Benefit–Cost Analysis of Addiction Treatment in Arkansas: Specialty and Standard Residential Programs for Pregnant and Parenting Women

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Substance Abuse [suba] PP337-364700 December 19, 2001 10:12 Style file version Nov. 19th, 1999

Substance Abuse, Vol. 23, No. 1, March 2002 ( C© 2002)

Benefit–Cost Analysis of Addiction Treatmentin Arkansas: Specialty and Standard ResidentialPrograms for Pregnant and Parenting Women

Michael T. French, PhD,1,2,5 Kathryn E. McCollister, PhD,1 John Cacciola, PhD,3

Jack Durell, MD,3 and Raymond L. Stephens4

A benefit–cost analysis of specialty residential treatment (Specialty) and standard res-idential treatment (Standard) was conducted on a sample of pregnant and parentingsubstance abusers from Arkansas. Economic benefits were derived from client self-reported information at treatment entry and at 6-month postdischarge with the useof an augmented version of the Addiction Severity Index (ASI). The average costof treatment in Specialty programs was $8,035 versus $1,467 for Standard residentialtreatment. Average net benefits (benefit–cost ratios) were estimated to be $17,144 (3.1)for Specialty and $8,090 (6.5) for Standard. The main policy implication of this re-search is that investment in Specialty residential treatment for pregnant and parentingsubstance-abusing women appears to be economically justified, but future evaluationsshould analyze larger and more comparable samples to improve power and precisionin the benefit–cost statistics.

KEY WORDS: benefit–cost analysis; addiction treatment; pregnant and parenting women.

INTRODUCTION

An important motivation for providing effective treatment to substance abusersis the concern for the substantial cost imposed on society from the abuse of illicit drugsand alcohol. Among substance abusers, the population of pregnant and parentingwomen has an especially pronounced impact on society considering that women aretypically the primary caregivers in a family. Furthermore, a host of individual and

1Health Services Research Center and Department of Epidemiology and Public Health, University ofMiami, Miami, Florida.

2Department of Economics, University of Miami, Miami, Florida.3DeltaMetrics, Philadelphia, Pennsylvania.4Bureau of Alcohol and Drug Abuse Prevention, Arkansas Department of Health, Little Rock, Arkansas.5To whom correspondence should be addressed at Department of Epidemiology and Public Health,University of Miami (D93), 1801 NW 9th Avenue, Third Floor, Miami, Florida, 33136; e-mail: [email protected].

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0889-7077/02/0300-0031/1 C© 2002 Association for Medical Education and Research in Substance Abuse

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32 French, McCollister, Cacciola, Durell, and Stephens

societal problems are associated with pre–post natal addiction, including the effectsof alcohol and drug exposure on infant development and the substantial resourcesnecessary to care for two generations of affected individuals (1–4).

A 1992 survey of the U.S. population by the National Institute on Drug Abuse(NIDA) revealed that 5.5% of women surveyed used illicit drugs while pregnant.The most commonly abused illicit drug was marijuana (2.9%), followed by cocaine(1.1%), psychotherapeutic drugs not obtained with a prescription (1.5%), and heroin(0.1%). The highest rates of substance use were reported for legal drugs: 18.8% ofwomen surveyed used alcohol and 20.4% used cigarettes (5).

Recently, an outcome evaluation of addiction treatment for pregnant and par-enting women was sponsored by the Center for Substance Abuse Treatment (CSAT)and conducted by the State of Arkansas and DeltaMetrics (6). The outcome eval-uation examined whether pregnant and parenting women with substance abuseproblems who entered specialty residential treatment programs (Specialty) showedbroader and more pronounced clinical improvements at follow-up than did womenwho entered standard residential treatment (Standard) (6). These Specialty pro-grams were developed to provide more intensive and more appropriate “gendersensitive” services than typically found in most residential settings. Thus, the eval-uation was also interested in differences between these two types of treatmentprograms in amount and type of services offered to pregnant and parentingwomen.

This paper presents an extension to the prior outcome assessment (6) by per-forming an economic evaluation of Specialty and Standard residential treatment.Following economic evaluation models developed by the authors (7–10), a benefit–cost analysis was performed for both treatment conditions. The empirical findings ofthis work may be useful for clinicians, researchers, and policymakers to help guide theefficient allocation of scarce resources to different treatment options for substance-abusing mothers. In addition, the methodological framework employed in this workand several earlier studies may serve as a foundation to future research performingcomparative economic analyses of treatment interventions.

BACKGROUND

Economic Evaluation Methods

Economic evaluations are popular with policymakers and funding agencies,given limited treatment resources and varying economic returns across interven-tions. Economic evaluation of substance abuse treatment consists of a variety ofanalytical methods to compare the costs and outcomes of different treatment op-tions (11–13). Cost analysis is typically the foundation of a full economic evaluation.Its primary purpose is to determine the opportunity cost of a project from a societalperspective, which requires information on the full value of all resources employedin a program (14).

An economic cost analysis can be extended to perform either a cost-effectivenessanalysis or a benefit–cost analysis. A cost-effectiveness analysis typically compares

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Benefit–Cost Analysis of Addiction Treatment 33

the incremental opportunity cost to an incremental nonmonetary health outcomethat is common to competing projects, such as “quality-adjusted life-years saved” or“cases of disease avoided” (14, 15). Benefit–cost analysis compares the opportunitycost of an intervention to the total benefit, while expressing both in a common mon-etary metric (16). The result is either presented as a benefit–cost ratio (i.e., benefitdivided by cost) or simply as net benefit (i.e., cost subtracted from benefit). An inter-vention is considered cost-beneficial if the benefit–cost ratio exceeds unity or if netbenefit is positive (15).

