Inequities in drug use and successful interventions: Umbrella study and analysis of emerging trends in Canada during the COVID-19 pandemic
Notice bibliographique
Résumé
Background: Current evidence suggests that certain subpopulations, such as ethnic or sexual minorities, bear a disproportional burden of population illicit drug use and related comorbidities. With drug use being a major risk factor for many chronic and acute diseases, addressing such an important health determinant could reduce health disparities prior to their clinical manifestation. This is especially relevant in the context of the stress and uncertainty associated with the COVID-19 pandemic, which, according to emerging research, may have led to increased drug use as a coping mechanism, potentially exacerbating sociodemographic disparities in drug use and associated comorbidities further. Given this context, it is pertinent to examine the effectiveness of illicit drug policies, and drug use trends during the COVID-19 pandemic with at-risk subpopulations and common risk factors in mind. Objectives: Firstly, this thesis aimed to assess the current state of evidence from systematic reviews pertaining to the effectiveness of illicit drug policy interventions among subpopulations, using the PROGRESS-Plus framework to classify relevant common disadvantage factors. A second objective was to investigate risk factors associated with an increase in both licit and illicit drug use and unmet need for care during the COVID-19 pandemic in Canada. The overarching objective of the thesis was to assess subpopulation representation in systematic reviews and identify those who faced the most excess drug use and service accessibility challenges during the COVID-19 pandemic in Canada. Methods: For the first objective, Manuscript 1 utilized umbrella review methodology. Three databases were searched for systematic reviews and meta-analyses. The data was double extracted, and quality of included studies was assessed using AMSTAR 2. Due to heterogeneity, the results were presented narratively in subgroups of relevant adapted PROGRESS-Plus domains. The second objective was addressed in Manuscript 2 using The Canadian Perspectives Survey Series 6 (CPSS6) from Statistics Canada and a set of binomial logistic regressions. Results: Firstly, our evidence suggests that targeted interventions, such as culturally adapted drug reduction and prevention programs, are more represented in systematic reviews of subpopulation-specific illicit drug intervention evaluations and appear to work better for most subgroups at-risk than universal interventions or standard treatment. Secondly, we found no existing systematic reviews that evaluate the effectiveness of drug policies for three domains: low social capital, disability, and unemployment, despite evidence suggesting greater risk of drug use among these subpopulations. Thirdly, our exploration of drug use change in Canada during COVID-19 showed that increased opioid and non-prescription drug use was significantly associated with lower mental health scores. Inability to access needed social services was associated with being in poor mental health and being single, divorced, or widowed, as opposed to being in excellent mental health, and living in a marital, or conjugal relationship. Discussion: Our findings suggested that subpopulations generally respond better to targeted, as opposed to universal interventions, pointing to the need to think about ‘targeting within universalism’, to ensure optimal population health outcomes. Most of the included reviews evaluated specific intervention types separately, as opposed to providing a comparative evaluation of targeted versus universal program effectiveness for different subgroups. Future research should aim to fill this gap. This is especially pressing as we emerge from the COVID-19 pandemic, which, as our initial findings suggest, may have disproportionately affected drug use trends and ability of certain subpopulations to find help for substance misuse
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 | 0,012 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,006 |
| Bibliométrie | 0,015 | 0,036 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».