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Profile of dependence symptoms among extramedicalopioid analgesic users

Silvia S. Martins ⁎, Lilian A. Ghandour, Howard D. Chilcoat 1

Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th floor,Baltimore, MD 21205-1900, USA

Abstract

Little is known about the extent of problems due to extramedical opioid analgesic use (‘analgesic misuse’) in theUS general population. This study explores the distribution of the seven DSM-IV-defined past-year dependencesymptoms in a total household sample of 7810 past-year extramedical opioid analgesic users using the 2002–2003National Survey on Drug Use and Health (NSDUH). We tested for differences in opioid analgesic dependencesymptom profiles across four subgroups of opioid analgesic users, different levels of deviant behaviors, andpresence/absence of serious mental health problems quantified by the Composite International DiagnosticInterview Short Form (CIDI-sf). Generalized Estimated Equations (GEE) models were used to analyze the data.The most common opioid analgesic dependence symptoms were ‘tolerance’ (17.0%) and ‘salience’ (13.3%).Opioid analgesic dependence symptom profiles were ‘parallel’ across the groups of past-year opioid analgesicusers, across deviant behavior groups and across presence/absence of serious mental health problems.Extramedical use of opioid analgesics associated with prescription drug use, having high levels of deviantbehaviors, and having serious mental health problems were more strongly associated with endorsement of opioidanalgesics dependence symptoms.© 2007 Elsevier Ltd. All rights reserved.

Keywords: Extramedical opioid analgesic users; Opioid dependence symptoms; Deviant behaviors; Psychiatric comorbidity

Addictive Behaviors 32 (2007) 2003–2019

⁎ Corresponding author. Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, 624 N. Broadway,8th floor, Suite 896, Baltimore, MD 21205-1900, USA. Tel.: +1 410 614 2852; fax: +1 410 955 9088.

E-mail addresses: [email protected] (S.S. Martins), [email protected] (L.A. Ghandour), [email protected](H.D. Chilcoat).1 Currently at GlaxoSmithKline WorldWide Epidemiology. GlaxoSmithKline 5 Moore Drive, 17.2131, Research TrianglePark, NC 27709, USA.

0306-4603/$ - see front matter © 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.addbeh.2007.01.006


1. Introduction

There is an increasing concern about the recent rise in extramedical (defined as non-medical use ormisuse) use of opioid analgesic medications (Compton & Volkow, 2006; Zacny et al., 2003). The overallincidence of extramedical use of opioid analgesics increased in the US from 627,000 initiates in 1990compared to 2.7 million initiates in 2000 (SAMSHA, 2003a, 2004a). There has also been a rise in thenumber of treatment admissions in which opioid analgesics are the primary drug of abuse and inemergency department visits involving opioid analgesics in the US (SAMHSA, 2003b, 2004b). Empiricaldata describing the nature and extent of the problems associated with the extramedical use of thesemedications (i.e. dependence symptoms) is still scarce (Sproule, Busto, Somer, Romach, & Sellers, 1999).Sproule et al. (1999) investigated the specific symptoms related to extramedical analgesic use in 124codeine dependent respondents in Canada and found that the most common symptoms were: tolerance(86%), withdrawal (82%), loss of control over use (81%) and difficulties cutting down or stopping (78%).To our knowledge, this is the only study that attempted to investigate specifically the problems associatedwith the use of opioid analgesics. However, Sproule et al. (1999) study investigated codeine dependencesymptoms in respondents who met criteria for the diagnosis of codeine dependence, and not among thetotal community sample of codeine users. Moreover, the authors relied on existing diagnostic categorieswhich may have obscured variation in the types of responses in terms of dependence problems related toextramedical use of analgesics. In our study, we hope to address these points by investigating the profile ofopioid analgesic dependence symptoms in a representative sample of opioid analgesic users in the USgeneral population which will allow us to identify the types of problems users experience regardless ofdependence status. [In this case, epidemiologic research can alert general practitioners and mental healthprofessionals to better identify the most prominent dependence symptoms and the subgroup of users moreprone to development of opioid analgesic dependence. It could also possibly contribute to futuredevelopment of intervention and prevention strategies that target a decrease in the development ofextramedical opioid analgesic dependence.]

While tolerance and withdrawal are two expected side effects of long-term opioid analgesic use, little isknown about what are the most commonly reported opioid analgesic dependence symptoms by opioidanalgesic users in the general population. Medical patients who need to receive analgesic medication areclassified under definitions developed from addicted populations without medical illness (Passik & Kirsh,2004). However, they may present with a different dependence symptom pattern than that presented byaddicted populations. It is important to make these distinctions as they help improve our understanding ofthe identification and management of extramedical use of opioid analgesics.

Some epidemiologic studies in the US describe demographic characteristics, drug use and deviantbehaviors associated with extramedical opioid analgesic use (McCabe, Boyd, & Teter, 2005; McCabe,Teter, & Boyd, 2005; McCabe, Teter, Boyd, Knight, & Wechsler, 2005; SAMSHA, 2003a; Simoni-Wastila, Ritter, & Strickler, 2004; Sung, Richter, Vaughan, Johnson, & Thom, 2005), but not withextramedical opioid analgesic dependence. Studies of clinical pain populations and of extramedicalopioid analgesic users in the general population have found positive associations between extramedicaluse of analgesic opioids with psychiatric comorbidity and illegal drug use (Busto, Sproule, Knight,Romach, & Sellers, 1998; Dowling, Storr, & Chilcoat, 2006; Reid et al., 2002; Romach, Sproule, Sellers,Somer, & Busto, 1999; Rosenblum et al., 2003). While these studies have increased greatly ourunderstanding of factors related to the use of opioid analgesics, none investigated the profile ofdependence symptoms related to extramedical opioid analgesic use.

