Abstract
Aim
Epidemiological data indicate that nonmedical use of prescription stimulants has increased over the past decade. However, little is known regarding the source of the misused stimulants and whether different sources correspond to differences in risk profiles and associated social and health problems.
Method
Data from the 2006 to 2011 National Survey on Drug Use and Health were used. A total of 4,945 participants who used prescription stimulants nonmedically and also reported their source of misused stimulants were categorized by the source: friend/relative, physician and illegal. Logistic regression models compared the socio-demographic, mental health and behavioral problems, and stimulant use-related problems (onset, recency, frequency, severity) according to the source of the misused stimulants.
Results
The most common sources of stimulants were friends/relatives, followed by physicians and illegal sources. Compared to participants reporting friends/relatives as the source, participants reporting an illegal source were more likely to be male, unemployed, have less than a high school education, a history of criminal behavior and an earlier age of use onset. Participants reporting a physician source were more likely to have mental health problems and mental health service use. Higher odds of past-month stimulant use, frequent use (≥10 days per year), drug dependence and substance service use were found in individuals reporting physician and illegal sources.
Conclusions
Identifying the source of misused stimulants may be useful in detecting distinct subgroups of nonmedical prescription stimulant users, which may inform development of tailored prevention and treatment programs and contribute to individual treatment planning.
Keywords: Prescription stimulants, Nonmedical use, Drug source, Mental health, Criminal behavior
1. Introduction
Prescription drugs are the second most commonly abused category of drugs after marijuana (SAMHSA, 2012). Prescription stimulants are a major contributor to this statistic. In 2012 about 1.2 million Americans reported using nonmedical prescription stimulants in the past year (SAMHSA, 2013). There has been a remarkable growth in nonmedical use of these medications over the past decade particularly among young adults and adolescents (Arria and Wish, 2006; Boyd et al., 2006; McCabe et al., 2005; SAMHSA, 2009a; Teter et al., 2006).
Previous studies have made a considerable contribution to investigating the epidemiology and risk factors associated with nonmedical use of prescription stimulants. In a recent review, the prevalence of past-year nonmedical prescription stimulant use ranged from 5% to 9% in adolescents and 5% to 35% in college-age adults (Wilens et al., 2008). College population studies have shown that the nonmedical prescription stimulant users are more likely to be male, white, members of fraternities and sororities, and earning lower grades (McCabe et al., 2005; Teter et al., 2006). The reported motives for use in this population range from cognitive enhancement and study aid to recreational use (Teter et al., 2005; White et al., 2006). Previous studies have consistently demonstrated that nonmedical prescription stimulant users are more likely to report other substance use and involvement in risky behaviors (McCabe et al., 2007, 2005; Teter et al., 2005).
Numerous studies have also revealed that a friend or relative is generally the primary source for the nonmedically used prescription stimulants (DeSantis and Hane, 2010; Garnier-Dykstra et al., 2012; Garnier et al., 2010; McCabe et al., 2006; White et al., 2006). A previous college-based study found that 61.7% of college students diagnosed with ADHD reported diverting their prescription stimulants (Garnier et al., 2010), suggesting that physicians could directly or indirectly become the sources for some stimulant misuse. Although availability of stimulants over the internet without a prescription has been reported (Schepis et al., 2008), the internet remains a relatively minor source for prescription drugs as a whole (Inciardi et al., 2010).
Previous studies have shown that sources of nonmedically used prescription drugs could distinguish the risk profiles of the users. A study focused on prescription opioids showed that those who obtained the opioids from a physician were twice as likely to report an opioid use disorder (Becker et al., 2011). Schepis et al. (2009) have evaluated sex or ethnic differences in sources of prescription drugs among adolescents and whether different sources correspond to differences in the risk profiles of other substance use. However, there is limited research on the implications of variations in specific source of nonmedically used stimulants in the general population. In particular, the implications for stimulant use associated problems remain unexplored, including the onset, frequency, recency, and severity of stimulant use—factors that are associated with social and health consequence of stimulant misuse (van der Meer et al., 2011).
Age of onset, recency, frequency and severity are frequently used indices in evaluating substance use disorders as they signal different concomitant substance use profiles and health and social consequences. For example, Lewinsohn.et al. (1999) examined the relationship between life-time use, recency, frequency and age of onset of adolescent cigarette smoking and later occurrence of substance use disorders). Also, the presence of cognitive deficits associated with lifetime stimulant use is dependent on the frequency of use (van der Meer et al., 2011).
