Despite media attention on the nonmedical use of prescription ADHD medication [37
], our understanding about the characteristics of NMU remains relatively underdeveloped compared to other substances such as alcohol and tobacco. This study contributes new data on a variety of issues related to NMU that provide insight into the patterns of NMU of prescription ADHD medications and associated risk factors in the United States. However, results from our study are not directly comparable to other data sources given that our study focuses on specific types of ADHD drugs rather than on stimulants in general. Most directly comparable is the study conducted by Kroutil and colleagues [7
] using the 2002 NSDUH. Based on those data, the past-year prevalence of NMU of ADHD stimulants was higher among young adults aged 18 to 25 (1.3%). This estimate is lower than our estimate of 4.3% for those aged 18 to 25 for at least two reasons. First, the current study covered a larger range of stimulant medications, including medications such as Adderall, than the NSDUH. Also, the Kroutil et al. [7
] study estimated NMU of ADHD medications for persons who had not misused other stimulants; that likely underestimated the prevalence of any NMU of ADHD medications.
Although not directly comparable because the estimates are based on only stimulants, the 2005 NSDUH estimated that 3.6% of persons aged 18 to 25 had used any stimulant nonmedically in the past year. We also estimated the past-year prevalence from the public use files of the National Epidemiologic Survey on Alcohol and Related Conditions [38
], which was found to be 1.7% among those aged 18 to 25. Follow-up data for young adults from the 2005 Monitoring the Future survey indicated that 5% to 7% of young adults aged 19 to 24 used amphetamines "not under a doctor's orders," and 2.9% to 5.5% did so for Ritalin [39
]. Results from the College Alcohol Study revealed that the rate of past-year NMU of prescription stimulants among college students was 4.1% [40
], which was similar to the 4.6% reported by students aged 18 to 25 in the current study.
Inspection of the prevalence rates from various community-based investigations revealed that, even among representative probability surveys, there is a wide degree of variation, which may be due to differences in methodology. For example, the NSDUH uses a definition of NMU based on use without a doctor's prescription or "only for the experience or feeling it caused." This final definition resulted from methodological testing [23
] to simplify the concept of "nonmedical" use for NSDUH respondents, some of whom are as young as 12. This definition has been criticized for leaving open the possibility that legitimate use may be reported by mistake [42
]. However, differences in prevalence estimates between surveys such as the NSDUH and National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) also may be related to differences in mode of administration; NSDUH questions about drug use are self-administered, but NESARC is administered directly by interviewers. Other research suggests that respondents may underreport sensitive behaviors if asked directly by interviewers [43
]. Nevertheless, findings on the correlates of NMU of ADHD medications from this study were largely consistent with previous research, providing a degree of confidence in our methods. In particular, we observed higher rates of NMU among young adults aged 18 to 25 than among older age groups, which parallels other national data [44
There also is significant interest in the rates of NMU among students. Recent studies suggest that young adults, and particularly students, may account for much of the NMU of prescription stimulants. For example, Herman-Stahl et al. [9
] found in analyses of NSDUH data that college students were at a greater risk of NMU of prescription stimulants than their noncollege peers. Further, previous college enrollment or graduation also was a significant risk factor, suggesting that NMU of stimulants in general may persist beyond college. However, the current study found no statistically significant (P
< .05) differences in the rates of NMU of ADHD medications between young adult students (4.6%) and young adult nonstudents (4.2%), although we considered a broader range of medications and focused on ADHD drugs rather than on prescription stimulants in general. It also should be noted that our definition of student status included technical students and did not distinguish between full-time and part-time students. Thus, future studies should probe for differences in NMU by student enrollment status, including full-time, part-time, public, private, technical, and community college.
Another important contribution of this study involves examination of the patterns of polydrug use among NMUs of ADHD medications. Binge alcohol use (69%) and marijuana use (54%) were fairly common among past-year NMUs, which also has been found in a previous study [9
]. Although most ADHD medications are classified as stimulants, only 16% of past-year NMUs of ADHD medications also were past-year methamphetamine users. In contrast, 46% of methamphetamine users commonly misused prescription ADHD medications. One possible explanation for this latter finding is that methamphetamine users may engage in NMU of ADHD stimulants as a substitute when methamphetamine is unavailable. However, additional studies, using items that capture aspects of substitution among methamphetamine users, are needed to specifically test this assertion. Overall, NMU of ADHD medications by methamphetamine users appears to constitute a relatively small portion of the overall diversion of ADHD medications in the United States, although use of methamphetamine is a powerful risk factor for NMU.