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This existing gap in the literature provided us with the impetus to investigate and estimate the overallprofile of dependence symptoms linked to extramedical opioid use. In addition, we set out to compare thedistribution of opioid analgesic dependence symptoms across different subgroups of past-year opioidanalgesic users, among individuals with varying levels of deviant behaviors, and across individuals withor without psychiatric comorbidity. We hypothesized that respondents who used other drugs besidesopioid analgesics in the past-year would be more likely to endorse any symptom of opioid analgesicdependence. We also hypothesized that respondents with high levels of deviant behaviors would be morelikely to endorse symptoms of opioid analgesic dependence because illegal drug use and dependence arecommonly associated with disruptive behaviors (Farrell, Kung, White, & Valois, 2000; Jessor, 1987;Jessor & Jessor, 1977; Milich et al., 2000). Similarly, we thought that respondents with psychiatriccomorbidity would be more likely to endorse symptoms of opioid analgesic dependence, due to previousstudies showing that psychiatric symptoms are associated with opioid analgesic use (Busto et al., 1998;Dowling et al., 2006; Reid et al., 2002; Romach et al., 1999; Rosenblum et al., 2003) and also due to thepossibility that there might be a common shared vulnerability between psychiatric disorders and opioidanalgesic dependence (Markou, Kosten, & Koob, 1998). Finally, we hypothesized that the opioiddependence symptoms endorsed would vary by different levels of deviant behaviors and psychiatriccomorbidity.

2. Methods

2.1. Sample and measures

We analyzed data from the 2002 (N=54,079) and 2003 (N=55,230) National Survey on Drug Use andHealth (NSDUH) public use data files with an aggregate sample size of 109,309. These were the most recentavailable NSDUH years when we conducted the data analyses, we decided to combine both years in order tohave a larger sample size (separate analyses of the NSDUH years yielded similar results, data not shown). Inthis report, we focus on 7810 respondents who reported use of opioid analgesics in the year prior to interview.The NSDUH is sponsored by the Substance Abuse and Mental Health administration (SAMHSA) and isdesigned to provide estimates of the prevalence of extramedical use of legal drugs and of illegal drugs in thehousehold population of the United States 12 years of age and older. Surveys have been conducted on aregular basis since 1971. In 1999, the surveys underwent a major redesign, changing from a paper-printedquestionnaire to a computer-assisted questionnaire (CAPI and ACASI-audio computer-assisted self-interviewing). Use of ACASI was designed to provide respondents with a highly private and confidentialmeans of responding to questions and to increase the level of honest reporting of illicit drug use and othersensitive behaviors (SAMSHA, 2003a, 2004a). African–American, Hispanics, and young people were oversampled to increase the precision of estimates for these groups. In 2002, several improvements to the surveywere also implemented. In addition to the change in the name of the survey, respondents were offered a $30incentive payment for participation in the survey and quality control procedures for data collection wereenhanced in 2001 and 2002 (SAMSHA, 2003a). Overall response rate was 91% for household screening forboth 2002 and 2003, and 79% and 71% for completed interviews in 2002 and 2003, respectively. Detailedinformation about the sampling and survey methodology in the NSDUH are found elsewhere (SAMSHA,2003a, 2004a). All respondents provided information about their drug experiences and other personal data(e.g., demographic data). Demographic variables selected for this study were age, sex, race/ethnicity,educational attainment, and annual family income.

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2.1.1. Measurement of analgesic opioid use and analgesic opioid dependence symptomsThe NSDUH includes separate sections about patterns of use of 12 drug classes. Symptoms of past-year

drug dependence are measured by several items based on diagnostic criteria (DSM-IV, APA, 1994). TheNSDUH includes an indicator for past-year dependence for respondents who meet DSM-IV criteria (3 outof 7 symptoms, Kandel, Huang, & Davies, 2001). Only respondents who reported extramedical analgesicuse during the 12 months prior to the interview were asked about opioid analgesic dependence symptoms,so we could not investigate lifetime extramedical opioid analgesic dependence (SAMSHA, 2003a, 2004a).

Extramedical opioid analgesic use is defined by the NSDUH as ‘…use of an analgesic that was notprescribed, or was taken for the experience or feeling it caused’ (SAMSHA, 2003a). If the response waspositive, the respondent was given a card with pictures of many types of opioid analgesics and was askedwhich he/she had ever used extramedically. Past-year analgesic use was based on the response to thefollowing question, ‘how long has it been since you last used any prescription pain reliever that was notprescribed for you or that you took only for the experience or feeling it caused.’ If the response indicatedthat extramedical use occurred during the preceding 12 months, the respondent was classified as anextramedical opioid analgesic user within the past-year.