While adolescents and adults differ in health problems, their healthcare access and use of healthcare, and criminal behavior, our preliminary tests found many correlates of sources of nonmedically used simulants to be similar among adolescent and adult nonmedical prescription stimulant users (Supplementay Table 11). Thus, this report combines both adolescent and adult data from the 2006 to 2011 National Surveys on Drug Use and Health (NSDUH) to answer the following questions: 1) Do individuals reporting different sources of misused stimulants show different socio-demographic, mental health and behavioral profiles? and 2) Is the source of the nonmedically used stimulants associated with factors such as age of onset of use, recency of use, and severity of the nonmedical stimulant use?
2. Methods
2.1. Sample
We analyzed data from the NSDUH public use data files for 2006 to 2011 (N= 338,495). We restricted our sample to participants who reported using prescription stimulants nonmedically in the past year after excluding those who used methamphetamine only (N=4,945). We conducted the analyses combining adolescent and adult data, as our previous analyses indicated few differences in sources of nonmedically used stimulants among adolescent and adult participants of NSDUH (Supplementary Table 12). The NSDUH is an annual cross-sectional survey sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA) and is designed to provide estimates of the prevalence of alcohol and drug use in the household population of the United States, 12 years of age and older. The survey employs a 50-state design with an independent multistage area probability sample for each of the 50 states and the District of Columbia. African-Americans, Hispanics, and youth are over-sampled to increase the precision of estimates for these groups. The response rate for household screening ranged from 87% to 91%, and for completed interviews from 74% to 76%, across the 6 years. Survey items were administered by computer-assisted personal interviewing (CAPI) conducted by an interviewer and audio computer-assisted self-interviewing (ACASI) for sensitive questions. Use of ACASI was designed to provide respondents with highly private and confidential means of responding to questions and to increase the level of honest reporting of illegal drug use and other sensitive behaviors. Respondents were offered a $30 incentive payment for participation in the survey. Detailed information about the sampling and survey methodology of NSDUH can be found elsewhere (SAMHSA, 2007, 2008, 2009b, 2010, 2011, 2012).
2.2. Assessments
2.2.1. Nonmedical prescription stimulant use
Lifetime nonmedical prescription stimulant use was ascertained through the use of a specified list of stimulants. To aid recall, pictures of the medications were provided. The stimulants included Ritalin or methylphenidate, amphetamines, dextroamphetamine, Cylert, methamphetamine, Methedrine or Desoxyn, Dexedrine, and selected diet pills. Participants were asked: “Have you ever, even once, used [Drug name] that was not prescribed for you or that you took only for the experience or feeling it caused?” Participants were also asked if they had used any other stimulants. We further ascertained past-year nonmedical prescription stimulant use if the respondents answered positively to the above question and indicated that the time since last use was within 12 months.
2.2.2. Sources of misused stimulants
NSDUH respondents who reported using prescription stimulants nonmedically in the past 12 months were asked a series of questions on how they had obtained these drugs most recently. In order to make our study comparable with previous literature (Garnier-Dykstra et al., 2012; McCabe et al., 2006), the 10 mutually exclusive choices were categorized into the following 3 groups: friend/relative source (“got from friend or relative for free, rdquo; “bought from friend or relative” or “took from friend or relative without asking”), physician source (“got from one doctor” or “got from more than one doctor”), and illegal source (“bought from drug dealer or other stranger,” “bought on the internet,” “wrote fake prescription,” “stole from doctor's office, clinic, hospital, or pharmacy”). These groupings focus upon the source, rather than the mechanism by which the medication was obtained. We excluded those who reported “other” source from the analyses as the source was not specified.
2.2.3. Socio-demographic characteristics
Socio-demographic variables included in the analyses were sex, age (12-17, 18-25, ≥ 26), race/ethnicity (non-Hispanic white, minorities), and annual household income (≤ $19,999, $20,000-$34,999, $ 35,000-$69,999, ≥ $70,000). For adults aged 18 and above we additionally included marital status (single, non-single), employment status (partial or full employment, unemployed, not in labor force), education (less than high school, high school, college and above).
