Measuring addiction propensity and severity: The need for a new instrument☆

Drug and Alcohol Dependence 111 (2010) 4–12

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Drug and Alcohol Dependence

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Review

Measuring addiction propensity and severity: The need for a new instrument!

Kevin P. Conwaya,!, Janet Levyb, Michael Vanyukovc, Redonna Chandlera,Joni Rutterd, Gary E. Swane, Michael Neale f

a Division of Epidemiology, Services, and Prevention Research, National Institute on Drug Abuse, National Institutes of Health, Department of Health and Human Services,6001 Executive Blvd., Room 5153, MSC 9589, Bethesda, MD 20892-9589, USAb Duke University School of Nursing, SON New Building, Room 1019, 307 Trent Drive, Durham, NC 27710, USAc Departments of Pharmaceutical Sciences, Psychiatry and Human Genetics, University of Pittsburgh, SALK 528, Pittsburgh, PA 15260, USAd Division of Basic Neuroscience and Behavioral Research, National Institute on Drug Abuse, National Institutes of Health, Department of Health and Human Services,6001 Executive Blvd., Room 4282, MSC 9589, Rockville, MD 20852, USAe Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USAf Department of Psychiatry, Virginia Commonwealth University, 1200 East Broad Street, PO Box 980710, Richmond, VA 23298-0710, USA

a r t i c l e i n f o

Article history:Received 30 June 2009Received in revised form 1 March 2010Accepted 2 March 2010Available online 11 May 2010

Keywords:TobaccoCannabis assessmentIndividual differencesAdolescents

a b s t r a c t

Drug addiction research requires but lacks a valid and reliable way to measure both the risk (propensity)to develop addiction and the severity of manifest addiction. This paper argues for a new measurementapproach and instrument to quantify propensity to and severity of addiction, based on the testableassumption that these constructs can be mapped onto the same dimension of liability to addiction. Thecase for this new direction becomes clear from a critical review of empirical data and the current instru-mentation. The many assessment instruments in use today have proven utility, reliability, and validity,but they are of limited use for evaluating individual differences in propensity and severity. The concep-tual and methodological shortcomings of instruments currently used in research and clinical practice canbe overcome through the use of new technologies to develop a reliable, valid, and standardized assess-ment instrument(s) to measure and distinguish individual variations in expression of the underlyinglatent trait(s) that comprises propensity to and severity of drug addiction. Such instrumentation wouldenhance our capacity for drug addiction research on linkages and interactions among familial, genetic,psychosocial, and neurobiological factors associated with variations in propensity and severity. It wouldlead to new opportunities in substance abuse prevention, treatment, and services research, as well as ininterventions and implementation science for drug addiction.

Published by Elsevier Ireland Ltd.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52. Severity measurement instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1. Addiction Severity Index (ASI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2. Drug Use Screening Inventory (DUSI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3. Global Appraisal of Individual Needs (GAIN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.4. Severity of Alcohol Dependence Questionnaire (SADQ) and Alcohol Dependence Scale (ADS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.5. Severity of Opioid Dependence Questionnaire (SODQ), the Severity of Amphetamine Questionnaire (SamDQ), and the Benzodiazepine

Dependence Questionnaire (BDEPQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.6. Chemical Use Abuse and Dependence (CUAD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.7. Substance Dependence Severity Scale (SDSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.8. Severity of Dependence Scale (SDS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.9. Substance Use Involvement Index (SUII) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

! Disclaimer: The views and opinions expressed in this paper are those of the authors and do not necessarily represent the views of the National Institute on Drug Abuse,National Institutes of Health, or any other governmental agency.! Corresponding author at: Division of Epidemiology, Services, and Prevention Research (DESPR), National Institute on Drug Abuse, 6001 Executive Blvd., Suite 5185,

Bethesda, MD 20892-9589, USA. Tel.: +1 301 443 6504; fax: +1 301 443 2636.E-mail address: [email protected] (K.P. Conway).

0376-8716/$ – see front matter. Published by Elsevier Ireland Ltd.doi:10.1016/j.drugalcdep.2010.03.011

K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12 5

3. Propensity measurement instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.1. Transmissible Liability Index (TLI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4. Summary and future directions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.1. Limitations of existing instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.2. Advantages of item-response theory (IRT) for measuring propensity and severity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.3. Advantages of an instrument measuring propensity to and severity of addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1. Introduction

Drug addiction research spans the gamut from neuroscienceand genetics to prevention, treatment and services. As in otherbiobehavioral disciplines, this research requires a valid and reli-able measure of both the propensity to develop addiction and theseverity of manifest addiction. This paper examines the need forsuch a measure as well as the assumption on which it is based,namely that these constructs share the same dimension. It pro-vides a critical review of theory, data, and current instrumentationto show how a new measurement tool could impact the field ofaddiction research. Finally, it considers the next steps required forthe development and fruition of a new instrument for measuringaddiction.

