which most who

fail are likely to fail (see, for example, Banks and Gottfredson (2003), in which

failure by re-arrest for drug court and control subjects stabilized at 16 months).

Finally, the analytic approach allows us to control Y in an approach that is

conceptually simpler and from an estimation standpoint Y for potential differences

between the treatment and comparison groups in determining the impact of non-

residential substance abuse treatment on recidivism. As noted by Linster (2000:

28), the approach provides B. . .one way by which estimated treatment effects may

be disentangled from risk-related population differences.^ In so doing, we are able

to answer the Fwhat-if_ question that distinguishes impact assessments from

prediction (see, e.g., Berk 1987). The output of the analyses provides estimates of

Farrest savings_ as a result of treatment that, in turn, can be used to identify when

treatment may be cost effective.

In addition to making methodological contributions to the field of substance

abuse treatment research, we fill a gap in the literature by focusing exclusively on

non-residential treatment. Very few studies, none of which was classified as


methodologically strong in Chanhatasilpa et al.’s (2000) review, have evaluated

the effectiveness of outpatient treatment among offender populations. Although

outpatient treatment appears to be promising with regard to reducing recidivism

among offenders (Finigan 1996; Oregon Department of Corrections 1994),

rigorous studies are necessary in order to contribute to our understanding of its

effectiveness. The following section describes the subjects included in the analysis;

subsequent sections detail our methods and findings.

Subjects

We obtained administrative data from the Florida Department of Corrections

(FLDC) and the Florida Department of Law Enforcement (FDLE) on the 293,156

offenders who entered community supervision in the State of Florida between

July 1, 1995 and June 30, 2000. Supervision includes felony probation, drug-

offender probation, community control and community release. The FLDC data

included a variety of indicators of prior criminal history, the instant offense and

sentence, the nature and content of supervision, and substance use and treatment.

The FDLE data included all arrests reported by local and state police departments

for the subjects.

Because the focus of our research is the impact of drug treatment, we needed to

identify those on supervision who were most likely to be drug involved. There was

no direct measure of current drug use, such as an assessment, in the data; however,

there were a variety of measures that we could use as proxies for drug involvement.

Specifically, we classified an offender as Bdrug involved^ if he or she met one or

more of the following criteria:

� Ever arrested for a drug-related offense� Ever participated in a drug court program� Ever enrolled in drug-offender probation� Ever tested positive on a criminal justice system-administered drug test� Ever referred to substance abuse treatment by the criminal justice system

The resulting sample of drug-involved offenders included 148,076. We then

excluded those who received residential treatment and those with missing or

ambiguous data. The decision to exclude those who began residential treatment

(n = 11,319) during the instant supervision was based primarily on two consid-

erations. First, those in residential treatment facilities face a systematically

different risk (presumably lower) of recidivism (arrest) than those who are free

in the community. Second, those in residential placement are a heterogeneous

group including those whose initial placement is residential and those who have

Ffailed_ non-residential treatment. Of the 1,721 observations dropped because of

missing values on one or more variables, most (1,597) were excluded because they

could not be linked to the arrest file obtained from FDLE (i.e., to the file from


which we generated our dependent variables, as well as some of our covariates).

Finally, we excluded 627 cases that were assigned to administrative probation, as

their supervision and contact requirements were minimal and 633 cases that were

classified as Bother race.^ The resulting data set contained data on 133,776 drug-

involved offenders who entered supervision in the State of Florida between July 1,

1995 and June 30, 2000.2

The characteristics of our subjects are shown in Table 1. We provide summary

measures for the complete sample of 133,776 and for our two subsamples Y the

51,979 who received non-residential substance abuse treatment and the 81,797 who

did not. We also include p-values for comparisons between those receiving and not

receiving treatment. The two groups are statistically different (at the 0.01 level) on

all measures except gender (about 81% of both groups are male) and one of the 21

judicial circuit indicator variables. Of course, with samples as large as ours, even

small differences reach statistical significance at conventional levels, but closer

examination suggests that the differences between the two groups are not only

statistically significant but meaningfully large on some variables that we would

expect to be related to recidivism outcomes.

Thus, we observe in our data what we might expect from non-experimental

data Y differences between our treatment and no-treatment groups along dimen-

sions that we might reasonably expect to be related to our outcome measures (e.g.,

arrest). Specifically, members of the no-treatment group have more prior arrests,

are less likely to be white, and are more likely to have prior supervisions/prison

terms Y all factors generally associated with higher recidivism rates. On the other

hand, our treatment group was supervised more intensely, which could be asso-

ciated with a higher recidivism rate, especially if supervision was not coupled with

treatment (Fulton et al. 1997; Petersilia 1998; Gendreau et al. 2000; Paparozzi

1999).

The average age of the sample is 31 years and there is little meaningful

difference between the average ages of our two subsamples. About 58% of the

treatment group was white, compared to 50% of the group that did not receive

treatment.

Current offense is the most serious offense associated with the supervision

term that led to inclusion in the sample. Current offense was categorized as

violent, property, drug, or Fother._ Comparing the two groups over this dis-

tribution, we see little difference in the proportions whose current offense is

violent or Fother_ Y 17% versus 16% with a violent offense and 7% versus 9%

with an Fother_ offense for the treatment and no-treatment groups, respectively.

There are substantial differences in the proportions with property and drug

offenses. Those in the treatment group were much more likely than those in the

no-treatment group to have a current drug offense (58% versus 47%, respectively),

while those in the no-treatment group were much more likely than the treatment

group to have a current property offense (28% versus 18%, respectively). We

should note, however, that since property Ftrumps_ drugs in the seriousness

hierarchy, those identified based on the most serious charge as property offenders

may also have a drug charge. Overall, the no-treatment group had a slightly


Table 1. Subject characteristics.

Variables All subjects

(N = 133,776)

Treatment

(N = 51,979)

No treatment

(N = 81,797)

t-test,

p-value

n % n % n %

Demographic characteristics

Age 31.002 31.089 30.946 0.0084

Male 107,929 80.68% 42,036 80.87% 65,893 80.56% 0.1557

African American 52,799 39.47% 18,189 34.99% 34,610 42.31% G0.0001

Hispanic 10,383 7.76% 3,764 7.24% 6,619 8.09% G0.0001

White 70,594 52.77% 30,026 57.77% 40,568 49.60% G0.0001

Criminal history

Current violent offense 21,830 16.32% 8,820 16.97% 13,010 15.91% G0.0001

Current property offense 32,403 24.22% 9,503 18.28% 22,900 28.00% G0.0001

Current other offense 11,008 8.23% 3,713 7.14% 7,295 8.92% G0.0001

Current drug offense 68,534 51.23% 29,942 57.60% 38,592 47.18% G0.0001

Prior arrests 4.6244 3.9892 5.028 G0.0001

Prior drug arrests 1.0331 0.8593 1.1435 G0.0001

# prior supervisions 0.8438 0.7241 0.9198 G0.0001

# prior prison terms 0.3306 0.2591 0.3759 G0.0001

Supervision

DOP sup 24,901 18.61% 10,364 19.94% 14,537 17.77% G0.0001

FP sup 92,955 69.49% 35,285 67.88% 57,670 70.50% G0.0001

CC sup 15,920 11.90% 6,330 12.18% 9,590 11.72% 0.0125

Field contacts/year 2.346 3.0938 1.8712 G0.0001

Home contacts/year 6.864 8.4178 5.8762 G0.0001

Office contacts/year 8.951 11.441 7.3683 G0.0001

UA tests/year 3.593 5.2257 2.4666 G0.0001

Positive UAs/year 0.8534 1.0515 0.7275 G0.0001

Positive UA first month 10,778 8.06% 5,053 9.72% 5,725 7.00% G0.0001

Geographic indicator

Judicial circuit 1 5,995 4.48% 3,102 5.97% 2,893 3.54% G0.0001

Judicial circuit 2 3,637 2.72% 1,302 2.50% 2,335 2.85% 0.0001














Judicial circuit 16 1,645 1.23% 526 1.01% 1,119 1.37% G0.0001





Judicial circuit none 1,845 1.38% 864 1.66% 981 1.20% G0.0001


more active criminal history than those who received treatment Y more prior arrests

(an average of 5 versus 4, respectively), more previous community supervision

terms (and average of 0.9 versus 0.7, respectively), and more prior prison terms (an

average of 0.4 versus 0.3, respectively). As criminal history is one of the most

reliable predictors of recidivism (Bonta et al. 1998; Gendreau et al. 1996), we

would expect that the no-treatment group would recidivate at higher rates than the

treatment group, other things being equal.