2.1.2. Subdivision in the four opioid analgesic subgroupsAs previously stated, we hypothesized that those who used other drugs besides opioid analgesics would

have a different dependence symptom profile as compared to those who only used opioid analgesics. Giventhe substantial heterogeneity in other drug use among past-year opioid analgesic users, we generated fourdifferent mutually exclusive groups, subdividing individuals according to their past-year other drug usestatus: 1) past-year opioid analgesic users who also had used other prescription drug (stimulants, sedativesand tranquilizers) extramedically in the past-year (referred to as AP); 2) past-year opioid analgesic userswho had used other prescription drugs extramedically as well as cocaine and/or heroin in the past-year(referred to as APCH), 3) past-year opioid analgesic users who also were past-year cocaine and/or heroinusers (referred to as ACH) but had not used other prescription drugs extramedically in the past-yearand 4) past-year opioid analgesics users only (referred to as A). We decided to subdivide past-yearanalgesic users as such based on the assumption that these groups would possibly represent differentpatterns of involvement with drugs with high abuse liabilty, from misuse of only opioid analgesics drugs,to misuse of opioid analgesics and other prescription drugs (tranquilizers, stimulants, sedatives), andfinally to misuse of opioid analgesics associated with illegal drug use (cocaine and heroin). It is necessaryto have a subgroup constituted only by misusers of opioid analgesics and other prescription drugs(tranquilizers, stimulants, sedatives), due to the fact that misuse of these drugs often co-occurs amongindividuals who do not use illegal drugs such as cocaine and heroin (McCabe et al., 2005). Cocaine is themost widely used illegal drug besides marijuana. Opioid analgesics and heroin share similarpharmacologic properties and there are reports showing that a large number of heroin users are alsoanalgesic misusers (Martyres, Clode, & Burns, 2004). We decided to combine cocaine and heroin users inthe same groups due to the fact that only 160 of the past-year opioid analgesic users were past-year heroinusers and 78.8% of them were also past-year cocaine users. Also, 90.8% of those in the APCH group and92.4% of those in the ACH group were past-year cocaine users but not past-year heroin users. Finally, wegrouped individuals who misused opioid analgesics and other prescription drugs as well as cocaine andheroin in the past-year; this group represents respondents involved in a wide array of drug use. Becausepast-year alcohol and marijuana use were highly prevalent in this study's population, past-year alcohol andmarijuana use were not taken into account in the process of dividing past-year opioid analgesic users in the



mutually exclusive groups (respondents in each of the four groups could or not be past-year alcohol andmarijuana users). Users of other illegal drugs assessed in the NSDUH, such as past-year hallucinogen andinhalant users were distributed in any of the four groups (inhalant use is relatively rare, see Table 2).

2.1.3. Past-year frequency of analgesic use and other drug usePast-year frequency of analgesic use among past-year opioid analgesic users was estimated through the

total number of days respondents used opioid analgesics extramedically in the past-year ranging from 1–365 days. For this report, we generated a categorical variable comprised of 5 categories: 1 day, 2 to 9 days,10 to 19 days, 20 to 49 days and 50 or more days. We estimated prevalence of past-year tobacco, alcohol,marijuana, hallucinogen and inhalant use among extramedical opioid analgesic users, as well as past-yearfrequency of use of these substances.

2.1.4. Assessment of psychiatric comorbidityTo investigate the association of past-year analgesic use with probable past-year psychiatric

comorbidity we tested for the association of past-year opioid analgesic use with the NSDUH seriousmental health indicator which assesses whether interviewees who were 18 years of age and older feltnervous, hopeless, restless, depressed, that everything was an effort, and/or had feelings of low self worthduring their worst month of the past-year through a modified version of the World Health Organization'sComposite International Diagnostic Interview Short Form (Kessler et al., 2003). If the cumulative score ofthe serious mental illness indicator was greater than 13 (on a scale of 0–24 representing the level of mentalillness) the respondent was classified as most likely having a serious mental health problem (referred to asrespondents with serious mental health problems). Identification of symptoms using the structuredscheduled interview does not confirm the presence of a psychiatric condition, but rather indicates aprobable diagnosis (Kessler et al., 1994). A more detailed description of the construction of the seriousmental illness indicator is available elsewhere (SAMSHA, 2003a, 2004a).

2.1.5. Assessment of deviant behaviorsTo assess deviant behaviors in past-year extramedical opioid analgesic users we used the following

2002–2003 NSDUH survey questions: ‘During the past 12 months, how many times have you attackedsomeone with the intent to seriously hurt them?’, ‘During the past 12 months, how many times have yousold illegal drugs?’, ‘During the past 12 months, howmany times have you stolen or tried to steal anythingworth more than US$50?’ Response options available for each of these deviant behaviors in the 2002–2003 NSDUH are: never, 1–2 times, 3–5 times, 6–9 times and 10 or more times (categorical response).Based on the past-year frequency of involvement in these deviant behaviors during the preceding12 months, we first summed the responses of the three deviant behavior variables and then created anordinal deviant behavior variable that represents: no involvement, low (1st tertile), medium (2nd tertile),and high (3rd tertile) involvement in deviant behaviors during the past-year. We selected these threemeasures of deviant behaviors because they were the only deviant behavior measures that were asked forboth adolescents and adults in the NSDUH.

2.2. Statistical analyses

Initially, cross-tabulations were performed to compare demographic characteristics, psychiatriccomorbidity (data available for adults only) and patterns of other drug use (past and current use, and past-



year frequency of analgesic use) across the four mutually exclusive opioid analgesic groups, the differentlevels deviant behaviors, and the presence or absence of serious mental health problems. To take intoaccount the complex sampling design, all analyses were based on Taylor series approximations using‘svy’ commands of STATA 8.0 (StataCorp, 2003). These models adjust standard errors for the samplingdesign and use sampling weights provided within the NSDUH dataset. Chi-square tests were run to testfor differences in demographic characteristics by groups of past-year opioid analgesic users.