2.2.4. Past-year criminal behaviors, mental health and substance use problems
Participants reported on how many times they had attacked someone with the intent to seriously hurting them, how many times they had sold illegal drugs, and how many times they had stolen or tried to steal anything worth more than $50 in the past year. Consistent with past research (Martins et al., 2009), participants who reported any of the three behaviors were categorized as having criminal behavior (0 for none of these behaviors and 1 for engaging in these behaviors 1 time or more). Past-year arrest was included to capture involvement with the law and justice systems. Based on the number of times in the past 12 months respondents had been arrested and booked for breaking the law, without counting minor traffic violations, past year arrest was categorized as 0 for none and 1 for one or more.
In the section of the survey assessing health care use, participants were asked if in the past year they had been told by a medical doctor or health care professional that they had an anxiety disorder or depression and whether they received any mental health treatment or substance use disorder treatment in the past year. The NSDUH team created separate indicators for past-year alcohol and drug dependence based on the responses to items that assessed the Diagnostic and Statistical Manual of Mental Disorders–IV (DSM-IV) criteria (American Psychiatric Association, 1994). A composite illegal drug dependence measure was created to demarcate individuals who were not dependent on any other drugs from those dependent on any one or more of the following eight substances: marijuana, crack/cocaine, heroin, hallucinogens, inhalants, prescription pain relievers, prescription tranquilizers and sedatives.
2.2.5. Onset, recency and severity of nonmedical use of stimulants
Based on past research (Grant and Dawson, 1998), early onset of stimulant use was defined by first time of use before or at age 13. Past-month stimulant use was defined as use within the past 30 days. Stimulant use disorders included both stimulant abuse and dependence, defined based on criteria specified in the DSM-IV (American Psychiatric Association, 1994). Frequency of use in the past month or year was a free response question in which participants entered the exact number of days the substance was used. We defined frequent use as using stimulant 10 days or more in the past year, consistent with past literature (Schepis and Krishnan-Sarin, 2009).
2.3. Statistical analyses
Analyses focused on exploring differences between the three past-year nonmedical prescription stimulant user groups categorized by their most recent source: a friends/relatives, physician, or illegal source group. We also examined the prevalence of nonmedical prescription drug use (opioids, sedatives, tranquilizers) among nonmedical stimulant users using cross-tabulation analysis. The group of friends/relatives source, the largest group, was set as the reference category. We used a series of logistic regression models to compare the groups with regard to socio-demographic characteristics, mental health problems, mental health and substance disorder service use, criminal behaviors and other substance dependence. Another set of models examined the onset, recency, and severity of stimulant use in each of these groups. The adjusted models were controlled for age, sex, race-ethnicity, education, marital status, employment status, household income, past-year clinician-identified depression or anxiety disorders, criminal behaviors, arrest, mental health and substance service use.
All percentages reported are weighted. Taylor series linearization method, as implemented in Stata 13 (StataCorp, 2013) was used to take into account the complex survey design of NSDUH.
3. Results
3.1. Sources of nonmedically used prescription stimulants
About 7.8% (n = 24,224) of participants from NSDUH 2006-2011 reported lifetime nonmedical use of prescription stimulants, and 1.1% (n=6,384) reported use in the past year. A specific source of misused stimulants was identified by 4,945 respondents, including 3,772 adults and 1,173 adolescents. The most commonly reported specific sources were getting from a friend/relative for free (52.5%), buying from a friend/relative (17.5%), and getting from just one doctor (10.3%; Table 1). A relatively small proportion of prescription stimulant users reported obtaining the medication from more than one doctor (2.9%) or the internet (1.8%). When these sources were summarized into the four predefined categories, the most commonly reported sources were friends/relatives sources (75.1%), followed by physicians (11.2%), illegal sources (9.8%), and “other” sources (3.9%).
Table 1.