The subjects were supervised via one of three supervision mechanisms, which

in increasing order of intensity are felony probation, drug offender probation, and

community control. About 68% of the treatment group and 70% of the no-

treatment group were on felony probation (FP). Twenty percent of the treatment

group and 18% of the no-treatment group were on drug offender probation

(DOP), an intensive supervision that is supposed to include frequent contacts,

urine testing, and drug treatment. About 12% of both groups were on community

control (CC), which is the most intensive supervision status and includes curfew

and/or house arrest, along with other conditions. In addition to type of

supervision, we also had some measures of supervision intensity. Although the

proportions of each group assigned to these three types of supervision were about

the same, we see that those in the treatment group were supervised much more

intensively than those in the no-treatment group Y having more contacts with

probation officers in the field, at home, and at FLDC’s offices, as well as more

drug tests. As the treatment group was Fwatched_ more closely, we might expect

higher failure rates for this group consistent with previous research on intensive

supervision (Fulton et al. 1997; Petersilia 1998; Gendreau et al. 2000; Paparozzi

1999).

The data contain 21 judicial circuit indicators that are used to control for

important differences within the state that could otherwise confound the es-

timated relationship between treatment and recidivism.3 Judicial circuits vary in

law enforcement practice, criminal opportunity, culture, urbanicity, population

size, geographic size, and treatment availability. The judicial circuits also vary

with respect to receipt of treatment; the percentage receiving treatment ranged

from 23.2% to 54.1% across the judicial circuits. In three of the circuits, less

than 30% of the probationers went to nonresidential treatment, while more

than 50% participated in five circuits. Five circuits contain almost half (48.6%)

of our sample but a slightly smaller proportion of those who received treatment

(46.9%).

Although there are differences between the groups, Table 1 also suggests that

there is considerable overlap between the groups in terms of the measured

characteristics, which is important for our methodology (as we discuss in the next

section). Specifically, there is evidence to suggest that (at least some of ) those in

the no-treatment group were currently drug involved Y 18% were in the drug

offender probation (DOP) program, a supervision status that requires treatment;

7% had a positive UA during their first month of supervision; and 47% had a drug

offense related to their current community supervision. Those in the no-treatment

group had more prior drug arrests, on average, than those in the treatment group


(1.1 versus 0.86, respectively). Finally, the no-treatment group averaged 0.73

positive drug tests per year during their supervision.4

As can be seen in Table 2, those who did not receive treatment were arrested

at a higher rate. During the first 12 months following placement on supervision,

33% of the no-treatment group was arrested for a felony compared with 21% of

the treatment group. Two years following placement on supervision, nearly 46%

of the no-treatment group had been arrested at least once compared with 33%

of the treatment group. A similar pattern is observed if we look only at arrests

for a felony drug charge Y at 12 months, 17% and 10% arrest rates were observed

for our no-treatment group and treatment group, respectively; and, at 24 months, the

rates were 25% and 16%.5 Members of the no-treatment group also had greater

numbers of arrests when compared with the treatment group Y an average of 0.49

felony arrests during the first 12 months of supervision compared with 0.29,

respectively.

The data in Table 2 provides our first potential indication that treatment may

reduce the recidivism of drug-involved probationers in Florida. But, because of the

differences in the two groups, we cannot be certain that the lower recidivism rates

are due to treatment and not to differences in the characteristics of the groups. In

other words, the treatment group may have had lower recidivism rates even

without treatment. In the next section, we describe our approach to disentangling

these issues.

Table 2. Outcome variables.

Variable All subjects

(N = 133,776)

Nonresidential

treatment

(N = 51,979)

No treatment

(N = 81,797)

t-test, p

n (%) n (%) n (%)

Any felony arrest past

12 months

37,915 28.3% 10,979 21.1% 26,936 32.9% G0.0001

Any felony arrest past

24 months

54,199 40.5% 16,957 32.6% 37,242 45.5% G0.0001

Any felony drug arrest past

12 months

18,740 14.0% 5,196 10.0% 13,544 16.6% G0.0001

Any felony drug arrest past

24 months

29,193 21.8% 8,550 16.5% 20,643 25.2% G0.0001

Mean # felony arrests past

12 months

0.4095 0.2860 0.4881 G0.0001

Mean # felony arrests past

24 months

0.7147 0.5206 0.8380 G0.0001

Mean # felony drug arrests

past 12 months

0.1732 0.1183 0.2081 G0.0001

Mean # felony drug arrests

past 24 months

0.3106 0.2170 0.3701 G0.0001


Methodology

We estimated the likelihood and numbers of arrests associated with treatment using

models that controlled for the rich set of covariates presented. The covariates in the

models include indicators of criminal history, demographics, and judicial circuit, as

well as eight measures of supervision. We present results for any felony arrest, any

drug-related arrest, total numbers of felony arrests, and total numbers of drug-

related arrests within 12 and 24 months of placement on supervision. We

considered treatment in two ways. First, we examined Fany treatment,_ which

represents the mix of treatment exposures across the entire treatment group.

Second, we created two treatment groups based on whether time in treatment was

less than 90 days or more. These models address the finding in the literature that

outpatient treatment is particularly effective beyond 90 days.

The approach is similar to that of Linster (1999), Lattimore (1999), and Linster

et al. (1998). To understand this approach, consider that we are interested in

predicting the number of arrests one would expect if treatment were provided to

those who were not treated Y in other words, to answer the question BWhat if

everyone received drug treatment?^ (or, conversely, BWhat if no one had received

drug treatment?^). We address these questions by stratifying our sample into

subsamples Y treatment and no-treatment Y and estimating separate models of

arrest for each of the subsamples.

We used logistic regression to estimate the likelihood of arrest and negative

binomial models to generate estimates of numbers of arrests for each individual.

The simplest models are the logistic regressions for the two groups, treatment and

no treatment. For these models, we estimated a logistic regression model of the

probability of arrest using only the data for those who received treatment Y in other

words, we estimated the probability of arrest conditional on treatment. We then

combined the coefficient estimates from that model with the corresponding data for

those who did not receive treatment to generate an estimate (or prediction) of the

probability of arrest under the assumption of treatment for each untreated person.

By summing over these probability estimates, we identify the expected number of

individuals arrested for the no-treatment group under the assumption of treatment.

The difference between this number and the observed number of arrests incurred

by the no-treatment group provides an estimate of the effect of treatment on felony

arrest within 12 months.

Secondly, we estimated a logistic regression model using only the data for those

who received no treatment Y i.e., we estimated the probability of arrest conditional

on not receiving treatment. By combining the coefficients from this model with the

data for the treatment group we generate an estimate for each individual of the

probability of arrest under the assumption of no treatment. The sum of these

individual predicted probabilities provides the expected number of individuals

arrested for the treatment group under the assumption of no treatment.

This approach is analogous to that of a forecaster who estimates a regression

equation using historical data and subsequently applies the resulting model to

answer Fwhat if_ questions for different values of the independent variables (e.g.,


BWhat is the impact on GDP if the price of oil increases?^). Such predictions are

valid as long as the estimation sample includes values for the independent

variables that are similar to those in the data from which predictions will be made.

As noted earlier, we have overlap in our two groups on our independent variables.

Thus, we can predict for each individual in the no-treatment group the effect of

treatment on recidivism.

In addition to modeling the probability of any arrest, we also used this approach

to model the number of felony arrests per individual in a two-year period using

negative binomial models. By applying the results from these models to the data

using the same approach described previously, the negative binomial models allow

us to compare expected differences in total numbers of felony arrests per in-

dividual under alternative assumptions about treatment.

In total, we conducted simulations for four groups of probationers: those who

received any treatment, the two sub-groups of treatment (less than 90 days and

more than 90 days of treatment), and no treatment. The six outcomes used in the

models were: arrested for a felony within 12 months, arrested for a felony within

24 months, arrested for a drug felony within 12 months, arrested for a drug felony

within 24 months, number of felony arrests within 12 months, and number of

felony arrests within 24 months. The logistic regression models are included in the

Appendices 1; the negative binomial models are available from the authors. In the

next section, we present the results of our analyses, which provide estimates of

the impact of treatment on felony and drug felony arrests.

Findings

In this section, we present the Barrests savings^ that could be expected as a result

of treatment. We compare no treatment with any non-residential treatment and

with either less than 90 days or more than 90 days of treatment. We first consider

the felony and drug arrest outcomes derived from the logistic regressions and then

examine the expected impact on the total number of felony arrests derived from the

negative binomial models.

Any felony arrest

Tables 3Y6 summarize the implications of the logistic regression models for the

impact of non-residential drug treatment on recidivism, as measured by arrest for a

felony within 12 and 24 months, respectively, of placement on supervision. Each

row in the tables presents the estimated numbers of failures (i.e., probationers who

were arrested after entering supervision) for the specified group under the

alternative, hypothetical treatment scenarios and the differences between the

estimates and the actual, observed numbers of those arrested.

