This article was downloaded by: [University of Tennessee, Knoxville]On: 19 December 2014, At: 06:37Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK

Journal of OffenderRehabilitationPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/wjor20

Recidivism Among High-RiskDrug FelonsSteven Belenko a , Carol Foltz a , Michelle A. Lang b

& Hung-En Sung ca University of Pennsylvania , USAb Samaritan Village, Inc , USAc Columbia University , USAPublished online: 24 Sep 2008.

To cite this article: Steven Belenko , Carol Foltz , Michelle A. Lang & Hung-En Sung(2004) Recidivism Among High-Risk Drug Felons, Journal of Offender Rehabilitation,40:1-2, 105-132, DOI: 10.1300/J076v40n01_06

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Recidivism Among High-Risk Drug Felons:A Longitudinal Analysis FollowingResidential Treatment

STEVEN BELENKO

University of Pennsylvania

CAROL FOLTZ

University of Pennsylvania

MICHELLE A. LANG

Samaritan Village, Inc.

HUNG-EN SUNG

Columbia University

ABSTRACT Recent interest in increasing access to substance abuse treat-ment for drug-involved offenders has been spurred by concerns over ex-panding prison and jail populations, high recidivism rates for drug-involved offenders, and the close link between illegal drug use and criminalactivity. Chronic untreated drug and alcohol abuse is likely to result in highrates of repeated contacts with the criminal justice system and a greaterlikelihood of reincarceration. Unless these offenders naturally desist fromdrug use, or are successfully engaged in treatment, recidivism is likely toremain high and the courts and correctional systems are likely to continueto be overwhelmed by large numbers of drug-involved offenders. This arti-cle uses multiple recidivism measures to assess the long-term effects of di-version to a highly coercive, long-term residential therapeutic communitytreatment for repeat felony drug offenders charged with drug sales and fac-

Journal of Offender Rehabilitation, Vol. 40 (1/2), 2004. Pp. 105-132.

http://www.haworthpress.com/web/JOR

© 2004 by The Haworth Press, Inc. All rights reserved.

Digital Object Identifier: 10.1300/J076v40n01_06

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ing mandatory incarceration in state prison. If the offenders completed the18-24 month program, all charges were dismissed; dropouts were returnedto court for prosecution and sentenced to state prison. Compared with aclosely matched sample of offenders sentenced to prison, Drug TreatmentAlternative to Prison (DTAP) program participation generally reduced theprevalence and annual rate (adjusting for time in the community) of recidi-vism, and delayed time to first rearrest. In multivariate models of rearrestprevalence and adjusted annual rearrest rate, DTAP program participationwas related to lower recidivism at significance levels between .05 and .10,after controlling for criminal history and other covariates. These findingssuggest that diverting high-risk, prison-bound felony drug sellers tolong-term treatment can yield significant, long-term reductions in recidi-vism. [Article copies available for a fee from The Haworth Document DeliveryService: 1-800-HAWORTH. E-mail address: <docdelivery@haworthpress.com>Website: <http://www.HaworthPress.com> © 2004 by The Haworth Press, Inc. Allrights reserved.]

KEYWORDS Treatment diversion, residential treatment, offenders, recid-ivism

INTRODUCTION

Recent interest in increasing access to substance abuse treatment fordrug-involved offenders has been spurred by concerns over rising prison andjail populations (Belenko, 2000; Belenko & Peugh, 1998; Blumstein & Beck,1999), high recidivism rates for drug-involved offenders (Blumstein & Beck,1999; Harer, 1995; Langan & Levin, 2002; Smith & Polsenberg, 1992), andthe close link between illegal drug use and criminal activity (Ball, Shaffer, &Nurco, 1983; Hawkins, Catalano, & Miller, 1992). Given the law enforcementfocus on drug selling and street drug markets (Kleiman, 1986; Zimmer, 1987),the high conviction and incarceration rates for drug law violators (DuRose &Langan, 2003), and the existence of mandatory minimum sentencing laws inmost states, chronic untreated drug and alcohol abuse is likely to result in highrates of repeated contacts with the criminal justice system and a greater likeli-hood of reincarceration. Unless these offenders naturally desist from drug use,or are successfully engaged in treatment, recidivism is likely to remain highand the courts and correctional systems are expected to continue to be over-whelmed by large numbers of drug-involved offenders.

In a recent national recidivism study of a sample of inmates released in1994 from prisons in 15 states (representing two-thirds of all state prisoners re-leased that year), 68% were rearrested within three years, 47% were

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reconvicted of a new crime, and 25% were sentenced to prison for a new crime(Langan & Levin, 2002). Released drug law violators had a 67% rearrest ratewithin three years, similar to the overall rate for the general prison population.From 1986 to 1989, almost half (49%) of state drug felons on probation wererearrested within three years, most for another drug law violation (Bureau ofJustice Statistics, 1992).

Alternative treatment programs for substance-abusing offenders seek to re-duce these reoffending rates by diverting them from the criminal justice sys-tem into community-based treatment (Belenko, 2000; Inciardi, McBride, &Rivers, 1996; Lurigio, 2000). For diverted offenders, participation in commu-nity-based treatment diminishes their contact with the criminal subculture andantisocial networks of prison environments (Stevens, 1997; Winfree, Mays, &Crowley, 1994), offers the opportunity to have their charges dismissed, andlessens their deviant self-concept (Bahn & Davis, 1991; Clear, Rose, & Ryder,2001).

Common models of treatment alternatives include diversion programs,Treatment Alternatives to Street Crime (TASC), early release from prison tocommunity treatment, and drug courts. Research on drug courts (Belenko,2001) and prison treatment programs (Knight, Simpson, & Hiller, 1999; Mar-tin, Butzin, Saum, & Inciardi, 1999) has demonstrated that treatment interven-tions can have a reductive effect on recidivism, but the effect sizes vary andmay be short-lived if not followed by continuing care. Research on prisontreatment in therapeutic communities generally finds that post-release recidi-vism and drug use are reduced only if inmates continue treatment in the com-munity in aftercare settings (e.g., Knight, Simpson, & Hiller, 1999; Martin,Butzin, Saum, & Inciardi, 1999; Wexler, Melnick, Lowe, & Peters, 1999).However, some of these findings are tempered by concerns about selectionbias and lack of matched non-treatment controls.

Since 1989, drug courts gained increasing popularity as a mechanism forlinking drug-involved offenders to long-term treatment (U.S. Department ofJustice, 1998). A number of studies have found that drug court participationreduces recidivism while under drug court supervision, and for one year fol-lowing drug court participation (Belenko, 2001), but little is know about theirlong-term impact on recidivism or other outcomes, and drug court researchhas often been hampered by weak designs (Belenko, 2002; General Account-ing Office, 2002).