Sources | N | Weighted % (95% confidence interval) |
---|---|---|
Friends/relatives source | 3813 | 75.1(72.8,77.3) |
Got the stimulants from a friend or relative for free | 2480 | 52.5 (50.2,54.9) |
Bought the stimulants from a friend or relative | 1044 | 7.5 (15.9,19.3) |
Took the stimulants from a friend or relative without asking | 289 | 5.0 (4.1,6.2) |
Physician source | 475 | 11.2(9.8,12.6) |
Got one or more prescriptions for stimulants from just one doctor | 427 | 10.3 (9.0,11.8) |
Got prescriptions for stimulants from more than one doctor | 48 | 2.9 (0.6,1.5) |
Illegal source | 485 | 9.8(8.1,11.5) |
Wrote fake prescriptions for stimulants | 20 | 0.5 (0.2,1.2) |
Stole the stimulants from a doctor's office, clinic, hospital, or pharmacy | 38 | 0.9 (0.5,1.6) |
Bought the stimulants from a drug dealer or other stranger | 363 | 6.7 (5.5,8.0) |
Bought the stimulants on the Internet | 64 | 1.8 (1.2,2.6) |
Other source | 172 | 3.9 (2.9,5.3) |
Got the stimulants in some other way | 172 | 3.9 (2.9,5.3) |
Overall, more than 50% of nonmedical prescription stimulants users also reported using other prescription drugs (opioids, sedatives, tranquilizers). Those who obtained stimulants from illegal sources had the highest prevalence of nonmedical use of other prescription drugs (66.3%), followed by those who obtain their stimulants via physician sources (58.8%) or friends/relatives sources (57.5%).
3.2. Correlates of source of nonmedically used prescription stimulants
Participants reporting an illegal source were predominantly male (58.3%), while no sex difference was observed in the other two groups (Table 2). Participants reporting a physician source or illegal source were more likely to be aged 12-17 (vs aged 18-25) compared to those reporting a friends/relatives source.
Table 2.
Characteristics | Friends/ Relatives source (N=3,813) | Physician source (N=475) | Physician source vs Friends/Relatives source | Illegal source (N=485) | Illegal source vs Friends/Relatives source |
---|---|---|---|---|---|
| |||||
N (Wgt%) | N (Wgt%) | OR (95% CI) | N (Wgt%) | OR (95% CI) | |
| |||||
Sex | |||||
Male | 1,792(49.0) | 220(46.7) | 1.00 | 281(58.3) | 1.00 |
Female | 2,021(51.0) | 255(53.3) | 1.01(0.67,1.50) | 204(41.7) | 0.48(0.32,0.72)‡ |
| |||||
Age | |||||
12-17 | 846(11.6) | 135(13.3) | 1.00 | 132(14.4) | 1.00 |
18-25 | 2,442(48.2) | 245(33.2) | 0.58(0.40,0.85)† | 281(40.6) | 0.60(0.44,0.82)† |
≥ 26 | 525(40.3) | 95(53.5) | 1.17(0.73,1.89) | 72(45.3) | 0.84(0.45,1.56) |
| |||||
Race | |||||
Non-Hispanic White | 3,103(83.2) | 350(78.9) | 1.00 | 389(83.1) | 1.00 |
Minorities | 710(16.8) | 125(21.1) | 1.05(0.64,1.73) | 96(16.9) | 1.04(0.62,1.74) |
| |||||
Marital statusa | |||||
Married | 270(18.9) | 52(27.5) | 1.00 | 41(28.7) | 1.00 |
No longer married | 152(9.1) | 27(20.4) | 2.01(0.85,4.75) | 28(14.5) | 0.71(0.22,2.22) |
Never married | 2,545(71.4) | 261(52.1) | 0.50(0.25,1.00) | 284(56.8) | 0.48(0.19,1.21) |
| |||||
Employmenta | |||||
Full/Partial | 2,054(72.2) | 210(65.8) | 1.00 | 227(63.0) | 1.00 |
Unemployed | 291(8.1) | 43(9.2) | 0.93(0.48,1.78) | 54(18.4) | 2.79(1.18,6.60)* |
Not in labor force | 622(19.7) | 87(25.1) | 1.26(0.73,2.15) | 72(18.6) | 1.26(0.68,2.34) |
| |||||
Educationa | |||||