In reality, the application of benefit–cost analysis or cost-effectiveness analysisto substance abuse interventions is rather complex. One issue is the multitude ofposttreatment outcomes, some of which affect the drug abuser or the health insurerper se, while others affect society at large (17). Second, imputing a dollar value tocertain outcomes may be difficult, especially when dealing with intangibles, suchas the number of days experiencing medical problems. A detailed description ofeconomic evaluation techniques is beyond the scope of this paper. However, severaluseful works on economic evaluation methods in the health care field are availablefor further consultation (7, 8, 11–15, 18–22).

Existing Research

The research literature on substance abuse treatment has expanded rapidly in re-cent years, in part because of the improvements in evaluation techniques. However,research focusing on interventions for women, and in particular pregnant or par-enting women, remains underdeveloped. The literature examining this populationtypically focuses on the various social and demographic characteristics of substance-abusing pregnant women (4, 23–25) or the moral and social issues surrounding preg-nant and parenting women and substance use (2, 3, 26). A recent study consideredthe costs and consequences of treating drug dependent pregnant women, such as thehigher cost of hospital care for drug abusing mothers and their infants (27). Anotherstudy determined that addicted mothers who participated in drug abuse treatmentduring their pregnancy and delivery incurred lower infant intensive care costs relativeto addicted mothers without treatment (28).

One of the earliest programs targeting pregnant and parenting women was de-veloped at New York Medical College in 1975. This program, entitled “PregnantAddicts/Addicted Mothers” (PAAM), focused on two goals: to help the addictedmother produce a living child and to rehabilitate the mother to be a stable andhealthy parent. Patients were assigned to a physician who was involved in all aspectsof treatment: prenatal care, detoxification, child delivery, postdelivery counseling,and medical care. Housing issues and the health of other family members were alsomonitored. Research on the outcomes of this program indicated that the mothersand their children reported improved prosocial, psychological, and emotional func-tioning as a result of treatment (see (25) for a complete discussion of the PAAMprogram).

A study by Svikis et al. (29) compared the outcomes and costs (though nota formal cost-effectiveness analysis) of an intervention designed to examine theeconomic and social impact of on-site support groups for substance-abusing pregnant

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women. The sample consisted of 121 pregnant women registered for prenatal carein an obstetric clinic located in an urban area. The cost analysis focused on maternalmedical costs from 1 week prior to delivery to 3-week postdelivery, as well as thecost of infant care through 3-week postdelivery. Patient progress was assessed usingthe Addiction Severity Index (ASI).

Women in the once-per-week on-site support group (“Attendees”) were com-pared to women that had been referred to neighborhood drug treatment programs(“Nonattendees”). Cost findings showed that the Attendees had both lower mater-nal costs from 1-week predelivery to 3-week postdelivery and lower infant medicalcosts from delivery to 3-week postdelivery. Attendees’ newborns also reported betterclinical outcomes (i.e., higher birth weights and better Apgar scores).

Considerably more research has focused broadly on the economic evaluationof substance abuse treatment. Recently, several interesting empirical contributionshave been made to the economic evaluation literature. Some papers focused exclu-sively on the cost of addiction treatment (30–36). Others took on the often-complextask of computing the monetary values of treatment outcomes, such as morbidity,criminal activity, and labor market behavior (37–39). Benefit–cost analyses of sub-stance abuse interventions, nevertheless, are still relatively scarce and sometimespresent methodological inconsistencies (7–10, 40–45).

This analysis is the first to examine, from an economic perspective, residentialtreatment programs specifically designed for pregnant and parenting women andtheir families. An augmented version of the ASI permitted coverage of a compre-hensive range of treatment outcomes, similar to the range typically requested by stateand other government agencies when deciding where to direct funds (46). Selectedoutcome variables were converted to economic benefits with the use of monetaryconversion factors from a variety of sources specific to the State of Arkansas. Impor-tantly, the research also provides a conceptual framework for performing benefit–costanalyses of substance abuse interventions.

ARKANSAS TOPPS I: BACKGROUND

Pregnant and Parenting Women’s Living Centers

Partially motivated by a documented increase in substance abuse among women(NIDA, 1996) (5), the Arkansas Bureau of Alcohol and Drug Prevention devel-oped specialty residential treatment programs directed exclusively at pregnant andparenting women. The programs, entitled Pregnant and Parenting Women’s Liv-ing Centers (PPWLCs), were designed to offer a comprehensive, gender-sensitiveapproach, focused on treating addicted mothers and their children. Specialty treat-ment consisted of a comprehensive set of “wrap around” services on site such as casemanagement, alcohol and other drug treatment, child care, transportation, medi-cal treatment, room and board, education/job skills training, parenting skills train-ing, aftercare planning, and family counseling. In addition, Specialty programs al-lowed children under age 5 to be in residence with their mother during the course oftreatment.

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The comparison group for this study consisted of women receiving standardpostdetoxification and rehabilitative treatment (Standard) in a residential settingthat was geographically proximate to the Specialty programs. Like the Specialtyprograms, Standard residential programs were intended to provide intensive care ina therapeutic setting with supportive living arrangements. Elements of care in theseStandard programs included services such as intake, individual and group therapy,case management, and room and board. The planned length of stay was 28 days forStandard treatment and up to 12 months for Specialty treatment.

Participants

Substance abuse treatment clients were recruited for the effectiveness studyfrom December 1997 to October 1998. Informed consent was obtained by clinical staffupon admission to treatment. No payment was offered for completing the baselineassessment, but subjects were paid $25 for the follow-up assessment. (The follow-uppayment was initially $5 until CSAT increased funding for the effectiveness study.)Recruitment into the study varied among the programs, but was comparable forSpecialty and Standard conditions. The overall recruitment rate was approximately70%. To be eligible for the study, women had to 1) be at least 18 years old, 2) bepregnant at the time of admission or have given birth within the past 18 years, and3) enter treatment at 1 of the 12 participating program sites. Most women enteredthese programs as self or criminal justice referrals.

