3.1. Nonmedical Opioid Users ()
About 5% (n=1,815) of all NESARC respondents used nonmedical prescription opioids in their lifetimes. A higher prevalence of nonmedical opioid use was found among young adults aged 18–29 years (7.4%), adults aged 30–44 years (5.7%), men (6.1%), whites (5.3%), those who had never married (7.0%), adults with a family income below $70K (4.8–5.3%), and those who reported a family history of substance abuse (6.9%) or a personal history of substance abuse treatment (26.6%). These findings are supported from adjusted logistic regression analyses.
Prevalence of lifetime nonmedical opioid use among adults aged 18 years or older: 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (N=43,093)
3.2. Prescription Opioid Use Disorders
Among all respondents (N=43,093), 1.4% met criteria for a lifetime DSM-IV prescription opioid use disorder (1.1%, abuse; 0.3%, dependence). Among the 1,815 nonmedical opioid users, 22.8% met criteria for opioid abuse, and another 7.2% met criteria for opioid dependence. Men were more likely than women to have abuse (26.6% [95% CI: 23.4–30.1] vs. 16.9% [95% CI: 13.7–20.6]) and any abuse/dependence diagnosis (33.3% [95% CI: 19.6–37.3] vs. 24.8% [95% CI: 21.3–28.6]), but were as likely to have a dependence diagnosis as women (6.8% vs. 7.9%).
3.3. LCA-defined Subtypes of Nonmedical Opioid Users ()
Marijuana (80%) was the drug most commonly used by nonmedical opioid users (n=1,815), followed by hallucinogens (49%), tranquilizers (47%), cocaine (46%), sedatives (45%), amphetamines (44%), inhalants (19%), and heroin (5%).
The 2-class model (BIC=13985; entropy=0.83) from LCA of the eight drug use variables was found to differ from a 1-class model in model fit (VLMR test, p<0.01). Next, the 2-class model was compared to a 3-class model (BIC=13644; entropy=0.77), which then was compared to a 4-class model using the VLMR test, and each comparison was significantly different (p<0.01), suggesting that a 4-class model (BIC=13559; entropy=0.78) was a better fit than others. The 4-class model did not differ from a 5-class (BIC=13582; entropy=0.74) model (VLMR test, p=0.432). These results suggested that a 4-class model was the best fit to the data, and each class size was adequate for further analysis (close to 10% for the smallest class). This model yielded the lowest BIC value (most parsimonious). The average conditional probability of correctly classifying each individual into each latent class was 0.91 for Class 1, 0.74 for Class 2, 0.89 for Class 3, and 0.90 for Class 4.
The four classes were distinguished by the extent of lifetime drug use (). Class 1 (opioid–marijuana users, 33% of all nonmedical opioid users) manifested a moderate probability of using marijuana as their other drug of use. Class 2 (opioid–other prescription drug users, 9%) comprised primary users of marijuana and other prescription drugs (tranquilizers and sedatives). Class 3 (opioid–marijuana–hallucinogen users, 28%) mainly used other illicit drugs. Class 4 (opioid–polydrug users, 30%) manifested a comparatively high probability of using all drug classes.
Latent class analysis of subtypes of nonmedical opioid users: 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (N=1,815)
3.4. Multinomial Logistics Regression Analysis of LCA-defined Subtypes ()
Relative to opioid–marijuana users, familial substance abuse and history of substance abuse treatment increased the odds for being in Classes 2–4; male gender and white race (vs. black race) increased odds for being in the opioid–marijuana–hallucinogen and the opioid–polydrug subtypes. Additionally, being widowed, separated, or divorced (vs. married) and opioid dependence were associated with being in the opioid–other prescription drug subtype; younger ages (18–29 years vs. 45+ years) increased the odds for being in the opioid–marijuana–hallucinogen subtype; and being widowed, separated, or divorced (vs. married), onset of nonmedical opioid use before 18 years, and opioid abuse increased odds for being in the opioid–polydrug subtype.
Supplemental comparisons among the more problematic groups (Classes 2–4) showed that opioid dependence was more likely to be found in the opioid–other prescription drug group (vs. the opioid–marijuana–hallucinogen group). Male gender and onset of nonmedical opioid use before 18 years were more likely to be in the opioid–polydrug subtype as compared with the opioid–other prescription drug subtype. Onset of nonmedical opioid use before 18 years, opioid abuse, and history of substance abuse treatment also increased odds for being in the opioid–polydrug subtype as compared with the opioid–marijuana–hallucinogen subtype.
