{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:34:47Z","timestamp":1762324487672,"version":"3.40.5"},"reference-count":42,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:00:00Z","timestamp":1557878400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:00:00Z","timestamp":1557878400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["www.cambridge.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol Psychiatr Sci"],"published-print":{"date-parts":[[2020]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S204579601900026X_sec_a1\"><jats:title>Aims<\/jats:title><jats:p>The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and\/or anxiety symptomatology. In order to do this without<jats:italic>a priori<\/jats:italic>assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach.<\/jats:p><\/jats:sec><jats:sec id=\"S204579601900026X_sec_a2\" sec-type=\"methods\"><jats:title>Methods<\/jats:title><jats:p>A large community sample from the Canadian general population (<jats:italic>N<\/jats:italic>= 254 443) was divided into data-driven clusters using non-parametric<jats:italic>k<\/jats:italic>-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared.<\/jats:p><\/jats:sec><jats:sec id=\"S204579601900026X_sec_a3\" sec-type=\"results\"><jats:title>Results<\/jats:title><jats:p>A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness).<\/jats:p><\/jats:sec><jats:sec id=\"S204579601900026X_sec_a4\" sec-type=\"conclusion\"><jats:title>Conclusion<\/jats:title><jats:p>Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.<\/jats:p><\/jats:sec>","DOI":"10.1017\/s204579601900026x","type":"journal-article","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T08:50:20Z","timestamp":1557910220000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":1,"title":["Subtyping psychological distress in the population: a semi-parametric network approach"],"prefix":"10.1017","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-4959","authenticated-orcid":false,"given":"S.","family":"de Vos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Patten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. 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