Relation of substance use disorders to mortality, accident and emergency department attendances, and hospital admissions: A 13-year population-based cohort study in Hong Kong
Bibliographic record
Abstract
BACKGROUND: The impact of substance use disorders (SUD) in an Asian population has not been fully explored. We aimed to assess the risk of mortality, accident and emergency (A&E) department attendances, and hospital admissions associated with SUD in a population-based cohort study. METHOD: Patients diagnosed with SUD in public A&E departments from 2004 to 2016 (N = 8,423) were identified in the Clinical Database Analysis and Reporting System of the Hong Kong Hospital Authority and 1:1 matched to patients without SUD by propensity score (N = 6,074 in each group). Relative risks of mortality, A&E attendances and hospital admissions were assessed using Cox regression and Hurdle negative binomial regression. RESULTS: Patients with SUD had higher mortality (hazard ratio=1.43; 95% confidence interval [CI]=1.26-1.62) and more often died from poisoning or toxicity and injuries. The odds ratio (OR) for A&E attendances and all-cause hospital admissions associated with SUD were 2.80 (95% CI=2.58-3.04) and 3.54 (95% CI=3.26-3.83), respectively. The impact of SUD on the above outcomes was greatest among school-aged individuals (≤ 21 years) and decreased with age. The relative risk of mental disorder-related hospital admissions was much higher than that for infections, respiratory diseases, and cardiovascular diseases. In patients with SUD, ketamine and amphetamine use were associated with increased A&E attendances than opioid use. CONCLUSIONS: SUD was associated with increased mortality, A&E attendances and hospital admissions, especially in school-aged individuals. Our findings suggest prioritising early treatment and preventive interventions for school-aged individuals and focusing on the management of comorbid mental disorders and the use of ketamine and amphetamine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".