Predicting Depressive and Anxiety Symptoms Among Lebanese and Syrian adults in a Suburb of Beirut during the Concurrent Crises: A Population-Based Study
Notice bibliographique
Résumé
Abstract Background People living in low socioeconomic conditions are more prone to depression and anxiety. This study aimed to develop and internally validate prediction models for depressive and anxiety symptoms in Lebanese adults and Syrian refugees residing in a suburb of Beirut, Lebanon. Methods This was a population-based study among COVID-19 vulnerable adults in low socioeconomic neighborhoods in Sin-El-Fil, Lebanon. Data were collected through a telephone survey between June and October 2022. The outcomes depressive and anxiety symptoms were investigated for Lebanese and Syrian populations. Depressive and anxiety symptoms were defined as having a PHQ-9 and GAD-7 score of 10 or more respectively. Outcomes’ predictors were identified through LASSO regression, discrimination and model calibrations were assessed using area under curve (AUC) and C-Slope. Results Of 2,045 participants, 1,322 were Lebanese, 664 were Syrian, and 59 were from other nationalities. Among Lebanese and Syrian populations, 25.3% and 43.9% had depressive symptoms, respectively. Additional predictors for depressive symptoms were not attending school, not feeling safe at all at home, and not having someone to count on in times of difficulty. Not having legal residency documentation for Syrian adults was a context-specific predictor for depressive symptoms. These predictors were similar to that of anxiety symptoms. Both Lebanese and Syrian models had good discriminations and excellent calibrations. Conclusion This study highlights the main predictors of poor mental health were financial, health, and social indicators for both Lebanese and Syrian adults during the concurrent crisis in Lebanon. Findings emphasise social protection and financial support are required in populations with low socioeconomic status. Research in context What is already known on this topic The prevalence of depression and anxiety has increased globally. Vulnerable populations, such as refugees and populations of low socioeconomic status, are more prone to depression and anxiety. What this study adds This study included Lebanese and Syrian adults residing in low socioeconomic status areas of Sin-El-Fil, Lebanon. This is a population-based comparison of the predictors to poor mental health in Lebanon between refugees and Lebanese. The study highlights the need to meet financial, physical, and social needs of individuals to address mental health. How this study might affect research, practice, or policy The findings of this study highlight the need to reduce financial stress, address physical pain and social isolation, and advocate for Syrian residency documentation to reduce the occurrence of anxiety and depressive symptoms in people living in low socioeconomic conditions.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».