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Article: Trajectory Pathways for Depressive Symptoms and Their Associated Factors in a Chinese Primary Care Cohort by Growth Mixture Modelling

TitleTrajectory Pathways for Depressive Symptoms and Their Associated Factors in a Chinese Primary Care Cohort by Growth Mixture Modelling
Authors
Issue Date2016
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
Citation
PLoS One, 2016, v. 11 n. 2, article no. e0147775, p. 1-15 How to Cite?
AbstractBackground: The naturalistic course for patients suffering from depressive disorders can be quite varied. Whilst some remit with little or no intervention, others may suffer a more prolonged course of symptoms. The aim of this study was to identify trajectory patterns for depressive symptoms in a Chinese primary care cohort and their associated factors. Methods and Results: A 12-month cohort study was conducted. Patients recruited from 59 primary care clinics across Hong Kong were screened for depressive symptoms using the Centre for Epidemiologic Studies Depression Scale (CES-D) and monitored over 12 months using the Patient Health Questionnaire-9 items (PHQ-9) administered at 12, 26 and 52 weeks. 721 subjects were included for growth mixture modelling analysis. Using Akaike Information Criterion, Bayesian Information Criterion, Entropy and Lo-Mendell-Rubin adjusted likelihood ratio test, a seven-class trajectory path model was identified. Over 12 months, three trajectory groups showed improvement in depressive symptoms, three remained static, whilst one deteriorated. A mild severity of depressive symptoms with gradual improvement was the most prevalent trajectory identified. Multivariate, multinomial regression analysis was used to identify factors associated with each trajectory. Risk factors associated with chronicity included: female gender; not married; not in active employment; presence of multiple chronic disease co-morbidities; poor self-rated general health; and infrequent health service use. Conclusions: Whilst many primary care patients may initially present with a similar severity of depressive symptoms, their course over 12 months can be quite heterogeneous. Although most primary care patients improve naturalistically over 12 months, many do not remit and it is important for doctors to be able to identify those who are at risk of chronicity. Regular follow-up and greater treatment attention is recommended for patients at risk of chronicity.
Persistent Identifierhttp://hdl.handle.net/10722/249114
ISSN
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 0.839
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChin, WY-
dc.contributor.authorChoi, PH-
dc.contributor.authorWan, YF-
dc.date.accessioned2017-10-27T05:59:08Z-
dc.date.available2017-10-27T05:59:08Z-
dc.date.issued2016-
dc.identifier.citationPLoS One, 2016, v. 11 n. 2, article no. e0147775, p. 1-15-
dc.identifier.issn1932-6203-
dc.identifier.urihttp://hdl.handle.net/10722/249114-
dc.description.abstractBackground: The naturalistic course for patients suffering from depressive disorders can be quite varied. Whilst some remit with little or no intervention, others may suffer a more prolonged course of symptoms. The aim of this study was to identify trajectory patterns for depressive symptoms in a Chinese primary care cohort and their associated factors. Methods and Results: A 12-month cohort study was conducted. Patients recruited from 59 primary care clinics across Hong Kong were screened for depressive symptoms using the Centre for Epidemiologic Studies Depression Scale (CES-D) and monitored over 12 months using the Patient Health Questionnaire-9 items (PHQ-9) administered at 12, 26 and 52 weeks. 721 subjects were included for growth mixture modelling analysis. Using Akaike Information Criterion, Bayesian Information Criterion, Entropy and Lo-Mendell-Rubin adjusted likelihood ratio test, a seven-class trajectory path model was identified. Over 12 months, three trajectory groups showed improvement in depressive symptoms, three remained static, whilst one deteriorated. A mild severity of depressive symptoms with gradual improvement was the most prevalent trajectory identified. Multivariate, multinomial regression analysis was used to identify factors associated with each trajectory. Risk factors associated with chronicity included: female gender; not married; not in active employment; presence of multiple chronic disease co-morbidities; poor self-rated general health; and infrequent health service use. Conclusions: Whilst many primary care patients may initially present with a similar severity of depressive symptoms, their course over 12 months can be quite heterogeneous. Although most primary care patients improve naturalistically over 12 months, many do not remit and it is important for doctors to be able to identify those who are at risk of chronicity. Regular follow-up and greater treatment attention is recommended for patients at risk of chronicity.-
dc.languageeng-
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action-
dc.relation.ispartofPLoS ONE-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleTrajectory Pathways for Depressive Symptoms and Their Associated Factors in a Chinese Primary Care Cohort by Growth Mixture Modelling-
dc.typeArticle-
dc.identifier.emailChin, WY: chinwy@hku.hk-
dc.identifier.emailChoi, PH: ephchoi@hku.hk-
dc.identifier.emailWan, YF: yfwan@hku.hk-
dc.identifier.authorityChin, WY=rp00290-
dc.identifier.authorityChoi, PH=rp02329-
dc.identifier.authorityWan, YF=rp02518-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0147775-
dc.identifier.pmid26829330-
dc.identifier.pmcidPMC4734622-
dc.identifier.scopuseid_2-s2.0-84958811330-
dc.identifier.hkuros263670-
dc.identifier.volume11-
dc.identifier.issue2-
dc.identifier.spagearticle no. e0147775, p. 1-
dc.identifier.epagearticle no. e0147775, p. 15-
dc.identifier.isiWOS:000369548200033-
dc.publisher.placeUnited States-
dc.identifier.issnl1932-6203-

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