Substances and substance combinations among accidental substance-related acute toxicity deaths (AATDs) in Canada from 2016 to 2017
Notice bibliographique
Résumé
BACKGROUND: Canada has seen a rise in substance-related accidental acute toxicity deaths (AATDs) in recent years. Research indicates that fentanyl opioids, non-fentanyl opioids, and stimulants are classes of concern and that multidrug AATDs have increased. However, there is limited information regarding the specific substances involved. This study aims to identify the substances and substance combinations as well as substance classes and substance class combinations most often involved in AATDs across Canada between 2016 and 2017. It also examines variations in substances by year and across sociodemographic, socioeconomic, and geographic factors. METHODS: Data were abstracted from the coroner and medical examiner files of all AATDs that occurred across Canada between 2016 and 2017. Top substances and classes detected in or contributing to AATDs were identified based on toxicology reports and cause of death statements. AATDs were stratified by year of death, age, sex, residence community type, neighbourhood income quintile, and province/region to understand variations in the substances contributing to AATDs. Combinations of substances and classes contributing to death were examined with UpSet plots and trends of select substances were visualized over time with ribbon charts. An algorithm was developed to report the source and origin of the substances based on prescription history and scene evidence. RESULTS: Fentanyl, cocaine, alcohol, and methamphetamine were the top substances contributing to the 7,902 AATDs identified between 2016 and 2017 in Canada. While stimulants and opioids were the most common substance classes contributing to AATDs, other classes, including benzodiazepines and acetaminophen also emerged as classes among the top contributors. Between 2016 and 2017, the proportion of AATDs attributable to diacetylmorphine (heroin) per quarter decreased while the proportion of AATDs attributable to carfentanil per quarter increased. AATDs involving more than one substance occurred across all sociodemographic, socioeconomic, and geographic groups. Substances contributing to AATDs more commonly originated from non-pharmaceutical sources than from pharmaceutical sources. CONCLUSIONS AND IMPACTS: Specific substances and substance combinations contributing to deaths vary over time and geographic areas. Opioids and stimulants are both detected in and contribute to a majority of AATDs, but the substance-related acute toxicity death crisis is complex and attributable to many substance classes. Understanding these differences will allow for targeted substance-related policies, prevention, and harm reduction efforts.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».