Fueled by technological innovations and multidisciplinary sci-entific collaborations, addiction science has advanced rapidly overthe past 20 years. The notion that addiction “runs in families”(Merikangas and Conway, 2009; Bierut et al., 1998; Merikangaset al., 1998; Reich et al., 1988; Schuckit et al., 1972; Winokuret al., 1970) is now attributed in large part to additive geneticfactors (Kendler et al., 2003a; Tsuang et al., 1996). This has leadscientists to search for genes that influence variation in the riskfor addiction, with a number of replication studies now emerg-ing on candidate genes (Foll et al., 2009; Kreek et al., 2005; Li andBurmeister, 2009; Saxon et al., 2005; Schuckit, 2009; Uhl, 2004;Vanyukov and Tarter, 2000). Scientific advances in neurosciencehave shown that addiction is a chronic and often relapsing diseasethat is linked to pathological changes in neural circuitry, includingthose involved in reward and motivation, learning and memory,cognitive control, decision making, mood, and interoceptive aware-ness (Adinoff, 2004; Hyman et al., 2006; Kalivas and Volkow,2005; Kalivas and O’Brien, 2007; Koob and Le, 1997; O’Brien, 2003;Volkow et al., 2003, 2004). Importantly, these changes involvethe same structures and processes that contribute to the mech-anisms of behavior regulation and its deviations, predating druguse (Vanyukov et al., 2003b). Impairment of the addicted brainprovides a neurological basis for the cardinal behavioral manifesta-tion of drug addiction—persistent drug use despite serious adverseconsequences. Longitudinal observations of addicts demonstratethe chronic relapsing nature of addiction and the need for long-term treatment strategies (Dennis and Scott, 2007; Dennis et al.,2005). However, the malleability of the liability phenotype, includ-ing “maturing-out” of conditions that meet diagnostic criteria fordependence (Dawson et al., 2006; Newcomb et al., 2001), under-scores the nonspecific character and construct validity of liabilitywhile still holding promise for effective intervention. In contrastto such static traits as stature in adults, liability to addiction isdynamic over time and development. It can be viewed as form-ing an ontogenetic trajectory that can be tracked and measured,and given its inherent dynamism, potentially changed (Tarter andVanyukov, 1991, 1994).

Advances in these and other important areas of addictionscience have outpaced progress in the measurement of addic-tion, and the ability to quantify and measure this constructaccurately remains a looming methodological challenge (Conway

et al., 2006; Craddock et al., 2008; Merikangas and Avenevoli,2000; Merikangas and Conway, 2008; Neale et al., 2006). Cur-rently, research usually measures drug addiction in the broadestsense by classifying and contrasting “addicts” to “non-addicts”according to diagnostic criteria. Genetic risk for addiction is sim-ilarly categorized based on a parent’s diagnosis or family history.This approach implicitly assumes categorical distinctions betweengroups and categorical similarities among the individuals withineach group regarding their symptoms and group-identified fea-tures of addiction. Few studies have focused on variations amongindividuals within each group, despite the common knowledgethat meaningful individual differences exist within any givencohort of addicts and controls. Such heterogeneity is reflectedin the hundreds of different combinations of addiction symp-toms that meet diagnostic criteria (Vanyukov et al., 2003b).Although needed for clinical practice, the diagnosis per se isless than optimal when it comes to genetic and other etiologyresearch, or for informing efforts in the primary prevention ofaddiction.

A precise method and instrument for measuring individualvariation in liability to addiction is therefore needed to build onand advance the science and understanding of the multifactorialnature of drug abuse and addiction. A term introduced in humangenetics by Falconer (1965, p. 52), liability is a latent (unobserv-able) quantitative trait that, if measured, “would give us a gradedscale of the degree of affectedness or of normality”, with these twocategories divided by a threshold. The “gradations of normality”(the subthreshold liability phenotypes) correspond to variation inthe risk (propensity), whereas “gradations of affectedness” (thesuprathreshold phenotypes, likely to be assigned a clinical diagno-sis) correspond to variation in severity, comprising the two portionsof the liability distribution. Applied to addiction, severity refersto the degree of maladaptive compulsive drug-seeking and usingbehavior displayed by an individual, corresponding to variationin liability above the diagnostic threshold. Propensity refers to theprobability of the disorder’s onset, corresponding to liability vari-ation below the diagnostic threshold. It follows that individualdifferences in severity manifest as different degrees of maladap-tive drug-seeking and drug-using behavior. Similarly, variation inpropensity manifest as behavioral precursors of addiction. The ulti-mate desirable goal of a new instrument of addiction would be toprovide a single scale (see Fig. 1) by which an individual’s liabil-ity to addiction (propensity or severity) can be quantified using anumeric score.

