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 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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".