A number of recent studies have examined post-treatment recidivism andfound lower recidivism rates for drug court participants (Bavon, 2000; Harri-son, Parsons, Byrnes, & Sahami, 1999; Miethe, Lu, & Reese, 2000; Peters &Murrin, 2000; Truitt, Rhodes, Seeherman, Carrigan, & Finn, 2000). Drugcourt participants in Douglas County (NE) had lower rates of 12-month recidi-vism (19%) than traditionally adjudicated felony drug offenders (35%)(Spohn, Piper, Martin, & Frenzel, 2001). Listwan, Sundt, Holsinger, andLatessa (2003) found that drug court participation had a significant independ-

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ent impact on new drug arrests within one year of drug court admission, but noimpact on rearrest more generally or on reincarceration within three years, relativeto a comparison sample of offenders who were eligible for the drug court but ei-ther refused to participate or were rejected. The effect on drug rearrests was signif-icant after controlling for background criminal history as well as other factors inmultivariate analyses; the adjusted probability of a new drug arrest was 10% fordrug court clients and 20% for comparison offenders.

Finally, several drug court evaluations using experimental designs foundevidence of reduced recidivism for drug court participants compared with ad-judication-as-usual controls (Gottfredson, Najaka, & Kearley, 2003; Harrell,Cavanagh, & Roman, 1999; Turner, Greenwood, Fain, & Deschenes, 1999).

Some evaluations of TASC programs generally have found lower recidi-vism for program participants (Law Enforcement Assistance Administration,1978; Potter & Starnes, 1981; Van Stelle, Mauser, & Moberg, 1994), but ex-perimental and quasi-experimental multi-site evaluations yielded mixed find-ings that are difficult to interpret (Anglin, Longshore, & Turner, 1999). TASCappears to generate better crime reduction effects among more serious or diffi-cult offenders, but is not as effective with lower risk offenders (Anglin, Long-shore, & Turner, 1999; Turner & Longshore, 1998).

More limited data are available on the crime-reduction effects of treatmentfor offenders diverted into community treatment programs. Spohn and col-leagues (2001) found that (controlling for age, gender, race, and number ofprior arrests) drug court clients had a significantly higher probability and num-ber of rearrests than offenders diverted into treatment. However, the general-izability of recidivism data for traditional diversion programs may be limiteddue to selection bias concerns. For example, eligibility criteria for diversionprograms tend to exclude higher-risk offenders. In fact, Spohn et al. (2001)found that after controlling for level of risk (based on the Level of Services In-ventory–see Andrews & Bonta, 1995), there was no difference in rearrest ratesbetween the drug court and diversion groups.

Both drug courts and other treatment diversion programs have been criti-cized for expanding social control to low-risk offenders who might have per-formed as well under less stringent supervision (Boldt, 1998; Nolan, 2002).Most drug courts exclude drug sellers or those with a prior violent offense;hence more research is needed to determine whether the drug court model canbe successfully adapted to higher-risk offenders. However, there is some evi-dence from other criminal justice-based treatment programs that targetinghigher-risk offenders is likely to have more potential cost and recidivism im-pacts (Gendreau, 1996; Griffith, Hiller, Knight, & Simpson, 1999).

Predictors of Recidivism in Alternative Treatment Programs

Studies of alternative treatment programs have identified several consistentpredictors of post-treatment reoffending. Controlling for treatment comple-

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tion status and length of treatment stay, only criminal history had a relevantimpact on recidivism in Wisconsin’s Treatment Alternative to Prison (TAP)program (Van Stelle, Mauser, & Moberg, 1994). In Escambia County (FL),Peters, Haas, and Murrin (1999) found that younger age, being single, not hav-ing a high school education (or GED), reporting cocaine as their primary drug,and having more prior arrests were associated with higher rearrest prevalenceover the 30-month follow-up period.

Spohn and colleagues (2001) found that age, gender, and criminal historywere significantly associated with 12-month rearrest in multivariate models.Being young or male, or having more arrests in the year prior to current arrestincreased a drug court participant’s likelihood to recidivate. In a national sam-ple of drug court graduates, age, race, gender, and size of the drug court wereall predictors of recidivism (Roman, Townsend, & Bhati, 2002). Offenderswho were 24 or younger, African-American, male, or enrolled in a small drugcourt were more likely to recidivate. However, an evaluation of NorthCarolina’s drug courts also found lower recidivism rates among smaller andrural courts, perhaps reflecting their greater coordination of treatment andmore intensive monitoring of participant progress than in drug court programsfrom large metropolitan areas (Irwin, 2002).

Measuring Recidivism

Previous research on offender recidivism has been hampered by method-ological and data quality problems. First, computerized criminal history data-bases (whether state, local, or federal) often are incomplete, have data entrybacklogs, or high error rates. Second, local law enforcement agencies may notreport lower-level arrests to state or federal data repositories. Third, informa-tion on convictions or sentences is commonly incomplete in many state data-bases. Fourth, in many jurisdictions, rearrest information is contained inseveral different agency databases. Fifth, because collecting recidivism datamanually is expensive and labor-intensive, relatively few studies have beenable to track recidivism for more than one-year post-treatment (e.g., Knight,Simpson, & Hiller, 1999; Martin, Butzin, Saum, & Inciardi, 1999). Thus, withthe exception of studies of prison therapeutic communities (TCs), little isknown about long-term treatment impacts on recidivism.

In addition, studies of recidivism among offenders also have been ham-pered by lack of control for time at risk in the community (Belenko, Fagan, &Dumanovsky, 1994). The failure to adjust for time in custody, or censoring,can artificially inflate estimates of rearrest rates and distort effect sizes. Soparadoxically, higher-risk offenders may appear to have lower rearrest preva-lence due to reduced time in the community.

Further complicating, analyses, specific measures each have limitations inproviding accurate estimates of the true rate of criminal behavior. Self-re-ported criminal behavior may reveal the existence of crimes that did not result

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in an official arrest, but has the undesirable property of being subject to under-or overreporting. Reconviction may be considered a more accurate measure ofcrime, because the constitutional presumption of innocence assumes that a de-termination of guilt is necessary before it can be concluded that a crime wascommitted. Moreover, the conditional probabilities of arrest given commis-sion of a crime may vary by factors such as neighborhood, class, or race thatcan consequently result in biased estimates of true rates of crime. Others haveargued for a focus on reincarceration, because it is the most expensive compo-nent of the criminal justice system and the outcome that generates the greatestsocial cost.

