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Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model

BACKGROUND: Given the rapidly rising proportion of the older population in China and the relatively high prevalence of depressive symptoms among this population, this study aimed to identify the trajectories of depressive symptoms and the factors associated with the trajectory class to gain a better...

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Autores principales: Xie, Yaofei, Ma, Mengdi, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276362/
https://www.ncbi.nlm.nih.gov/pubmed/37328803
http://dx.doi.org/10.1186/s12877-023-04048-0
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author Xie, Yaofei
Ma, Mengdi
Wang, Wei
author_facet Xie, Yaofei
Ma, Mengdi
Wang, Wei
author_sort Xie, Yaofei
collection PubMed
description BACKGROUND: Given the rapidly rising proportion of the older population in China and the relatively high prevalence of depressive symptoms among this population, this study aimed to identify the trajectories of depressive symptoms and the factors associated with the trajectory class to gain a better understanding of the long-term course of depressive symptoms in this population. METHODS: Data were obtained from four wave’s survey of the China Health and Retirement Longitudinal Study (CHARLS). A total of 3646 participants who aged 60 years or older during baseline survey, and completed all follow-ups were retained in this study. Depressive symptoms were measured using the 10-item version of the Center for Epidemiologic Studies Depression Scale (CES-D-10). Growth mixture modelling (GMM) was adopted to identify the trajectory classes of depressive symptoms, and both linear and quadratic functions were considered. A multivariate logistic regression model was used to calculate the adjusted odds ratios (ORs) of the associated factors to predict the trajectory class of participants. RESULTS: A four-class quadratic function model was the best-fitting model for the trajectories of depressive symptoms in the older Chinese population. The four trajectories were labelled as increasing (16.70%), decreasing (12.31%), high and stable (7.30%), and low and stable (63.69%), according to their trends. Except for the low and stable trajectory, the other trajectories were almost above the threshold for depressive symptoms. The multivariate logistic regression model suggested that the trajectories of chronic depressive symptoms could be predicted by being female, living in a village (rural area), having a lower educational level, and having chronic diseases. CONCLUSIONS: This study identified four depressive symptom trajectories in the older Chinese population and analysed the factors associated with the trajectory class. These findings can provide references for prevention and intervention to reduce the chronic course of depressive symptoms in the older Chinese population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04048-0.
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spelling pubmed-102763622023-06-18 Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model Xie, Yaofei Ma, Mengdi Wang, Wei BMC Geriatr Research BACKGROUND: Given the rapidly rising proportion of the older population in China and the relatively high prevalence of depressive symptoms among this population, this study aimed to identify the trajectories of depressive symptoms and the factors associated with the trajectory class to gain a better understanding of the long-term course of depressive symptoms in this population. METHODS: Data were obtained from four wave’s survey of the China Health and Retirement Longitudinal Study (CHARLS). A total of 3646 participants who aged 60 years or older during baseline survey, and completed all follow-ups were retained in this study. Depressive symptoms were measured using the 10-item version of the Center for Epidemiologic Studies Depression Scale (CES-D-10). Growth mixture modelling (GMM) was adopted to identify the trajectory classes of depressive symptoms, and both linear and quadratic functions were considered. A multivariate logistic regression model was used to calculate the adjusted odds ratios (ORs) of the associated factors to predict the trajectory class of participants. RESULTS: A four-class quadratic function model was the best-fitting model for the trajectories of depressive symptoms in the older Chinese population. The four trajectories were labelled as increasing (16.70%), decreasing (12.31%), high and stable (7.30%), and low and stable (63.69%), according to their trends. Except for the low and stable trajectory, the other trajectories were almost above the threshold for depressive symptoms. The multivariate logistic regression model suggested that the trajectories of chronic depressive symptoms could be predicted by being female, living in a village (rural area), having a lower educational level, and having chronic diseases. CONCLUSIONS: This study identified four depressive symptom trajectories in the older Chinese population and analysed the factors associated with the trajectory class. These findings can provide references for prevention and intervention to reduce the chronic course of depressive symptoms in the older Chinese population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04048-0. BioMed Central 2023-06-16 /pmc/articles/PMC10276362/ /pubmed/37328803 http://dx.doi.org/10.1186/s12877-023-04048-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Xie, Yaofei
Ma, Mengdi
Wang, Wei
Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model
title Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model
title_full Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model
title_fullStr Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model
title_full_unstemmed Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model
title_short Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model
title_sort trajectories of depressive symptoms and their predictors in chinese older population: growth mixture model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276362/
https://www.ncbi.nlm.nih.gov/pubmed/37328803
http://dx.doi.org/10.1186/s12877-023-04048-0
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