Opioid-related deaths during hospital admissions or shortly after discharge in the United Kingdom: A thematic framework analysis of coroner reports
Bibliographic record
Abstract
BACKGROUND: People who use heroin and other illicit opioids are at high risk of fatal overdose in the days after hospital discharge, but the reasons for this risk have not been studied. METHODS: We used the National Programme on Substance Abuse Deaths, a database of coroner reports for deaths following psychoactive drug use in England, Wales, and Northern Ireland. We selected reports where the death occurred between 2010 and 2021, an opioid was detected in toxicology testing, the death was related to nonmedical opioid use, and death was either during an acute medical or psychiatric hospital admission or within 14 days after discharge. We used thematic framework analysis of factors that may contribute to the risk of death during hospital admission or after discharge. RESULTS: We identified 121 coroners' reports; 42 where a patient died after using drugs during hospital admission, and 79 where death occurred shortly after discharge. The median age at death was 40 (IQR 34-46); 88 (73%) were male; and sedatives additional to opioids were detected at postmortem in 88 cases (73%), most commonly benzodiazepines. In thematic framework analysis, we categorised potential causes of fatal opioid overdose into three areas: (a) hospital policies and actions. Zero-tolerance policies mean that patients conceal drug use and use drugs in unsafe places such as locked bathrooms. Patients may be discharged to locations such as temporary hostels or the street while recovering. Some patients bring their own medicines or illicit opioids due to expectations of low-quality care, including undertreated withdrawal or pain; (b) high-risk use of sedatives. People may increase sedative use to manage symptoms of acute illness or a mental health crisis, and some may lose tolerance to opioids during a hospital admission; (c) declining health. Physical health and mobility problems posed barriers to post-discharge treatment for substance use, and some patients had sudden deteriorations in health that may have contributed to respiratory depression. CONCLUSION: Hospital admissions are associated with acute health crises that increase the risk of fatal overdose for patients who use illicit opioids. Hospitals need guidance to help them care for this patient group, particularly in relation to withdrawal management, harm reduction interventions such as take-home naloxone, discharge planning including continuation of opioid agonist therapy during recovery, management of poly-sedative use, and access to palliative care.
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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 | 0.030 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".