Looking first at Table 3, we see the results for no-treatment versus any

treatment (which is actually Fsome treatment_ or the mix of treatment delivered;


i.e., the status quo). Across all subjects, 37,915 were arrested at least once for a

felony during the first 12 months of supervision. Specifically, 26,936 of the no-

treatment group were arrested during the first year. If this group had received

treatment, the treatment model predicts that only 20,735 or 6,201 fewer would

have been arrested Y a 23% reduction in the expected number of probationers in

this group who were arrested. We see a similar effect in the expected number of

failures among the treatment group predicted in the absence of treatment. In this

case, 10,979 of the Fany treatment group_ were arrested during the first 12 months.

The no-treatment model suggests that without treatment, 13,504 members of this

group Y or an additional 2,525 Y would have been arrested Y a 23% increase.

The expected benefit of more extensive treatment is apparent in Table 4. If all

subjects had received less than 90 days of treatment, the expected total number of

Table 3. Expected numbers of subjects with at least one felony arrest, first 12 months of supervision,

under alternative treatment scenarios Y no treatment versus some treatment.

Group Number in

group

No treatment Any treatment

Expected from

model

Difference

from actual

Expected from

model

Difference

from actual

No treatment 81,797 26,936a 0 20,735 j6,201

Any treatment 51,979 13,504 +2,525 10,979a 0

Total 133,776 40,440 +2,525 31,714 j6,201

a Estimates from model equal the observed number of subjects with at least one felony arrest during the

initial 12 months of supervision. Overall, 37,915 subjects had a felony arrest.


under alternative treatment scenarios Y no treatment versus less than or 90 days or more.

Group Number in

group

No treatment G90 Days treatment Q90 Days treatment

Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

No

treatment

81,797 26,936a 0 23,301 j3,635 17,538 j9,398

G90 Days

treatment

23,896 6,740 +723 6,017 a 0 4,594 j1,423

Q90 Days

treatment

28,083 6,764 +1,802 6,262 +1,300 4,962 a 0

Total 133,776 40,440 +2,525 35,581 j2,335 27,094 j10,821

aEstimates from model equal the observed number of subjects with at least one felony arrest during the



probationers who would be arrested across the three groups is 35,581 Y a reduction

of 2,334 failures (6.1%) over the 37,915 that were observed. The expected total of

35,581 includes an expected increase of 1,300 failures among the group who

actually received at least 90 days of treatment. Finally, we see that if all subjects

were given at least 90 days of treatment, the model predicts that only 27,093

probationers would have been arrested Y a reduction of 10,822 failures (28.5%).

The results in Tables 5 and 6 show similar expected effects extending over a

two-year period. More than 40% of the subjects (54,199) had been arrested at least

once for a felony within two years of placement on supervision. The model

suggests that by providing treatment to the no-treatment group, the number of

probationers arrested could have been reduced by 6,603 from 54,199 to 48,136 Yan 11% reduction. If everyone had received at least 90 days of treatment, the


under alternative treatment scenarios Y no treatment versus some treatment.

Group Number

in group


Expected from

model

Difference from

actual

Expected from

model

Difference from

actual

No treatment 81,797 37,242a 0 31,179 j6,063

Any treatment 51,979 19,639 +2,682 16,957 a 0

Total 133,776 56,882 +2,682 48,136 j6,063




under alternative treatment scenarios Y no treatment versus less than or 90 days or more.

Group Number in

group


Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

No

treatment

81,797 37,242a 0 33,188 j4,054 28,651 j8,591

G90 Days

treatment

23,896 9,613 + 903 8,710a 0 7,625 j1,085

Q90 Days

treatment

28,083 10,026 +1,779 9,197 +950 8,247a 0

Total 133,776 56,882b +2,682 51,095 j3,104 44,523 j9,676


initial 24 months of supervision. Overall, 54,199 subjects had a felony arrest.bTotal does not sum perfectly due to rounding of estimates.


model predicts that 44,523 probationers (or 9,676 fewer) would have been arrested

within the 24-month period Y an 18% reduction.

Any felony drug arrest

Tables 7Y10 summarize the implications of the models of non-residential drug

treatment on recidivism, as measured by felony arrest on a drug charge within 12

and 24 months of placement on supervision. The observed numbers of those

probationers who were arrested on a felony drug charge within 12 and 24 months

were 18,740 and 29,194, respectively. As with the earlier tables, each row presents

the expected numbers of failures for the specified group under the alternative,

Table 7. Expected numbers of subjects with at least one felony drug arrest, first 12 months of

supervision, under alternative treatment scenarios Y no treatment versus some treatment.

Group Number in

group


Expected from

model

Difference

from actual

Expected from

model

Difference

from actual

No treatment 81,797 13,544a 0 9,581 j3,963

Any treatment 51,979 6,741 +1,545 5,196a 0

Total 133,776 20,285 +1,545 14,777 j3,963


initial 12 months of supervision. Overall, 18,740 subjects had a felony drug arrest.


supervision, under alternative treatment scenarios Y no treatment versus less than or 90 days or more.

Group Number

in group


Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

No

treatment

81,797 13,544a 0 11,146 j2,398 7,634 j5,910

G90 Days

treatment

23,896 3,404 +435 2,969a 0 2,091 j878

Q90 Days

treatment

28,083 3,336 +1,109 3,037 +810 2,227a 0

Total 133,776 20,285 +1,544 17,152 j1,588 11,952 j6,788


initial 12 months of supervision. Overall, 18,740 subjects had a felony drug arrest.


hypothetical treatment scenarios. Looking first at the results for no treatment

versus any treatment, we see in Table 7 that 13,544 of the no-treatment group were

arrested during the first year. If this group had received the same treatment, or mix

thereof, as the treatment group, the treatment model predicts only 9,581 would

have been arrested Y a 29% reduction in the expected number of failures for this

group. Of those in the Fany treatment group,_ 5,196 were arrested for a felony drug

offense Y the no-treatment model suggests that without treatment 6,741 members

of this group would have been arrested Y a 30% increase over what was observed.

The expected benefit of more extensive treatment is also apparent in Table 8. If all

subjects had received less than 90 days of treatment, the expected total number of

failures across the three groups drops from 18,740 to 17,152 Y a reduction of 1,588

(8.5%), which includes an expected 810 additional arrestees in the group who


supervision, under alternative treatment scenarios Y no treatment versus some treatment.

Group Number in

group


Expected from

model

Difference

from actual

Expected from

model

Difference

from actual

No treatment 81,797 20,643a 0 15,671 j4,972

Any treatment 51,979 10,690 +2,140 8,550a 0

Total 133,776 31,333 +2,140 24,220 j4,972




supervision, under alternative treatment scenarios Y no treatment versus less than or 90 days or more.

Group Number

in group


Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

Expected

from

model

Difference

from

actual

No

treatment

81,797 20,643a 0 17,120 j3,523 13,910 j6,733

G90 Days

treatment

23,896 5,277 +758 4,519a 0 3,794 j725

Q90 Days

treatment

28,083 5,413 +1,382 4,680 +649 4,031a 0

Total 133,776 31,333 +2,140 26,319 j2,874 21,734 j7,458




actually received at least 90 days of treatment. If all subjects were given at least 90

days of treatment, the model predicts that 11,952 would have been arrested Y a

reduction of 6,788 over the 18,740 (36.2%) who were arrested for a felony drug

offense under the status quo treatment.

The results in Tables 9 and 10 show similar expected effects over a two-year

period. More than 22% of the subjects (29,193) had been arrested at least once for

a felony drug offense within two years of placement on supervision. The model

suggests that by providing treatment to the no-treatment group, the number

arrested in this group could have been reduced from 20,643 to 15,671 Y a 24.1%

reduction (see Table 9). If everyone had received at least 90 days of treatment,

Table 10 shows that the model predicts that 21,734 would have been arrested

within the 24-month period Y a 25.6% reduction over what was observed.

Total number of felony arrests

The previous findings suggest that current policy with respect to providing

substance abuse treatment reduced the number of probationers arrested during the

first two years of supervision over what would have been expected if non-

residential drug treatment had not been provided to some of these subjects. The

results also suggest that providing treatment to those who did not receive it or that

providing more treatment could reduce the expected number of arrests. Of course

some offenders are arrested multiple times over the course of two years, and the

probationers in our data were no exception. Overall, during the first 12 months of

supervision, the group experienced an average of 0.41 felony arrests, with a range

of 0 to 15. During the first 24 months, the average was 0.71 arrests and the range

was 0 to 18. Just as the likelihood of arrest varied by group, the groups varied

regarding the numbers of felony arrests, where again those who received more

treatment were arrested fewer times. Mean numbers (and maximum numbers) of

arrests during the first 12 months were 0.49 (maximum of 15), 0.29 (maximum of

10), 0.35 (maximum of 8), and 0.23 (maximum of 10) for the no treatment, any

treatment, less than 90 days treatment, and at least 90 days treatment groups,

respectively. Mean numbers (and maximum numbers) of arrests during the first

24 months were 0.84 (maximum of 18), 0.52 (maximum of 17), 0.61 (maximum of

17), and 0.44 (maximum of 15) for the no treatment, any treatment, less than

90 days treatment, and at least 90 days treatment groups, respectively.