Although 177 clients were recruited, 24 subjects did not complete a baseline ASIand were excluded from the effectiveness study. Follow-up surveys were completedby telephone interview, 6 months following discharge, on 85 individuals (56%) fromthe admission sample. Thus, the data set for the full economic analysis included 44clients from the Specialty residential treatment programs and 41 clients from theStandard residential treatment programs.

Table I presents mean values for selected individual characteristics at base-line. The mean values are reported for four distinct groups: Specialty treatmentparticipants, Standard treatment participants, all respondents at follow-up, and allnon-respondents at follow-up. In addition, tests were conducted for statistically sig-nificant differences between 1) Specialty and Standard participants, and 2) respon-dents and nonrespondents at follow-up. Results of the significance tests indicatethat respondents and nonrespondents differed for only two variables—age and ASIcomposite score for medical. However, Specialty and Standard clients differed acrossmore measures, including age, education, currently pregnant, and three ASI compos-ite scores (drug, legal, and family/social). Thus, these findings suggest that potentialnonresponse bias should be less of a concern than subject heterogeneity across thetwo study conditions. This issue is explained further in the Discussion section thatfollows.

Clinical Outcomes

The study was conducted as part of the CSAT funded Treatment Outcome PilotProspective Study (TOPPS) set of evaluations. The project was an intent-to-treat,

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Table I. Variable Means at Baseline

Specialty Standard All Alltreatment treatment respondents nonrespondents

Variable (N = 44) (N = 41) (N = 85) (N = 68)

Age∗††† 31.93 (5.79) 34.59 (7.05) 33.21 (6.53) 30.85 (5.97)Education∗∗ 11.02 (1.77) 11.67 (1.51) 11.33 (1.67) 11.19 (1.76)Married (%) 0.14 0.22 0.18 0.10White (%) 0.48 0.61 0.54 0.54Pregnant (%)∗∗ 0.18 0.02 0.11 0.13Parenting (%) 0.68 0.63 0.66 0.62Number of children 2.75 (1.31) 2.90 (1.41) 2.82 (1.36) 2.90 (1.89)ASI composite score

Medical† 0.27 0.38 0.32 0.23Employment 0.79 0.72 0.75 0.80Alcohol 0.37 0.36 0.37 0.30Drug∗∗ 0.24 0.18 0.21 0.19Legal∗∗ 0.34 0.21 0.28 0.26Family/social∗∗∗ 0.52 0.34 0.43 0.43Psychiatric 0.40 0.34 0.37 0.36

Note. Standard deviations in parentheses for continuous variables.Statistically significant difference in variable means between study conditions (Specialty and Standard),Kruskal-Wallis equality of populations rank-sum test: ∗ p < 0.10; ∗∗ p < 0.05; ∗∗∗ p < 0.01.Statistically significant difference in variable means between follow-up respondents and follow-up non-respondents, Kruskal-Wallis equality of populations rank-sum test: †p < 0.10; ††p < 0.05; †††p < 0.01.

naturalistic effectiveness trial investigating consecutively recruited samples of preg-nant and parenting women patients entering two separate types of residential treat-ment. A repeated measures design was implemented and performed by trained andindependent research staff from DeltaMetrics, an outcome evaluation contractor.The project was conducted over a period of approximately 2 years. Recruitmentoccurred from October 15, 1997, through September 30, 1998, and follow-up wascompleted by July 12, 1999 (See (6) for more details).

The effectiveness study examined four main hypotheses. Women entering Spe-cialty treatment settings were expected to 1) have more severe admission problems;2) receive more intensive and a broader range of services; 3) show a greater andbroader range of improvement following treatment; and 4) have better personalhealth, social functioning, and drug use outcomes than did the women entering theStandard treatment programs. The first hypothesis was partially supported by thedata (see Table I). As indicated by the ASI composite scores, women entering Spe-cialty treatment tended to have more severe problems in the areas of drug use, legalstatus, and family/social items. In light of these results, it is important to emphasizethat direct comparisons across treatment conditions should be viewed with cautionbecause the samples differed at baseline for several measures.

The second hypothesis, which tested if women in Specialty programs receivedmore and broader services, was only partially supported by the data. The Specialtyclients stayed significantly longer in treatment (M = 94 days, SD = 114.01) than theStandard clients (M = 30 days, SD= 6.58) (p < 0.01). However, in the first month oftreatment, there was some evidence that Standard clients actually reported receivingsignificantly more family and psychiatric services (6).

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The third hypothesis tested for improvement in all problem areas for women re-ceiving treatment. Both groups of women showed significant levels of improvement,but the nature and amount of improvements were approximately equal between thegroups.

Finally, following from the third hypothesis, there were no statistically significantacross-group differences at 6-month postdischarge in any of the outcome domainsexamined (6).

METHODS

Economic Cost Analysis

Cost estimates were approximated from available information on average treat-ment reimbursement rates for residential treatment (Specialty and Standard) pro-vided by the State of Arkansas and included a one-time intake and assessment feeper admission to a program. Although this approach was convenient and has directapplications for public agencies in Arkansas, two potential problems should be ac-knowledged. First, reimbursement rates represent the overall cost to the state, butdo not necessarily reflect the per client economic cost of treatment services. Sec-ond, reimbursement rates usually underestimate the opportunity costs of treatment,especially if a program is using donated or subsidized resources.

Opportunity cost is the recommended measure for use in any economic evalua-tion because it includes the value of all resources used. For example, the opportunitycost of a donated building would be equal to what the building would rent for in acompetitive market (i.e., in its best alternative use), whereas the accounting cost ofthis resource would be zero (13). Thus, using reimbursement rates potentially createdsome measurement error in the cost calculations, but this was the best informationavailable because a formal cost analysis of all participating programs was not possiblewithin the financial constraints of the evaluation study.