We generated a symptom profile among all individuals who reported past-year use of opioidanalgesics by estimating and graphically depicting the prevalence of each symptom of opioidanalgesic dependence, indicating which symptoms were relatively common and those that were rare.Next, we compared symptom profiles across groups of past-year opioid analgesic users, differentlevels of deviant behaviors and presence or absence of serious mental health problems using graphicaldisplays.

Logistic regression using generalized estimating equations (GEE, Liang & Zeger, 1986; Zeger &Liang, 1986) was used to detect differences in profiles of past-year opioid analgesic dependencesymptoms across the four analgesic groups, across the different levels of deviant behaviors and acrosspresence/absence of serious mental health problems, similar to the methods proposed by Andrade, Eaton,and Chilcoat, (1994) and applied by Chen and Anthony (2004) to NSDUH data. For example, in Chen andAnthony's (2004) study the use of GEE was informative in describing that crack users were more likely toexperience many symptoms of cocaine dependence as compared to cocaine HCl powder users. GEEaccounts for dependency in the data such as multiple symptoms within the same respondent (Andradeet al., 1994). Specifically, we tested whether the magnitude of observed subgroup differences was similarfor all symptoms (parallel profile), or varied for a subset of symptoms (non-parallel profile). Weconstructed a series of three hierarchical equations. In the first step we created a baseline equation toaccount for the odds of individual opioid analgesic dependence symptoms relative to an arbitrarilyselected symptom in order to model the overall profile of symptoms.

In the second step we added indicators for the four analgesic groups, for the four levels of deviantbehaviors and for the serious mental health indicator to the previous model This step assumes that profilesare parallel across presence/absence of serious mental health problems and tests whether groups differ inodds of having any symptom. In the third step interaction terms for indicators presence/absence of seriousmental health problems by dependence symptom were added to the model to test whether the symptomprofiles were parallel or not and whether serious mental health problem subgroup differences could varyby symptom. We created similar models for the four analgesic groups, and for the four levels of deviantbehaviors. The models were then expanded to adjust for potential confounders (demographiccharacteristics and past-year frequency of opioid analgesic use).

3. Results

There were a total of 7810 past-year opioid analgesic users in the 2002–2003 NSDUH. The fourmost common types of opioid analgesics used by extramedical opioid analgesic users in the past-yearwere: hydrocodone (prevalence=66.5%, which includes Hydrocodone®, Vicodin®, Lortab® andLorcet® tablets), propoxyphene (prevalence=66.4%, including Propoxyphene®, Darvocet® andDarvon® tablets), oxycodone (prevalence=42.6%, which includes Oxycontin®, Percocet®, Percodan®,and Tylox®tablets), and codeine (prevalence=28.4%, including Codeine® and Phenaphen withcodeine®). The main socio-demographic differences found include: a) males were twice as likely as



females to be in the ACH group (10.8% vs 4.8%, see Table 1), in contrast to the other groups in whichthe male/female ratio was fairly evenly divided (Table 1); b) Whites were more likely to be in the twoother prescription drug groups (AP and APCH) as compared to other race/ethnicities; c) individualsaged 18–34 years old were twice as likely to be in the ACH group as compared to the other age groups(Table 1).

3.1. Overall profile of opioid analgesic dependence symptoms in past-year opioid analgesic users

As shown in Fig. 1, the most common past-year opioid analgesic dependence symptoms in all past-yearopioid analgesic users were: tolerance (17.3%), salience (measured by the item: ‘spending a great deal oftime getting or using opioid analgesics/recovering from its effects’, 13.5%) and withdrawal (7.1%).Opioid analgesic dependence symptom profiles appear to be ‘parallel’ across all four past-year analgesic

Table 1Socio-demographic characteristics among subgroups of past-year opioid analgesic users, National Survey on Drug Use andHealth, 2002–2003

Variables All opioid analgesic users N=7810

Opioid analgesicsonly(A)(N=4241) 54.3%

Opioid analgesicsand prescriptiondrugs (AP)(N=1709)21.9%

Opioid analgesicsand cocaine/heroin (ACH)(N=648)8.3%

Opioid analgesics andprescription drugs andcocaine/heroin (APCH)(N=1212)15.5%

N % % % % χ2 (df )

GenderMale 4032 54.85 19.05 10.18 15.92 49.93 (3)Female 3778 62.27 21.80 4.79 11.15

RaceWhite 5847 52.50 23.64 7.40 16.46 288.13 (9)African–Americans 613 77.88 9.80 9.18 3.14Hispanics 911 74.43 10.94 8.55 6.08Others 439 64.46 17.05 5.63 12.87

Age12–17 2424 59.67 24.23 4.73 11.3818–25 4020 48.67 22.23 9.73 19.38 199.91(9)26–34 646 62.26 14.34 10.62 12.7835 or older 720 65.28 20.23 5.04 9.44

Education a

High school 3119 58.16 20.39 8.14 13.31 25.60 (6)Some college 1641 56.09 18.71 8.21 16.99College or more 626 61.70 18.68 8.29 11.33

Total family income0 to $19,999 2215 53.89 21.53 7.95 16.63 32.36 (9)$20,000 to $49,999 2970 58.54 20.42 6.82 14.22$50,000 to $74,999 1212 62.59 19.68 8.09 9.63$75,000+ 1413 59.94 19.25 8.40 12.42

a Reflect values at the time of interview, only measured for those 18 years and older.



groups. The relative prevalence of individual dependence symptoms was similar across groups but theAPCH group consistently had a higher prevalence of each symptom than all the other groups. The APgroup had higher prevalence of all symptoms except withdrawal as compared to the ACH group. Thegroup with the lowest prevalence of all symptoms was the opioid analgesics only group.

