According to the DSM-IV
an abuse diagnosis is limited to individuals who do not meet criteria for a drug dependence diagnosis and is defined only by the harmful consequences of repeated use.1
The dependence syndrome
was first introduced by Edwards and Gross,2
who posited that the unidimensionality of a set of behavioral, cognitive, and physiological components constituted a clinical syndrome for alcoholism. This syndrome was considered distinct from the consequences of heavy drinking (e.g., social or legal problems),3
and this biaxial conceptualization guides the separation of dependence and abuse for alcohol and drug use disorders in DSM-III-R4
. Dependence is described as unidimensional in nature and noncategorical,2
but because of considerations of clinical practice, it was dichotomized at a threshold of three or more criteria. The validity of the distinction between dependence and abuse is controversial, as some studies have suggested that dependence and abuse constitute a single construct.5
The present relative positions of dependence and abuse along a continuum of severity are of concern because the manner in which a given disorder is defined directly affects prevalence estimates of the disorder and its needs for treatment.
To date, little is known about whether the distinction between abuse and dependence is applicable to adolescents who have opioid use disorders (OUDs). Data from the 2007 National Survey on Drug and Health (NSDUH) revealed that OUDs constitute the second most prevalent nonalcohol drug use disorder,6
following only marijuana use disorders. Analgesic opioids have become one of the most commonly used drugs by new drug users and also are much more likely to be used nonmedically than all other abusable psychotherapeutic drugs considered in aggregate.6
Among American adolescents aged 12 to 17 years, approximately 1 in 10 has used analgesic opioids nonmedically.7
Hydrocodone, propoxyphene, and codeine are the opioids of choice among adolescents.7
A recent study of a national sample of adolescents further revealed that at least one third of nonmedical users of prescription opioids manifest OUD symptoms.8
Among adolescents who reported nonmedical opioid use in the previous year, 7% and 9% met DSM-IV
criteria for opioid abuse and dependence, respectively. An additional 20% of the sample was classified as diagnostic orphans
(endorsing one to two dependence criteria and no abuse). Although diagnostic orphans are not currently captured by DSM-IV
, they also exhibited elevated odds of depression, alcohol use disorders, and polydrug use. Furthermore, symptoms of opioid dependence among adolescents are twice as prevalent as those of opioid abuse (24% versus 12%), and dependence on opioids can occur in the absence of abuse.8
This finding implies that dependence may not be more severe than abuse as suggested by DSM-IV
Together, recent findings show not only that the nonmedical use of opioids poses a threat to the nation’s youth but also that DSM-IV
’s criteriamay be inadequate in classifying OUDs in adolescents. Given that the valid classification of OUDs is fundamental to estimating the populations’ needs for prevention and treatment, it is important to investigate the validity of the hierarchical distinction between dependence and abuse for OUDs.
As demonstrated by recent studies,9–11
item response theory (IRT)12
modeling constitutes an appropriate method to evaluate the latent construct underlying DSM-IV
criteria for abuse and dependence. It assumes that a single construct is tapped by a set of items or criteria and can be used to evaluate the psychometric properties of each constituent indicator for a disorder. Item response theory uses information from each item and item endorsement patterns to estimate the level of severity that each item assesses along the latent trait severity of the OUD liability construct as measured by the 11 criteria (i.e., item severity). It also provides estimates to describe how precisely each item discriminates among users at different risk for OUDs (i.e., item discrimination). An item with a low discrimination value would suggest low precision (i.e., higher measurement error). Low discrimination could indicate that the indicator is unrelated to the underling construct of OUD risk or the indicator is poorly defined.13
Recent IRT studies have found that DSM-IV
criteria for alcohol and specific drug use disorders in adult samples reflect a single continuum of severity.9,10
Among adolescents, DSM-IV
criteria for alcohol and marijuana use disorders have also yielded a single dimensional construct.11,14,15
These studies demonstrate that DSM-IV
criteria for abuse and dependence may represent only one condition and not the two specified by DSM-IV
Whereas a unidimensional construct of DSM-IV
criteria for alcohol and drug use disorders is consistently observed, IRT studies also reveal substance-specific variations in item-level contributions to the underlying construct.9,11,14,15
For example, two abuse criteria (social problems and role interference) and one dependence criterion (time spent) were found to indicate the lowest severity levels on the marijuana use disorder continuum,14
whereas tolerance and social problems tapped the lowest level on the alcohol use disorder continuum.15
The study of adolescent marijuana users further revealed that several criteria exhibited comparatively low levels of precision in distinguishing among problematic users (e.g., items measuring continued use despite reporting resulting problems and an inability to cut down).14
It has been suggested that the DSM-IV
criteria are developed mainly from studies of adult samples and that they may be less reliable in assessing adolescent drug use disorders.16
Furthermore, recent studies have found differential item functioning (DIF)— differential reporting of criterion symptoms of alcohol and marijuana use disorders—by sex and race/ethnicity,14,17
which indicates that the severity level of certain criteria along the latent continuum may vary by these characteristics. An ideal criteria set would show no evidence of DIF because its presence could lead to errors in comparing the disorder across groups, if these differences were not controlled statistically.18
This issue has important implications for risk factor assessments of substance use disorders because traditional epidemiological studies have often not taken DIF into account, and the resulted associations between two groups could be confounded by differential reporting across groups (e.g., measurement artifact). We are not aware of any studies of predictors of OUDs or risk among adolescents that take DIF into account.
Taken together, recent findings emphasize the need to move from a reliance on generic IRT analyses to a more in-depth investigation of psychometric performance of each item used to determine a given disorder. This investigation should be performed for each substance of use, given the variability in pharmacological effects. Specifically, a greater level of understanding of the contributions of specific item-level variations to the latent construct of each substance use disorder is needed, and DIF across key demographic groups should be elucidated to inform revisions of the DSM
criteria. Given increased rates of nonmedical opioid use and related disorders,6–8
it is also critically important to identify subgroups that exhibit a high risk for the disorder using a regression framework that controls for the potentially confounding effects of DIF in reports of diagnostic criteria.
To date, criteria for current OUDs in adolescents have yet to be subjected to IRT analysis. Whereas recent IRT analyses of the latent construct of alcohol and marijuana use disorders among adolescents have used IRT,11,14,15
we apply both IRT and multiple indicators–multiple causes (MIMIC) methods to examine DSM-IV
criteria and DIF. Importantly, MIMIC modeling can detect the DIF of criteria for multiple demographic variables while controlling for the overall level of OUD liability (as measured by the 11 criteria of OUDs). It also facilitates the statistical adjustment for DIF in the analysis of predictors of the latent construct within a regression framework, thereby reducing the potential for distortion of the risk factor assessment.17,18
To reflect latent variable modeling, we use the term OUD liability
to describe the latent trait severity of the OUD construct. This investigation relies on newly released data from the 2006 NSDUH,19
which constitutes the largest nationally representative sample of American adolescents. We examine DIF by participants’ sex, age, and race/ethnicity because these characteristics are associated with nonmedical use of opioids8
and because prevalence estimates of substance use disorders are often disaggregated by these variables to reflect disparities in problems and to guide health policies related to treatment and research.19,20
This study addresses the following questions:
- Do the criteria of OUDs form a single continuum of severity (one factor)?
- If so, where along this continuum does each criterion lie, relative to the others (item severity or what level of severity does each assess)?
- How well does each criterion perform, or discriminate, relative to the others (item discrimination)?
- Does the probability of endorsing the criteria of OUDs at any given level of severity differ across sex, age, and race/ethnicity (DIF)?
- What demographic subgroups manifest a comparatively high severity along the OUD continuum?