Identification of Mood Disorders in Self‐Reported Versus Health Administrative Data
Notice bibliographique
Résumé
BACKGROUND: Producing relevant knowledge on the prevalence of mood disorders (MDs) requires a clear identification of people living with the condition. Analyzing this multifaceted disease from the perspective of health administrative data and population-based surveys could contribute to document inconsistencies between these data sources and highlight the strengths and limitations of each methodological approaches. OBJECTIVES: The aim of this study was to estimate the prevalence of MD disease, assess concordance of MD patterns in population-based surveys versus health administrative data, and investigate statistical differences in characteristics between individuals presenting the disease in each data sources. METHODS: This study used the Care Trajectories-Enriched Data (TorSaDE) cohort. The TorSaDE cohort is built by merging five waves of the Canadian Community Health Survey (CCHS) with health administrative data of the province of Quebec, Canada. The sample includes individuals who participated in at least one round of CCHS and for whom evidence of use of health services in the year of CCHS completion and the year before were present in health administrative data. The cohort was split into four groups based on the presence and absence of MD in self-reported versus health administrative data. Groups' characteristics were compared using chi-square tests and ANOVA. RESULTS: The study cohort was composed of 96,079 individuals, of which 10,418 (10.8%) had MD, regardless of the data sources. Self-reported prevalence of MD was 6.03%, while the prevalence from health administrative data was about 7.79%. Estimates showed a low level of concordance between the two measures, as only 27.4% of people presenting this medical condition were identified in both data sources. Furthermore, individuals identified with MD only in survey data had poorer socioeconomic outcomes but better health outcomes than those from the concordant group (i.e., identified in both data sources). In addition, people presenting MD in health administrative data only had better socioeconomic and health outcomes than those who reported MD diagnosis only in survey data. CONCLUSION: Findings suggest that each measure capture different specific subpopulations. Estimates obtained from each source should thus be contextualized and interpreted with caution.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».