Economic Benefits Analysis

Economic benefits were derived from patient self-reported information col-lected at treatment entry and at 6-month postdischarge with the use of treatmentoutcome variables measured through an augmented version of the fifth edition ofthe ASI. The ASI is a 45–60 min structured clinical interview developed to diagnoseand evaluate lifetime as well as recent (past 30 days) severity of addiction-relatedproblems in the following areas: medical status, employment, alcohol and drug use,legal status, family and social relationships, and psychiatric symptoms (47, 48). Theadmission version of the ASI covered both the past 30 days, previous 6 months, andlifetime experiences. The 6-month follow-up version measured matching variablesfor either the preceding 6 months or the past 30 days.

The ASI was augmented to meet specific data collection requirements of theArkansas TOPPS project. First, questions related to past 6-month experience (inaddition to lifetime and past 30 days) were added at baseline and follow-up in the

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areas of medical and psychiatric services, employment, and criminal justice systeminvolvement. These questions were added to create reasonably long and comparabletime frames and to provide more sensitive cost-related items. Second, more specificquestions on medical and psychiatric care were included to measure specific unitsof service for inpatient days and ambulatory care (i.e., emergency room, hospitaloutpatient, and clinical provider visits). Because the original ASI was slightly aug-mented for this study, the actual reliability and validity of the study instrument isunknown. However, the augmented ASI was derived directly from the original ASI,which is widely accepted as a reliable and valid instrument for assessing outcomesrelated to drug abuse treatment. Thus, we expect the study instrument to have similarproperties.

Since the ASI is primarily administered to measure clinical outcomes, manyof the measures were not suitable for economic analysis (8, 9). Other variablescould not be associated with a monetary equivalent due either to the absence ofreasonable dollar counterpart estimates (e.g., family conflicts) or their monetaryirrelevance (e.g., client demographics). Furthermore, measures collected at base-line only, such as lifetime severity measures relating to the behavior of the sub-ject leading up to admission (e.g., number of lifetime arrests), had no follow-upequivalent.

All economic benefits in this analysis were determined from a societal perspec-tive. The U.S. Public Health Service recommends the societal perspective because itis comprehensive and comparable across interventions (13–15).

Outcome Variables

The first outcome category was medical status, which was examined using the fol-lowing four measures from the augmented ASI: days experiencing medical problems,days hospitalized for medical problems, number of ER visits for medical treatment,and number of visits to a clinic for medical treatment. The four measures for psy-chiatric status were number of days experiencing psychological problems, days ininpatient psychological treatment, days in hospital outpatient psychological treat-ment, and days in outpatient (clinic) treatment. Days of criminal activity was usedto assess changes in criminal behavior.

Fluctuations in employment and substance use were already expressed in mon-etary terms. The economic value of change in employment was captured by incomereceived from employment, and the economic value of change in substance use wasmeasured by money spent on drugs and money spent on alcohol.

As mentioned earlier, some variables had a 6-month time frame whereas othersmeasured past 30-day experiences. Baseline and 6-month follow-up variables thatmeasured past 30-day experiences were multiplied by 6 to cover the entire follow-upperiod. While certainly not ideal, this practice was unavoidable given the currentstructure of most questions on the ASI. The possible implications of this practiceare discussed in the Limitations section. The selected ASI variables not expressedin dollar terms were converted to a monetary equivalent by applying monetary con-version factors (unit cost estimates), which were obtained from several publishedsources.

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Unit Cost Estimates

The outcome variables and respective unit cost estimates are presented inTable II. All cost estimates are expressed in 1998 dollars for consistency and compara-bility with other financial data. The cost of a day experiencing medical problems wasvalued at $20.03 and represents the dollar-equivalent decrement to a quality-adjusted

Table II. Outcome Variables and Unit Cost Estimates ($1998)

ASI Variable Unit cost ($)

Days experiencing medical problems 20.03a

Overnight hospitalizations for medical problems 814.00b

Emergency room visits for medical treatment 179.49c

Clinic or physician visits for medical treatment 56.73d

Psychiatric statusDays experiencing psychological problems 7.73e

Days in inpatient psychiatric treatment 178.93 f

Days in hospital outpatient psychiatric treatment 85.05g

Days in outpatient psychiatric treatment 84.36h

Employment statusIncome received from employment N/A

Substance useExpenditures on alcohol N/AExpenditures on drugs N/A

Legal statusDays engaged in illegal activities, for profit 718.04i

N/A—not applicable.aEstimated by summing the dollar-equivalent decrement in a quality adjusted

life day (QALD) associated with medical problems related to drug abuse(value of statistical life = $1,000,000).

bRepresents the expenses of one inpatient day in a hospital for the State ofArkansas (American Hospital Association, 2000).

cPhysician fee for the first hour of critical care, evaluation, and managementof the unstable critically ill or unstable critically injured patient, requiring theconstant attendance of the physician (American Medical Association, 1999).Although most emergency room episodes for the present sample may be lessserious than this estimate would imply, it is best to employ this value as anaverage estimate because the most serious emergency room cases are infre-quent, yet extremely costly.

dFee for an office consultation with a new or established patient, which re-quires a detailed history, a detailed examination, and medical decisionmaking of low complexity (American Medical Association, 1999).

eEstimated by summing the dollar-equivalent decrement in a quality adjustedlife day (QALD) associated with psychiatric problems related to drug abuse(value of statistical life = $1,000,000).

f Sum of the fee for evaluation and management services provided to a patientin a psychiatric residential treatment center and the fee for a visit to an officeof outpatient facility for psychiatric treatment (American Medical Associa-tion, 1999).

gFee for individual psychotherapy, insight oriented, behavior modifying and/orsupportive, in an inpatient hospital, partial hospital, or residential hospital,with medical evaluation and management services (American MedicalAssociation, 1999).

hFee for a visit to an outpatient facility for psychiatric treatment (AmericanMedical Association, 1999).

i Value represents the weighted average cost of crimes associated with sub-stance abuse. Number of crimes is assumed to be one act per day.