Nevertheless, because of data collection constraints and the common unre-liability and incompleteness of official criminal justice arrest and convictionrecords, previous research on recidivism most often has utilized rearrest as thesole outcome measure. One recent exception is the study by Spohn and col-leagues (2001), who analyzed recidivism outcomes for drug court and com-parison samples using several different measures of recidivism (rearrest,felony rearrest, felony drug rearrest, reconviction, felony reconviction, felonydrug reconviction, time to first arrest, time to first felony rearrest). They foundthat drug court reduced recidivism on most but not all measures. Drug courtparticipants had significantly lower rates of rearrest, any reconviction, andnumber of new arrests, but differences in felony convictions and time to firstrearrest were not significant. Until sufficient data exist to determine whatmight be the most reliable and appropriate recidivism measure, the inclusionof multiple measures is probably warranted.

The present article uses multiple recidivism measures to analyze thelong-term effects of participation in long-term residential treatment forhigh-risk felony drug offenders. These offenders were charged with drug salesand had prior nonviolent felony convictions that made them subject to manda-tory imprisonment (both are factors that typically preclude eligibility for treat-ment diversion programs) and were diverted to long-term treatment inresidential TCs. Participants that completed the 18-24 month program had allcriminal charges dismissed. Participants who dropped out of the program orwere expelled were brought back to court and prosecuted on the originalcharges; most were then sentenced to state prison (Hynes & Swern, 2002).

The Drug Treatment Alternative to Prison (DTAP) program was developedby the Office of the District Attorney in Kings County (Brooklyn), New York,and began in October 1990. The DTAP program is unique among criminal jus-tice-based alternative drug treatment programs for several reasons. First, it ac-cepts felony offenders charged with selling drugs, who have an underlyingdrug abuse problem. Second, because offenders must have one or more priornonviolent felony convictions to be eligible for DTAP, they are facing a man-datory prison sentence under New York State sentencing laws if convicted onthe new charge. Third, the District Attorney screens cases for evidentiarystrength, to ensure that only cases with a high likelihood of conviction are ac-

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cepted into the program. Accordingly, in contrast to most diversion programs,the DTAP program attempts to avoid net-widening and maximize the proba-bility that it is serving a truly prison-bound population. In previous studies, wefound that DTAP participants have substantially higher retention rates thanthose typically observed in residential treatment programs (Lang & Belenko,2000). In a study of recidivism among DTAP’s early participants, Dynia andSung (2000) found that 32% of program participants were rearrested during 3years follow-up, compared with 47% in a comparison sample of traditionallyprosecuted defendants.

Our analyses of the recidivism impacts of DTAP are important for severalreasons. First, as noted above, DTAP differs from most treatment alternativeprograms in serving a high-risk, prison-bound population. Research suggeststhat treatment programs that target higher-risk offenders are more likely toyield tangible economic and public safety benefits (Griffith, Hiller,Knight, & Simpson, 1999). Second, DTAP participants receive 18-24months of intensive residential treatment, a higher treatment dose as com-pared to community-based offender treatment (e.g., drug court, treatment di-version, probation- supervised treatment), which tend to only incorporateshort-term residential or outpatient treatment (Peyton & Gossweiler, 2000).Third, we were able to track recidivism for up to five years post-treatment orpost-prison release, allowing examination of longer-term outcomes and trendsover time. Fourth, we had access to multiple measures of recidivism from acomprehensive offender-based database that includes arrest and court pro-cessing data for all New York City arrests. Fifth, although the study was notable to incorporate an experimental design, our sample matching produced ex-perimental and comparison samples that were quite comparable; a screeninginterview allowed us to match on drug use history and motivation for treat-ment as well as other commonly used matching criteria such as demographiccharacteristics, arrest charge, and criminal history. Finally, we controlled fortime at risk in the community, making our between-groups arrest rate mea-sures and comparisons more accurate.

METHODS

The data for these analyses were collected as part of a larger evaluation ofthe impact of DTAP. The longitudinal quasi-experimental design included aprospective experimental sample of 150 offenders diverted to DTAP (90 ofwhom completed DTAP and 60 of whom dropped out or failed), and a com-parison sample of 130 offenders sentenced to state prison, matched on arrestcharges, prior felony convictions, age, race, gender, drug use, and desire fordrug treatment. Additionally, retrospective samples of 64 DTAP graduatesand 68 DTAP dropouts were randomly selected among all DTAP participantswho entered DTAP before the current evaluation began in May 1995, but who

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graduated or dropped out during the period of time that the prospective samplewas in DTAP treatment. Because DTAP residential treatment lasts up to 24months, and both DTAP dropouts and comparison participants are sentencedto several years of prison, it was necessary to draw the additional retrospectivesample to assure that a sufficient number of participants would be available totrack recidivism in the community for a multiyear follow-up period.

For the prospective sample of 150 DTAP participants and 130 compari-sons, an extensive battery of intake interview instruments was administered,including items related to drug use, prior treatment history, criminal activity,employment and earnings, physical and psychological health, HIV risk behav-iors, and social stability. Standardized assessments included the Addiction Se-verity Index (McLellan et al., 1985), the Michigan Alcohol Screening Test(MAST) (Selzer, 1971), the Risk Behavior Assessment (Coyle, 1993), and theTexas Christian University’s Self Rating Form (Knight, Holcom, & Simpson,1994). Details on the sampling and data collection procedures are presentedelsewhere (Lang & Belenko, 2000, 2001). Participants in the retrospectivesamples were administered a more limited intake interview by staff of theKings County (Brooklyn) District Attorney’s Office. On most baseline indica-tors there were no significant differences between prospective and comparisonparticipants (Belenko, Cowan, & Lang, 1998) with the exception that compar-ison participants had a greater number of prior misdemeanor convictions,which were controlled for in our analyses.

Recidivism data were drawn in several waves from the offender trackingdatabase maintained by the New York City Criminal Justice Agency (CJA).The CJA, the pretrial services agency for New York City, conducts assessmentinterviews of all individuals arrested in New York City, and tracks casesthrough sentencing from data provided by the New York State Office of CourtAdministration. For all research participants, we provided CJA with name,date of birth, sample arrest date, and the New York State Identification Num-ber (NYSID). The NYSID is a fingerprint-based ID used by all New YorkState law enforcement agencies to track offenders. Each arrest record containsthe NYSID number, and was used to link our research participants to new ar-rests, while name and date of birth were used to verify the link. The final waveof data collection reflected all arrests in the CJA database as of July 31, 2002.Because our research participants completed treatment (DTAP Completers) orwere released from prison for their original target arrest (DTAP Dropouts andComparisons) between January 6, 1994 and April 14, 2003, the available timefor follow-up ranged from 0 to 103 months.

The analytical complexities of this study were driven by the fact that DTAPparticipants spent up to 24 months in intensive residential treatment, much ofit spent in isolated upstate residences, which severely limited their opportuni-ties to be arrested. Similarly, DTAP dropouts and comparison sample partici-pants were generally sentenced to prison for several years, and mandated toserve prison time before being released and having an opportunity to reoffend.

