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Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study

BACKGROUND: Depression and anxiety are common mental health conditions in the older adult population. Understanding the trajectories of these will help implement treatments and interventions. AIMS: This study aims to identify depression and anxiety trajectories in older adults, evaluate the interrel...

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Autores principales: Cheng, Yanzhao, Thorpe, Lilian, Kabir, Rasel, Lim, Hyun Ja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515663/
https://www.ncbi.nlm.nih.gov/pubmed/34645416
http://dx.doi.org/10.1186/s12877-021-02501-6
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author Cheng, Yanzhao
Thorpe, Lilian
Kabir, Rasel
Lim, Hyun Ja
author_facet Cheng, Yanzhao
Thorpe, Lilian
Kabir, Rasel
Lim, Hyun Ja
author_sort Cheng, Yanzhao
collection PubMed
description BACKGROUND: Depression and anxiety are common mental health conditions in the older adult population. Understanding the trajectories of these will help implement treatments and interventions. AIMS: This study aims to identify depression and anxiety trajectories in older adults, evaluate the interrelationship of these conditions, and recognize trajectory-predicting characteristics. METHODS: Group-based dual trajectory modeling (GBDTM) was applied to the data of 3983 individuals, aged 65 years or older who participated in the Korean Health Panel Study between 2008 and 2015. Logistic regression was used to identify the association between characteristics and trajectory groups. RESULTS: Four trajectory groups from GBDTM were identified within both depression and anxiety outcomes. Depression outcome fell into “low-flat (87.0%)”, “low-to-middle (8.8%)”, “low-to-high (1.3%)” and “high-stable (2.8%)” trajectory groups. Anxiety outcome fell into “low-flat (92.5%)”, “low-to-middle (4.7%)”, “high-to-low (2.2%)” and “high-curve (0.6%)” trajectory groups. Interrelationships between depression and anxiety were identified. Members of the high-stable depression group were more likely to have “high-to-low” or “high-curved” anxiety trajectories. Female sex, the presence of more than three chronic diseases, and being engaged in income-generating activity were significant predictors for depression and anxiety. CONCLUSIONS: Dual trajectory analysis of depression and anxiety in older adults shows that when one condition is present, the probability of the other is increased. Sex, having more than three chronic diseases, and not being involved in income-generating activity might increase risks for both depression and anxiety. Health policy decision-makers may use our findings to develop strategies for preventing both depression and anxiety in older adults. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-021-02501-6.
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spelling pubmed-85156632021-10-20 Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study Cheng, Yanzhao Thorpe, Lilian Kabir, Rasel Lim, Hyun Ja BMC Geriatr Research BACKGROUND: Depression and anxiety are common mental health conditions in the older adult population. Understanding the trajectories of these will help implement treatments and interventions. AIMS: This study aims to identify depression and anxiety trajectories in older adults, evaluate the interrelationship of these conditions, and recognize trajectory-predicting characteristics. METHODS: Group-based dual trajectory modeling (GBDTM) was applied to the data of 3983 individuals, aged 65 years or older who participated in the Korean Health Panel Study between 2008 and 2015. Logistic regression was used to identify the association between characteristics and trajectory groups. RESULTS: Four trajectory groups from GBDTM were identified within both depression and anxiety outcomes. Depression outcome fell into “low-flat (87.0%)”, “low-to-middle (8.8%)”, “low-to-high (1.3%)” and “high-stable (2.8%)” trajectory groups. Anxiety outcome fell into “low-flat (92.5%)”, “low-to-middle (4.7%)”, “high-to-low (2.2%)” and “high-curve (0.6%)” trajectory groups. Interrelationships between depression and anxiety were identified. Members of the high-stable depression group were more likely to have “high-to-low” or “high-curved” anxiety trajectories. Female sex, the presence of more than three chronic diseases, and being engaged in income-generating activity were significant predictors for depression and anxiety. CONCLUSIONS: Dual trajectory analysis of depression and anxiety in older adults shows that when one condition is present, the probability of the other is increased. Sex, having more than three chronic diseases, and not being involved in income-generating activity might increase risks for both depression and anxiety. Health policy decision-makers may use our findings to develop strategies for preventing both depression and anxiety in older adults. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-021-02501-6. BioMed Central 2021-10-13 /pmc/articles/PMC8515663/ /pubmed/34645416 http://dx.doi.org/10.1186/s12877-021-02501-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Cheng, Yanzhao
Thorpe, Lilian
Kabir, Rasel
Lim, Hyun Ja
Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study
title Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study
title_full Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study
title_fullStr Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study
title_full_unstemmed Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study
title_short Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study
title_sort latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515663/
https://www.ncbi.nlm.nih.gov/pubmed/34645416
http://dx.doi.org/10.1186/s12877-021-02501-6
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