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life-day caused by health conditions typically related to drug abuse. Intuitively, itmay seem that the expenses resulting from medical treatment are more relevantthan the loss in a quality-adjusted life-day, the former directly imposing a burdenupon society. However, the societal perspective cannot ignore impaired functioningcaused by health problems, leading to a decrease in overall well-being of society. Theestimated value of a statistical life ($1 million was used as a lower bound estimate),the resulting value of a quality-adjusted life-day, and quality-adjustment factorsused to calculate the dollar-equivalent decrement in a quality-adjusted life-day wereobtained from French et al. (38), where the quality-adjustment factors originatedfrom the Rosser/Kind Index of health status (49). The Rosser/Kind Index has goodreliability and validity, and it has been employed in numerous economic studies ofhealth and health care (38, 50–51). For more information on the calculation of thedollar-equivalent decrement to a quality-adjusted life-day as a result of a day expe-riencing medical problems, refer to French et al. (8).

One day in inpatient medical treatment was valued at $814.00, based on theaverage cost of 1 day in a community hospital in Arkansas (52). One emergencyroom visit for medical treatment was valued at $179.49 (53). A clinic or physicianvisit for medical treatment was valued at $56.73, and represents the fee for an officeconsultation with a new or established patient (53). All estimates are specific to theState of Arkansas.

A day experiencing psychological or emotional problems was valued at $7.73and, similar to a day with medical problems, was calculated as the dollar-equivalentdecrement to a quality-adjusted life-day resulting from psychological or emotionalproblems. A day with psychological or emotional problems was equated to a severesocial/slight work disability and a moderate level of stress on the Rosser/Kind scale.

A day in inpatient psychiatric treatment was valued at $178.93 (53), a day inhospital outpatient psychiatric treatment was valued at $85.05 (53), and a day inoutpatient psychiatric treatment was valued at $84.36 (53). All estimates are for theState of Arkansas.

Income received from employment was already expressed in dollars. Incomereceived from other (illegal) sources was excluded from the benefit calculationsbecause illegal income involves currency transfers from one individual to another,which does not result in a net loss or gain to society (39).

Money spent on alcohol and illicit drugs reflects the value of changes in theconsumption of these substances, not including the negative consequences, whichwere captured by other measures. It could be argued, however, that possible ben-efits of reduced alcohol and drug consumption have moral rather than economicimplications. Moreover, consumption of illicit substances may generate social wel-fare by supporting a parallel economy. Since it is difficult to precisely value the loss tosociety caused exclusively by the consumption of addictive substances (ignoring neg-ative consequences), it was decided to use money spent on alcohol and illicit drugs asa proxy.

Reported number of days engaged in illegal activities was used to estimate thereduction in crime-related costs. The value of crime-related benefits could potentiallyexceed all other benefits of substance abuse treatment because criminal activityaccounts for as much as two thirds of the total economic cost of substance abuse (54).

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Estimates of the cost of various criminal acts associated with substance abuse wereobtained from Rajkumar and French (39). However, the ASI asks about criminalactivity days rather than acts committed for individual crimes since the former isa more stable self-reported estimate of crime. Hence, a weighted average cost ofa criminal activity day (i.e., one “undefined” crime) for drug abusers, where theweights were the relative probabilities of committing each type of act, was estimatedat $718.04. (These calculations are available upon request.)

To apply the unit cost estimate for an undefined crime to the number of daysengaged in illegal activities, an assumption had to be made about the number ofcrimes an average subject would commit during 1 day of illegal activity. Althoughsome studies report up to five addiction-related crimes per day for an active drugabuser (55), this study assumed, conservatively, that only one crime was committedon each criminal activity day.

Total Benefit Calculations

The previous sections described how outcomes from the ASI were converted toeconomic (dollar) benefits. The total benefit of the Specialty and Standard treatmentconditions was derived by summing the benefits (i.e., differences between baselineand follow-up values in monetary terms) across categories. The magnitude and sta-tistical significance of the total benefit estimates are more important than benefitestimates for specific measures, and so the total benefit estimates will be empha-sized in the Results section. All significance tests for the total benefit and net ben-efit estimates applied to the null hypothesis, H0: u = 0. The null hypothesis for thebenefit–cost ratios was H0: u > 1.

Because of missing values for some variables, the drop in number of cases forthe total benefit estimates of Specialty and Standard (i.e., the sum of all variables)was as high as 32%. To correct for this item nonresponse, a data imputation rulewas applied whereby missing values for any variable were assigned the mean valuefor that variable and condition. One variable in particular was most affected bythe imputations. For both the Specialty and Standard treatment conditions, baselineinformation on “days in outpatient psychiatric treatment” had the greatest numberof missing values (20–32% of this variable required imputation).

Benefit–Cost Analysis

Cost and benefit estimates permitted a full economic evaluation of treatmentalternatives. The results are presented as either benefit–cost ratios (i.e., benefit di-vided by cost), or as net benefit estimates (i.e., cost subtracted from benefit). Allresults were derived from the societal perspective, with outcomes covering a diver-sity of categories and behaviors (see Background section). Although the societalperspective includes costs and benefits from all sources in the benefit–cost calcula-tions, some individuals or policymakers may only be interested in a subset of costsand benefits (e.g., medical care benefits for a managed care company). The proposedapproach can easily accommodate a disaggregated benefit–cost analysis, whenevernecessary.

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RESULTS

Economic Costs

Table III provides the results of the economic cost analysis. A unit of servicein the Specialty program was 1 day for a family, with a cost per day of $83. Ev-ery participant, whether in Specialty or Standard treatment was also subject to aone-time charge of $200 for intake and assessment. This involved the administrationof an interview to provide information on the client, the client’s history of substanceuse, employment history, family background, and prior treatment episodes.