A significant strength of this study was that we also asked about the concomitant use of ADHD medications with other drugs and alcohol. We observed that approximately 68% of past-year NMUs had used at least one other substance either at the same time or within a couple of hours of NMU of ADHD medications. Findings from McCabe et al. [11
] using a sample drawn from a single university showed that most polydrug use involving alcohol and prescription drugs occurred simultaneously rather than on separate occasions. The high rate of alcohol use in combination with NMU of ADHD drugs is alarming in these studies, given that ADHD stimulants can counteract the depressant effects of alcohol. This leaves open the possibility that persons who drink alcohol may be able to prolong their drinking by also using stimulants, thereby increasing their risk of alcohol poisoning. Findings from the Drug Abuse Warning Network [46
] indicate that over 2% of all emergency department visits involving alcohol also involved prescription stimulants [46
]. We also found that nearly one in five NMUs of ADHD medications had used them in combination with cocaine, which may increase the risk of adverse cardiac events, given known risks associated with cocaine use [47
] and recent concerns about potential cardiovascular risks associated with specific ADHD medications [48
]. The issue of co-occurrence is particularly germane because alcohol and other drugs have the potential to affect the synaptic transmission produced by stimulant and nonstimulant ADHD medications, thus affecting regulation and causing adverse immediate and long-term health consequences [49
Recent research has found elevated rates of substance use among those with ADHD [16
]. Similarly, findings from our study indicated that those who self-identified that they had been diagnosed by a physician with ADHD and had been prescribed medication were at increased risk for NMU of ADHD medications. However, NMU by this subpopulation constitutes a relatively small proportion (less than 5%) of the total past-year NMU in the United States. Further, interpretation of the association between nonmedical use of ADHD medications and a history of pharmacologic treatment for ADHD requires caution. Our data are cross-sectional and preclude causal inferences, such as concluding that prescribing medications with potential abuse liability increases the likelihood of NMU. Accumulating evidence from prospective research also suggests that treatment of ADHD with stimulants in and of itself does not confer additional liability to NMU of ADHD stimulants or other substances [53
One possible explanation for the relation between NMU and an ADHD diagnosis or receipt of medications is that some patients receiving pharmacological treatments may be undermedicated or noncompliant with their prescribed therapeutic dosage. Consequently, they may take excessive quantities to self-medicate or overcompensate for earlier noncompliance because of an increase in the quantity and severity of untreated symptoms. However, undermedication seems a less plausible explanation for NMU in this study, as the question wording for NMU primes the respondent to answer about use beyond the therapeutic value of the drug as prescribed by a physician or medical professional (see the appendix). However, productivity (39.8%) was a commonly endorsed motivation for NMU for all respondents, regardless of ADHD status. Thus, it is possible that a proportion of NMUs who are taking ADHD medications to be more productive may be self-medicating ADHD symptoms. It is also possible that a large proportion of those whose primary motivation for NMU is productivity may actually meet the criteria for ADHD, but have not received a formal medical diagnosis. In contrast to self-medication, another prominent subgroup of NMUs – nearly 30% – could be considered recreational users whose principal motivations were tension relief, euphoria, or thrill-seeking. Another possible explanation linking ADHD and NMU is that an additional set of vulnerability factors linked to NMU, such as co-occurring psychiatric disorders, may increase the likelihood of NMU of ADHD medications. We are conducting additional analyses to examine the complex issues related to treatment status, ADHD symptoms, NMU, and motivations for use. Thus, additional research is needed with persons being treated pharmacologically for ADHD to compare NMU between those with and without psychiatric comorbidity to identify the combination of risk factors that can account for vulnerability to NMU.
