Quality of life and its correlated factors among patients with substance use disorders: a systematic review and meta-analysis
Notice bibliographique
Résumé
Abstract Background Patients with substance use disorder (SUD) usually report lower quality of life (QoL) than other patients and as much as patients with other mental disorders. The present study investigated variables associated with QoL domains among patients with SUD. Methods Studies in English published before December 1st 2021, were searched for on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on factors associated with QoL domains among patients with SUD. After reviewing for study duplicates, the full-texts of selected papers were assessed for eligibility using PECO (Participants, Exposures, Comparison and Outcome) criteria: (a) participants: patients with SUD; (b) exposures: sociodemographic factors, clinical, and service use variables; (c) comparison: patient groups without SUD; and (d) outcomes: four domains of QoL (physical, mental, social, and environmental domains). Three researchers recorded the data independently using predefined Excel spreadsheets. The Newcastle–Ottawa Scale (NOS) was used for assessing risk of bias and rated each study in terms of exposure, outcome, and comparability. Pooled odds ratios (ORs) and β coefficient were utilized at a 95% confidence level, and because sampling methods differed between studies’ pooled estimates, a random effects model was utilized. Results After the assessment of over 10,230 papers, a total of 17 studies met the eligibility criteria. Five studies (1260 participants) found that patients with SUD who were older were less likely to have a good physical Qol (OR = 0.86, 95% CI = 0.78, 0.95). Two studies (1171 participants) indicated that patients with SUD who were homeless were less likely to have a good environmental Qol (β = -0.47, p = 0.003). However, a better mental QoL was observed in four studies (1126 participants) among those receiving support from their family or friends (social networks) (OR = 1.05, 95% CI = 1.04, 1.07). Two studies (588 participants) showed that those using cocaine were less likely to have a good mental QoL (OR = 0.83, 95% CI = 0.75, 0.93). Two studies (22,534 participants) showed that those using alcohol were less likely to have a good physical QoL (β = -2.21, p = 0.001). Two studies (956 participants) showed that those having severe substance use disorders were less likely to have a good mental (β = -5.44, p = 0.002) and environmental (β = -0.59, p = 0.006) QoL respectively. Four studies (3515 participants) showed that those having mental disorders were less likely to have a good physical QoL (β = -1.05, p = 0.001), and another three studies (1211 participants) that those having mental disorders were less likely to have a good mental QoL (β = -0.33, p = 0.001). Finally, two studies (609 and 682 participants) showed that individuals who experienced trauma symptoms or mental disorders were less likely to have good social and environmental QoL, respectively (OR = 0.78, 95% CI = 0.61, 1.00) and (OR = 0.92, 95% CI = 0.9, 0.94). Conclusions The findings suggest the need for mental health services to improve the QOL among patients with SUD but further study is needed. Cocaine may cause behavioral changes which can increase the possibility of reckless and suicidal behaviors. Therefore, identifying cocaine user access, adherence, and satisfaction with treatment is recommended as an important component of adaptive functioning. Interventions that help patients with SUD get support from people within their social networks who support their recovery are also essential to their QoL.
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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 | 0,015 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,020 | 0,041 |
| Bibliométrie | 0,011 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».