Given that the average Specialty client stayed in treatment for 94.40 days, theestimated average cost of direct treatment services was $7,835 (94.40 days× $83 perday). Adding the intake and assessment fee, the total cost of treatment for the averageSpecialty client was $8,035. The Standard clients stayed in treatment for an average of30.17 days—significantly less than the Specialty clients (p < 0.01). The cost of 1 dayin Standard residential treatment was $42, leading to an estimated average cost of$1,267 for Standard treatment services. With the intake and assessment fee, the totalcost of treatment for the average Standard client was $1,467.

Economic Benefits

Table IV provides the results of the economic benefits analysis, by treatmentcondition (Specialty and Standard) as well as for the combined sample. Mean val-ues are provided for the matched pairs of variables at baseline (column 2) and atthe 6-month follow-up (6-month postdischarge from treatment) (column 4). Cor-responding standard deviations are presented in columns 3 and 5. Benefit esti-mates are reported for each outcome category and then aggregated to calculatetotal benefit. The column labeled Group under Economic Benefits (column 6) rep-resents the difference in variable means for Specialty and Standard conditions, at the6-month follow-up. The column labeled Time (column 7) denotes the difference inmatched variable means at baseline and the 6-month follow-up, by treatment condi-tion. Finally, the column labeled G× T (i.e., Group and Time differences) (column 8)represents the difference-in-mean-difference (baseline to 6-month follow-up) forSpecialty and Standard conditions.

Although group differences in benefits (column 6) may appear most relevantfor comparisons of Specialty and Standard treatment, these estimates are misleading

Table III. Cost of Treatment for Specialty and Standard Treatment Conditions ($1998)

Average Intake and Total cost ofTreatment length of Reimbursement assessment treatment percondition stay (days) rate ($) fee ($) client ($)

Specialty 94.40 (114.01) 83a 200 8,035Standard 30.17 (6.58) 42b 200 1,467

Note. Standard deviations are in parentheses.aA unit of service in Specialty treatment is equivalent to one day per family.bA unit of service for Standard residential service is one day per individual.

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Table IV. Economic Benefits of Treatment ($1998)

Baseline 6-month follow-up Economic benefits

Outcome category Mean SD Mean SD Group Time G× T

Medical statusSpecialty 1,174 1,550 877 1,387 14 297 −1,089∗∗Standard 2,278 3,156 891 1,536 1,387∗∗∗Full 1,707 2,506 883 1,452 823∗∗∗

Psychiatric statusSpecialty 857 896 508 672 −49 349∗∗ −113Standard 922 1,376 460 978 462∗∗Full 888 1,146 485 829 404∗∗∗

Employment statusSpecialty 521 1,477 1,205 2,082 −284 684∗∗ −108Standard 697 1,900 1,489 2,345 793Full 606 1,686 1,342 2,204 736∗∗

Drug/alcohol useSpecialty 4,763 12,105 106 316 −41 4,657∗∗ 2,804Standard 1,917 3,374 65 294 1,853∗∗∗Full 3,390 9,082 86 304 3,304∗∗∗

Legal statusSpecialty 22,129 46,005 2,937 19,485 −2,937 19,191∗∗∗ 14,129∗Standard 5,062 20,136 0 0 5,062Full 13,897 36,744 2,937 19,485 12,376∗∗∗

TotalsSpecialty 28,402 53,661 3,223 19,973 −3,297 25,178∗∗∗ 15,622∗Standard 9,483 21,232 −74 3,121 9,557∗∗∗Full 19,276 42,179 1,632 14,547 17,643∗∗∗

∗ p < 0.10; ∗∗ p < 0.05; ∗∗∗ p < 0.01.Specialty—Specialty treatment condition (N = 44).Standard—Standard treatment condition (N = 41).Full—Specialty + Standard, the full intent-to-treat sample (N = 85).Group—difference in variable means for Specialty and Standard conditions at the 6-month follow-up.Time—difference in variable means from baseline to the 6-month follow-up, by group.G× T—difference in mean difference for Specialty and Standard conditions.

because they do not account for significant differences in outcome measures betweenthe two conditions at baseline. By analyzing the differential benefits between thetwo conditions at baseline and follow-up, the group-by-time differences (G× T,column 8) offer the most meaningful comparisons between conditions.

It may be interesting and informative to review the benefit estimates by outcomecategory and for individual measures, but the most important estimates appear inthe last row of Table IV (total benefit). The total benefit estimates represent the sumof all individual measures. All variables were entered additively because they werereviewed for conceptual acceptability, while guarding against double counting.

Looking first at benefits within conditions, the direction of change in mean valuesfrom baseline to the 6-month follow-up corresponded to positive economic benefits,as shown by the numbers reported in the Time column. In addition, most of theseestimates were statistically significant. Total benefit of treatment amounted to $25,178(p < 0.01) for Specialty, $9,557 (p < 0.01) for Standard, and $17,643 (p < 0.01) forFull. Since all total benefit estimates were statistically different from zero, there isevidence that the treatment conditions separately and in the aggregate generatedpositive total benefits.

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Further examination of the results in Table IV shows that group differences(column 6) favored Standard over Specialty for most outcome categories and inthe aggregate. However, none of the results in this column approached significance.Moreover, since individuals in Specialty and Standard differed at baseline, the Groupestimate is a poor measure of incremental change.

The G×T estimates (column 8) were calculated through the more appropriatedifference- in-difference method, and these provide a more suitable measure for ben-efit comparisons across conditions. The G × T results indicate that the incrementaltotal benefit of Specialty relative to Standard amounted to $15,622 (p < 0.10). Thus,the total economic benefit of Specialty treatment was significantly different fromthe total economic benefit of Standard treatment. Since Specialty treatment was amore expensive package of treatment services (see Table III), and the incrementaltotal benefit of Specialty was significant (subject to the important qualification thatthe samples were significantly different at baseline for some measures), these resultshave important implications for the benefit–cost analysis that follows.