We used negative binomial regression, following the same procedures described

previously, to estimate Fcount models_ for each of our four groups.6 For these mo-

dels, the outcome was number of felony arrests during the 12 (or 24 month) period.7

Tables 11 and 12 summarize the implications of the negative binomial models.

These tables contain the expected numbers of arrests for each group under each of

the alternative treatment scenarios for 12 and 24 months, respectively, following

placement on supervision. The 133,776 subjects were arrested for 54,787 felonies

during the initial 12 months and a total of 95,606 during the first 24 months of

supervision.


As can be seen in Table 11, our models suggest that in the absence of treatment,

we would have expected to see during the first 12 months a total of 59,317 arrests

or 4,530 additional arrests (an 8.3% increase). If status quo treatment had been

provided to everyone, the models suggest there would have been 45,323 felony

arrests Y a 17% reduction over what was observed. The most substantial effects,

however, are observed when we examine the results of providing at least 90 days

Table 12. Expected numbers of felony arrests, first 24 months of supervision, under alternative treatment

scenarios.

Group Number in

group

Treatment

No

treatment

Any

treatment

G90 Days

treatment

Q90 Days

treatment

No treatment 81,797 69,256a 54,746

Any treatment 51,979 33,664 27,476a

Total 133,776 102,920 82,221

No treatment 81,797 69,256a 59,878 47,872

G90 Days

treatment

23,896 16,900 14,924 a 12,105

Q90 Days

treatment

28,083 16,764 15,023 12,550 a

Total 133,776 102,920 89,825 72,527

a Estimates from model during the initial 24 months of supervision. Overall, the subjects had 95,606

actual felony arrests.

Table 11. Expected numbers of felony arrests, first 12 months of supervision, under alternative treatment

scenarios.

Group Number in

group

Treatment

No

treatment

Any

treatment

G90 Days

treatment

Q90 Days

treatment

No treatment 81,797 40,193a 30,287

Any treatment 51,979 19,125 15,036a

Total 133,776 59,317 45,323

No treatment 81,797 40,193a 34,227 24,987

G90 Days

treatment

23,896 9,694 8,501a 6,256

Q90 Days

treatment

28,083 9,431 8,543 6,529a

Total 133,776 59,317 51,271 37,772

a Estimates from model during the initial 12 months of supervision. Overall, the subjects had 54,787

actual felony arrests.


of treatment to everyone. Under this scenario, the models predict 37,772 arrests Y a

31% reduction over what was observed and a 36% reduction over what we would

expect in the absence of any treatment.

Table 12 presents the findings for the total number of felony arrests expected

during the first 24 months of supervision. We observed a total of 95,606 felony

arrests for the 133,776 subjects during this period. Table 12 shows that if no

treatment had been provided, we would have expected to see 102,920 arrests

during the first 24 months, about 8% more than were actually observed and

substantially more than we would expect to see if some treatment had been

provided to everyone. Under the Fany treatment_ scenario, the models suggest a

total of 82,221 felony arrests, 13,385 fewer than were observed (a 14% decrease).

As was true with the other models, the most substantial Farrest savings_ are

observed when everyone is provided at least 90 days in treatment. Under this

scenario, the expected number of felony arrests drops to 72,527 Y 24% less than

was observed under the provided or status quo mix of treatment. Overall, the

models identify a difference of 30,393 felony arrests over 24 months between the

no treatment and at least 90 days of treatment options.

Discussion and conclusions

We have used a large administrative database provided by the FLDC and FDLE

to examine the expected effects of different amounts of non-residential substance

abuse treatment on drug-involved probationers. The four non-residential

treatment alternatives were no treatment, any treatment (non-residential treatment

of any length as experienced by those who received at least one day of

treatment), less-than-90-days treatment, and at least 90 days treatment. To

identify the expected effects, we stratified our data by treatment and estimated

separate models for each of our treatment groups for any (and total numbers of)

felony arrest(s) and felony drug arrest(s) within 12 and 24 months of placement

on supervision. We used these models to predict the expected likelihood (and

numbers) of failures for all groups and were able to quantify the impact of

non-residential substance abuse treatment on arrest. Specifically, we were able

to identify the effect of current treatment policy compared with no treatment,

and to examine the differential effect of less than versus at least 90 days of

treatment. The results suggested that the treatment provided reduced the number

of recidivists and the numbers of arrests that would have been expected by these

arrestees over the 24 months following admission to community supervision.

Further, the results showed that additional Farrest savings_ were possible by

providing treatment to those drug-involved offenders who did not receive

treatment.

The analyses that we conducted are only reasonable if we assume that not all

of those who would benefit from treatment actually received treatment. In other

words, it is necessary that the no-treatment group includes many who should be


in treatment and, thus, some who would benefit. Is this assumption a reasonable

one? We believe so for the following two reasons: First, our data include

indicators of current (as well as past) drug use by many of those in the no-

treatment group. To assume that these individuals would not benefit from

treatment is to assume that there are numerous individuals under community

supervision who are using drugs but for whom treatment is deemed unnecessary

and/or inappropriate. Second, using the same criteria statewide for inclusion in

our sample, there was considerable variability across judicial districts in terms

of the proportions of individuals who were in treatment. This variability, again,

suggests that many members of the no-treatment group could have been in

treatment. Otherwise, we would need to assume, for example, that in one

district only about one-fourth of those identified by our criteria should be in

treatment while in another the proportion exceeds one-half. For this to be true,

we would have to assume that many more of those in the first district as

compared to the second were either (a) former but not current drug users and/or

(b) drug sellers/distributors rather than drug users. In fact, we believe that it is

more reasonable to assume that treatment availability, attitudes about treatment

(among probation officers, offenders and, perhaps, judges), and Fthe luck of the

draw_ contribute to whether or not someone began treatment Y creating a quasi-

experiment on which the methods described here can be used to estimate

effects.

The results of the models are interesting in that they yield the Farrest savings_that can accrue to treatment. We can take these savings one step further and

comment on the implications of these Fsavings_ for economic analysis. We will

focus on the implication of the results of the models for total numbers of felony

arrest within 24 months and, in particular, the no- and any-treatment models (see

Table 12). These models suggest that the drug treatment provided to 51,979

offenders generated savings in numbers of arrests of 6,188. The simplest economic

model identifies the Fbreakeven_ point in terms of investment in drug treatment

with the only returns being a reduction in the (costs associated with) arrests. In

other words, it will be worthwhile to invest in drug treatment as long as the

following is true:

NT:CT � NA

:CA

From our data we know (or have assumed) the number treated, NT, and our

models provided us with an estimate of the reduction in the total number of arrests,

NA. Given that treatment was provided to 51,979 individuals and if the (average)

cost of treatment, CT, is $1,000 per individual, the above equation suggests that

that such an investment is worthwhile as long as the (average) cost of an arrest

(and all related criminal justice processing), CA, is at least $8,400. If we extend the

analysis and assume that everyone is treated at an (average) cost of $1,000 per

person, it will be cost effective to provide treatment as long as the (average) cost of

arrest (and all related criminal justice processing and corrections) exceeds about


$6,463.8 For comparison, the annual average cost of incarceration in Florida in

2001 was $20,190 (Stephan 2004).

Results of our analyses suggest that the nonresidential substance abuse

treatment provided to about 52,000 of the nearly 134,000 probationers in our

sample reduced both the expected number of individuals who were arrested and the

expected total number of felony arrests during the initial 12 and 24 months

following placement on supervision. More than 28% (37,915) of the 133,776

subjects had been arrested on a felony charge within 12 months of placement on

supervision. Within 24 months, this percentage grew to more than 40% (54,199 of

133,776). In total, this group had 54,787 and 95,606 felony arrests within 12 and

24 months, respectively, of placement on supervision.

Our results suggest that for the 51,979 who received some non-residential

treatment, 10,979 were arrested at least once within one year Y about 2,500

fewer than expected in the absence of treatment. Those arrested had a total of

15,036 arrests, compared with an expected number of arrests of 19,125 under the

no-treatment scenario. Thus, the results suggest that the treatment that was

provided reduced the number arrested and the number of arrests by 18.7% and

21.4%, respectively, for the group. Results were similar when we examined the

impact of treatment on arrest over 24 months: 2,682 fewer probationers with a

felony arrest and 6,188 fewer arrests. More substantial reductions in number

arrested and numbers of arrests occurred under the scenario of lengthier treat-

ment (i.e., at least 90 days).

