Causes of death among people who used illicit opioids in England, 2001–18: a matched cohort study
Notice bibliographique
Résumé
BACKGROUND: In many countries, the average age of people who use illicit opioids, such as heroin, is increasing. This has been suggested to be a reason for increasing numbers of opioid-related deaths seen in surveillance data. We aimed to describe causes of death among people who use illicit opioids in England, how causes of death have changed over time, and how they change with age. METHODS: In this matched cohort study, we studied patients in the Clinical Practice Research Datalink with recorded illicit opioid use (defined as aged 18-64 years, with prescriptions or clinical observations that indicate use of illicit opioids) in England between Jan 1, 2001, and Oct 30, 2018. We also included a comparison group, matched (1:3) for age, sex, and general practice with no records of illicit opioid use before cohort entry. Dates and causes of death were obtained from the UK Office for National Statistics. The cohort exit date was the earliest of date of death or Oct 30, 2018. We described rates of death and calculated cause-specific standardised mortality ratios. We used Poisson regression to estimate associations between age, calendar year, and cause-specific death. FINDINGS: We collected data for 106 789 participants with a history of illicit opioid use, with a median follow-up of 8·7 years (IQR 4·3-13·5), and 320 367 matched controls with a median follow-up of 9·5 years (5·0-14·4). 13 209 (12·4%) of 106 789 participants in the exposed cohort had died, with a standardised mortality ratio of 7·72 (95% CI 7·47-7·97). The most common causes of death were drug poisoning (4375 [33·1%] of 13 209), liver disease (1272 [9·6%]), chronic obstructive pulmonary disease (COPD; 681 [5·2%]), and suicide (645 [4·9%]). Participants with a history of illicit opioid use had higher mortality rates than the comparison group for all causes of death analysed, with highest standardised mortality ratios being seen for viral hepatitis (103·5 [95% CI 61·7-242·6]), HIV (16·7 [9·5-34·9]), and COPD (14·8 [12·6-17·6]). In the exposed cohort, at age 20 years, the rate of fatal drug poisonings was 271 (95% CI 230-313) per 100 000 person-years, accounting for 59·9% of deaths at this age, whereas the mortality rate due to non-communicable diseases was 31 (16-45) per 100 000 person-years, accounting for 6·8% of deaths at this age. Deaths due to non-communicable diseases increased more rapidly with age (1155 [95% CI 880-1431] deaths per 100 000 person-years at age 50 years; accounting for 52·0% of deaths at this age) than did deaths due to drug poisoning (507 (95% CI 452-562) per 100 000 person-years at age 50 years; accounting for 22·8% of deaths at this age). Mirroring national surveillance data, the rate of fatal drug poisonings in the exposed cohort increased from 345 (95% CI 299-391) deaths per 100 000 person-years in 2010-12 to 534 (468-600) per 100 000 person-years in 2016-18; an increase of 55%, a trend that was not explained by ageing of participants. INTERPRETATION: People who use illicit opioids have excess risk of death across all major causes of death we analysed. Our findings suggest that population ageing is unlikely to explain the increasing number of fatal drug poisonings seen in surveillance data, but is associated with many more deaths due to non-communicable diseases. FUNDING: National Institute for Health Research.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».