Fig. 1. Past-year opioid analgesic dependence symptoms in subgroups of past-year opioid analgesic users, in subgroups of deviantbehaviors and according to presence of serious mental health problems, NSDUH 2002–2003. ⁎Symptoms: Used more: use is inlarger amounts or for longer periods than intended; Time spent: salience (great deal of time spent getting or using substance/recovering from its effects);Problems: use is continued despite problems; Imp act: important activities are given up or reduced dueto substance use; W/D: withdrawal; Tol: tolerance; Desire: persistent desire/unsuccessful efforts to cut down/control use.



Opioid analgesic dependence symptom profiles appear also to be ‘parallel’ across levels of deviantbehaviors and by presence/absence of serious mental health problems (Fig. 1). Respondents with moderateand high involvement on deviant behaviors in the past-year consistently had a higher prevalence of eachsymptom compared to the other deviant behavior groups. Similarly, those with past-year serious mentalhealth problems also had a higher prevalence of each symptom compared to those without serious mentalhealth problems reflecting a parallel profile.

3.2. Past-year tobacco, alcohol and other drug use among past-year extramedical opioid analgesic users

Further exploratory analyses (Table 2) show the proportion of respondents among all extramedicalopioid analgesic users groups who used other substances in the past-year. Most of the past-yearextramedical opioid analgesic users were past-year alcohol users (N82.0%), and with the exception of theA group, more than 70% of them used marijuana in the past-year. Analyzing frequency of use, those whowere alcohol and marijuana users were mostly regular users of these substances and those who werehallucinogen and inhalant users were mainly occasional users of these drugs. Those in the AP and in theAPCH groups were regular users of prescription drugs.

Table 2Past-year use of other substances and frequency of past-year analgesic use by subgroups of past-year analgesic users, NationalSurvey on Drug Use and Health, 2002–2003

All analgesic users N=7810

Variables Opioid analgesicsonly (A) N=4241

Opioid analgesics &prescription drugs (AP)N=1709

Opioid analgesics &cocaine/heroin (ACH)N=648

Opioid analgesics & prescriptiondrugs and cocaine/heroin (APCH)N=1212

% % % %

Past-year substance useTobacco 56.7 76.5 87.2 95.0Alcohol 82.0 92.8 98.0 98.9Marijuana 44.8 70.0 90.9 94.4Cocaine 0 0 97.4 98.6Heroin 0 0 7.6 9.2Hallucinogens 10.0 28.0 45.2 64.5Inhalants 6.3 15.1 12.2 27.8Stimulants a 0 40.8 0 56.8Sedatives a 0 10.7 0 14.2Tranquilizers a 0 72.3 0 76.7

Frequency of past-year use b

1 day 13.89 4.25 7.38 3.352–9 days 43.90 34.88 44.69 31.3610–19 days 8.37 10.46 12.86 10.2620–49 days 13.35 18.84 11.44 20.3550 ormore days

20.49 31.57 23.63 34.58

a Listed in the groups as prescription drugs.b χ2: 481.75, pb .001, df: 12.



3.3. Past-year opioid analgesic dependence and other drug dependence among past-year extramedicalopioid analgesic users

Overall, 8.3% of the past-year extramedical opioid analgesic users met DSM-IV criteria (APA, 1994)for past-year opioid analgesic dependence. Prevalence of dependence was highest in the APCH group(16.1%), followed by the ACH (10.7%) and the AP (9.3%), with lower prevalence in the A groups (5.5%,χ2:192.78, df=1, pb .001).

Eighteen percent of the APCH group and 13% of the ACH group met criteria for past-year cocainedependence. Three percent of the APCH group and 7.4% of the ACH met criteria for past-year heroindependence. Approximately 8% of the AP and APCH groups met criteria for past-year other prescriptiondrug dependence.

3.4. Frequency of past-year extramedical analgesic use among past-year opioid analgesic users

Table 3 points to differences in frequency of past-year analgesic use among past-year opioid analgesicusers groups. Frequency of past-year extramedical use of opioid analgesics was quite similar between the

Table 3Unadjusted odds ratios and 95% confidence intervals for each DSM-IV symptom of analgesic opioid dependence and analgesicgroups, deviant behaviors and serious mental health problems, based on GEE multivariate analysis of adult past-year analgesicusers, NSDUH 2002–2003

Variables in the model All adult opioid analgesic usersN=5386OR 95% CI

Symptoms Model 1Use is in larger amounts or for longer periods than intended 1.00 –Great deal of time spent getting or using substance/recovering from its effects 4.40 3.83, 5.05Use is continued despite problems 1.41 1.21, 1.64Important activities are given up or reduced due to substance use 1.37 1.17, 1.60Withdrawal 1.80 1.54, 2.10Tolerance 5.66 4.93, 6.50Persistent desire/unsuccessful efforts to cut down/control use 0.95 0.81, 1.11

Analgesic groups a Model 2 b

Analgesic users only (N=2514) 1.00 –Analgesics and prescription drugs (N=938) 1.77 1.51, 2.08Analgesics and prescription drugs and cocaine/heroin (N=735) 2.44 2.07, 2.88Analgesics and cocaine/heroin (N=463) 1.12 0.87, 1.44

Deviant behaviors a Model 3 b

None 1.00 –Low 1.36 1.12, 1.65Medium 2.13 1.71, 2.66High 2.30 1.89, 2.80

Serious mental health problems a Model 4 b

No 1.00 –Yes 2.72 2.38, 3.11

a Odds of reporting any opioid analgesic dependence symptoms.b Models 2–4 are adjusted for analgesic opioid dependence symptoms.