We also looked at the impact of providing treatment to the 81,797 who did

not receive treatment. As discussed earlier, this group had a 12-month felony

arrest rate of 32.9% (26,936 with at least one arrest) and a 24-month felony

arrest rate of 45.5%. The models suggest that providing this group access to

treatment as it was provided to the group who received it could have reduced the

number arrested within one year from 26,936 to 20,735 (23% reduction) and the

number arrested within two years from 37,242 to 31,179 (16.3% reduction). This

group experienced 40,193 and 69,256 felony arrests within 12 and 24 months,

respectively. The models suggest with treatment, we could expect 30,287 and

54,746 arrests within 12 and 24 months, respectively. These numbers represent

reductions in the expected numbers of arrests of 24.6% and 21.0%, respectively.

Once again, greater reductions were forecast under the lengthier treatment

scenario.

Many offenders who are arrested and supervised in the community are drug-

involved. Some portion of these offenders are referred to and receive substance

abuse treatment, which appears to reduce the likelihood, as well as the number of

times, they will recidivate. A substantial proportion of state (20%) and Federal

(55%) prison inmates are being held on drug charges, and the costs associated

with imprisonment are substantial. At a time when state and Federal budgets are

stretched, it seems that the provision of treatment might yield a number of

societal and criminal justice system benefits Y both in terms of improvements in

public safety and potential cost savings. Treatment, although not free, is, on

average, substantially less expensive than incarceration and the financial and


personal (e.g., victim impact) benefits of avoiding future crimes are difficult to

overestimate.

The policy implications of our findings are straightforward. Providing non-

residential substance abuse treatment to offenders being supervised in the community

appears to be a feasible approach to reducing the likelihood and collateral impacts of

recidivism, and thus enhancing public safety. The provision of treatment, to the

extent that it reduces recidivism and the related need for incarceration, might also

result in valuable public resource savings.

Many quasi-experimental studies of the impact of substance abuse treatment on

outcomes like recidivism do not adequately control for the biases that differentiate

treatment and comparison group members. Failing to control for these differences

often invalidates the findings and reduces the credibility of the literature. This

study employs an analytic approach that controls for meaningful differences

between the treatment and comparison groups, utilizes a large administrative

dataset, and produces findings suggesting that the provision of non-residential

substance abuse treatment can substantially reduce the likelihood and impact of

recidivism.

The approach presented here provides an alternative to the other approaches that

have been used to address selection bias issues.9 The approach does require that the

untreated group contains subjects that could potentially benefit from the

intervention. If this requirement is met, the approach has the added benefit of

being simpler to estimate (and explain) than more complex models based on, for

example, Heckman’s approach or an instrumental variable approach.

Acknowledgements

This study was supported by grant DA13086 from the National Institute on Drug

Abuse (NIDA). Points of view are those of the authors and do not necessarily

represent the views of the NIDA. The authors would like to acknowledge Bill

Bales of Florida State University (formerly of the Florida Department of

Corrections), Kristine Dougherty of the Florida Department of Corrections, and

Sue Burton of the Florida Department of Law Enforcement for their assistance in

developing the data, as well as their guidance and support. We would also like to

thank the Journal of Experimental Criminology editor and reviewers for their

insights and recommendations.

Appendix A: Model

Tables A-1 and A-2 show the odds ratio estimates from the logit models for anyfelony arrest and any felony drug arrest, respectively, within 12 and 24 months.Shown are the estimates of the ratio of the odds of failure (i.e., felony arrest within


the specified time period) to the odds of success Y values greater than one implyincreasing odds of arrest and values less than one imply decreasing odds of arrest,other things being equal. Values significantly greater than one (Wald test, 0.05significance level) are shaded. Two measures of goodness of fit, the R2 and themaximum-rescaled R2 are also included.

Notes

1 The non-residential substance abuse treatment provided is contracted by the FLDC

throughout the state. There are several different models supported (details available from

authors or by going to the FLDC website, http://www.dc.state.fl.us), but overall the fact

that the contracting is done at the state level suggests at least some comparability among

the treatment provided across the state.2 For the cases with missing data, the non-residential treatment participation rate was

40.7% Y about equal to the rate for those for whom we had complete data (38.9%).3 Florida has 20 judicial circuits. The judicial circuit reflects the circuit in which the

individual was sentenced. An individual was identified with the judicial circuit of

sentencing. We have 21 indicators for judicial circuit because we included 1,845

individuals in our analysis who either had a missing value for judicial circuit or who had

been sentenced out of state. Those in this group comprise the Bjudicial circuit none.^ We

estimated the models excluding these individuals and found results (not shown) very

similar to those presented here.4 We also examined the reasons why members of each group were included in our drug-

involved sample. Again, although there are differences between the groups, it is important

to note that both groups were represented in each of the criteria. For the treatment and no-

treatment groups, respectively, (1) 37.1% versus 55.6% had at least one drug-related

arrest; (2) 2.7% versus 2.0% had any history of drug court participation; (3) 22.0% versus

21.4% had ever been on DOP supervision; (4) 46.6% versus 53.2% had any FLDC record

of a positive drug test ever; and (5) 100% versus 21.1% had ever attended treatment. (By

definition, of course, all of the treatment group would have had to meet this last criterion.)5 Members of the no-treatment group were also more likely than the treatment group to be

arrested for violent or property offenses and to have a probation violation (data not

shown).6 The ability of a subject to accumulate arrests depends partly on being free in the

community. If the first arrest results in confinement, the likelihood of a subsequent arrest

during that confinement period is substantially reduced, including only additional arrests

for offenses committed prior to the incarceration or to arrests stemming from incidents

that occur while incarcerated. For our purposes, we are only interested in predicting the

numbers of arrests. We are not interested in predicting how many arrests would have been

expected over the period in the absence of any incarceration.7 Negative binomial model results are available from the authors upon request.8 This estimate is based on the estimate of 82,221 total arrests with all receiving some

treatment differenced from the 102,920 arrests estimated under the no treatment scenario.

This results in a total reduction in arrests of 20,699 (NA) following from the treatment of

133,776 (NT).9 We are currently completing work of a series of propensity score-based analyses that

provide treatment Y effect results similar to those presented here (Lattimore et al. 2004).


Ta

ble

A-1

.O

dd

sra

tio

esti

mat

esfr

om

felo

ny

arre

stre

cid

ivis

mm

od

els.

Va

ria

ble

Fel

on

ya

rres

tw

ith

in1

2m

on

ths

Fel

on

ya

rres

tw

ith

in2

4m

on

ths

An

y

trea

tmen

t

(N=

51

,97

9)

Tre

atm

ent

G9

0d

ays

(N=

23

,89

6)

Tre

atm

ent

Q9

0d

ays

(N=

28

,08

3)

No

trea

tmen

t

(N=

81

,79

7)

An

y

trea

tmen

t

(N=

51

,97

9)

Tre

atm

ent

G9

0d

ays

(N=

23

,89

6)

Tre

atm

ent

Q9

0d

ays

(N=

28

,08

3)

No

trea

tmen

t

(N=

81

,79

7)

Dem

og

rap

hic

cha

ract

eris

tics

Ag

e(y

ears

)0

.966

0.9

61

0.9

71

0.9

64

0.9

63

0.9

58

0.9

67

0.9

59

Mal

e1

.095

1.0

75

1.1

10

1.0

79

1.1

03

1.0

79

1.1

18

1.0

92

Bla

ck*

1.2

77

1.2

63

1.2

41

1.2

81

1.3

64

1.3

59

1.3

35

1.4

22

His

pan

ic*

0.9

71

0.9

81

0.9

51

1.1

00

0.9

99

1.0

02

0.9

91

1.1

37

Cri

min

al

his

tory

Curr

ent

vio

lent

off

ense

00

.931

0.9

28

0.9

43

1.0

07

0.9

17

0.9

08

0.9

32

1.0

71

Curr

ent

pro

per

tyoff

ense

01

.470

1.3

87

1.5

25

1.4

78

1.4

63

1.4

46

1.4

54

1.6

08

Curr

ent

oth

ero

ffen

se0

0.7

77

0.6

96

0.8

52

0.8

15

0.8

07

0.7

80

0.8

26

0.9

24

#p

rio

rar

rest

s1

.061

1.0

64

1.0

58

1.0

62

1.0

73

1.0

78

1.0

67

1.0

76

#p

rio

rd

rug

off

ense

s1

.070

1.0

81

1.0

60

1.0

55

1.0

82

1.0

90

1.0

76

1.0

67

#p

rio

rsu

per

vis

ion

s0

.991

0.9

91

0.9

90

0.9

97

0.9

85

0.9

87

0.9

82

0.9

97

#p

rio

rp

riso

nte

rms

0.9

71

0.9

68

0.9

73

0.9

83

0.9

65

0.9

50

0.9

78

0.9

69

Su

per

visi

on

DO

Psu

per

vis

ion-

1.5

15

1.4

40

1.6

29

1.3

81

1.3

64

1.4

14

1.3

25

1.3

52

FP

sup

erv

isio

n1

.278

1.1

85

1.4

78

1.1

08

1.1

68

1.1

76

1.1

97

1.1

54

Fie

ldco

nta

cts/

yea

r1

.007

1.0

01

1.0

10

1.0

06

1.0

03

1.0

04

1.0

02

1.0

00

Ho

me

con

tact

s/y

ear

1.0

12

1.0

13

1.0

12

1.0

07

1.0

12

1.0

13

1.0

11

1.0

09

Offi

ceco

nta

cts/

yea

r0

.967

0.9

67

0.9

72

0.9

68

0.9

68

0.9

65

0.9

72

0.9

69

UA

test

s/yea

r0

.982

0.9

78

0.9

96

0.9

41

0.9

86

0.9

81

0.9

95

0.9

60

Po

siti

ve

UA

s/yea

r1

.068

1.0

53

1.0

74

1.1

00

1.0

82

1.0

73

1.0

89

1.0

85

Po

siti

ve

UA

firs

tm

on

th1

.083

1.1

17

1.0

27

1.0

70

0.9

87

1.0

03

0.9

60

1.0

38


Geo

gra

ph

icin

dic

ato

r

Jud

icia

lci

rcu

it1.

