Global country-level network analysis of mental disorders
Résumé fourni par la source
Mental disorders are major contributors to the global burden of disease and may show correlated incidence patterns across countries. We characterized the country-level ecological network of age-standardized incidence rates for 10 mental disorders, identified statistically central disorders and examined how individual countries influenced estimated network connectivity. Data for 204 countries and territories, including Sociodemographic Index values, were obtained from the Global Burden of Disease Study 2023. A regularized network model using the mgm framework estimated conditional associations among country-level variables. Strength centrality and case-deletion delta-centrality analyses assessed disorder connectedness and country influence, with bootstrap procedures evaluating stability. Approximately 400 million incident cases of the studied disorders were estimated globally in 2023. Anorexia nervosa and bipolar disorder had the highest strength centrality, indicating broader conditional connections with other disorders. Australia, the United States, Iran, China and Finland showed relatively large delta-centrality values, suggesting greater influence on estimated centrality than most countries. Strength centrality stability was acceptable but modest. These findings provide a descriptive, hypothesis-generating view of how modeled incidence rates covary across countries and should not be interpreted as individual-level comorbidity, causal influence or direct policy priority.
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Contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Global country-level network analysis of mental disorders
- Date Crossref
- 01/01/2026
- Éditeur
- Cambridge University Press (CUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.
Institutions déclarées
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