< High school | 350(11.1) | 50(14.3) | 1.00 | 84(18.9) | 1.00 |
High school | 763(23.5) | 94(26.9) | 1.83(0.90,3.73) | 115(26.5) | 0.56(0.31,0.99)* |
≥ College | 1,854(65.4) | 196(58.8) | 1.20(0.65,2.20) | 154(54.6) | 0.37(0.20,0.69)† |
| |||||
Income | |||||
< $20,000 | 1,260(28.8) | 160(27.1) | 1.00 | 125(23.3) | 1.00 |
$20,000- $49,999 | 1,107(30.6) | 142(33.4) | 1.07(0.67,1.73) | 169(37.4) | 1.73(0.92,3.19) |
$50,000- $74,999 | 508(13.2) | 68(14.6) | 1.35(0.72,2.53) | 61(9.8) | 1.02(0.63,1.65) |
≥ $75,000 | 938(27.4) | 105(24.9) | 0.82(0.46,1.46) | 130(29.6) | 1.24(0.75,2.05) |
| |||||
Past-year depression | |||||
No | 3,128(86.4) | 350(73.3) | 1.00 | 361(78.1) | 1.00 |
Yes | 650(13.6) | 110(26.7) | 1.63(1.05,2.52)* | 124(21.9) | 1.28(0.80,2.07) |
| |||||
Past-year anxiety disorder | |||||
No | 3,362(89.1) | 352(69.2) | 1.00 | 411(86.2) | 1.00 |
Yes | 451(10.9) | 123(30.8) | 4.37(2.66,7.18)‡ | 74(13.8) | 1.29(0.85,1.97) |
| |||||
Past-year mental health treatment | |||||
No | 2,787(72.7) | 262(51.3) | 1.00 | 339(71.7) | 1.00 |
Yes | 1,026(27.3) | 213(48.7) | 2.92(1.96,4.35)‡ | 146(28.3) | 1.02(0.65,1.61) |
| |||||
Past-year SUD treatment | |||||
No | 3,413(90.3) | 408(86.9) | 1.00 | 381(81.4) | 1.00 |
Yes | 400(9.7) | 67(13.1) | 2.10(1.30,3.39)† | 104(18.6) | 2.50(1.49,4.18)† |
| |||||
Past-year criminal behavior | |||||
No | 2,370(89.1) | 309(69.2) | 1.00 | 204(53.4) | 1.00 |
Yes | 1,443(10.9) | 166(30.8) | 1.07(0.67,1.70) | 281(46.6) | 2.62(1.67,4.10)* |
| |||||
Past-year arrest | |||||
No | 3,233(86.4) | 398(87.3) | 1.00 | 348(75.5) | 1.00 |
Yes | 580(13.6) | 77(12.8) | 1.09 (0.73,1.64) | 137(24.5) | 2.27(1.47,3.49)* |
| |||||
Past-year alcohol dependence | |||||
No | 2,950(78.3) | 362(77.6) | 1.00 | 361(78.4) | 1.00 |
Yes | 863(21.7) | 113(22.4) | 1.50(0.93,2.42) | 124(21.6) | 1.28(0.78,2.12) |
| |||||
Past-year drug dependence | |||||
No | 2,796(76.9) | 339(71.6) | 1.00 | 279(65.0) | 1.00 |
Yes | 1,017(23.1) | 136(28.4) | 1.73(1.15,2.61)* | 206(35.0) | 2.36(1.52,3.66)* |
p<0.05,
p<0.01,
p<0.001
Education, marital status, and employment status were examined in adults aged 18 and above only. Drug dependence did not include stimulant dependence.
Compared to the group who obtained stimulants from friends/relatives, the physician source group was more likely to report clinician-identified depression (Odds ratio [OR]: 1.63, 95% confidence interval [95% CI]: 1.05, 2.52), or anxiety disorder (OR: 4.37, 95% CI: 2.66, 7.18) and receiving mental health treatment (OR: 2.92, 95% CI: 1.96, 4.35). The illegal source group was significantly less likely to be female (OR: 0.48, 95% CI: 0.32, 0.72), have college or above education (OR: 0.37, 95% CI: 0.20, 0.69) and more likely to be unemployed (OR: 2.79, 95% CI: 1.18, 6.60). The illegal source group also had higher odds of criminal behaviors (OR: 2.62, 95% CI: 1.67, 4.10), and arrests (OR: 2.27, 95% CI: 1.47, 3.49) (Table 2). Both the physician source and the illegal source groups were more likely to meet the criteria for drug dependence (physician source group OR: 1.73, 95% CI: 1.15, 2.61; illegal source group OR: 2.36, 95% CI: 1.52, 3.66), and to receive substance abuse treatment in the past year (physician OR: 2.10, 95% CI: 1.30, 3.39; illegal OR: 2.50, 95% CI: 1.49, 4.18).