The plausibility of such a single common (versus drug-specific)liability dimension (latent trait) and the feasibility of its mea-surement are supported by clinical, neurobiological, genetic, andstatistical findings (Vanyukov et al., 2003a). Liabilities to addictionsto specific drugs are both phenotypically and genetically highlycorrelated, with minimal specific genetic variance; most of the vari-ance in addiction liability is associated with common (thus, likely,brain-behavioral) mechanisms rather than with drug action per se(Kendler et al., 2007; Tsuang et al., 1998). Indeed, neurobiologi-cal data pertaining to drug reward suggest that many drug effects

6 K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12

Fig. 1. Addiction liability distribution.

involve the dopaminergic and other major neurobiological sys-tems, despite differences in specific routes of administration, thebiotransformation pathways of different drugs, or in their primarytargets (Koob and Volkow, 2009). These same systems substantiallyoverlap with those involved in mechanisms of natural reward andincentive motivation, stress response, and social behavior as well(Goldstein and Volkow, 2002; Koob and Volkow, 2009; Vanyukovet al., 2003a). Moreover, data increasingly show that drug addic-tion can be located on the same dimension as premorbid (and evenpre-drug use) behaviors that indicate a highly heritable latent traitvariably known as dysregulation, disinhibition, behavior under-control or externalizing behavior, including risks for disruptivebehavior disorders (Button et al., 2006; Hicks et al., in press; Kendleret al., 2007; Kirisci et al., 2009; Krueger et al., 2002; Tarter et al.,1990, 2003, 2004; Vanyukov et al., 2009; Young et al., 2009). Fur-ther support comes from research on an instrument designed tomeasure variation in propensity to addiction (the TransmissibleLiability Index, TLI; described in detail below). Comprised largelyof items indicating dysregulation, scores on the TLI have beenfound to be elevated among offspring of drug abusers (Tarter etal., 2003), highly heritable (Vanyukov et al., 2009; Hicks et al., inpress), predictive of addiction subsequent to drug use initiation(Vanyukov et al., 2009; Kirisci et al., 2009), and prior to exposureto cannabis, higher in boys who later develop cannabis use dis-order compared to those who used cannabis but did not developthis disorder (Kirisci et al., 2009). It is noteworthy that no sharedenvironment component was detected for TLI (Vanyukov et al.,2009), a finding that is consistent with studies showing that lia-bility to drug use initiation is affected by shared and non-sharedenvironmental influences, whereas liability to addiction is largelyinfluenced by genetic factors (Kendler et al., 2000) and phenotype–(and genotype-) environment correlation (i.e., when individualswith certain characteristics are more likely to seek, and be foundin, environments conducive to accessing and using drugs) (e.g.,Kirillova et al., 2008). These findings are part of the cumulativescience on the biobehavioral foundations of addiction. They rein-force the plausibility of mapping the propensity for and severity ofaddiction along a common dimension of liability, in the Falconersense, i.e., extending across a graded scale whose degrees signifygradations between sub- and suprathreshold liability phenotypes,respectively.

Genes found to be associated with addiction-related charac-teristics, both in candidate gene and whole genome scan studies(Uhl et al., 2008), are not specific to a particular drug, or evento addictions as such, given their involvement in basic neurolog-ical mechanisms and signal transduction in the central nervoussystem. It is thus likely that these associations are mediated bypleiotropic nonspecific effects of these genes on neurobiologicalprocesses involved in behavior regulation and control. This hypoth-esis is supported, for instance, by significant genetic correlations

between an index of behavior disinhibition (locating symptoms ofconduct and attention-deficit hyperactivity disorder, novelty seek-ing, and substance use on the same dimension) and an index ofperformance on neuropsychological tasks measuring attentionalcontrol and response inhibition (Young et al., 2009). A similar brain-behavior connection is noted for disinhibition/externalizing andthe reduced amplitude of the P300 event-related potential (Iaconoet al., 2002; Justus et al., 2001), particularly its main time–frequencycomponents, theta and delta, indexing, respectively, memoryencoding and attention processes, and signal matching, decisionmaking, and memory updating processes (Gilmore et al., 2009).The association between P300 amplitude and an index of thelatent externalizing trait (accounting for variance shared betweenliabilities to alcohol, nicotine and drug dependence, conduct dis-order and adult antisocial behavior) is of genetic origin (Hickset al., 2007). As shown by genetic studies of phenotypes basedon neuroimaging, genetic associations of behavioral response reg-ulation and personality characteristics comprising the behaviordysregulation construct are mediated by the function of neural cir-cuitry involving targets of drug action (Hariri et al., 2008; Hariri,2009).

Statistically, substance use disorders and related symptomshave been shown to best fit a model whereby their covariancesare substantially accounted for by a single dominant factor (Kirisciet al., 2002, 2006; Compton et al., 2009; Saha et al., 2007; Wu etal., 2009). Unidimensionality was also established for items thatcomprise the TLI, the instrument (described above) that measurestransmissible liability to illicit drug-related substance use disor-der in children. Interestingly, the TLI items are largely relatedto a set of externalizing problems (e.g., conduct disorder, anti-social personality disorder) and temperament-like indicators ofnonspecific addiction risk (e.g., behavioral disinhibition, constraint,sensation seeking, and novelty seeking) collectively referred toas behavioral undercontrol, dysregulation, or externalizing psy-chopathology (Cadoret et al., 1995; Chassin et al., 1991; Conway etal., 2002, 2003; Elkins et al., 2004; Iacono et al., 1999, 2008; Kruegeret al., 2002, 2007; Sher et al., 2000). Evidence suggests a commongenetic variance between these traits and addiction (Mustanski etal., 2003). Some studies suggest that the broad externalizing factoris more heritable than the constituent individual disorders (80–85%versus 40–70%) (Hicks et al., 2004; Moffitt, 2005; Young et al., 2000),as well as being more useful for gene identification (Dick et al.,2008).