A and ACH groups vs. the AP and APCH. While slightly more than a third of the A and the ACH groupreported using opioid analgesics extra-medically for 20 or more days during the past-year, slightly morethan half of the AP and APCH reported doing so.

3.5. Deviant behaviors and psychiatric comorbidity among past-year analgesic users

Among all past-year analgesic users, 22.8% had engaged in deviant behaviors (selling illegaldrugs, attacking someone or stealing more than $50 at least once during the past-year) during thepast-year.

Table 4Estimated odds ratios and 95% confidence intervals of reporting any analgesic opioid dependence symptom for analgesic groups,deviant behaviors and serious mental health problems, based on GEE multivariate analysis of adult past-year opioid analgesicusers, NSDUH 2002–2003

Variables in the model All adult opioid analgesic users N=5386

Model 1 a Model 2 b Model 3 c

OR 95% CI OR 95% CI OR 95% CI

Analgesic groupsOpioid analgesic users only (A) (N=2514) 1.00 – 1.00 – 1.00 –Opioid analgesics and prescription drugs (AP) (N=938) 1.66 1.41, 1.96 1.57 1.33, 1.85 1.16 0.98, 1.37Opioid analgesics and cocaine/heroin (ACH) (N=735) 1.22 0.95, 1.57 1.10 0.86, 1.42 0.97 0.75, 1.25Opioid analgesics and prescription drugs andcocaine/heroin (APCH) (N=463)

2.43 2.06, 2.87 2.01 1.69, 2.40 1.41 1.18, 1.69

Deviant behaviors d

None – – 1.00 – 1.00 –Low – – 1.25 1.03, 1.53 1.21 0.99, 1.48Medium – – 1.72 1.36, 2.18 1.47 1.16, 1.87High – – 1.93 1.56. 2.76 1.51 1.21, 1.87

Serious mental health problems d

No 1.00 – 1.00 – 1.00 –Yes 2.52 2.20,2.88 2.40 2.09,2.76 2.34 2.04,2.69

Frequency of analgesic use d

1 day – – – – 1.00 –2 to 9 days – – – – 2.64 1.69,4.1310 to 19 days – – – – 4.43 2.77,7.0920 to 49 days – – – – 7.25 4.54,11.5950 or more days – – – – 13.85 9.01,21.27

a Model 1 is adjusted for demographics (race, age, and gender), analgesic opioid dependence symptoms, and for the presence ofserious mental health problems.b Model 2 is adjusted for demographics (race, age, and gender), analgesic opioid dependence symptoms, past-year deviant

behaviors, and for the presence of serious mental health problems.c Model 3 is adjusted for demographics (race, age, and gender), analgesic opioid dependence symptoms, past-year deviant

behaviors, for the presence of serious mental health problems, and for past-year frequency of analgesic use.d Odds of reporting any opioid analgesic dependence symptoms.



Table 5Estimated odds ratios and 95% confidence intervals for each DSM-IV symptom of dependence, analgesic groups, and deviantbehaviors, based on GEE multivariate analysis of adolescent past-year analgesic users, National Survey on Drug Use and Health2002–2003

All adolescent analgesic users N=2424

Variables in the model Model 1 Model 2 Model 3 Model 4 a Model 5 b

OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI

SymptomsUse is in larger amountsor for longer periodsthan intended

1.00 – 1.00 – 1.00 – 1.00 – 1.00 –

Great deal of time spentgetting or usingsubstance/recoveringfrom its effects

6.96 5.46, 8.89 7.28 5.70, 9.32 7.10 5.56, 9.07 7.38 5.77, 9.45 7.73 6.02, 9.94

Use is continued despiteproblems

2.16 1.65, 2.82 2.18 1.66, 2.85 2.17 1.66, 2.83 2.19 1.67, 2.87 2.21 1.68, 2.91

Important activities aregiven up or reduced dueto substance use

2.76 2.11, 3.61 2.81 2.14, 3.68 2.78 2.13, 3.64 2.82 2.15, 3.70 2.87 2.18, 3.78

Withdrawal 3.57 2.75, 4.63 3.65 2.80, 4.75 3.60 2.77, 4.68 3.67 2.82, 4.78 3.76 2.88, 4.91Tolerance 8.71 6.81, 11.13 9.21 7.19, 11.79 8.92 6.97, 11.41 9.36 7.30, 11.99 9.90 7.70, 12.74Persistent desire/unsuccessful efforts tocut down/control use

1.24 0.93, 1.66 1.24 0.93, 1.67 1.24 0.93, 1.66 1.25 0.93, 1.67 1.25 0.93, 1.68

Analgesic groups c

Analgesic users only(N=1296)

– – 1.00 – – – 1.00 – 1.00 –

Analgesics and prescriptiondrugs (N=449)

– – 2.68 2.21, 3.25 – – 2.42 1.97, 2.97 2.08 1.70, 2.56

Analgesics and prescriptiondrugs and cocaine/heroin (N=182)