0.9

57

0.8

43

1.0

21

0.8

98

1.0

66

0.9

73

1.1

44

0.8

99

Jud

icia

lci

rcu

it2.

0.8

51

0.7

55

0.9

12

0.9

30

0.8

90

0.7

57

0.9

83

0.9

43

Jud

icia

lci

rcu

it3.

1.0

55

0.9

02

1.1

57

0.8

27

1.0

67

0.9

79

1.1

23

0.8

01

Jud

icia

lci

rcu

it4.

0.9

94

0.9

87

0.8

57

1.1

02

1.1

09

1.1

64

0.9

59

1.1

44

Jud

icia

lci

rcu

it5.

0.9

09

1.0

56

0.6

76

0.9

53

1.0

37

1.2

13

0.8

35

0.9

96

Jud

icia

lci

rcu

it6.

1.0

57

1.1

12

0.8

96

1.1

91

1.1

18

1.1

86

1.0

02

1.1

78

Jud

icia

lci

rcu

it7.

1.0

63

1.0

43

0.9

11

1.0

85

1.1

28

1.1

71

0.9

83

1.0

72

Jud

icia

lci

rcu

it8.

1.6

25

1.7

32

1.5

43

1.6

50

1.7

35

1.7

90

1.6

93

1.6

97

Jud

icia

lci

rcu

it9.

0.9

56

0.9

92

0.7

76

1.0

98

0.9

88

1.0

05

0.9

06

1.1

11

Jud

icia

lci

rcu

it1

0.

1.0

92

1.2

11

0.8

96

1.3

12

1.1

95

1.3

38

1.0

35

1.4

29

Jud

icia

lci

rcu

it1

1.

1.1

50

1.2

15

1.1

34

1.1

08

1.2

63

1.3

30

1.2

40

1.1

60

Jud

icia

lci

rcu

it1

2.

1.1

17

1.0

65

1.0

86

0.9

66

1.1

27

1.0

88

1.1

16

0.9

51

Jud

icia

lci

rcu

it1

3.

1.3

49

1.4

54

1.0

66

1.3

83

1.2

52

1.3

56

1.0

78

1.3

08

Jud

icia

lci

rcu

it1

4.

1.1

03

1.1

79

1.0

07

0.8

08

1.1

01

1.1

04

1.0

85

0.7

98

Jud

icia

lci

rcu

it1

5.

0.7

74

0.8

50

0.6

66

0.8

61

0.8

11

0.9

24

0.7

07

0.9

29

Jud

icia

lci

rcu

it1

6.

1.7

12

1.7

61

1.3

12

1.1

58

1.6

55

1.7

07

1.4

14

1.0

67

Jud

icia

lci

rcu

it1

8.

1.0

08

0.9

93

0.9

67

0.9

75

1.0

30

1.0

88

0.9

58

0.9

90

Jud

icia

lci

rcu

it1

9.

1.0

71

1.0

07

1.0

53

1.0

05

1.1

68

1.1

68

1.1

36

0.9

75

Jud

icia

lci

rcu

it2

0.

0.9

64

1.1

00

0.8

34

1.0

19

1.0

28

1.1

70

0.9

22

1.0

02

Jud

icia

lci

rcu

itn

on

e.0

.513

0.4

26

0.5

64

0.6

32

0.4

95

0.4

67

0.5

07

0.6

52

R2

0.0

66

80

.08

45

0.0

48

40

.097

60

.095

20

.11

46

0.0

75

40

.122

6

Max

imu

m-r

esca

led

R2

0.1

03

80

.12

49

0.0

79

70

.135

90

.132

80

.15

29

0.1

07

40

.163

9

Ital

odds

rati

os

init

alic

sar

est

atis

tica

lly

signifi

cant

atth

e0.0

5le

vel

.

*W

hit

eis

refe

rence

cate

gory

..Ju

dic

ial

circ

uit

17

isth

ere

fere

nce

cate

gory

.-C

om

munit

yco

ntr

ol

isth

ere

fere

nce

cate

gory

.0C

urr

ent

dru

goff

ense

isth

ere

fere

nce

cate

gory

.


Ta

ble

A-2

.O

dd

sra

tio

esti

mat

esfr

om

felo

ny

dru

gar

rest

reci

div

ism

mo

del

s.

Va

ria

ble

Fel

on

yd

rug

arr

est

wit

hin

12

mo

nth

sF

elo

ny

dru

ga

rres

tw

ith

in2

4m

on

ths

An

y

trea

tmen

t

(N=

51

,979

)

Tre

atm

ent

G9

0d

ays

(N=

23

,89

6)

Tre

atm

ent

Q9

0d

ays

(N=

28

,08

3)

No

trea

tmen

t

(N=

81

,797

)

Tre

atm

ent

(N=

51

,97

9)

Tre

atm

ent

G9

0d

ays

(N=

23

,896

)

Tre

atm

ent

Q9

0d

ays

(N=

28

,083

)

No

trea

tmen

t

(N=

81

,797

)

Dem

og

rap

hic

cha

ract

eris

tics

Ag

e(y

ears

)0

.96

40

.96

20

.967

0.9

70

0.9

64

0.9

62

0.9

67

0.9

66

Mal

e1

.15

21

.09

21

.228

0.9

97

1.1

75

1.1

25

1.2

24

1.0

33

Bla

ck*

1.5

53

1.6

05

1.4

21

1.5

89

1.6

50

1.6

69

1.5

90

1.6

62

His

pan

ic*

0.9

93

0.9

78

0.9

86

1.1

33

1.0

46

0.9

79

1.0

99

1.1

69

Cri

min

al

his

tory

Curr

ent

vio

lent

off

ense

00

.49

00

.49

00

.490

0.5

57

0.4

86

0.4

96

0.4

78

0.5

93

Curr

ent

pro

per

tyoff

ense

00

.71

40

.66

90

.752

0.6

62

0.7

63

0.7

37

0.7

79

0.7

33

Curr

ent

oth

eroff

ense

00

.59

30

.59

70

.576

0.6

38

0.6

05

0.6

29

0.5

77

0.7

37

#p

rio

rar

rest

s1

.01

91

.01

81

.018

1.0

21

1.0

21

1.0

19

1.0

22

1.0

26

#p

rio

rd

rug

off

ense

s1

.19

51

.20

71

.188

1.1

61

1.2

12

1.2

20

1.2

09

1.1

72

#p

rio

rsu

per

vis

ion

s0

.99

00

.99

50

.981

0.9

95

0.9

87

0.9

95

0.9

77

0.9

91

#p

rio

rp

riso

nte

rms

0.9

61

0.9

64

0.9

58

0.9

72

0.9

56

0.9

50

0.9

63

0.9

69

Su

per

visi

on

DO

Psu

per

vis

ion-

1.5

92

1.4

99

1.8

06

1.4

22

1.4

55

1.4

60

1.4

75

1.3

26

FP

sup

erv

isio

n1

.32

51

.16

91

.730

1.2

08

1.2

53

1.1

78

1.4

02

1.2

26

Fie

ldco

nta

cts/

yea

r1.0

00

0.9

96

1.0

04

1.0

06

0.9

96

0.9

98

0.9

95

1.0

01

Hom

eco

nta

cts/

yea

r1

.01

21

.01

01

.013

1.0

08

1.0

12

1.0

12

1.0

11

1.0

09

Offi

ceco

nta

cts/

yea

r0

.97

30

.97

30

.978

0.9

72

0.9

73

0.9

70

0.9

78

0.9

74

UA

test

s/yea

r0

.97

60

.97

60

.989

0.9

27

0.9

81

0.9

78

0.9

90

0.9

52

Po

siti

ve

UA

s/yea

r1

.09

41

.06

71

.115

1.1

05

1.0

94

1.0

78

1.1

11

1.0

75

Po

siti

ve

UA

firs

tm

on

th1

.12

51

.19

31

.011

1.0

14

1.0

61

1.1

00

1.0

03

1.0

07


Geo

gra

ph

icin

dic

ato

r

Jud

icia

lci

rcu

it1.

