Trends in Medical and Nonmedical Use of Prescription Opioids Among US Adolescents: 1976–2015
Bibliographic record
Abstract
OBJECTIVES: Most US studies of national trends in medical and nonmedical use of prescription opioids have focused on adults. Given the limited understanding in these trends among adolescents, we examine national trends in the medical and nonmedical use of prescription opioids among high school seniors between 1976 and 2015. METHODS: The data used for the study come from the Monitoring the Future study of adolescents. Forty cohorts of nationally representative samples of high school seniors (modal age 18) were used to examine self-reported medical and nonmedical use of prescription opioids. RESULTS: Lifetime prevalence of medical use of prescription opioids peaked in both 1989 and 2002 and remained stable until a recent decline from 2013 through 2015. Lifetime nonmedical use of prescription opioids was less prevalent and highly correlated with medical use of prescription opioids over this 40-year period. Adolescents who reported both medical and nonmedical use of prescription opioids were more likely to indicate medical use of prescription opioids before initiating nonmedical use. CONCLUSIONS: Prescription opioid exposure is common among US adolescents. Long-term trends indicate that one-fourth of high school seniors self-reported medical or nonmedical use of prescription opioids. Medical and nonmedical use of prescription opioids has declined recently and remained highly correlated over the past 4 decades. Sociodemographic differences and risky patterns involving medical and nonmedical use of prescription opioids should be taken into consideration in clinical practice to improve opioid analgesic prescribing and reduce adverse consequences associated with prescription opioid use among adolescents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".