Variation in common (nonspecific) liability to addiction couldhelp explain the typical pattern of progression “softer” to “harder”types of substances used (Tarter et al., 2006). Variation in external-izing traits could also mediate heritability of liability to addiction(Button et al., 2006; Kirillova et al., 2008; Slutske et al., 2002;Swendsen et al., 2002; Tarter et al., 2004; Vanyukov et al., 2007).From a genetic-risk perspective, familial antisocial addiction maybe particularly potent (or severe). Studies of adopted-away off-spring of fathers who are antisocial addicts (compared to eitherantisocial or addicted), for example, have found the offspring tobe at greatest risk for substance abuse themselves (Langbehn etal., 2003). Prevention research has demonstrated how interven-tions that directly or indirectly target externalizing problems caneffectively prevent drug abuse (Hawkins et al., 2008; Kellam et al.,2008; Poduska et al., 2008), perhaps through impacting underlyingneuroregulation systems (Romer and Walker, 2007) and familialprocesses that enhance behavioral regulation (Brody et al., 2009).

2. Severity measurement instruments

The following briefly reviews instruments that measure addic-tion propensity and severity (see Table 1). The instruments inthis review were identified through a literature search of sev-

K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12 7

Table 1Instruments designed to measure addiction severity.

Instrument What it measures Operationalization of severity Item-selection methodology

Addiction Severity Instrument (ASI) Severity of alcohol anddrug use

Need for treatment across 6 domains Clinical judgment

Alcohol Dependence Scale (ADS) Severity of alcoholdependence

DSM symptomatology, loss of control,obsessive drinking style, two aspects ofwithdrawal

Clinical judgment, item/factorcorrelation

Benzodiazepine Questionnaire (BDEQ) Severity of benzodiazepinedependence

DSM symptomatology, pleasurable effectsof drug, perceived need for drug in orderto function properly

Clinical judgment, item/factorcorrelation

Chemical Use, Abuse, and DependenceScale (CUAD)

Severity of alcohol anddrug use

DSM symptomatology Clinical judgment

Drug Use Screening Inventory (DUSI) Severity of alcohol anddrug use

Consequences of drug use Clinical judgment

Global Appraisal of Individual Needs(GAIN)

Severity of alcohol anddrug use

DSM symptomatology, substance usefrequency, behavioral complexity

Clinical judgment

Severity of Alcohol DependenceQuestionnaire (SADQ)

Severity of alcoholdependence

DSM symptomatology, three aspects ofwithdrawal, rapidity of reinstatementafter abstinence

Clinical judgment, item/factorcorrelation

Severity of Amphetamine DependenceQuestionnaire (SamDQ)

Severity of amphetaminedependence

DSM symptomatology, three aspects ofwithdrawal, rapidity of reinstatementafter abstinence, depression, lethargy

Clinical judgment, item/factorcorrelation

Severity of Dependence Scale (SDS) Severity of drugdependence

DSM symptomatology, compulsivity ofdrug use

Clinical judgment, item/factorcorrelation

Severity of Opiate DependenceQuestionnaire (SODQ)

Severity of opiatedependence

DSM symptomatology, three aspects ofwithdrawal, rapidity of reinstatementafter abstinence

Clinical judgment, item/factorcorrelation

Substance Dependence Severity Scale(SDSS)

Severity of drugdependence

DSM symptomatology Clinical judgment

Substance Use Involvement Index(SUII)

Severity of alcohol anddrug use

DSM symptomatology, liability ofsubstance use

Clinical judgment, item/factorcorrelation, item-response theory

eral databases (e.g., PubMed) for key terms such as “drug abuseseverity”, “drug addiction severity”, “substance abuse severity”,“substance addiction severity”. To be included, an instrument hadto be explicitly designed to measure severity of drug or alco-hol addiction. Excluded were instruments that measure nicotineaddiction,1 screening instruments, and those that measure only onespecific domain of addiction (e.g., craving). The review includes thepurpose and content of each instrument and other information onthe applicability of the instrument for liability measurement.

