[Commentary] UNDERSTANDING THE PARAMETERS OF NON‐MEDICAL USE OF PRESCRIPTION DRUGS: MOVING BEYOND MERE NUMBERS
Bibliographic record
Abstract
In this issue of Addiction, McCabe and colleagues present a study on the expanding problem of non-medical use of prescription drugs in the USA [1]. Based on a representative sample of adult Americans, they show that early onset (i.e. as low as 13 years or younger) of non-medical prescription drug use functions as a predictor of future adult prescription drug abuse and dependence. As such, this paper provides complementary information on the epidemiology of the rapidly expanding phenomenon of the non-medical use of prescription drugs—including the specific problem element of prescription opioids (e.g. Oxycontine, Hydromorphone, Codeine, etc.)—abuse and harms in North America [2–4]. Since 1990, the incidence of prescription opioid abuse in the US general population alone has grown approximately fourfold within the wider context of substantial increases of medical usage of prescription opioid analgesics [3]. More disturbingly, these increases have been associated with similarly pronounced increases in morbidity (e.g. emergency room mentions) and mortality [5,6], expressing the extensive and growing harm toll of this phenomenon. Specifically, there were 5528 accidental poisoning deaths involving prescription opioids in the United States in 2002, a figure comparatively larger than that for either heroin- or cocaine-related fatalities [6]. Although some initial efforts have been undertaken, the wider societal and economic tolls of these developments are yet to be determined systematically. McCabe and colleagues' contribution towards a better understanding of the phenomenon of non-medical prescription drug use (as is true for most other forms of substance use) is that the individual trajectories often begin early in life (i.e. in teenage years) [7–9]. The authors interpret this association as causal and point to the implications for interventions. However, the results may simply reflect a life-time course in drug use-related deviance, which would necessitate distinct interventions from those assuming causal associations. There are a couple of other aspects that would have enhanced the analytical value of McCabe's study. First, their analyses hinge primarily upon the utilization of ‘life-time’ occurrence reports for both predictors and outcomes of non-medical prescription drug use. Such indicators tend to produce inflationary and biased results in favour of the putative ‘problem’ examined (just as someone running a red-light once in their life-time may hardly be labelled a dangerous traffic offender solely by this indicator) [10]. A second key question is: to what extent are the observed use trajectories from early to adult abuse associated with socio-economic, class or educational status? [11,12]. Relevant indicators are available in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) data set, and would have been of particular interest in the specific context of the study, given its focus on the use of medical (i.e. prescription) substances which for many (especially poor or uninsured) Americans may be less affordable or accessible by legitimate (i.e. medical) means [13,14]. However, McCabe's study also provides a useful basis to ‘push the envelope’ on urgently needed research in the emerging field of prescription drug abuse. A main challenge here is to overcome or enlighten empirically the fallacy of the widely accepted binary between ‘medical’ (i.e. legitimate) and ‘non-medical’ (i.e. illegitimate) prescription drug use [4]. Using the specific example of the McCabe et al. study, non-medical use is operationalized by incidents of use reported to have occurred without or outside explicit sanctioning or directives by the medical system. To assume that all (or even most) such use is ‘non-medical’, or occurring for reasons unrelated to the user's health, is probably erroneous and leading to false and practically irrelevant conclusions. In fact, many so-labelled ‘non-medical users’ of prescription drugs—e.g. those in disadvantaged circumstances regarding access to medical assistance, as mentioned above—may need to be understood more as ‘self-medicators’ of medically undiagnosed or untreated health conditions, whether these relate to pain, mental health or others, rather than ‘drug abusers’[15,16]. For example, the main reason for adolescents' and young adults' non-medical use of prescription opioids has indeed been shown to be pain (rather than the seeking of pleasure or psychoactive intoxication) [17,18], and evidence exists for histories of or presence of pain symptoms in adult non-medical opioid users [19]. In these contexts, many of the abusers of prescription drugs may be more of a stark testimony of inadequate systems or operations of medical care than an indicator of deviance or even illegal conduct (potentially to be followed by punitive interventions, as especially prominent in the United States). In order to inform sensible and adequate interventions, systematic and thorough user-focused inquiries are thus needed on the detailed motivations, dynamics and contexts of the growing phenomenon of ‘nonmedical prescription drug use’, in order to both inform our understanding and future interventions of this growing problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".