S241. FACTORS ASSOCIATED WITH EARLY RISK OF DISENGAGEMENT FROM EARLY PSYCHOSIS INTERVENTION SERVICES
Notice bibliographique
Résumé
Abstract Background Despite the body of evidence supporting early psychosis intervention (EPI) programs for young people with psychotic disorders, approximately 30% of individuals with first-episode psychosis disengage from care. To date, two factors, lack of family involvement and presence of a substance use disorder, have emerged as robust predictors of EPI disengagement. Several factors associated with service disengagement in mental health care more broadly have not been well-studied in EPI; some of these, such as homelessness and ethnicity, may be of particular importance to urban, multicultural populations, and ethnicity in particular has been shown to affect pathways into EPI services. Early missed appointments may signal risk for subsequent service disengagement. We sought to identify early predictors of disengagement risk in an urban EPI program. Methods We conducted a prospective chart review of consecutive patients accepted for services in a large, urban EPI program in Toronto, Canada in a 3-month period from July 4-October 3, 2018. Patients were observed in their first 3 months of treatment. The primary outcome of interest was risk of disengagement, defined as having missed at least 1 appointment without cancellation. Extracted data included a variety of demographic and clinical information. The principal investigator trained 2 data abstractors on the first 50 charts; subsequent agreement on the next 5 charts was 88%. Based on previous literature, we hypothesized that risk of disengagement would be increased in individuals with problem substance use, experiences of homelessness, and nonwhite race/ethnicity and decreased in individuals with family involvement in their care. We used logistic regression to examine the odds of disengagement associated with univariate predictors individually, and then together in a multivariate model. Results Seventy-three patients were consecutively admitted to EPI services in the 3-month period. Of these individuals, 59% (N=43) were identified as being at risk of disengagement based on having missed at least 1 appointment without cancellation. In the full sample, 71% (N=52) identified as nonwhite, 23% (N=17) had a documented experience of homelessness, 52% (N=38) had problem substance use, and 73% (N=53) had family involved in their care. In univariate logistic regression, only problem substance use was associated with risk of disengagement (OR=2.91, 95% CI 1.11–7.66); no significant associations were identified with race/ethnicity, experience of homelessness, or family involvement. In multivariate logistic regression, once we controlled for these other factors, the association between risk of disengagement and problem substance use was attenuated and no longer statistically significant (OR=2.15, 95% CI 0.77–5.97). Discussion In this small study of early disengagement in an urban EPI program, only problem substance use was associated with increased odds of missing an appointment, but not when we controlled for other factors thought to be associated with disengagement. Larger studies may be required to identify factors with small but important effects. These factors may be used to identify young people at risk of disengagement from EPI services early in care in order to target them for increased engagement efforts.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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) | 0,005 | 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 ».