Patterns and predictors of 12-month treatment of common anxiety, mood, and substance use disorders in the World Mental Health (WMH) surveys: treatment in the context of perceived need
Notice bibliographique
Résumé
BACKGROUND: Data from the World Mental Health (WMH) surveys on the coverage cascade has underscored the importance of perceived need for seeking treatment of mental disorders. However, little research has focused on treatment contact after adjusting for perceived need. We do so here in analysis of WMH data. METHODS: The WMH data considered here come from 25 community surveys implemented between 2001 and 2019 across 21 countries. n = 12,508 of the n = 117,739 respondents in these surveys aged 18 and older met criteria for one or more 12-month DSM-IV anxiety, mood, or substance use disorders assessed across all these surveys. Information was obtained about 12-month treatment of each disorder. The predictors considered were disorder type, socio-demographics, and history of prior treatment. RESULTS: Twelve-month treatment was obtained for 17.7% of the n = 18,702 12-month person-disorders in the sample, including 34.1% for the 46.5% with perceived need and 3.5% for the 54.5% without perceived need. After adjusting for perceived need, receiving treatment was most strongly associated with disorder characteristics (severity, and highest for major depressive, panic/agoraphobia, and generalized anxiety disorders; lowest for substance use disorders), health insurance, employment status (highest for students, the retired, and the unemployed/disabled), and several aspects of prior treatment. These associations were generally similar in cases with and without perceived need for treatment. 12-month treatment among cases who without perceived need and without history of prior treatment was rare (1.1%). CONCLUSIONS: Findings highlight the critical importance of perceived need for obtaining 12-month treatment in the context of other significant predictors involving complexity and severity of disorders and socio-demographic factors. The importance of prior treatment history was quite striking, as was the finding that absence of both perceived need and prior treatment history were associated with a nearly complete absence of treatment. Policy recommendations emerging from these results include the need to increase health literacy, reduce the stigmatization of mental disorder, enhance access through health insurance, and improve the quality of care given the clear evidence that prior experiences with treatment play an important role in determining the likelihood of again seeking treatment for current problems.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».