Bridging diagnostic safety and mental health: a systematic review highlighting inequities in autism spectrum disorder diagnosis
Notice bibliographique
Résumé
INTRODUCTION: There is increased recognition that diagnostic errors disproportionately affect marginalised and underserved patient populations in the USA. However, evidence on diagnostic inequities in mental disorders is sparse and not well integrated into the overall diagnostic safety literature. OBJECTIVE: We systematically reviewed and narratively synthesised evidence on inequities in diagnosis of mental disorders, guided by the Diagnostic Process Framework developed by The National Academies of Sciences, Engineering, and Medicine. METHODS: We conducted a systematic review and a narrative synthesis. Medline, Embase, PsycInfo and CINAHL were searched for studies published between 2015 and 2024. Studies were eligible if they reported on inequities in the diagnosis of mental disorders and applied a quantitative, qualitative or mixed-methods design. Studies had to be peer reviewed, US based and published in English. The Mixed-Methods Appraisal Tool was used for quality appraisal. Data were analysed with a descriptive intent, and inequities were mapped into the diagnostic process. RESULTS: 20 studies of varying methodological quality were included. Though not the initial focus, autism spectrum disorder (ASD) emerged as the most studied mental disorder (n=17). Of the diagnostic errors identified, most fell into the category of delayed diagnosis. 11 factors emerged as contributors to diagnostic inequities. Limited health literacy among patients and caregivers was the leading cause of diagnostic error in symptom recognition. Insurance coverage issues delayed patient engagement with the healthcare system. Provider bias during clinical history-taking and interviewing was seen as a key cause of delays and misdiagnoses. Within diagnostic testing and interpretation, culturally inequivalent assessment measures might cause misdiagnosis and delayed diagnosis for Black/African American and Hispanic/Latino patients. The use of medical jargon and lack of qualified language interpreters during communicating the diagnosis were associated with diagnostic errors impacting patients with limited health literacy and low English language proficiency. CONCLUSIONS: Diagnostic inequities in ASD and other mental disorders persist across US patient populations. Multiple factors such as parental health literacy, provider bias and limited access interact and impact the diagnostic process. Addressing these interconnected barriers is essential to ensure timely, accurate and equitable care. PROSPERO REGISTRATION NUMBER: CRD42024581271.
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 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 | 0,010 | 0,199 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,001 |
| 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 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».