Trends in Mortality Due to Stimulants Use in Adolescents and Young Adults
Bibliographic record
Abstract
Objectives: Stimulant medications including illegal use of Methamphetamine (MA) continues to rise in adolescents and young adults. This study aims to examine mortality trends because of the stimulant overdose in this age group (15 to 34 years). Methods: Age-adjusted mortality data, including 95% confidence intervals and standard errors, were extracted using publicly available multiple causes of death files from the United States Centers for Disease Control Wide-ranging ONline Data for Epidemiologic Research (WONDER). The data was filtered using International Classification of Disease (ICD-10) codes: F15.0 (Mental and behavioral disorders because of use of other stimulants, acute intoxication), F15.1 (Mental and behavioral disorders because of use of other stimulants, harmful use), T43.6 (Psychostimulants with abuse potential). The trends analysis for 1999 to 2019 was conducted using Joinpoint regression statistical software. Results: The mortality rate has been consistently increasing in the last decade across all races and ethnicities in adolescents and young adults. Non-Hispanic White population had the highest mortality rates (7.6 per 100,000 in 2019) compared with non-Hispanic Black (3.08 per 100,000 in 2019) and Hispanic population (3.33 per 100,000 in 2019). But the annual percent change in mortality was shown to be highest in non-Hispanic Black population (34.3% between 2009 and 2019). Conclusion: The increase in overall mortality rate because of stimulants use reflects the increase of MA use in this age group. The difference in the rate of change shows worsening racial inequality. Public health policies should be implemented to include evidence-based strategies to prevent MA misuse or overdose.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".