– – 3.91 3.09, 4.94 – – 3.16 2.41, 4.13 2.32 1.77, 3.04

Analgesics and cocaine/heroin (N=112)

– – 1.57 1.01, 2.43 – – 1.36 0.87, 2.11 1.40 0.89, 2.19

Deviant behaviors c

None – – – – 1.00 – 1.00 – 1.00 –Low – – – – 1.64 1.31, 2.07 1.48 1.17, 1.86 1.42 1.13, 1.79Medium – – – – 1.92 1.49, 2.47 1.51 1.15, 1.98 1.38 1.04, 1.82High – – – – 2.55 2.00, 3.24 1.93 1.47, 2.53 1.67 1.28, 2.18

Frequency of analgesic use c

1 day – – – – – – – – 1.00 –2 to 9 days – – – – – – – – 1.35 0.93, 1.9410 to 19 days – – – – – – – – 2.29 1.53, 3.4220 to 49 days – – – – – – – – 2.95 2.03, 4.2950 or more days – – – – – – – – 4.56 3.22, 6.47

a Model 4 is adjusted for demographics (race, age, and gender), analgesic groups and past-year deviant behaviors.



The serious mental illness indicator was only available for subjects aged 18 years and older in the2002–2003 NSDUH. Among the adult past-year analgesic users, 21.8% had a serious mental healthproblem in the past-year.

3.6. GEE profile analyses

We conducted separate GEE profile analyses for adults and adolescents due to lack of mental healthmeasures for adolescents. The results of the GEE profile analyses confirmed that past-year opioidanalgesic dependence symptom profiles were ‘parallel’ across all four past-year analgesic groups, acrossthe deviant behavior groups and across the serious mental health problem groups, specifically because nostatistically significant symptom-group interactions were found. We found no evidence of interactionsbetween past-year opioid analgesic dependence symptoms and the other covariates (demographics andfrequency of past-year analgesic use).

Table 4 shows that among adults in the unadjusted models, those in the AP group were almost twotimes more likely to have any past-year opioid analgesic dependence symptom as compared to those in theA group; APCH users were 2.5 times more likely to have any past-year opioid analgesic dependencesymptom as compared to A users only, while ACH users did not differ significantly from those in the Agroup. Respondents with high levels of past-year deviant behaviors were two times more likely to presentany past-year opioid analgesic dependence symptom as compared to those who did not engage in anydeviant behavior in the past-year. Respondents with serious mental health problems in the past-year werealmost three times more likely to experience any opioid analgesic dependence symptom as compared tothose without serious mental health problems.

The magnitude of the differences in profiles diminished somewhat across the four analgesic groups,across deviant behaviors and across presence/absence of serious mental health problems when adjustedfor past-year frequency of analgesic use (Table 4, model 3). This suggests that some, but not all of thedifferences in the prevalence of opioid analgesic dependence symptoms among the four extramedicalopioid analgesic subgroups, can be accounted for frequency of opioid analgesic use.

The associations between the AP, APCH, and ACH groups with all opioid analgesic dependencesymptoms were stronger for adolescents as compared to adults (Table 5, model 2). Among adolescents,associations between high levels of deviant behavior with all opioid analgesic dependence symptoms weresimilar to the adult associations (Table 5, model 3). Those in the APCH group were almost four times morelikely to have any past-year opioid analgesic dependence symptom as compared to those in the A group;AP users were almost three times more likely to have any past-year opioid analgesic dependence symptomas compared to A users only, and ACH users were 1.6 four times more likely to have any past-year opioidanalgesic dependence symptom as compared to A users only. Those with moderate and high levels ofdeviant behaviors in the past-year were two times and 2.6 times more likely to have any past-year opioidanalgesic dependence symptom as compared to those not involved in deviant behaviors, respectively.Adjustment for frequency of past-year opioid analgesic use diminished these differences somewhat, but,with the exception of the ACH group they remained statistically significant (Table 5, model 5).

a Model 4 is adjusted for demographics (race, age, and gender), analgesic groups and past-year deviant behaviors.b Model 5 is adjusted for demographics (race, age, gender), analgesic groups, past-year deviant behaviors, and past-year

frequency of analgesic use.c Odds of reporting any opioid analgesic dependence symptoms.

Notes to Table 5:



3.7. GEE profile analysis of subjects who met DSM-IV criteria for past-year analgesic dependence

We also conducted a GEE profile analysis of the 598 past-year analgesic users who fulfilled the DSM-IV (APA, 1994) criteria for past-year analgesic dependence. Symptom profiles across groups wereparallel, subjects in the ACH group were 1.7 times more likely and those in the APCH group were 1.5times more likely to have past-year analgesic dependence symptoms as compared to those in the A group.Those with moderate and high levels of deviant behaviors in the past-year were 1.3 times more likely tohave past-year analgesic dependence symptoms as compared to those not involved in deviant behaviors.Results did not change with adjustment for past-year frequency of analgesic use.

4. Discussion

Analysis of symptom profiles indicated that salience and tolerance were the most common symptomsof extramedical opioid use. Past-year opioid analgesic users who used other prescription drugsextramedically (stimulants, sedatives and tranquilizers) regardless of combination with cocaine/heroinhad highest prevalence of opioid analgesic dependence symptoms. Similarly, past-year analgesicprevalence of opioid analgesic dependence was highest among the APCH group, followed by the APgroup, reinforcing the impression that ‘prescription poly-drug users’ are those who are more likely tobecome dependent on opioid analgesics independent of frequency of use. Therefore, they should beidentified by health care professionals for strategies that prevent the development of opioid analgesicdependence.

