Amphetamine-related care in the USA, 2003–2014: cross-sectional analyses examining inpatient trends and factors associated with hospitalisation outcomes
Bibliographic record
Abstract
OBJECTIVES: Although amphetamine use is a growing health problem in the USA, there are limited data on amphetamine-related hospitalisations. The primary objective of our study was to examine trends in amphetamine-related hospitalisations in the USA between 2003 and 2014, including by age and sex. Our secondary objectives were to examine whether demographic, clinical and care setting characteristics were associated with select outcomes of amphetamine-related hospitalisations, including in-hospital mortality, prolonged length of stay and leaving against medical advice. DESIGN, SETTING AND PARTICIPANTS: Using the 2003-2014 National Inpatient Sample, we estimated the rate of amphetamine-related hospitalisations for each year in the USA among individuals 18+ years of age, stratified by age and sex. Subgroup analyses examined hospitalisations due to amphetamine causes. Unconditional logistic regression modelling was used to estimate the adjusted odds of admission outcomes for sociodemographic, clinical and hospital indicators. PRIMARY AND SECONDARY OUTCOMES: Our primary outcome was amphetamine-related hospitalisations between 2003 and 2014; secondary outcomes included in-hospital mortality, prolonged length of stay and leaving against medical advice. RESULTS: Amphetamine-related hospitalisation rates increased from 27 to 69 per 100 000 population between 2003 and 2014. Annual rates were consistently greater among younger (18-44 years) individuals and men. Regional differences were observed, with admission to Western hospitals being associated with increased mortality (adjusted OR, AOR 5.07, 95% CI 1.22 to 21.04) and shorter (0-2 days) lengths of stay (AOR 0.70, 95% CI 0.58 to 0.83) compared with Northeast admissions. Males (AOR 1.26, 95% CI 1.15 to 1.38; compared with females) and self-pay (AOR 2.30, 95% CI 1.90 to 2.79; compared with private insurance) were associated with leaving against medical advice. CONCLUSIONS: Increasing rates of amphetamine-related hospitalisation risk being overshadowed by other public health crises. Regional amphetamine interventions may offer the greatest population health benefits. Future studies should examine long-term outcomes among patients hospitalised for amphetamine-related causes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".