2.1. Addiction Severity Index (ASI)

The ASI (McLellan et al., 1980) is a widely used instrument thatassesses the need for alcohol or drug treatment across six domainsof functioning (Chemical Abuse, Medical, Psychological, Legal, Fam-ily/Social, and Employment/Support). Within each domain, theinterviewer provides global ratings of severity (referred to as Inter-viewer Severity Ratings, or ISRs) based on the patient’s responsesto objective items as well as the patient’s assessment of how both-ered he/she is by problems in each domain. The ISRs, which rangefrom 0 to 9, form the basis for the patient’s profile and treat-ment plan. Revised versions of the ASI (McDermott et al., 1996)use more advanced psychometric methods, partially in responseto studies that failed to replicate high inter-rater reliabilities ofthe original version of the ASI (McLellan et al., 1985; Hodgins andElguebaly, 1992). The ASI is one of the most frequently used instru-ments in addiction research and practice, and it serves as one ofthe core measures in the NIDA Clinical Trials Network. The relia-bility and validity of the ASI is well documented, but less is knownabout its dimensionality or whether its items reflect differences inseverity.

1 Considerable research attention has been focused on the severity of nicotineaddiction, and results suggest a latent trait for nicotine dependence (Strong et al.,2003a,b, 2007, 2009). Less is known about the severity of alcohol and/or illicit drugaddiction, however, which underscores the need for additional research of this kind.

2.2. Drug Use Screening Inventory (DUSI)

The DUSI (Kirisci et al., 1995; Tarter et al., 1990) was devel-oped as a diagnostic instrument for the treatment of alcohol ordrug problems in adolescents. Despite the word “screening” in itsname, this is a phenotypic assessment instrument comprising aseries of scales measuring severity of substance abuse as reflectedin various life domains: Substance Use, Behavior Patterns, HealthStatus, Psychiatric Disorder, Social Skills, Family System, SchoolAdjustment, Work, Peer Relationships, Leisure/Recreation. Kirisciet al. (1995) found that the items are scalable as indicators ofa unidimensional latent trait, that the scores among individualswith high or moderate substance use were measured with greaterprecision than those at lower levels, and that scores accurately pre-dicted the level of substance use and consequences at 6-monthfollow-up.

2.3. Global Appraisal of Individual Needs (GAIN)

The GAIN (Dennis et al., 2003) is a large set of questionnairesdesigned to provide diagnostic and severity information for bothadolescents and adults seeking treatment for drug or alcohol prob-lems. The GAIN’s 16-item Substance Problem Scale (SPS) scaleconsists of the DSM-IV criteria for substance use diagnosis plus fivescreener items that measure variation in severity (i.e., weekly use,family and friends complaining about use, continued use despitefights, and use being time consuming). Acceptable reliability andvalidity estimates have been reported (1999 manual and Dennis etal., 2006). Riley et al. (2007) found that the five screener items hadrelatively low location parameters, indicating that the items reflectvariation at the lower (or “milder”) end of a severity continuum.

2.4. Severity of Alcohol Dependence Questionnaire (SADQ) andAlcohol Dependence Scale (ADS)

The SADQ (Stockwell et al., 1979) and the ADS (Skinner andAllen, 1982) were explicitly designed to extend the Edwards and

8 K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12

Gross (1976) conceptualization of alcoholism to severity of addic-tion to illicit drugs. The SADQ includes four subscales: physicalwithdrawal, affective withdrawal, drinking to relieve withdrawal,and rapidity of reinstatement after abstinence. The ADS includesfour slightly broader subscales: psychophysical withdrawal, psy-choperceptual withdrawal, loss of behavioral control, and obsessivedrinking style. Reliability and validity estimates were high for bothinstruments. Kahler et al. (2003a,b) reported that the ADS itemsprimarily measure a single dimension of alcoholism severity, withsome items corresponding to greater severity than others.

2.5. Severity of Opioid Dependence Questionnaire (SODQ), theSeverity of Amphetamine Questionnaire (SamDQ), and theBenzodiazepine Dependence Questionnaire (BDEPQ)

The SODQ and the SamDQ adapted the SADQ for opioids andamphetamines, respectively. These instruments consist of sub-scales designed to measure the severity of physical withdrawalsymptoms, affective withdrawal symptoms, the extent to whichdrugs are used to relieve withdrawal symptoms, and the rapid-ity of reinstatement after periods of abstinence. The BDEPQ (Baillieand Mattick, 1996) assesses aspects of benzodiazepine dependenceincluding the extent to which pleasurable effects were anticipatedas a result of drug use, the extent to which drug use is needed tocomplete daily life activities, and a general dependence factor. Thereliability and validity of these instruments are well documented(Baillie and Mattick, 1996; Churchill et al., 1993; Sutherland et al.,1986; Topp and Mattick, 1997), but information is limited regard-ing dimensionality and whether some items reflect greater severitythan others.

2.6. Chemical Use Abuse and Dependence (CUAD)

The CUAD scale (Mcgovern and Morrison, 1992) is a semi-structured interview designed to provide DSM-III-R substance usediagnoses as well as severity indicators in both clinical and researchsettings. For each substance used, weights based on clinical judg-ment are assigned for each symptom to reflect increasing clinicalseverity, and total severity scores are computed by summing theweights across symptoms. The CUAD has demonstrated reliabil-ity and validity (Mcgovern and Morrison, 1992), but it is unknownwhether it is unidimensional or whether some items reflect greaterseverity than others.