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However, several participants were in fact rearrested while in DTAP or inprison; these new arrests were included in two of our overall prevalence esti-mates (see Table 2).

Recidivism Measures

Our basic measures of recidivism included new arrests post-admission (ex-cluding traffic offenses), and new convictions for a criminal offense after re-lease from prison or DTAP treatment. For analyses of reconviction, wedistinguished charge severity (felony, misdemeanor) and charge type (drug,nondrug), and minor violations were included (although not differentiated bydrug/non-drug type). Additionally, we captured the occurrence of new prisonand jail sentences across all arrests that occurred after release from DTAP treat-ment or prison. We also computed the total months incarcerated for these newsentences, separately for prison and jail sentences, by summing the minimumsentences received for all prison and jail sentences occurring after release fromprison or DTAP treatment (these variables reflected the total incarceration timereceived post-release, and include time served after the data collection cutoffdate of July 31, 2002). Given that data on the actual time incarcerated were notavailable, we opted to use lower-bound estimates: a majority of nonviolent NewYork State prison inmates are released on or near their minimum sentence (pa-role eligibility) date, although some are denied parole initially and serve addi-tional time. Jail inmates in New York City are also typically released before theexpiration of their sentences. The time spent in pretrial detention for any arrestwhich later received a jail/prison sentence was not added onto the minimumsentence, because the time in pretrial detention usually is credited as time servedtoward that jail/prison sentence. Participants who never received a prison or jailsentence were assigned 0 months in these calculations.

The prevalence of recidivism was calculated both (a) overall, regardless ofthe person’s time in the community, and (b) only for cases who were in thecommunity for a given period of time or longer (we calculated prevalence forthose with at least 1-, 3-, and 5 years of community time post-release fromtreatment or prison). By including only those cases who had an opportunity toreoffend, the second calculation provides a recidivism rate which was not arti-ficially deflated by including cases who did not have, or had very limited op-portunity to reoffend. It should be noted that this second measure of recidivismalso possesses potential shortcomings, given that it excludes an arrest in agiven period if the person was not in the community during that entire fol-low-up period.

Adjusted Annual Arrest Rate

Given that DTAP dropouts and comparisons were incarcerated for part ofthe follow-up period, they had less opportunity to reoffend. In contrast, DTAP

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completers had longer community times than any other group and thus had thegreatest opportunity to recidivate. Both factors would partly confound any ob-served treatment effect. Indeed, except in a few instances, the groups differedsignificantly on both their unadjusted and adjusted community times(Bonferroni adjusted significance level was p < .005, see Table 1). For thesereasons, it was important to calculate an adjusted annualized rearrest rate to ad-dress potential confounds caused by differential time in the community acrosssamples.

We controlled for actual time in the community by first calculating thenumber of months between DTAP completion or release from prison and July31, 2002, and then subtracting the number of months spent in jail, prison, pre-trial detention, and out of the country or state during this same follow-up pe-riod. In addition, a small number of cases absconded from the DTAP programbut were allowed to later return to treatment (n = 18). This absconded time wasincluded in their time in the community post-release. The final value resultedin a measure of “adjusted community time,” which ranged from 0 to 86.5months.1 The total number of post-treatment/prison arrests was then dividedby the adjusted community time and multiplied by 12 to yield an adjusted an-nual arrest rate, which reflects the number of arrests per year in the community.

Belenko et al. 115

� Table 2: Post-Admission and Post-Release Recidivism:Prospective Sample versus Comparison Sample

(from 0 to 103 Months Follow-Up)

DTAP Prospective(n = 150)

Comparison(n = 130)

n P n P Wald X2 N p

Rearresteda 86 57 98 75 9.88 280 .002

Reconvictedb 61 42 84 65 13.73 275 .000

New Jail Sentenceb 43 30 66 51 12.54 275 .000

New Prison Sentenceb 10 7 23 18 7.08 275 .008

M SD M SD F dfd p

Mean # Rearrestsac 1.77 2.61 2.12 2.23 6.12 278 .014

Adjusted # Arrests perYear at Riskbc

0.49 0.77 0.84 1.27 8.80 264 .003

a Includes all new arrests occurring after admission to treatment (DTAP) or incarceration onsample offense (comparisons).b Reconvictions and sentences are post-treatment or post-prison release.c Analysis of variance was conducted on the natural log of number of rearrest, but meansand standard deviations are presented in terms of raw scores.Note. dfd = degrees of freedom denominator.

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Because this measure controls for the “availability” to be rearrested, it is a truermeasure of reoffending rates than simple prevalence.

Logistic regression was used to make basic comparisons of various recidi-vism measures, while analysis of variance was used to compare group arrestrates. In addition, survival analyses were conducted on time to first rearrest,and regression models were used to predict the adjusted annual rearrest rate.Covariates included factors found to differentiate our samples at baseline, fac-tors associated with recidivism in other studies, and factors with significantcorrelations with the dependent measures.

Because participants in the retrospective sample exited DTAP earlier thanthe prospective sample, more participants in the retrospective sample could beobserved for longer follow-up periods. However, it would not be appropriateto contrast the recidivism of the comparison sample with a combined prospec-tive-retrospective DTAP sample, because the comparison sample wasmatched to the prospective DTAP sample only. Accordingly, the analysescontrast the comparison group separately with each experimental sample. Theregression analyses of rearrest rates and the annual adjusted rearrest rates,which accommodate for prior criminal history, contrasts only the prospectivesamples with the comparison sample, for this same reason and for the addi-tional reason that data on background variables used in the predictive modelwere not available for the retrospective samples who entered DTAP prior tothis research study.

RESULTS

Across multiple outcome measures and with adjustments to post-releasecommunity time, DTAP program participation generally reduced the preva-lence and rate of recidivism, and delayed time to first rearrest, compared withsimilar offenders sentenced to prison.

Overall Recidivism Prevalence:DTAP Prospectives versus Comparisons

Table 2 presents basic descriptive data on all recidivism events for the pro-spective sample without any adjustment for time in the community. Thus,these measures indicate the overall likelihood of a new recidivism event (a) afterrelease from prison or DTAP treatment or (b) the arrest rate since either being ad-mitted to treatment (DTAP prospective sample) or to prison (comparisons). Aseries of logistic regression analyses for each recidivism event indicated sig-nificant reductions in nearly all recidivism measures for prospective DTAPparticipants. Fifty-seven percent of all prospective DTAP participants were re-arrested for any offense at least once over the follow-up period compared with75% of the comparison participants, 42% were reconvicted of any offense

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(compared with 65% of comparisons, 30% had a new jail sentence (versus51% of comparisons), and only 7% incurred a new prison sentence (versus18% of comparisons). Being placed in DTAP drug treatment reduced the oddsof a new rearrest by 56%, a new reconviction by 60%, a new jail sentence by59%, and a new prison sentence by 65%.