Net Benefits and Benefit–Cost Ratios

Table V reports the average costs and benefits of Specialty and Standard treat-ment, separately and combined. Per-client net benefit for each treatment conditionwas obtained by deducting average total cost from average total economic benefit(column 3). Benefit–cost ratios were calculated by dividing average total benefit byaverage total cost (column 4).

Recall that average cost of treatment amounted to $8,035 for Specialty and$1,467 for Standard (see Table III), and average economic benefit amounted to$25,178 for Specialty and $9,557 for Standard (see Table IV). It can be observedfrom Table V (column 3) that Specialty treatment generated the highest net bene-fit ($17,143). At $8,090, the average net benefit for Standard treatment was $9,053below that of Specialty.

The benefit–cost ratios offer a different interpretation of the economic returnsto treatment. Specialty treatment generated a benefit–cost ratio of 3.1 implying that,for this sample, each dollar invested in Specialty treatment yielded 3.1 dollars inaccumulated economic benefit to society. However, the benefit–cost ratio for

Table V. Comparison of Treatment Costs and Benefits ($1998)

Average Average Average Benefit–costTreatment condition economic cost economic benefit net benefit ratio

Specialty 8,035∗∗∗ 25,178∗∗∗ 17,143∗∗ 3.1††

Standard 1,467∗∗∗ 9,557∗∗∗ 8,090∗∗ 6.5††

Full (Specialty + Standard) 4,830∗∗∗ 17,643∗∗∗ 12,813∗∗∗ 3.7†††

Note. The average economic cost estimate for Full condition (Specialty + Standard) represents theweighted average cost of the Specialty and Standard conditions, where the weights are the number ofclients at each program.∗∗Significantly different from zero, p < 0.05; ∗∗∗Significantly different from zero, p < 0.01; ††Significantlydifferent from 1, p < 0.05; ††† Significantly different from 1, p < 0.01.

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Standard treatment was considerably higher (6.5), implying that an additional dollarinvested in Standard treatment would yield a higher economic benefit to society thanan additional dollar invested in Specialty treatment. Despite the differing interpreta-tions for net benefit estimates and benefit–cost ratios, these findings strongly suggestthat both conditions generated treatment benefits that were significantly higher thantreatment costs.

The last set of calculations considered Specialty and Standard programs togetherto increase sample size and improve the precision of the estimates. The estimatednet benefit and the benefit–cost ratio for the full intent-to-treat condition (Full)amounted to $12,813 and 3.7, with both estimates showing statistical significance(p < 0.01).

DISCUSSION

The main objective of this benefit–cost analysis was to determine whether theeconomic benefits of Specialty and Standard treatments exceeded the economic costsof these conditions for a sample of pregnant and parenting women receiving treat-ment in Arkansas. Based on differences in treatment outcomes from baseline to the6-month follow-up, the total economic benefit of each condition was significantly dif-ferent from zero. In addition, the average net benefit estimates were significantly dif-ferent from zero, and the benefit–cost ratios were significantly greater than unity. Thenet benefit estimate for Specialty ($17,143) was higher than the net benefit estimatefor Standard ($8,090), implying that Specialty residential treatment may offer an eco-nomically prudent enhancement to Standard residential treatment for pregnant andparenting women. However, future studies should reexamine these estimates withlarger and more equivalent samples to determine whether the benefit–cost statisticsare stable and significant in all cases.

Additional analysis of the data showed that economic benefits were not dis-tributed evenly over the different outcome categories, and Specialty and Standardclients exhibited significantly different improvements for certain outcomes. Furtheranalysis of these differential effects could be a fruitful area of investigation to learnmore about the reasons for client improvement in both conditions. Moreover, theimplicit assumption underlying both the initial effectiveness study and the presenteconomic analysis was that either treatment condition would be appropriate for allpatients. Significant differences in patient severity at admission suggest that thereis reason to question this assumption. Conceivably, the more severe patients whovoluntarily entered Specialty treatment would not have improved in Standard treat-ment. The number of subjects was too low to adequately test this assumption throughmatched analyses. Nevertheless, an important finding is that both the Specialty andStandard clients showed significant clinical improvements and positive net benefitsresulting from treatment. Thus, it appears that the State of Arkansas is receivingvalue for its treatment investments in both clinical and financial terms—at least tothe extent that these samples are representative of the types of patients enteringtreatment.

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Limitations

It is important to note that several significant limitations that are common tononrandomized field studies were also present with the current analysis. Since thebenefit–cost analysis was methodologically innovative, limitations were present inthis area as well.

Sample Size

The sample size used for the statistical analysis was relatively small (N = 44for Specialty, N = 41 for Standard), potentially compromising statistical significanceof some benefit estimates and benefit–cost statistics within and between treatmentconditions. Of potentially greater significance was the low follow-up rate (56%),raising the possibility that the samples were not truly representative of their clinicalpopulations.

Another sample issue concerns differences at baseline across Specialty and Stan-dard clients. As described in the original effectiveness evaluation, women enteringSpecialty treatment had greater drug, legal, and family/social problems (p < 0.05).The difference-in-difference benefit measure (G× T in Table IV) partially controlsfor differences at baseline, but it is not a perfect substitute for equivalent samples.Further analysis estimated a regression model for total cost, total benefit, and net ben-efit, controlling for demographic characteristics, ASI composite scores at baseline,and treatment condition. The regression results are presented in Table VI. Theseestimates confirm earlier findings, as total cost, total benefit, and net benefit arelarger for the Specialty condition relative to the Standard condition. However, theestimated differentials for total benefit and net benefit were not statistically

Table VI. Regression Analysis of Total Cost, Total Benefit, and Net Benefit (N = 85)