2.7. Substance Dependence Severity Scale (SDSS)

The SDSS (Miele et al., 2000) is a semi-structured interviewdesigned to provide DSM-IV diagnoses and severity indicators inboth clinical and research settings. For each substance used, theSDSS provides DSM-IV diagnoses and four different 30-day severityindicators; two for intensity of symptoms (SEV, WORST SEV), andtwo for frequency of symptoms (DAYS, WORST DAYS). The relia-bility and validity of the SDSS is documented, but information islacking about its dimensionality and ability to assess severity.

2.8. Severity of Dependence Scale (SDS)

The SDS (Gossop et al., 1995) was developed as a brief scale tomeasure the degree of dependence experienced by different typesof drug users. In contrast to the other instruments discussed here,the five-question SDS “is primarily a measure of compulsive useand it does not include items to measure tolerance, withdrawal, orreinstatement” (p. 612). Responses are self rated on a 4-point Likertscale, and summed to form a total score. Scale reliability and valid-ity has been reported and factor analytic methods have confirmedunidimensionality of the five items.

2.9. Substance Use Involvement Index (SUII)

The Substance Use Involvement Index (SUII; Kirisci et al., 2002),derived using item-response theory (IRT; described below), isbased on the respondent’s endorsement of lifetime use (yes/no)of 10 categories of drugs: alcohol, cannabis, cocaine/crack, opiates,amphetamines, methylphenidate, sedatives, tobacco, hallucino-gens, PCP and inhalants. One study reported that a unidimensionalfactor model adequately fit the data for both adult males andadult females, and the factor loadings were not significantly dif-ferent between gender groups (Kirisci et al., 2002). Interestingly,Kirisci and colleagues also reported that equated item-locationparameters were higher in females than males, suggesting thatendorsement of substance use by a male is (by itself) indicativeof a lower level of severity than the same endorsement by a female.These results are consistent with data showing that females havea higher liability threshold (Kendler et al., 2003b), i.e., they requirehigher factor scores in order to manifest a disorder.

3. Propensity measurement instruments

Measurement of propensity to addiction has rarely beenattempted and is considerably more difficult than measurementof addiction severity, especially given the absence of face-valueindicators of risk with high construct validity. Obviously, onlypremorbid characteristics may be safely used as indicators ofpropensity because drug use and addiction have behavioral andpsychological effects. Despite knowledge of many “risk” and “pro-tection” factors (psychological and psychopathological variablesassociated with addiction risk) (Glantz et al., 2005; Hawkins et al.,1992), it is not evident on an a priori basis that premorbid indica-tors can be used, or how, in constructing an index of propensity toaddiction.

3.1. Transmissible Liability Index (TLI)

One approach to the selection of propensity indicators has beenused in the derivation of the index of transmissible liability to addic-tion related to illicit drugs (the Transmission Liability Index, TLI),based on a high-risk/family design and IRT in the NIDA-fundedCenter for Education and Drug Abuse Research (CEDAR). Inasmuchas addiction risk is transmissible within families (mostly due toits high heritability), characteristics that discriminate groups ofchildren with affected versus unaffected parents (high- and low-average-risk groups, HAR and LAR, respectively) are likely to beindicators of the transmissible component of children’s addictionliability/propensity (the variance component correlated betweenrelatives/generations).

The TLI derivation method involves using a large set of itemsfrom numerous psychological and psychiatric instruments thatwere originally selected based on their potential for measuringvariables related to addiction risk and psychopathology. Througha variety of statistical methodologies (e.g., factor analysis, IRT),the items were selected and transformed into a set of unidimen-sional constructs characterizing individual behavior/personality(e.g., antisociality, attention, mood) (see measurement model andother details in Vanyukov et al., 2003a). The resulting 45-item setselected for sons of the probands at age 10–12 was used to assessthe quality of items and estimate TLI. The findings show that the TLIis a valid and reliable scale, and highly predictive (e.g., O.R. = 1.81,95% C.I.: 1.12–2.30) of substance use disorder (Vanyukov et al.,2009; Kirisci et al., 2009). Twin studies have also shown that TLIis highly heritable (H2 = 0.79) (Vanyukov et al., 2009; Hicks et al.,in press), further supporting its derivation as a measure of a trans-missible trait.

K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12 9

The longitudinal design of the CEDAR study permits furtherrefinement of the method used in the creation of the TLI, such thatthe liability/propensity indicator constructs and items will be ref-erenced to the offspring’s own, rather than parental, outcomes suchas drug use and drug addiction. Such referencing, aside from beingbased entirely on the offspring’s own phenotype, directly connectsindicators and a given manifest trait, which will enable indexingof the resultant total, rather than only transmissible, liability. Thisextended liability index methodology has potential as a prototypefor development of an index to cover the entire range of phenotypicdistribution (i.e., both propensity and severity) and identify candi-date biologic and genetic mechanisms of drug use and its precursorsand antecedents.

