Causes of hospital admission and mortality among 6683 people who use heroin: A cohort study comparing relative and absolute risks
Bibliographic record
Abstract
BACKGROUND: Mortality in high-risk groups such as people who use illicit drugs is often expressed in relative terms such as standardised ratios. These measures are highest for diseases that are rare in the general population, such as hepatitis C, and may understate the importance of common long-term conditions. POPULATION: 6683 people in community-based treatment for heroin dependence between 2006 and 2017 in London, England, linked to national hospital and mortality databases with 55,683 years of follow-up. METHOD: Age- and sex-specific mortality and hospital admission rates in the general population of London were used to calculate the number of expected events. We compared standardised ratios (relative risk) to excess deaths and admissions (absolute risk) across ICD-10 chapters and subcategories. RESULTS: Drug-related diseases had the highest relative risks, with a standardised mortality ratio (SMR) of 48 (95% CI 42-54) and standardised admission ratio (SAR) of 293 (95% CI 282-304). By contrast, other diseases had an SMR of 4.4 (95% CI 4.0-4.9) and an SAR of 3.15 (95% CI 3.11-3.19). However, the majority of the 621 excess deaths (95% CI 569-676) were not drug-related (361; 58%). The largest groups were liver disease (75 excess deaths) and COPD (45). Similarly, 80% (11,790) of the 14,668 excess admissions (95% CI 14,382-14,957) were not drug-related. The largest groups were skin infections (1073 excess admissions), alcohol (1060), COPD (812) and head injury (612). CONCLUSIONS: Although relative risks of drug-related diseases are very high, most excess morbidity and mortality in this cohort was caused by common long-term conditions.
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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) | 0.001 | 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".