Variable Total cost Total benefit Net benefit

Specialty condition 6,708∗∗∗ (1,889) 8,057 (9,369) 1,349 (9,613)Age −7475 (922) −1,653 (4,572) −906 (4,691)Age squared 11.36 (13.81) 6.06 (68.49) −5.30 (70.27)Education 975∗ (540) 3,869 (2,678) 2,895 (2,748)Married −1,336 (2,113) −3,019 (10,479) −1,684 (10,752)White −1,935 (1,824) 4,883 (9,045) 6,818 (9,281)Pregnant 2,556 (2,994) −34,051∗∗ (14,845) −36,607∗∗ (15,231)Parenting −26.20 (1,730) −6,489 (8,580) −6,463 (8,804)Number of children 161 (555) −2,397 (2,751) −2,558 (2,822)ASI composite score

Medical −1,408 (2,558) 20,795 (12,683) 22,203∗ (13,014)Employment 2,212 (3,564) 20,089 (17,675) 17,876 (18,136)Alcohol −965 (2,567) −2,734 (12,730) −1,770 (13,062)Drug 4,660 (6,253) 35,375 (31,008) 30,715 (31,816)Legal −2,941 (3,414) 68,002∗∗∗ (16,931) 70,943∗∗∗ (17,373)Family/social −2,101 (3,726) 8,420 (18,477) 10,521 (18,958)Psychiatric 2,081 (3,276) 3,437 (16,247) 1,356 (16,671)

Constant 1,924 (16,663) −23,209 (82,627) −25,133 (84,780)Adjusted R2 0.15 0.31 0.30

Note. Standard errors are in parentheses.∗Statistically significant, p < 0.10; ∗∗Statistically significant, p < 0.05; ∗∗∗Statistically significant, p < 0.01.

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significant. Again, it would be fruitful to reestimate these models with largersamples.

In part because of the low follow-up rates, data imputations were also nec-essary for a small percentage of the variables to avoid losing additional cases forthe total benefit calculations. However, imputations were approximations of thetrue, missing values, which may have caused some estimation bias (56). A sensi-tivity analysis of the imputation procedures, however, indicated that the net ben-efit estimates and benefit–cost ratios with and without imputation were verysimilar.

Self-Reported Data

ASI outcome data were derived from patient self-reported information, whichmay be less reliable than sources such as physical specimens or administrative records(8). However, numerous studies have demonstrated that self-reported data on druguse and other functional status measures can be quite accurate (57–59) and even ifimperfect, there is no reason to assume differential levels of accuracy were presentbetween the two groups.

Evaluation Timeline

The posttreatment follow-up interval was rather short (6 months), and improve-ments may have extended beyond this period. Thus, the calculations may have un-derestimated protracted benefits. Furthermore, some follow-up questions referredto a period (past 30 days) during which benefits may have already begun to erodesomewhat. Treatment benefits typically peak soon after discharge and then steadilydecline thereafter.

Multiplying the benefits accrued during a 30-day period by 6 to estimate treat-ment benefits for a 6-month follow-up period implies that progress was consistentacross all 6 months. Some studies have shown that treatment outcome measures(e.g., drug use, health care costs) ramp up before admission and then ramp downsoon after treatment discharge (60–61). If this ramp effect was present in the currentsample, then multiplying outcomes by 6 may have overestimated the level of severityof the preadmission period, but underestimated the level of improvement for thepostadmission period.

Potential Double Counting and Overvaluation

Although certain overlap in outcomes was accounted for (see Methods section),some residual double counting may be present in the total benefit calculations. Forexample, money spent on alcohol and illicit drugs may have included some of thenegative consequences resulting from the abuse of addictive substances, such ascrime. Criminal activity, however, was already valued in the benefit calculationsthrough days engaged in illegal activities.

The valuation of a day of illegal activity deserves additional attention. The unitcost estimate for this variable ($718.04) represents a weighted average of various

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crimes, ranging from drug law violation to violent assault. It may be justified, however,to assume that this population was committing less severe crimes, such as prostitutionor drug law violations, rather than predatory crimes. Since less severe crimes have aconsiderably lower social cost, the benefit analysis may be overvaluing the reportedillegal activity for this sample of pregnant and parenting women.

Selection Bias

Since patients entered these treatment programs voluntarily, the analysis maypossess some degree of selection bias. Substance abusers seeking treatment usuallypresent a certain degree of motivation. It could thus be argued that patients wouldhave improved somewhat even without formal treatment (62).

Generalizability

Results from formal hypothesis testing with a convenience sample such as thesedata must be interpreted cautiously. It is not clear to what extent these results wouldgeneralize to other patients or programs, as the participants were not obtainedthrough sampling procedures designed to enhance generalizability.

CONCLUSION

Despite the limitations noted above, the empirical findings are informative and,with appropriate caveats, are potentially useful for clinicians, researchers, and poli-cymakers at the State and Federal level regarding the allocation of scarce resourcesto the treatment of pregnant or parenting women with substance dependence issues.Equally, if not more important, the methodological framework presented here con-tributes to the foundation of economic evaluation studies with the use of the ASIand similar clinical instruments (7, 9-10). This body of economic research offers con-ceptual and empirical guidelines for future studies when performing comparativeeconomic analyses of treatment options.

ACKNOWLEDGMENTS

Financial assistance for this study was provided by the Center for SubstanceAbuse Treatment (Grant No. 270-97-7020) and the National Institute on Drug Abuse(Grant Nos. 1R01 DA11506 and 2P50 DA07705). We are grateful to Kathy Geary,Jarrod Cecere, and Walter Hathaway for project management and data collection;Quansheng Shen for his data management support; Silvana Zavala for her researchassistance; Tom McLellan and Helena Salome for helpful suggestion on previousversions of the paper; and Carmen Martinez for her administrative and editorialassistance. The authors are entirely responsible for the research and results reportedin this paper, and their position or opinions do not necessarily represent those of theUniversity of Miami, DeltaMetrics, or the State of Arkansas.

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Documents

Benefit–Cost Analysis of Addiction Treatment in Arkansas: Specialty and Standard Residential Programs for Pregnant and Parenting Women