4. Summary and future directions

4.1. Limitations of existing instruments

The assessment instruments in use today for measuring addic-tion severity (described above and appearing in Table 1) haveproven utility, reliability, and validity, but they are of limited usefor evaluating individual differences in propensity/severity of thehallmark characteristic(s) of drug addiction, i.e., compulsivity inseeking and using drugs despite harmful consequences. First, exist-ing instruments are based on a variety of related but differentconstructs of addiction severity including behavioral and socialconsequences, quantity or type of DSM symptoms endorsed, usepatterns within and across substances, and number of differentDSM diagnoses. Only one, the SDS, focuses on compulsive drugtaking and seeking. Second, the content of existing measures isunlikely to reflect the full range of addiction severity. This limi-tation can be partly attributed to the fact that the content of manyinstruments derives from the DSM, which virtually by design, isnot optimal for measuring variability in the severity of addiction.Indeed, clinical and epidemiologic studies applying IRT modelsshow that DSM symptoms are redundant and fail to capture vari-ability across the full range of the addiction continuum (Compton etal., 2009; Gillespie et al., 2007; Langenbucher et al., 1995; Saha et al.,2007; Wu et al., 2009). Many existing instruments also rely on clin-ical judgment in the absence of item-selection techniques, therebyincreasing the likelihood of including items that are redundant,collinear, or weakly related to the construct of interest.

Third, only one extant instrument (TLI) is currently available tomeasure propensity to addiction, let alone both the propensity andthe severity of addiction on the same metric. It should be notedthat the latent liability phenotype assessed by the TLI, while per-taining to the risk in the nonaffected individuals and thereby toprospective prediction, is nevertheless a cross-sectional measure.Substance use or lack thereof, or consumption with multiple gradedcategories (e.g., none, low, heavy) are observed phenotypic out-comes that may or may not be used as indicators of latent liability.Using items immediately related to drug use may be problematic,of course, because that would preclude the index’s use in study-ing respective behaviors as outcomes. Although substance use isa necessary condition for addiction development, it seems likelythat the psychological/behavioral components of liability such aspersonality largely influence substance use initiation and/or, moreimportantly, development of addiction. Substance use can thus beviewed as a manifestation of liability forming an ontogenetic tra-jectory that can be tracked and measured.

Fourth, most extant instruments were constructed using clas-sical test theory methodology, which has its own psychometriclimitations that directly bear on severity measurement. The use offactor analysis, for example, does not utilize the full power of mod-ern scaling techniques (Embretson and Reise, 2000; Hambleton

and Swaminathan, 1985; Lord and Novick, 1968) that take itemproperties into account (e.g., whether one item indicates greater“severity” than another item). While producing scales with highlevels of internal consistency, the use of factor analytic methodsalone does not allow one to build instruments to discriminate indi-viduals along selected ranges of an underlying trait.

4.2. Advantages of item-response theory (IRT) for measuringpropensity and severity

Whereas various methods are available to analyze latent traits,IRT appears particularly suited for the derivation of an index of lia-bility to addiction. Briefly, IRT (e.g., Embretson and Reise, 2000) is apsychometric test theory that relates the performance of an exami-nee on a test item to a latent trait (ability) that the test is intended tomeasure. This relationship (e.g., in a simple case, between the traitand the probability of a correct response) is described by an item-response function (IRF). While ability level is a characteristic of theexaminee, the examinee’s performance will also depend on param-eters that characterize the test items themselves. The widely usedtwo-parameter model includes location (difficulty) or b, the traitvalue at which the probability of a correct response exceeds 0.5, anddiscrimination or a, which is proportional to the slope of the IRF atpoint b on the trait scale. In this model, the parameters provide forgradients in item difficulty and capacity to discriminate differentvalues of the trait. In contrast to the classical psychometric test the-ory, which was used to develop most of the instruments reviewedherein, IRT provides testable models. A data-fitting IRT model yieldsestimates that have unique value for trait measurement; i.e., itemparameters are invariant across samples (subpopulations) of sub-jects (the trait distribution does not influence the estimates), andtrait estimates are invariant across items used.

The many advantages of IRT have been underutilized in severityinstrument development, though it has successfully been used inthe measurement of transmissible addiction risk (Vanyukov et al.,2003a,b, 2009; Kirisci et al., 2006, 2009). Selection of items basedon the information they contribute is a key advantage of item-response theory (Hambleton et al., 1991), which is likely to informthe development of the liability index. The most difficult questioncenters on the problem of selecting and constructing items that arehighly informative for measuring low propensity values. Neverthe-less, some such items (e.g., “I don’t move around much at all in mysleep”, “I usually eat the same amount each day”, and “Changes inplans make my child restless”) have been identified and applied inan existing propensity measurement instrument, the TLI (Kirisci etal., 2009; Vanyukov et al., 2009). These and other items are poten-tial candidates for an instrument that measures both propensityand severity on the same scale.