These significant differences remained after controlling for several mea-sures of prior criminal record (being arrested prior to 16 years of age, numberof prior drug and non-drug convictions, total number of months ever incarcer-ated) in a second parallel set of logistic regression (not shown in Table 2): anew rearrest post-admission (Wald 2 [1, N = 280] = 4.85, p < .05, Odds Ratio[OR] = 0.54), a new reconviction (Wald 2 [1, N = 275] = 8.98, p = .003, OR =0.45), a new jail sentence (Wald 2 [1, N = 275] = 6.83, p = .009, OR = 0.49), and anew prison sentence (Wald 2 [1, N = 275] = 7.32, p = .007, OR = 0.32). Hencecomparisons were twice as likely as DTAP participants to be rearrested, bereconvicted, or incur a new jail sentence, and three times more likely to acquire anew prison sentence.

An analysis of variance of the natural log of the number of post-admissionrearrests indicated that the prospective DTAP sample also had a significantlyfewer number of rearrests post-admission than the comparison sample (seeTable 2). Additionally, the adjusted annual arrest rate for post-treatment/post-prison rearrests (based on adjusted community time) was almost twice as highfor the comparison participants than for prospective DTAP participants (seeTable 2). In separate analyses (not shown), which compared the adjusted an-nual rearrest rates only for participants with 3, 4, or 5 years time in the commu-nity, significant differences were not found. However, these results areconfounded by the fact that the number of participants was notably reduced af-ter three years (e.g., there were only 33 and 13 comparison participants, re-spectively, with at least 4 or more years of community time available for thoseanalyses), and participants with more than three years of community time areoverrepresented by lower risk participants.

Rearrest by Time in the Community, All Study Samples

Table 3 depicts how the cumulative probability of rearrest rose for all sam-ples as time in the community increased. Each follow-up interval includes cu-mulative rearrests through the end of that period for all participants who had atleast that amount of time in the community following completion of DTAP treat-ment (completers) or release from prison (DTAP dropouts and comparisons). Forexample, 80% of the 61 comparison participants who had at least four years in thecommunity following prison release on the original baseline arrest, had at leastone rearrest within that four-year period. The highest rearrest prevalence occurredamong the comparison and the retrospective dropout samples.

Combining data for all DTAP prospective participants, rearrest prevalencewas significantly lower for DTAP prospective participants than comparison

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participants for each discrete follow-up period, suggesting that treatment ef-fects are robust over time. For example, 23% of DTAP prospectiveparticipants were rearrested in the first year after release from treatment orprison, compared with 45% of comparisons (Wald 2 [1, N = 273] = 13.83, p <.001, OR = 0.37). Among those with at least four years in the community followingtreatment or prison, 55% of prospective DTAP participants had at least one rearrestduring that period compared with 80% of the comparisons (Wald 2 [1, N = 157] =9.84, p = .002, OR = 0.30). Significant group differences were also found at 2 and3 years of community time, respectively, Wald 2 (1, N = 60) = 7.81, p = .005, OR =0.49 and Wald 2 (1, N = 226) = 11.22, p < .001, OR = 0.39.

New Incarceration Time: DTAP Prospectives versus Comparisons

DTAP participation also significantly reduced the mean number of totalmonths sentenced to jail or prison received after DTAP treatment or releasefrom prison. T-tests were computed on the natural log of the total number ofmonths incarcerated (separately for prison sentences and jail/time served sen-tences). DTAP prospectives averaged a total of 2.6 months (SD = 10.7) in newprison time, compared with 6.8 months (SD = 15.8) for comparisons (t[218] =2.75, p = .006, degrees of freedom adjusted for unequal group variance). Thus, forevery group of 100 felony drug offenders diverted to residential treatment, therewould be a reduction of 420 person-months of state prison time over an averageadjusted community time of 46.8 months for DTAP prospectives. New time sen-

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� Table 3: Post-Treatment/Prison Rearrest Prevalence by Yearsat Risk in the Communitya (Cumulative Percent Rearrested

Within Time Period)

1 Year 2 Years 3 Years 4 Years

n at risk P n at risk P n at risk P n at risk P

DTAP Samples:

Prospective Completers 90 20 90 38 86 48 76 55

Retrospective Completers 64 16 64 39 64 45 64 53

Prospective Dropouts 53 28 47 40 38 40 20 55

Retrospective Dropouts 68 43 68 56 65 75 60 80

DTAP Prospectives 143 23 137 39 124 45 96 55

Comparison Sample 130 45 123 56 102 68 61 80

a Includes rearrests after completion of treatment (DTAP completers) or release from prison(DTAP dropouts and comparisons).

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tenced to jail (including pretrial detention which comprised “time served” sen-tences) was also significantly lower for DTAP (M = 0.44 months, SD = 2.1)relative to the comparison group (M = 1.08, SD = 2.7, t[218] = 3.26, p < .001, de-grees of freedom adjusted for unequal variances).

Reconviction Within Three Years, All Study Samples

Table 4 compares reconviction prevalence within three years by type ofcrime, for all participants who had at least three years in the community afterrelease from treatment or prison. The patterns were similar when examiningother discrete follow-up periods, although the number of participants in theprospective dropout and comparison samples was too small at the 4- or 5-yearfollow-up periods for meaningful comparisons. Generally, the data showlower rates of reconvictions for DTAP participants than comparison partici-pants across all classes of crime, where lower rates are particularly noted forDTAP participants who completed treatment (according to a logistic regres-sion analysis which contrasted each DTAP sample with the comparison sam-ple separately for each recidivism measure).

With the exception of the retrospective dropout sample (Wald 2 [1, N =352] = 0.49, p = .49, OR = 1.26), about one-third of the DTAP participants werereconvicted of any crime during the 3-year follow-up period, a significantly lowerpercent than the 62% for the comparison sample (prospective dropouts: Wald 2

[1, N = 352] = 10.87, p = .001, OR = 0.26; retrospective completers: Wald 2 [1, N =

Belenko et al. 119

� Table 4: Percentage of Reconvictions Post-Release by Chargefor Participants with 3 Years at Risk