0.6

34

0.6

12

0.6

07

0.5

91

0.6

64

0.6

25

0.6

78

0.5

44

Jud

icia

lci

rcu

it2.

0.6

73

0.6

06

0.7

17

0.6

36

0.7

43

0.6

67

0.7

91

0.6

77

Jud

icia

lci

rcu

it3.

0.7

78

0.6

53

0.8

70

0.5

54

0.6

52

0.4

89

0.7

82

0.5

16

Jud

icia

lci

rcu

it4.

0.8

83

0.7

82

0.8

93

0.7

53

0.8

95

0.8

46

0.8

49

0.8

16

Jud

icia

lci

rcu

it5.

0.6

92

0.7

68

0.4

95

0.6

58

0.7

48

0.7

62

0.6

60

0.6

59

Jud

icia

lci

rcu

it6.

0.8

88

0.9

06

0.7

28

1.0

01

0.8

96

0.8

53

0.8

55

0.9

11

Jud

icia

lci

rcu

it7.

0.8

98

0.8

51

0.7

35

0.9

17

0.9

13

0.9

15

0.7

46

0.8

43

Jud

icia

lci

rcu

it8.

1.4

14

1.2

80

1.5

05

1.2

23

1.3

72

1.3

14

1.3

96

1.2

15

Jud

icia

lci

rcu

it9.

0.8

10

0.8

00

0.6

65

0.9

10

0.8

63

0.8

34

0.7

93

0.8

95

Jud

icia

lci

rcu

it1

0.

0.8

42

0.8

98

0.6

82

0.9

49

0.9

69

0.9

96

0.8

70

0.9

52

Jud

icia

lci

rcu

it1

1.

0.9

01

0.9

06

0.9

19

0.8

61

0.9

39

0.9

36

0.9

45

0.8

72

Jud

icia

lci

rcu

it1

2.

0.8

61

0.8

53

0.7

76

0.9

10

0.8

89

0.8

46

0.8

66

0.8

73

Jud

icia

lci

rcu

it1

3.

1.1

03

1.1

35

0.8

61

1.1

56

0.9

92

0.9

83

0.8

89

1.0

77

Jud

icia

lci

rcu

it1

4.

0.8

40

0.9

18

0.7

18

0.6

47

0.7

89

0.7

43

0.7

96

0.6

18

Jud

icia

lci

rcu

it1

5.

0.7

21

0.7

38

0.6

48

0.8

02

0.7

70

0.7

95

0.7

09

0.8

16

Jud

icia

lci

rcu

it1

6.

1.5

11

1.4

97

1.1

04

1.0

75

1.5

45

1.4

42

1.3

77

0.9

54

Jud

icia

lci

rcu

it1

8.

0.8

76

0.8

80

0.7

90

0.8

78

0.8

50

0.8

17

0.8

33

0.8

27

Jud

icia

lci

rcu

it1

9.

0.8

34

0.8

53

0.7

34

0.8

54

0.8

76

0.9

57

0.7

67

0.8

27

Jud

icia

lci

rcu

it2

0.

0.8

62

0.8

76

0.7

78

0.8

18

0.9

59

0.9

63

0.9

04

0.7

81

Jud

icia

lci

rcu

itn

on

e.0

.506

0.3

62

0.6

10

0.5

74

0.4

86

0.3

93

0.5

54

0.5

78

R2

0.0

52

20

.06

78

0.0

36

80

.068

00

.07

63

0.0

90

60

.061

90

.086

1

Max

imum

-res

cale

dR

20

.109

30

.12

84

0.0

86

60

.114

80

.12

91

0.1

45

90

.110

30

.127

2

Ital

od

ds

rati

os

init

alic

sar

est

atis

tica

lly

sign

ifica

nt

atth

e0

.05

lev

el.

*W

hit

eis

refe

ren

ceca

teg

ory

..Ju

dic

ial

circ

uit

17

isth

ere

fere

nce

cate

gory

.-C

om

munit

yco

ntr

ol

isth

ere

fere

nce

cate

gory

.0C

urr

ent

dru

go

ffen

seis

the

refe

ren

ceca

teg

ory

.


References

Anglin, D. M., Longshore, D., Turner, S., McBride, D., Inciardi, J. & Prendergast, M.

(1996). Studies of the functioning and effectiveness of Treatment Alternatives to Street

Crime (TASC) programs: Final report.

Banks, D. & Gottfredson, D. C. (2003). The effects of drug treatment and supervision on

time to rearrest among drug treatment court participants. Journal of Drug Issues 33(2),

385Y412.

Belenko, S. (2001). Research on drug courts: A critical review 2001 update. The National

Center on Addiction and Substance Abuse.

Berk, R. A. (1987). Causal inference as a prediction problem. In D. M. Gottfredson &

M. Tonry (Eds.), Prediction and classification: Criminal justice decision making (pp.

183Y200). Chicago: The University of Chicago Press.

Bonta, J., Law, M. & Hanson, K. (1998). The prediction of criminal and violent recidivism

among mentally disordered offenders: A meta-analysis. Psychological Bulletin 123,

123Y142.

Brewster, M. P. (2001). An evaluation of the Chester County (PA) Drug Court Program.

Journal of Drug Issues 31(1), 177Y206.

Bureau of Justice Statistics. (1995). Characteristics of adults on probation, 1995. (Publica-

tion No. NCJ 164267). Washington, DC: Department of Justice, Bureau of Justice Statistics.

Bureau of Justice Statistics. (2003a). Probation and parole in the United States, 2002.

(Publication No. NCJ 201135). Washington, DC: Department of Justice, Bureau of Justice

Statistics.

Bureau of Justice Statistics. (2003b). Prisoners in 2002. (Publication No. NCJ 200248).

Washington, DC: Department of Justice, Bureau of Justice Statistics.

Chanhatasilpa, C., MacKenzie, D. L. & Hickman, L. J. (2000). The effectiveness of

community-based programs for chemically dependent offenders: A review and assessment

of the research. Journal of Substance Abuse Treatment 19, 383Y393.

Deschenes, E. P., Moreno, K. & Condon, C. (2001). Success of drug court participants:

Central and South Justice Centers Superior Court of Orange County, California.

Falkin, G. P., Strauss, S. & Bohen, T. (1999). Matching drug-involved probationers to appro-

priate drug interventions: A strategy for reducing recidivism. Federal Probation 63(1), 3Y8.

Fielding, J. E., Tye, G., Ogawa, P. L., Imam, L. J. & Long, A. M. (2002). Los Angeles County

drug court programs: Initial results. Journal of Substance Abuse Treatment 23(3), 217Y224.

Finigan, M. (1996). Societal outcomes and cost savings of drug and alcohol treatment in the

state of Oregon. Report prepared for Office of Alcohol and Drug Abuse Programs, Oregon

Department of Human Resources, and Governor’s Council on Alcohol and Drug Abuse

Programs.

Flynn P. M., Craddock, S. G., Hubbard, R. L., Anderson, J. & Etheridge, R. M. (1997).

Methodological overview and research design for DATOS. Psychology of Addictive

Behaviors 11(4), 230Y243.

Fulton, B., Latessa, E., Stichman, A. & Travis, L. (1997). The state of ISP: Research and

policy implications. In R. P. Corbett, Jr. & M. K. Harris (Eds.), Federal Probation 61(4),

65Y75.

Gendreau, P., Goggin, C. & Little, T. (1996). Predicting adult offender recidivism: What

works! (User Report No. 1996-07). Ottawa: Department of the Solicitor General of

Canada.

Gendreau, P., Goggin, C., Cullen, F. T. & Andrews D. A. (2000). The effects of community

sanctions and incarceration on recidivism. Forum on Corrections Research 12(3), 10Y13.


Glaze, L. (2004). Probation and parole in the United States, 2003. (Publication No. NCJ

205336). Washington, DC: Department of Justice, Bureau of Justice Statistics. http://

www.ojp.usdoj.gov/bjs/pub/pdf/ppus03.pdf.

Gottfredson, D. C. & Exum, M. L. (2002). The Baltimore city drug treatment court: One-

year results from a randomized study. Journal of Research in Crime and Delinquency

39(3), 337Y356.

Harrell, A. (1998). Drug courts and the role of graduated sanctions. (Publication No. NCJ

169597). Washington, DC: National Institute of Justice.