3.3. Stimulant use onset, recency, and severity
In the adjusted models, the physician source group were more likely to report past-month stimulant use (adjusted OR [aOR]: 3.30, 95% CI: 2.23, 4.86), frequent use (≥10 days in the past year) (aOR: 3.05, 95% CI: 1.93, 4.81), and to meet the criteria for a stimulant use disorder (aOR: 2.71, 95% CI: 1.49, 4.93), compared to the friends/relatives source group. The illegal source group was more likely to report early onset (onset ≤ age 13) (aOR: 2.10, 95% CI: 1.05, 4.18), frequent use (aOR: 2.36, 95% CI: 1.58, 3.54) and to meet criteria for stimulant use disorder (aOR: 2.71, 95% CI: 1.75, 4.20) compared to the friends/relatives source group. We conducted pair-wise comparisons between the physician source and the illegal source groups. In these comparisons, the physician source group was found to be more likely to report past-month use compared to the illegal source group (P=.002). The other comparisons were not statistically significant.
4. Discussion
To the best of our knowledge, this is the first study using nationally representative data to examine the implications of the sources of nonmedically used stimulants with regard to socio-demographic and mental health characteristics of users and seriousness of prescription stimulant use. Consistent with past research (DeSantis and Hane, 2010; Garnier-Dykstra et al., 2012; McCabe et al., 2006; White et al., 2006), in this study friends/relatives were found to be the major sources of misused stimulants, comprising 78.1% of all users.
Those who reported an illegal source for their stimulants were more likely than those obtaining the drug from friends/relatives to be male, unemployed, and less likely to have a higher education. They were also more than two times as likely to report criminal behaviors and history of arrests. It is important to acknowledge that the association between illegal source of stimulants and criminal behavior rating might be simply because the value of the stolen stimulants exceeded $50, thus qualifying as a criminal behavior according to our definition. This group comprised only 10.2% of all nonmedical prescription stimulant users.
The physician source group had markedly greater mental health burden including depression, anxiety disorder, and mental health service use compared to the friends/relatives source group. This finding could indicate the use of prescription stimulants for self-medication by some in this group. In another study, up to 50% of frequent nonmedical prescription stimulant users reported depressed mood (Teter et al., 2010). Additionally, many users self-diagnose themselves as having an attention disorder (Judson and Langdon, 2009). In a 2009 survey study, 39% of college students who misused stimulants did so with the desire to self-treat target symptom such as concentration (McCabe et al., 2009). It is also possible that the physician source group had greater access to medical providers, which in turn could provide a greater opportunity for being diagnosed with mental disorders (Wang et al., 2005). Future studies need to explore the reasons for the greater prevalence of mental health problems in the physician source group.
Obtaining stimulants from physicians or via illegal methods were both strongly correlated with recent use (in the past month), frequent use (10 days or more in the past year) as well as meeting stimulant use disorder criteria. Those obtaining these drugs via illegal methods were more likely to report an early-onset of stimulant use. Early onset of any substance use has been found to be linked to subsequent dependence on other drugs (Anthony and Petronis, 1995); thus, it is not surprising that the illegal source group was also more likely to have other drug dependence and substance service use compared to the friends/relatives source group.
The finding that the physician source group had significantly higher odds of stimulant use problems and particularly past-month use is intriguing. It is possible that a physician is a more sustainable source of drugs, leading users who obtain their misused drug from this source to continue using, which in turn may then eventually lead to a stimulant use disorder. On the other hand, the psychological vulnerabilities of this group may contribute to the persistent use of stimulants for self-medication. The more serious and persistent nature of stimulant misuse among individuals who report obtaining their drugs from physicians points to the need for better monitoring of dispensation and use of these drugs in medical practice. In one study, almost half of physicians found it difficult to discuss the abuse potential of prescription drugs with their patients (McCabe et al., 2002). This finding suggests that physicians should receive more training to identify nonmedical users, to discuss the potential danger of the stimulants, and to educate their patients regarding the risks associated with misuse and the legal responsibility of drug diversion.
In clinical practice, identifying the source of misused stimulants may also serve as a useful indicator of the specific needs and vulnerabilities of the patients. For example, patients who report obtaining their misused stimulants illegally or from doctors appear to be an increased risk of other substance use disorders.
The availability of prescription drugs on the internet has dramatically increased in recent years (McCabe and Boyd, 2005; Schepis and Krishnan-Sarin, 2009). Nevertheless, internet appears to be a relatively minor source of prescription stimulants in this sample.
Similarly, doctor-shopping which has been recognized as an important signal of misuse and prescription drug fatalities (Hall et al., 2008) was the source of only 0.7% of misused stimulants in this study.