AnyType

FelonyDrug

MisdemeanorDrug

FelonyNon-drug

MisdemeanorNon-drug

Violations

DTAP Sample: N P P P P P P

ProspectiveCompleters

86 37 12 14 0 11 20

RetrospectiveCompleters

64 33 5 6 5 14 17

ProspectiveDropouts

3 30 0 14 8 8 19

RetrospectiveDropouts

65 68 17 17 6 19 34

DTAP Prospectives 12 34 8 14 3 10 20

Comparison Sample 102 62 18 22 3 21 29

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352 = 13.21, p < .001, OR = 0.30; prospective completers: Wald 2[1, N = 352] =12.10, p = .001, OR = 0.35). Within the combined DTAP prospective sample, 34%were reconvicted, which again is a significantly lower percent than the comparisons(Wald 2 [1, N = 223] = 16.84, p < .001, OR = 0.31). Only 8% of the DTAP prospec-tive sample had a reconviction for a felony drug offense, compared with 18% of thecomparisons, Wald 2 (1, N = 223) = 4.46, p = .04, OR = 0.41. Non-drug felonycrimes were rare among all participants and there was no difference between theDTAP prospectives and comparisons, Wald 2 (1, N = 223) = 0.06, p = .82, OR =0.82; which is not surprising given that DTAP and comparison participants werescreened for having no prior violent felony convictions. Although there was a signifi-cant difference between comparison and DTAP prospective participants for any newmisdemeanor non-drug conviction (Wald 2 [1, N = 223] = 5.05, p = .03, OR = 0.41),the difference was not significantly lower for DTAP prospective participants for lessserious offenses (misdemeanor drug conviction: Wald 2 [1, N = 223] = 2.33, p = .13,OR = 0.58; violation conviction: Wald 2 [1, N = 223] = 2.47, p = .12, OR = 0.60).

Time to First Rearrest, All Study Samples

We conducted survival analyses using Cox regression to determine the ef-fect of the DTAP program relative to the comparison group on the number ofmonths to first rearrest after release from DTAP treatment or prison. To con-trol for the impact of prior criminal record on time to rearrest, the Cox regres-sion model consisted of two predictive blocks, with the first block includingfour prior criminal history variables, and the second block comprised of agrouping variable which contrasted each DTAP sample with the comparisonsample. The specific measures of criminal history included: any self-reportedarrest prior to 16 years of age (coded 1 if yes), and, from official records, thenumber of prior drug convictions, the number of prior non-drug convictions,and the total number of months previously incarcerated. Because the continu-ous prior crime covariates were rather skewed, the natural log of these vari-ables was used. Although all participants who had any time in the communitywere included in the analysis, the number of months to first rearrest was maxi-mized at 3 years because the number of participants available to study afterthat time was deemed too small to obtain reliable estimates for those latermonths. Thus, all participants who were in the community for more than 3years were coded as non-arrestees even if rearrested after the 3-year period.

Figure 1 shows the survival curves from the Cox regression analysis of theeffect of the DTAP program on time to first rearrest across 3 years in the com-munity after release from the DTAP program or prison for all study partici-pants, after controlling for several measures of prior criminal history. Theresults indicate a significant overall effect of DTAP participation, even afteradjusting for prior criminal history (see Table 5). Table 5 shows the results ofthe more specific tests that contrasted each DTAP sample with the comparison

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Belenko et al. 121

� Table 5: Results of Cox Regression for All Study Samples

Block Predictor Wald X2 df p OR

1 Months incarcerated lifetimea 0.17 1 .68 1.02

1 Prior number drug convictionsa 0.26 1 .61 1.07

1 Prior number non-drug convictionsa 3.35 1 .07 1.18

1 Arrested prior to 16 years old 1.29 1 .26 1.19

2 Comparison-prison group 22.46 4 .000

2 DTAP retrospective dropouts 0.20 1 .66 1.09

2 DTAP prospective dropouts 4.46 1 .04 0.60

2 DTAP retrospective graduates 10.07 1 .002 0.47

2 DTAP prospective graduates 9.92 1 .002 0.53

Notes. N = 379. OR = odds ratio.a Natural log of predictor was entered into model. The likelihood ratio test for block 1 was

2(4, N = 379) = 10.72, p = .03; and the likelihood ratio test for block 2 was 2 (4, N = 379) =22.89, p < .001.

1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.00 6 12 18 24 30 36

Months in the Community

Cum

ulat

ive

Sur

viva

l Rat

e

� Figure 1: Time to Rearrest by Sample

× DTAP Prospective Graduates* DTAP Retrospective Graduateso DTAP Prospective Dropouts+ DTAP Retrospective Dropouts×× Comparisons

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group. The results indicated that, with the exception of the retrospective drop-outs, DTAP participation significantly delayed the time to first rearrest, withthe strongest effect evident among the DTAP completers. Relative to the com-parison group, the odds of being rearrested were reduced by 47% for the pro-spective DTAP completers, by 53% for the retrospective completers, and by40% for the prospective dropouts. While the criminal history measures weresignificantly related to outcome (block 1) prior to entering the grouping vari-able, none of the criminal history variables was individually significant oncethe grouping variable was entered into the model in block 2.

Multivariate Analyses of Adjusted Annual Rearrest Rate:DTAP Prospectives versus Comparisons

The natural log of the adjusted annual rearrest rate was entered as the de-pendent variable into a multiple regression analysis to determine group differ-ences in rearrest rate after controlling for time in the community. This analysiswas limited to the DTAP prospective and comparison samples given that thesamples were prospectively matched and the larger baseline battery was onlyadministered to these participants. Preliminary correlational analyses wereconducted to test for collinearity and multi-collinearity and reduce a set of po-tential control variables. Potential control predictors that were retained fromthese analyses were then subjected to an initial stepwise regression where allpredictors were entered in the first block (the significance level for allowingpredictors to enter into the model was p < .25, whereas the significance criteriafor removing variables from the model was p < .30) in order to further reduce thepredictive set. After eliminating predictors which did not enter into the initialstepwise regression, the final analytic model consisted of 4 predictive blocks (us-ing the entry method). Block 1 consisted of age and employment status (em-ployed full- or part-time versus unemployed); block 2 included previous criminalhistory variables (specifically, arrested prior to 16 years of age, and the natural logof the number of prior misdemeanor convictions); block 3 consisted of status ondynamic variables at entry into DTAP treatment or prison for the original offense(history of injection drug use, the natural log of money from drug dealing in the 30days prior to admission to treatment-prison, the natural log of the MAST alcohol-ism score, whether the major problem substance is cocaine or crack, natural log ofthe number of prior drug treatment admissions, and the TCU Self Rating Formhostility subscale score and the TCU Self Rating Form depression subscale score[Knight et al., 1994]); and block 4 consisted of a grouping variable (Group) whichcontrasted DTAP prospective and comparison participants. Group was entered inthe final block in order to provide a more stringent test of the efficacy of DTAP.

The final model was significant, F(12, 251) = 5.17, p < .001, adjusted R2 =.16. All predictive blocks containing control variables were significant: block 1,F(2, 261) = 3.87, p = .02, adjusted R2 = .02, block 2, F(2, 259) = 6.77, p = .001, ad-justed R2 = .04, and block 3, F(7, 252) = 4.85, p < .001, adjusted R2 = .09). The

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grouping variable in block 4 was marginally significant, F(1, 251) = 3.65, p = .06,adjusted R2 = .01).

