Comparing mental health semi-structured diagnostic interviews and symptom checklists to predict poor life outcomes: an 8-year cohort study from childhood to young adulthood in Brazil
Notice bibliographique
Résumé
BACKGROUND: Semi-structured diagnostic interviews and symptom checklists present similar internal reliability. We aim to investigate whether they differ in predicting poor life outcomes in the transition from childhood to young adulthood. METHODS: For this longitudinal study, we used data from the Brazilian High Risk Cohort Study for Childhood Mental Health Conditions. Eligible participants were aged 6-14 years on the day of study enrolment (January to February, 2010) and were enrolled in public schools by a biological parent in Porto Alegre and São Paulo, Brazil. 2511 young people and their caregivers were assessed at baseline in 2010-11, and 1917 were assessed 8 years later (2018-19; 76·3% retention). Clinical thresholds were derived using semi-structured parent-report interview based on the Diagnostic and Statistical Manual of Mental Disorders, according to the Developmental and Well-being Assessment (DAWBA), and clinical scores as defined by the Child Behavior Checklist (CBCL; T-score ≥70 considered positive caseness). At 8 years, participants were assessed for a composite life-threatening outcome (a composite of death, suicide attempts, severe self-harm, psychiatric inpatient admission, or emergency department visits) and a composite poor life chances outcome (a composite of any criminal conviction, substance misuse, or school dropout). We evaluated the accuracy of DAWBA and CBCL to predict these outcomes. Logistic regression models were adjusted for age, sex, race or ethnicity, study site, and socioeconomic class. FINDINGS: DAWBA and CBCL had similar sensitivity, specificity, predictive values, and test accuracy for both composite outcomes and their components. Any mental health problem, as classified by DAWBA and CBCL, was independently associated with the composite life-threatening outcome (DAWBA adjusted odds ratio 1·62, 95% CI 1·20-2·18; CBCL 1·66, 1·19-2·30), but only CBCL independently predicted poor life chances (1·56, 1·19-2·04). Participants classified by both approaches did not have higher odds of the life-threatening outcome when compared with participants classified by DAWBA or CBCL alone, nor for the poor life chances outcome when compared with those classified by CBCL alone. INTERPRETATION: Classifying children and adolescents based on a semi-structured diagnostic interview was not statistically different to symptom checklist in terms of test accuracy and predictive validity for relevant life outcomes. Classification based on symptom checklist might be a valid alternative to costly and time-consuming methods to identify young people at risk for poor life outcomes. FUNDING: Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; and Medical Research Council, European Research Council. TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.
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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 | 0,001 |
| 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.
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