Several limitations to this study and of the NSDUH data should be noted. First, the NSDUH asks for the most recent source of misused stimulants, but not the most commonly used source. Second, Adderall, a commonly used stimulant, was not included in the list of stimulants specifically enquired about and was included in the “other stimulants.” There is some evidence that this approach to ascertaining Adderall may have missed a large proportion of Adderall users (Kroutil et al., 2010). Third, given the cross-sectional nature of the NSDUH, causal-relationship cannot be established here. Thus, for example, it is not clear from these data whether mental health problems preceded or followed stimulant misuse. Fourth, all of the data were based on self-report, which is open to recall or social-desirability bias. However, the validity of these reports has been established in a previous study (Harrison, 2007). Fifth, although we combined adults and adolescents in this study due to similarity in the distribution of sources across age groups and similarity in the correlates of stimulant use, the stimulant source may be differentially associated across age groups with other characteristics of participants that were not examined in this study. Finally, questions used by the NSDUH do not differentiate between those who obtained their stimulant from friends versus relatives. The implications and correlates of these two sources may be different (Schepis and Krishnan-Sarin, 2009).
In the context of the limitations noted earlier, findings from this study suggest that different sources of misused stimulants are associated with different risk profiles and the extent and severity of stimulant use involvement. Those who obtained their misused stimulants from a physician source carry a greater burden of mental health problems, whereas there is a stronger correlation between criminal activities and obtaining stimulants from illegal sources. Both these groups tend to have more serious stimulant use problems than the group who obtained their stimulants from friends or relatives. The implications of these differences in the source of misused stimulants may inform future prevention and treatment campaigns.
Supplementary Material
Table 3.
Early Onset (≤ age 13) | Past-month use | ≥ 10 days in the past year | Past-year stimulant use disorder | |||||
---|---|---|---|---|---|---|---|---|
Odds Ratio (95% CI) | Adjusted Odds ratioa (95% CI) | Odds ratio (95% CI) | Adjusted Odds ratioa (95% CI) | Odds ratio (95% CI) | Adjusted Odds ratioa (95% CI) | Odds ratio (95% CI) | Adjusted Odds ratioa (95% CI) | |
Physician source vs Friends/Relatives source | 1.82(1.16,2.84)† | 1.06(0.52,2.19) | 3.25(2.39,4.41)‡ | 3.30(2.23,4.86)‡ | 3.10(2.09,4.60)‡ | 3.05(1.93,4.81)‡ | 2.88(1.82,4.58)‡ | 2.71(1.49,4.93)† |
Illegal source vs Friends/Relatives source | 2.36(1.41,3.95)† | 2.10(1.05,4.18)* | 1.66(1.12,2.47)* | 1.53(0.99,2.37) | 2.36(1.58,3.54)‡ | 2.17(1.42,3.31)‡ | 2.80(1.93,4.06)‡ | 2.71(1.75,4.20)‡ |
p<0.05,
p<0.01,
p<0.001
Adjusted models included sex, age race, education, marital status, employment, income, past-year major depression, anxiety disorder, criminal behaviors, arrest, mental health and substance service use.
Nonmedical prescription stimulant users have different characteristics by sources.
Different sources indicate different severity of stimulant use problems.
Future directions include development of prevention programs by sources.
Acknowledgments
Role of funding source: This study was supported by K24 DA023186 (PI: Dr. Eric C. Strain). NIDA has no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
Footnotes
Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…
Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…
Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…
Conflict of Interest Disclosures: Dr. Mojtabai has received consulting fees from Lundbeck Pharmaceuticals. Dr. Strain has provided consultation to the following pharmaceutical companies in the past 12 months: Reckitt Benckiser, DemeRx, Zogenix, and Jazz Pharmaceuticals, and has served on an advisory board for the Oak Group.
Other authors declare they have no conflicts of interest.
Author Contributions: Study concept and design: Lian-Yu Chen, Ramin Mojtabai
Acquisition of data: The data reported herein come from the 2006–2011 National Survey of Drug Use and Health (NSDUH) public data files available at the Substance Abuse and Mental Health Data Archive and the Inter-university Consortium for Political and Social Research, which are sponsored by the Office of Applied Studies, Substance Abuse and Mental Health Services Administration.
Analysis and interpretation of data: Lian-Yu Chen, Rosa M. Crum & Ramin Mojtabai
Critical revision of the manuscript for important intellectual content: Drs. Eric C. Strain, Carla L. Storr, Ramin Mojtabai
Statistical analysis: Lian-Yu Chen
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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