Hubbard, R. L., Rachael, J. V., Craddock, S. G. & Cavanaugh, E. (1984). Treatment

outcome prospective study (TOPS): Client characteristics and behaviors before, during,

and after treatment (DHHS Publication No. [ADM] 84-1349). In F. Tims & J. Ludford

(Eds.), Drug abuse treatment evaluation: Strategies, progress, and prospects (NIDA

Monograph No. 51) (pp. 29Y41). Rockville, MD: National Institute on Drug Abuse.

Hubbard, R. L., Craddock, S. G., Flynn, P. M., Anderson, J. & Etheridge, R. M. (1997).

Overview of 1-year follow-up outcomes in the Drug Abuse Treatment Outcome Study

(DATOS). Psychology of Addictive Behaviors 11(4), 261Y278.

Lattimore, P.K. (July 1999). Effectiveness of residential drug treatment for Florida

probationers. Paper presented at the Annual Conference on Criminal Justice Research

and Evaluation: Enhancing Policy and Practice, Washington, DC.

Lattimore, P. K., Krebs, C. P., Cowell, A. J. & Koetse, W. (2004). Alternative approaches to

the analysis of large, administrative criminal justice databases. Paper presented at the

2004 American Society of Criminology Conference, Nashville.

Linster, R. L. (1999). Evaluation of Florida’s residential treatment program prison diversion

program, Final report. Washington, DC: Unpublished final report submitted to the

National Institute of Justice.

Linster, R. L., Lattimore, P. K. & Dougherty, K. (1998). Residential drug treatment for

Florida probationers: Preliminary findings. Paper presented by P. Lattimore at the

American Probation and Parole Association Winter Training Institute, Orlando, FL.

Lurigio, A. J. (2000). Drug treatment availability and effectiveness: Studies of the general

and criminal justice populations. Criminal Justice and Behavior 27, 495Y528.

Martin, S. S., Butzin, C. A. & Inciardi, J. (1995). Assessment of a multi-state therapeutic

community for drug involved offenders. Journal of Psychoactive Drugs 27, 109Y116.

Miethe, T., Lu, H. & Reese, E. (2000). Reintegrative shaming and recidivism risks in

drug court: Explanations for some unexpected findings. Crime and Delinquency 46(4),

522Y541.

Oregon Department of Corrections. (1994). Comparison of outcomes and costs: Residential

and outpatient treatment programs for inmates Y alcohol and drug, mental health, sex

offender, and social skills treatment. Salem, OR: Oregon Department of Corrections,

Research and Evaluation Unit.

Paparozzi, M. (1999). Evaluation of intensive supervision programs (ISP) in Montreal and

Toronto. Unpublished manuscript.

Pelissier, B., Wallace, S., O’Neil, J. A., Gaes, G. G., Camp, S., Rhodes, W. & Saylor, W.

(2001). Federal prison residential drug treatment reduces substance use and arrests after

release. American Journal of Drug and Alcohol Abuse 27(2), 315Y337.

Petersilia, J. (1998). A decade of experimenting with intermediate sanctions: What have we

learned? Federal Probation 62(2), 3Y9.

Prendergast, M. L., Anglin, M. D. & Wellisch, J. (1995). Up to speed: Treatment for drug-

abusing offenders under community supervision. Federal Probation 59(4), 66Y75.

Prendergast, M. L., Wellisch, J. & Wong, M. M. (1996). Residential treatment for women


parolees following prison-based drug treatment: Treatment experiences, needs, and

service outcomes. Prison Journal 76, 253Y274.

Rhodes, W. & Gross, M. (1997). Case management reduces drug use and criminality among

drug-involved arrestees: An experimental study of an HIV prevention intervention. A final

summary report presented to the National Institute of Justice and the National Institute on

Drug Abuse.

Rhodes, W., Pelissier, B., Gaes, G. G., Saylor, W., Camp, S. & Wallace, S. (2001).

Alternative solutions to the problem of selection bias in an analysis of federal residential

drug treatment programs. Evaluation Review 25(3), 331Y369.

Simpson, D., Joe, G., Broome, K., Hiller, M., Knight, K. & Rowan-Szal G. (1997a).

Program diversity and treatment retention rates in the Drug Abuse Treatment Outcome

Study (DATOS). Pyschology of Addictive Behaviors 1194, 279Y293.

Simpson, D., Joe, G. W., Rowan-Szal, G. A. & Greener, J. M. (1997b). Drug abuse

treatment process components that improve retention. Journal of Substance Abuse

Treatment 14(6), 565Y572.

Simpson, D., Joe, G. W. & Rowan-Szal, G. A. (1997c). Drug abuse treatment retention and

process effects on follow-up outcomes. Drug and Alcohol Dependence 47(3), 227Y235.

Stephan, J. J. (2004). State prison expenditures, 2001. (Publication No. NCJ 202949).

Washington, DC: Department of Justice, Bureau of Justice Statistics. http://www.ojp.

usdoj.gov/bjs/pub/pdf/spe01.pdf.

Truitt, L., Rhodes, W. M., Seeherman, A. M., Carrigan, K. & Finn, P. (March 2000). Phase

I: Case studies and impact evaluations of Escambia County, Florida and Jackson County,

Missouri drug courts. Cambridge, MA: Abt Associates.

Turner, S., Longshore, D., Wenzel, S., Deschenes, E., Greenwood, P., Fain, T., Harrell, A.,

Morral, A., Taxman, F., Igushi, M., Greene, J. & McBride, D. (2002). A decade of drug

treatment court research. Substance Use and Misuse 37(12Y13), 1489Y1527.

Van Stelle, K. R., Mauser, E. & Moberg, D. P. (1994). Recidivism to the criminal justice

system of substance-abusing offenders diverted into treatment. Crime and Delinquency

40, 175Y196.

Vito, G. F., Wilson, D. G. & Holmes, S. T. (1993). Drug testing in community corrections:

Results from a four-year program. Prison Journal 73, 343Y354.

Weisburd, D., Petrosino, A. & Mason, G. (1993). Design sensitivity in criminal justice

experiments. In M. Tonry (Ed.), Crime and justice: A review of research, (Vol. 17,

pp. 337Y379). Chicago: University of Chicago Press.

Wexler, H. K., Graham, W. F., Koronowski, R. & Lowe, L. (1995). Evaluation of amity in-

prison and post-release substance abuse treatment programs. Washington, DC: National

Institute of Drug Abuse.

Wolfe, E., Guydish, J. & Termondt, J. (2002). A drug court outcome evaluation comparing

arrests in a two year follow-up period. Journal of Drug Issues 32(4), 1155Y1172.

About the authors

Pamela K. Lattimore is Professor and Director of the Center for the Management of Risk

Behavior, Department of Criminology and Criminal Justice at the University of South

Carolina. Dr. Lattimore is affiliated as a senior research social scientist with Research

Triangle Institute and has directed a number of research and evaluation projects, including

currently serving as co-principal investigator of the Multi-site Evaluation of the Serious and


Violent Offender Reentry Initiative (SVORI), a five-year evaluation of a federal initiative to

improve outcomes for offenders returning to the community from prison. Her research

focuses on the evaluation of interventions, investigation into the causes and correlates of

criminal behavior, particularly among young offenders and probationers, and development

of approaches to improve criminal justice operations. She served as Chair of the Division on

Corrections and Sentencing of the American Society of Criminology from 2001 to 2003 and

serves on the editorial boards of several professional journals. She received her Ph.D. in

economics from the University of North Carolina in 1987.

Christopher P. Krebs is a Senior Research Social Scientist at the Research Triangle

Institute. He earned his B.A. in Sociology from Emory University and his M.S. and Ph.D. in

Criminology from Florida State University. His research interests include research

methodology, juvenile justice and delinquency, corrections and correctional policy, inmate

and offender behavior, and substance use and abuse treatment.

Willem Koetse is a Statistician and Research Analyst at Research Triangle Institute who

works exclusively on criminal justice, substance abuse treatment and violence related

projects. He has a solid background in statistical methodology, experimental design and

using software to manage, model and analyze data. He has a B.A. in Psychology and

Philosophy from SUNY Binghamton, and a master`s degree in Statistics from North

Carolina State University.

Christine Lindquist is a Research Sociologist at Research Triangle Institute. She earned a

Ph.D. in Medical Sociology from the University of Alabama at Birmingham. Her research

interests and experience include multisite evaluation design, violence and substance abuse

prevention, alternative treatment for drug-involved criminal offenders, prisoner reentry, and

violence against women.

Alex J. Cowell is an economist with the Behavioral Health Economics Program at RTI

International. Examples of his work include cost analyses in the areas of substance use,

mental health and criminal justice. Using data for these populations, Dr. Cowell also has

conducted analyses focusing on econometric issues and on topics in services research.


Predicting the effect of substance abuse treatment on probationer recidivism

Documents

Transcript of Predicting the effect of substance abuse treatment on probationer recidivism