Table 6 depicts the predictors retained in the final model. Except for theTCU Self Rating Form hostility and depression subscale scores, all retainedcontrol predictors were individually significant after controlling for all otherterms in the model. In terms of the significant control predictors, being older,being employed full- or part-time, having a history of injection drug use, earn-ing more money from drug dealing in the 30 days prior to admission, and having ahigher MAST alcoholism score, all significantly predicted lower annual rearrestrates, whereas being arrested for a crime prior to age 16, having more prior misde-meanor convictions, having crack or cocaine as the primary substance of abuse,and having a greater number of prior drug treatment admissions significantly pre-dicted higher rearrest rates. After controlling for all of the control variables,DTAP treatment remained marginally significant in terms of predicting lowerrearrest rates relative to the comparison group.

Multivariate Analyses of Recidivism Likelihood:DTAP Prospectives versus Comparisons

The same analytic model was repeated, this time using logistic regressionto predict the likelihood of any new rearrest after release from prison or DTAPtreatment after controlling for various background covariates. The results ofthe regression model indicated that the entire model was only marginally sig-nificant, 2(12, N = 264) = 19.55, p = .08, and the grouping factor was also onlymarginally significant as a block in the final step after controlling for all of theother background variables, 2(1, N = 264) = 3.45, p = .06. More specifically, be-ing in the DTAP prospective group decreased the odds of a rearrest after releasefrom prison or treatment by 42%. Although competing predictors were selectedbecause they were significant in preliminary analyses, none of the individualcovariates were significant in the final model.

Effect of Length of Treatment Stay and Graduation Status:DTAP Prospective Sample

Finally, we analyzed whether length of stay in DTAP treatment was relatedto the adjusted annual rearrest rate and the occurrence of any new rearrest forthe prospective DTAP participants. The above analytic model was repeated,replacing the grouping variable with the total number of months in DTAPtreatment (including any time in treatment beyond official graduation). Re-sults of the multiple regression analysis of the natural log of the adjusted an-nual rearrest rate, F(12, 127) = 1.39, p = .18, adjusted R2 = .03, and from thelogistic regression of any rearrest post-release, 2(12, N = 140) = 8.76, p = .72)were not significant. Given the absence of an effect for length of stay, we repeated

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these analyses to determine whether graduation status per se predicted the occur-rence and rate of rearrests. Again, the models were not significant, either for thenatural log of the adjusted annual arrest rate, F(12, 127) = 1.35, p = .20, adjustedR2 = .03), or for any rearrest post-release, 2(12, N = 140) = 7.42, p = .83).

DISCUSSION

The diversion to long-term residential treatment of prison-bound drug sell-ers (DTAP) resulted in significant reductions in recidivism, over a multiyearpost-treatment period. A significantly smaller proportion of DTAP partici-pants were convicted (of both drug-related and non-drug crimes), or sentencedto new jail or prison terms relative to the comparison group. Additionally, theadjusted annual rate of arrests and the total number of months of new incarcer-ation were significantly reduced, and the time to first rearrest was longer.Multivariate analyses found significant or marginally significant treatment ef-fects after controlling for a number of background covariates, using the moststringent block entry method. Given the high cost of incarceration, diverting

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� Table 6: Multiple Regression of Natural Log of Adjusted RearrestRate Post Release: DTAP Prospectives versus Comparisons

Block Predictor ßa t p pr

1 Age �.17 �2.59 .010 �.15

1 Employed full/part-time (coded 1/yes) �.13 �2.24 .026 �.13

2 Arrested prior 16 years (coded 1/yes) .15 2.56 .011 .14

2 Number of prior misdemeanor convictionsb .17 2.62 .009 .15

3 History of any injection drug use (coded1/yes)

�.13 �2.03 .043 �.12

3 Money drug dealing 30 days prior entrytreatment/prisonb

�.13 �2.16 .032 �.12

3 MAST alcoholism scoreb �.12 �2.08 .039 �.12

3 Crack/cocaine is primary abuse substance(coded 1/yes)

.13 2.06 .040 .12

3 Number of prior drug treatment admissionsb .18 2.98 .003 .17

3 TCU hostility total score �.08 �1.22 .224 �.07

3 TCU depression total score �.07 �1.10 .272 �.06

4 DTAP prospective sample (coded 1/yes) �.12 �1.91 .057 �.11

a Standardized Betab Natural log of predictor was used in analysis

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drug sellers to long-term treatment can also generate substantial criminal jus-tice cost savings (Zarkin, Dunlap, Mamo, Belenko, & Dynia, 2003), comparedwith programs that serve low-risk offenders (Griffith et al., 1999). These find-ings confirm other research that suggests offender treatment and diversionprograms can improve public safety by engaging high-risk drug offenders intreatment (including drug sellers, a population that is generally not eligible fordrug courts or other treatment diversion programs).

The programmatic and clinical elements of DTAP treatment that promotereduced recidivism remain to be determined. Whether similar recidivism re-ductions can be achieved with less expensive outpatient treatment, shorter res-idential programs, or non-TC models is not known at this time but worthfurther empirical study. Relative to other criminal justice-based treatment,DTAP is a highly coercive program (Young & Belenko, 2002). Participantsface a high certainty of incarceration in state prison following dropout or ex-pulsion from the program, and the District Attorney’s Office employs a “war-rant squad” to seek out and return participants to court who fail in the programor abscond from the residential treatment facility. Compared with standardcriminal justice treatment, such as probation or parole supervision, DTAP par-ticipants perceive the program to be highly coercive, understand that they arebeing closely monitored by criminal justice authorities, and believe that therewill be legal consequences as a result of treatment failure (Young, 2002).

DTAP’s high retention rates (Lang & Belenko, 2000) suggest that these co-ercive elements operate to keep participants in treatment longer and increasegraduation rates; however, we found no evidence for a partial treatment effectamong treatment dropouts. Time in treatment was not related to rearrest preva-lence or adjusted rearrest rate, after controlling for multiple covariates, includ-ing prior criminal record and drug use. In previous studies, we found nosignificant differences among dropouts who leave treatment at different times(Lang & Belenko, 2000).

Other research on highly structured programs with high levels of account-ability, such as drug courts and TASC programs, has been fairly consistent indemonstrating the ability to achieve reductions in post-program recidivism(Anglin, Longshore, & Turner, 1999; Belenko, 2001). However, the limitedrecidivism impact seen in studies of a few drug courts and diversion programsmay reflect that: (a) these programs serve low-risk offenders who would havehad low recidivism rates anyway; (b) the treatment was neither sufficientlylong nor intense; and/or (c) the program had low accountability. Spohn et al.(2001) suggested that the low rates of recidivism found among diversion pro-gram clients reflected their relatively low risk profile. These findings are con-sistent with the “risk/responsivity principle” of criminological theory, whichsuggests that low-risk offenders may alter their behavior without substantialsupervision, treatment, or threat of sanctions, while high-risk offenders willbenefit more from intensive monitoring and accountability (Gendreau, Smith, &Goggin, 2001; Marlowe, 2003).

