Patterns of association of chronic medical conditions and major depression
Notice bibliographique
Résumé
AIMS: Age and sex-related patterns of association between medical conditions and major depressive episodes (MDE) are important for understanding disease burden, anticipating clinical needs and for formulating etiological hypotheses. General population estimates are especially valuable because they are not distorted by help-seeking behaviours. However, even large population surveys often deliver inadequate precision to adequately describe such patterns. In this study, data from a set of national surveys were pooled to increase precision, supporting more precise characterisation of these associations. METHODS: The data were from a series of Canadian national surveys. These surveys used comparable sampling strategies and assessment methods for MDE. Chronic medical conditions were assessed using items asking about professionally diagnosed medical conditions. Individual-level meta-analysis methods were used to generate unadjusted, stratified and adjusted prevalence odds ratios for 11 chronic medical conditions. Random effects models were used in the meta-analysis. A procedure incorporating rescaled replicate bootstrap weights was used to produce 95% confidence intervals. RESULTS: Overall, conditions characterised by pain and inflammation tended to show stronger associations with MDE. The meta-analysis uncovered two previously undescribed patterns of association. Effect modification by age was observed in varying degrees for most conditions. This effect was most prominent for high blood pressure and cancer. Stronger associations were found in younger age categories. Migraine was an exception: the strength of association increased with age, especially in men. Second, especially for conditions predominantly affecting older age groups (arthritis, diabetes, back pain, cataracts, effects of stroke and heart disease) confounding by age was evident. For each condition, age adjustment resulted in strengthening of the associations. In addition to migraine, two conditions displayed distinctive patterns of association. Age adjusted odds ratios for thyroid disease reflected a weak association that was only significant in women. In epilepsy, a similar strength of association was found irrespective of age or sex. CONCLUSIONS: The prevalence of MDE is elevated in association with most chronic conditions, but especially those characterised by inflammation and pain. Effect modification by age may reflect greater challenges or difficulties encountered by young people attempting to cope with these conditions. This pattern, however, does not apply to migraine or epilepsy. Neurobiological changes associated with these conditions may offset coping-related effects, such that the association does not weaken with age. Prominent confounding by age for several conditions suggests that age adjustments are necessary in order to avoid underestimating the strength of these associations.
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 | 0,010 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,005 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».