Unfortunately, it is not possible to distinguish whether the past-year extramedical opioid analgesicusers in the NSDUH first started using these drugs because the drugs were legitimately prescribed forthem or if they initiated analgesic use illegally. The high prevalence of ‘tolerance’ symptomatology mightbe related to the pharmacologic properties of opioid analgesics, since tolerance is a natural expectedphysiologic response of regular opioid users (Heit, 2003). Tolerance can be present in extramedical opioidanalgesic users as well as in individuals who take opioid analgesics legally prescribed for them tominimize pain. The high prevalence of salience is associated with the craving component of opioidaddiction, assuming that addiction is defined as a neurobiological disease which disrupts the mesolimbicdopamine system (Heit, 2003). The prevalence of withdrawal symptoms was much lower than that oftolerance symptoms across all groups. This might have occurred because tolerance can be present evenwithout physical dependence. However, the GEE profile analysis was able to show which were the mostprominent analgesic opioid dependence symptoms in this general population sample, and how theydiffered in relation to other drug use, presence of deviant behaviors and presence of mental healthproblems.

Interestingly, frequency of past-year opioid analgesic use accounted for some of the qualitativedifferences between the four analgesic subgroups, especially among the adult sample. Partly becausethose in the AP and APCH groups were more frequent users of opioid analgesics, they were more likelyreport any opioid analgesic dependence symptom as compared to those in the A and ACH groups. It isimportant to note that, among adolescents, differences between the four analgesic subgroups inexperiencing any opioid analgesic dependence symptom remained statistically significant even afteradjustment for frequency of use. Future studies need to focus in this specific subpopulation.

The high prevalence of deviant behaviors (23%) among past-year opioid analgesic users is inaccordance with prior research (McCabe et al., 2005; Sung et al., 2005). Respondents with moderate and



high involvement on deviant behaviors in the past-year consistently had a higher prevalence of eachsymptom compared to the other deviant behavior groups. The nature and potential pathways of theassociation of opioid analgesic dependence symptoms with deviant behaviors needs to be betterunderstood and investigated.

Those who had serious mental health problems in the past-year were two times more likely to have anyof the past-year opioid analgesic dependence symptoms as compared to those without serious mentalhealth problems even when frequency of use and types of drugs used were taken into account.Longitudinal studies are needed to clarify the extent and the potential causal pathways betweenpsychiatric comorbidity and opioid analgesic use and dependence.

It is necessary to note some strengths and potential limitations of this study. This study has severalstrengths mostly due to the NSDUH research design and methods, including its large sample size andgeneralizability to the US household population. While large epidemiologic datasets are useful forexamining factors associated with extramedical analgesic use and symptom profiles of extramedicalopioid analgesic users, one limitation of this study is that it is not possible to distinguish whether theseextramedical opioid analgesic users first started using these drugs because they were prescribed forthem or if they initiated analgesic use illegally. Even though past-year dependence questions fordifferent drug classes are asked separately in the NSDUH, there might be some misclassification amongrespondents who are past-year users of more than one class of drugs (e.g., respondents who are past-year analgesic and cocaine users might attribute cocaine dependence symptoms to the analgesic theyuse and vice-versa). While the NSDUH has questions regarding frequency of analgesic use in the past-year, it does not have data available on the exact amount of opioid analgesics an individual has taken inthese occasions or on the average dose of analgesic used. Another limitation of the dataset is the factthat psychiatric comorbidity data was not collected for respondents aged 12–17 years old. Also, there isthe possibility that respondents might be under-reporting their extramedical analgesic use in theNSDUH survey, and extramedical analgesic opioid users who do not report use might present with adifferent dependence symptom profile as compared to those reporting extramedical opioid analgesicuse.

Withstanding these limitations, this study brings forth several important findings that have significantimplications, primarily at the screening and prevention level. As has been noted in the literature (Sprouleet al., 1999), we found that tolerance and salience are the most commonly reported analgesic dependencesymptoms associated with extramedical analgesic use, suggesting that placing equal weight on allsymptoms may not be the case when screening for opioid analgesic misuse. The fact that the profiles wereparallel provides support that the magnitude but not the pattern of problems differs between analgesicusers only and poly-drug users. In particular, one should note that the likelihood of all opioid analgesicdependence symptoms was highest among ‘prescription poly-drug users’. Similarly, while profiles wereparallel across the different levels of deviant behaviors, the magnitude of the analgesic dependenceproblems were highest among those with moderate and high levels of deviant behaviors. In the same way,although the analgesic dependence symptom profile was parallel across presence/absence of past-yearserious mental health problems, the magnitude of the opioid dependence problems was higher amongthose with serious mental health problems.

This study fills a gap by addressing the nature of dependence problems associated to extramedicalopioid analgesic use. In particular, health care providers should always keep in mind to screen for opioidanalgesic dependence among ‘prescription poly-drug users’, and extramedical analgesic users with highlevels of deviant behaviors and psychiatric comorbidity.



Acknowledgements

This study was supported by a grant from Janssen Medical Affairs, L. L. C. Dr. Martins received apostdoctoral scholarship from the Brazilian National Council of Research (CNPq-Brazil) whileconducting this study. Preliminary results of this study were presented at the College on Problems onDrug Dependence, June 21st 2005.

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