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Our findings suggest a need to develop and test other treatment models forserious drug offenders, and additional research to disentangle the client andprogram factors that promote and maintain reduced levels of crime amongtreatment participants (Belenko, 2002; Sung & Belenko, in press; Taxman,1999).

Several limitations of this study should be noted. First, we were unable toimplement an experimental design. The matched comparison group designwith multiple matching criteria enabled us to obtain a comparison sample thatclosely paralleled the prospective treatment sample on most measures. Wheredifferences occurred (such as some criminal history measures) we controlledfor these factors in our analyses and still found a significant treatment effect.When a number of other covariates were included, the program effect re-mained marginally significant. The retrospective experimental samples werenot directly matched to the comparison samples, and although these partici-pants had similar characteristics as the prospective samples, there may havebeen unmeasured differences that might account for differences in recidivismoutcomes. In particular, the retrospective sample of DTAP dropouts had recid-ivism rates that were similar to the prison comparison sample and significantlyhigher than the prospective dropout sample. Because we only had limitedbackground data for the retrospective samples, the reasons for this differenceare unclear.

DTAP serves a population of offenders charged with drug sales who haveprior nonviolent felony convictions and are facing mandatory prison sen-tences if convicted. Accordingly, the results may not be generalizable to othersamples of felony drug offenders, lower level offenders, or offender popula-tions outside of New York City. Nonetheless, the data are consistent withsome other studies in suggesting that treatment program impacts on recidi-vism are greater for higher risk offenders.

Because DTAP participants are treated in long-term residential TCs, thefindings may not be generalizable to other types of treatment interventions fordrug-involved offenders. However, the data are consistent with other researchthat suggests that any type of treatment of high-risk offenders, even if comple-tion rates are modest, can achieve substantial criminal justice and societal costsavings (Belenko & Peugh, 1998; Finigan, 1999).

The recidivism data source was complete and comprehensive for arrests oc-curring in New York City, but did not include arrests made outside the city.Accordingly, the recidivism measures may underestimate the number ofrearrests, and it is not known whether this bias was randomly distributedacross study samples or participant characteristics.

Finally, although we were able to analyze recidivism patterns for a rela-tively long follow-up period, there was a potential confound in our analysis.Because this was a high-risk sample of repeat felony offenders, recidivismrates were fairly high, and new arrests often resulted in reincarceration. Ac-cordingly, the number of participants with sufficient time in the community in

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which to observe recidivism decreased as the follow-up length increased. Be-cause rearrested offenders had a greater likelihood of receiving pretrial deten-tion and new incarceration, it is possible that the subgroup of participants withlonger follow-up periods was skewed toward lower risk offenders. This sug-gests a need for some caution when interpreting the recidivism results for lon-ger than three years post-treatment.

NOTE

1. Five cases from the DTAP prospective dropout group had no community timeprior to making any adjustments (unadjusted community time). In 2 of these cases,there was a warrant for the original-target arrest; and in 3 cases, the participant was stillin prison for the original offense as of July 31, 2003. Because we were certain these 5participants had no community time, their cases could only be included in the analysesof the rearrest rate post-admission (which included arrests prior to release). There were anadditional 7 cases who had no community time after making adjustments (adjusted com-munity time). One of these cases was from the DTAP prospective completer sample andwas out of state, presumably for the entire follow-up period, whereas 6 cases (3 compari-sons and 3 DTAP prospective dropouts) were deported or died immediately or shortly afterrelease. Because we could not say with certainty that they were truly not in the communityduring the entire follow-up period (e.g., deported individuals could later return; in fact, 1deported case was rearrested in New York at a later date), these participants were excludedonly from analyses involving the adjusted rearrest rates.

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AUTHORS’ NOTES

Steven Belenko, PhD, is a senior scientist at the Treatment Research Institute at theUniversity of Pennsylvania. Prior to joining TRI, he spent five years as a CASA Fellowat the National Center on Addiction and Substance Abuse at Columbia University. Hisprimary research interests have included the impact of drug offenders on the criminaljustice system, substance abuse treatment for criminal offenders, treatment and HIVservices access, crack cocaine and crime, and drug courts. His book Crack and the Evo-lution of Anti-Drug Policy received an Outstanding Academic Book Award from theAmerican Library Association’s Choice Magazine, and another book, Drugs and DrugPolicy in America: A Documentary History, was published by Greenwood Press in 2000.Dr. Belenko received his bachelor’s degree in applied mathematics and PhD in experimen-tal psychology from Columbia University.

Carol Foltz, PhD, is a research psychologist at the Treatment Research Institute atthe University of Pennsylvania, where she is responsible for data analysis of immediateand long-term treatment outcomes of substance abuse treatment. Dr. Foltz also servesas a statistical, methodological, and psychometric consultant for faculty and researchstaff at TRI and other universities. She received her doctorate in developmental psy-chology from Temple University and postdoctoral training at the University of Penn-sylvania School of Medicine.

Michelle A. Lang, PhD, is the director of research and evaluation at Samaritan Village,Inc., a leading provider of drug treatment services in New York City. Dr. Lang is a licensedclinical psychologist, specializing in health psychology and treatment for severe mental ill-ness and substance abuse. She has extensive research experience with program evaluationfor statewide mental health initiatives and policy-related issues involving criminal justicepopulations. Dr. Lang received her doctorate in clinical psychology at the University ofRhode Island, and was a postdoctoral scholar at Yale University.

Hung-En Sung, PhD, is a research associate at the National Center on Addiction andSubstance Abuse at Columbia University (CASA). His research interests revolvearound drug abuse, policing, and comparative criminology, and he has published anumber of journal articles and also a book titled The Fragmentation of Policing in Amer-ican Cities: Toward an Ecological Theory of Police-Citizen Relations (Praeger, 2002). Dr.Sung received his doctorate in criminology from the State University of New York at Al-bany.

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This research was supported by grant number R01-DA09075 from the National In-stitute on Drug Abuse to the National Center on Addiction and Substance Abuse at Co-lumbia University (Steven Belenko, principal investigator). The conclusions andopinions expressed in this paper are those of the authors and do not represent the viewsof the National Institute on Drug Abuse, the Treatment Research Institute, SamaritanVillage, Inc., or the National Center on Addiction and Substance Abuse at ColumbiaUniversity.

Address correspondence to Steven Belenko, Treatment Research Institute at the Uni-versity of Pennsylvania, 600 Public Ledger Building, 150 South Independence Mall West,Philadelphia, PA 19106-3475 (E-mail: sbelenko@tresearch.org).

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