EXAMINING THE RELATIONSHIP BETWEEN SOCIODEMOGRAPHIC FACTORS AND MENTAL HEALTH IN CHILDHOOD-ONSET SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
O068 / #494 Topic:AS18 - Pediatric SLE ABSTRACT CONCURRENT SESSION 12: PEDIATRIC SLE – ADVANCES IN DISEASE OUTCOMES AND MENTAL HEALTH 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Childhood-onset systemic lupus erythematosus (cSLE) is a chronic autoimmune disease with significant adverse impact on mental health. Furthermore, patients with cSLE often face health disparities due to marginalization involving individual-level race and ethnicity, household-level and neighborhood-level socioeconomic factors. We aimed to understand the impact of these multilevel sociodemographic factors for marginalization on mental health in youth with cSLE. Methods We conducted a retrospective cross-sectional cohort study of publicly insured cSLE patients (9-18 years) recruited from an outpatient lupus clinic in Ontario, Canada from October 2017-December 2023. All patients met ACR or SLICC criteria for SLE classification. The exposure of marginalization included measures for individual race and ethnicity, household low-income status, and neighborhood-level Ontario Marginalization Index (material resources, racialized and newcomer population dimensions). Mental health outcomes included the presence of clinically elevated anxiety symptoms, measured by the Screen for Childhood Anxiety Related Disorders (SCARED), and depression symptoms, measured by the Center for Epidemiologic Studies Depression Scale for Children (CES-DC) or Children’s Depression Inventory/Beck Depression Inventory (CDI/BDI). Logistic regression models examined associations between the marginalization exposure variables and the mental health outcomes, adjusting for age, sex, disease duration and activity. Results 100 cSLE patients were included. Marginalization characteristics are shown in Figure 1. 27% of the cohort lived in low-income households, and 52% lived in areas with the highest density of racialized and newcomer populations. Symptoms for anxiety were present in 43% and depression in 40%. Patients living in the most marginalized quintile of neighborhood material resources had higher odds of depressive symptoms compared to those in more material resourced neighborhoods (OR=4.2, 95% CI 1.2-13.9, p=0.02, Table 2). No other significant associations were observed for depressive symptoms, and no associations were found for anxiety symptoms. Figure 1: Shown are marginalization characteristics and mental health outcomes for the cSLE cohort (n=100). The “Other” race and ethnic category included individuals identifying as Indigenous, Middle Eastern, Southeast Asian, Latin American or multiethnicity. Table 2: Multivariable Logistic Regression Model for Association between Marginalization, Depression, and Anxiety Symptoms Conclusions In a publicly insured Canadian cohort of youth with cSLE, we found that those living in neighborhoods with the lowest material resources were at highest risk for depression symptoms. Further research into other social determinants of health is essential to improve mental health support for youth with cSLE from diverse socioeconomic backgrounds.
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 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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,002 | 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 ».