Overall, these findings should be interpreted in light of several considerations. First, the validity of self-reported drug use among Internet respondents depends on their willingness to answer truthfully about drug use in that data collection mode and their ability to recall use of specific drugs within designated time frames. To aid in recall, our online survey questionnaire supplied respondents with reference dates to establish recall periods such as the past 12 months and, where possible, made pictures of medications available to respondents. We have also shown that our findings are consistent with data from a large national probability survey (NSDUH), as well as with findings from other studies on NMU. Confidence in our findings is also supported by the low prevalence of self-reported NMU of the fictitious drug Supraval (lifetime: 0.11%; past year: 0.09%), suggesting that respondents were able to discriminate between real and fictitious drugs. Substance use data have been shown to be statistically comparable between data collection modes involving mail surveys and the Internet [55
], as well as between the Internet and telephone [56
]. HI also conducts routine screening of Internet IP addresses and conducts follow-ups with members to ensure that each participant is not using multiple e-mail accounts to opt into the panel. For security reasons, each panelist is also assigned a unique and confidential ID to ensure privacy. Together, these findings strengthen our confidence in the accuracy of the self-reports and use of the Internet recruitment design. Although the convergence of our findings with those from other studies does not rule out misreporting of drug use in our data, it does not suggest a markedly greater misreporting problem in our study than in others. Nevertheless, the possibility of a mode effect does remain [57
], and researchers should be sensitive to its presence by comparing estimates from other data sources to check for reliability of the data.
Second, concerns about selection bias may arise from the low overall number of responses relative to the number of e-mail invitations that were sent and from our use of a nonprobability Internet survey to estimate NMU. With regard to the numbers responding, it should be noted that this method differs from other survey research methods, in that the current study did not employ numerous follow-up attempts or refusal conversion efforts. Further, bias is defined as the difference between the sample estimate and the true population estimate. If the results from other nationally representative data systems (e.g., NSDUH, NESARC) are used as the "true" gold standard population estimates, then comparing our results as the sample estimates to those found in other studies as the population estimates reveals no appreciable degree of bias. In addition to the studies noted above (e.g., NESARC, MTF), a direct comparison of estimates between the current study and the 2005 NSDUH, the most methodologically parallel study, for variables that were not used in our weighting procedures suggests that we reduced to a large degree the potential biases resulting from the sample selection for the online survey. In additional analyses not shown, we also estimated parallel regression models using the 2005 NSDUH and our Internet sample. For the outcomes of past-year marijuana use and past-month binge alcohol use, we observed high consistency in the estimated odds ratios for common demographic characteristics measured in both studies. The primary difference in the estimates for these variables was that the width of the confidence interval around each estimate was wider in the Internet study than in the NSDUH. This could be expected given the differences in the sample sizes between the NSDUH (N = 32,104) and our study (N = 4,297). If bias existed in the propensity score algorithm used to generate the sample weights, we would have expected to observe differences in the estimates in addition to the confidence intervals. Epidemiologic studies of NMU of ADHD drugs can suffer from other biases inherent to sample selection, such as samples drawn from clinical populations, local or regional samples, or samples of demographic subgroups that are accessible for surveys (e.g., students). Therefore, we believe that our study adds to the base of knowledge about NMU of ADHD medications through its national focus and depth of coverage of relevant issues.
We note that the value of the Internet panel design is meant to complement, not replace a national probability study. Internet surveys may be a promising approach for conducting formative research or gathering information on a focused topic for less time and expense than other survey methods. Internet panels are burgeoning in many countries around the world, and this study design is appearing in the research literature with greater frequency. For example, Schlenger et al. (2002) [58
] used such an approach to examine the psychological reaction to the terrorist attacks of September 11, 2001, within weeks after the event. Another study by West et al. (2006) [59
] used the Harris Interactive Online Poll, a panel with several million members in 125 countries, to examine patterns of smoking cessation. Their combined sample consisted of 2,009 participants from the United States, Canada, the United Kingdom, and France. They compared characteristics of cigarette smokers from a standing Internet panel who were selected for the study to characteristics of smokers in national data systems in each respective country and found a high degree of correspondence on major demographic characteristics. In particular, the rapidity with which Internet surveys can be conducted may open new opportunities for cross-national studies of NMU of prescription medications. Under careful methodological rigor, this approach may be favorable to a small face-to-face survey or a large telephone survey. Nevertheless, results from an Internet design should be regarded as preliminary estimates that should be replicated using national probability samples.