Adjusted odds ratios (AOR) and 95% confidence intervals (CI) of LCA-defined subtypes of nonmedical opioid users: 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (N =1,815)
3.5. Substance Use Disorders by LCA-defined Subtypes ()
Gender variations in lifetime SUDs by subtypes were determined (). Among men, opioid–polydrug users had the highest prevalence of opioid abuse (40%) and disorders related to use of opioids (48%), nicotine (64%), alcohol (92%), marijuana (81%), cocaine (62%), amphetamines (46%), hallucinogens (46%), sedatives (41%), tranquilizers (41%), inhalants (14%), and heroin (10%). Opioid–other prescription drug users also exhibited the highest prevalence of nicotine dependence (64%) and tranquilizer use disorders (25%). Opioid–marijuana–hallucinogen users differed from opioid–other prescription drug users in having a higher rate of disorders related to use of inhalants (2.6% vs. 0%), hallucinogens (26% vs. 0.7%), and heroin (2% vs. 0%). Opioid–marijuana users showed the lowest rate of disorders related to use of marijuana (24%) and other drugs (≤ 3%).
Among women, opioid–other prescription drug users resembled opioid–polydrug users in having the highest rates of opioid dependence (22%, 14%, respectively) and disorders related to use of opioids (41%, 36%), nicotine (71%, 71%), tranquilizers (23%, 30%), and sedatives (28%, 33%); opioid–marijuana–hallucinogen drug users had a higher rate of marijuana and hallucinogen use disorder than opioid–other prescription drug users. In both genders, the mean number of SUDs was highest among opioid–polydrug users (5.4 [95% CI: 5.04–5.83] in men; 4.9 [95% CI: 4.37–5.39] in women), following by opioid–other prescription drug users (2.9 [95% CI: 2.45–3.38] in men; 3.3 [95% CI: 2.76–3.78] in women), opioid–marijuana–hallucinogen users (3.2 [95% CI: 2.96–3.43] in men; 2.9 [95% CI: 2.61–3.26] in women), and opioid–marijuana users (1.6 [95% CI: 1.45–1.78] in men; 1.1 [95% CI: 0.94–1.21] in women).
Substance use disorders by LCA-defined subtypes of nonmedical opioid users (N =1,815)
3.6. Lifetime Mental Disorders by LCA-defined Subtypes ()
Among men, opioid–polydrug users had higher rates than opioid–marijuana users of major depression (42% vs. 25%), dysthymia (16% vs. 4%), mania (16% vs. 4%), and antisocial personality disorder (34% vs. 13%). Among women, the other three subtypes (Classes 2–4) had similar rates of mental disorders. However, only opioid–other prescription drug users showed higher rates than opioid–marijuana users in major depression (67% vs. 41%), dysthymia (36% vs. 11%), mania (26% vs. 11%), generalized anxiety disorder (32% vs. 9%), and paranoid personality disorder (24% vs. 11%); the three more problematic subtypes in women had higher rates of antisocial personality disorder than opioid–marijuana users (16–25% vs. 6%). For all subtypes, women tended to have a higher rate of mood or anxiety disorders than men in their corresponding subtypes.
Mental disorders by LCA-defined subtypes of nonmedical opioid users (N =1,815)
3.7. Quality of Life by LCA-defined Subtypes ()
Finally, there was little variation in quality of life by subtype among men. Among women, opioid–other prescription drug users exhibited lower quality of life in mental disability (mean scores: 42.59 [95% CI: 39.17–46.02] vs. 48.93 [95% CI: 47.60–50.27]), emotional role (mean scores: 42.50 [95% CI: 38.86–46.14] vs. 49.04 [95% CI: 47.58–50.50]), and mental health (mean scores: 43.05 [95% CI: 40.07–46.02] vs. 48.08 [95% CI: 46.65–49.52]) than opioid–marijuana users. For all subtypes, women reported lower quality of life in mental health than men in their corresponding subtypes.
Indicators of quality of life (SF-12, version 2) by LCA-defined subtypes of nonmedical opioid users (N =1,811*)