Health Reporting from Different Data Sources: Does it Matter for Mental Health?
Notice bibliographique
Résumé
BACKGROUND: Mental disorders are typically stigmatized conditions associated with negative stereotypes, which may lead individuals to underreport them. Thus, survey data may be subject to biases. Although administrative data has some limitations, it is an alternative data source that may be considered more objective. AIMS OF THE STUDY: This study aimed to identify the degree of agreement between survey and administrative health care data for mental health conditions, factors affecting underreporting, and whether underreporting also occurs for physical health conditions. METHODS: We used Ontario data from the Canadian Community Health Survey linked to health records to examine the presence of mental health conditions (i.e., schizophrenia and mood disorders) and select physical health conditions (i.e., diabetes and cancer). Using administrative data as the reference standard, we created four categories for each health condition based on the level of agreement between the two data sources: consistent cases and non-cases (i.e. individuals with concordant data based on their reported health condition), and people who were found to underreport and overreport a condition (i.e. where the condition was present in the administrative data, but not in the survey data and vice-versa, respectively). The overall level of agreement was assessed using Cohen's kappa statistic. Probit regressions were estimated to determine the factors affecting underreporting. RESULTS: The Kappa statistics for mood disorder was fair (k= 0.26) and moderate for schizophrenia (k = 0.49). Physical health conditions had higher kappa values (diabetes, k = 0.81; ever having cancer, k = 0.68), with the exception of currently having cancer (k = 0.24). Underreporting was highest for the most stigmatizing condition, schizophrenia (63%), followed by mood disorders (39%) and cancer (39%), and lowest for diabetes (25%). Older age, being born in Africa and Asia, and being employed all increased the probability of underreporting among individuals identified in the administrative data; the opposite held for social assistance. DISCUSSION: We extended previous work on mental health reporting by combining survey data with administrative data to examine the level of agreement between respondents' self-reported mental health and administrative records. The data include some mental disorders not studied previously. We examined the entire adult population; this is important because prevalence of schizophrenia may be less common among older population groups due to higher mortality among this patient population. Additionally, there may be potential age-related differences in stigma and mental health conditions. The administrative health data captured only health services covered by the public provincial health insurance plan and thus did not capture medical care provided by psychologists, social workers, and nurses. While this would affect Kappa statistic values, it does not directly affect the underreporting analyses. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: Our results suggest that disclosure of mental health conditions may differ by the level of stigma, which has implications for obtaining accurate estimates of mental health prevalence from self-reported data sources.
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 | 0,433 | 0,701 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,007 | 0,019 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».