How and to what extent the propensity and severity phenotypescan be characterized by a common, underlying, and continuous uni-dimensional trait of liability to addiction (Chan et al., 2008) remainlarge, empirical challenges suitable for IRT analyses. Dimension-sharing between propensity and severity need not be perfect forthe derivation of a unidimensional index to quantify sub- andsuprathreshold liability; showing that a unidimensional modeloffers the best fit for the data is sufficient. The unidimensionalityof a trait essentially refers to the structure of covariance betweenitems used in its measurement (and testable for their dominancein accounting for the covariance) rather than to a single causalinfluence or the total number of influences potentially determin-ing the shared variance. Even if there is specificity to the unaffectedand affected phenotypic distributions (i.e., those of propensity andseverity), the variance they share with the common liability dimen-sion, based on genetic data, will likely be large and sufficientlyinformative to be targeted for measurement. If such tests fail andthe two constructs do not share the same dimension to an appre-

10 K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12

ciable degree, continuous indices will need to be developed thatquantitate these constructs separately and are applied to the nonaf-fected (or asymptomatic) and affected (symptomatic) populations.Measurement of common rather than drug-specific liability is alsodesirable from a practical standpoint, given its universal applica-tion.

4.3. Advantages of an instrument measuring propensity to andseverity of addiction

The conceptual and methodological shortcomings of instru-ments currently used in research and clinical practice can beovercome through the use of emerging technologies to develop areliable, valid, and standardized assessment instrument to mea-sure and distinguish variations in phenotypic expression of theunderlying latent trait that comprises propensity and severity ofdrug addiction. The distribution of the hypothesized latent traitin the population is depicted in Fig. 1. Key requirements of thisassessment instrument include the ability to accurately and com-prehensively measure gradations along the propensity/severitycontinuum using broad and discriminating content that capturesthe essence of addiction; to detect meaningful variation between,within, and across individuals over time that is scalable along theunderlying dimension; and to allow for efficient assessment of theconstruct with minimal burden for administration, training, andcost to the researcher, clinician, research participant, or patient.Coupling modern psychometric methodology with sophisticatedcomputer technology (e.g., IRT-based computerized adaptive test-ing [CAT]) enables measurement covering the full phenotypic scale,with maximum flexibility, accuracy, and efficiency (Drasgow andOlson-Buchanan, 1999; Kirisci and Hsu, 1992). CAT-based instru-ments, for example, can administer different sets of items todifferent people optimal for their liability phenotypes, can provideanalyses to explore the relative difficulty of items, and can gen-erate a single score to represent an individual’s location along anunderlying trait (the liability phenotype). This technique is rou-tinely applied in the education field (e.g., the administration ofthe Graduate Record Examination applies IRT analyses), and cancertainly be adapted for use in the field of addiction.

Whether propensity to and severity of addiction are locatable onessentially the same dimension remains to be determined, yet thecumulative evidence on neurobiological mechanisms of addiction,combined with psychometric evidence, gives merit to a commonmetric of liability to addiction extending across subclinical to clin-ical diagnostic thresholds. The concept of addiction liability as anunidimensional trait that is complex, i.e., contributed to by manydiverse influences, and dynamic is evidenced from a number ofother complex, multifactorial traits, such as stature (an observedtrait) or the Intelligence Quotient (IQ, an index of a latent trait). Suchan instrument, as a continuous measure of addiction propensityand severity at the behavioral level, would enhance the capacity,statistical power, and precision of research on the etiology, neu-robiology, and genetics of addiction (Embretson and Reise, 2000;Hambleton and Swaminathan, 1985; Lord and Novick, 1968; Nealeet al., 2006), and for use in developing improved approaches inprimary prevention, treatment, and intervention. For instance, indi-viduals with high severity scores may require different types oftreatments than those with lower scores, and individuals withelevated propensity scores may require preventive interventionstailored to their particular needs. In addition, severity measureshave been rarely applied to identify neurobiological correlates ofaddiction. Volkow et al. (2006) found that cue-induced dopaminechanges in cocaine-dependent subjects positively correlated withthe average ratings on the seven domains of the ASI. Patkar et al.(2008) reported that blunted responses to a serotonergic challengeamong cocaine-dependent subjects positively correlated with the

drug subscale scores of the ASI. Clearly, research of this kind canbe enhanced by improved measurement of individual differencesin the propensity and severity of addiction. Finally, from a transla-tional perspective, such an instrument would unify and potentiallytransform research on the addiction liability phenotype by using asingle instrument. The field of addiction science has advanced inremarkable ways from discoveries in psychometrics, computeriza-tion, etiology, neurobiology, genetics, prevention, and treatment.The challenge before us now is to build on these advances todevelop a new instrument that measures the propensity and sever-ity of addiction.

Conflict of interest

Author Swan served on the Pfizer National Advisory Board (1day, 2008) to consult on issues related to varenicline.

Acknowledgements

Role of funding source: NIDA had no role in the analysis, prepa-ration, and writing of the report, or in the decision to submit thepaper for publication.

Contributors: Author Conway took primary responsibility forwriting the manuscript drafts. Authors Conway and Levy managedthe literature searches and summaries of related work. AuthorsLevy, Vanyukov, Chandler, Rutter, Swan, and Neale contributed tothe manuscript design, writing, and editing of manuscript drafts.All authors contributed to and have approved the final manuscript.

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