Trajectories of Depression and Their Predictors in a Population-Based Study of Korean Older Adults

Lim, Hyun J.; Cheng, Yanzhao; Kabir, Rasel; Thorpe, Lilian

doi:10.1177/0091415020944405

Cited by 7 publications

(10 citation statements)

References 26 publications

(71 reference statements)

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“…This is also supported by previous studies (27,28). Most chronic diseases have a long course and may even require lifelong treatment, affecting their quality of life and increasing the financial burden on families, which may trigger and exacerbate the onset and progression of depression (29). Furthermore, this study found that sleep duration 6-8 h and >8 h were protective factors against depressive symptoms in older adults in both urban and rural areas, relative to sleep duration <6 h. It has been confirmed that adjusting sleep duration decreases the incidence of depressive symptoms (30).…”

Section: Discussion Risk Factors a Ecting Depressive Symptoms In Th...supporting

confidence: 80%

Association between specific social activities and depressive symptoms among older adults: A study of urban-rural differences in China

Han

Moreira

et al. 2023

Front. Public Health

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BackgroundEngaging in social activities can help older persons with their depressed symptoms. Few studies, however, have looked into the connection between social interactions and depressed symptoms in Chinese older persons. The aim of this study was to investigate differences in older Chinese individuals' social activity involvement and depressive symptoms across urban and rural settings.MethodsA cross-sectional investigation using information from the 2018 China Health and Retirement Longitudinal Study (CHARLS), which was limited to older individuals aged 60 and over. Generalized linear models were constructed to assess the effects of participants' characteristics and specific social activities on CES-D scores. The association between specific social activities and depressed symptoms was investigated using multivariate logistic regression analysis.ResultsIn this study, it was discovered that older individuals had a prevalence of depressed symptoms of 36.2%, with rural older adults having a greater prevalence of depressive symptoms (39.7%) than urban older adults (30.9%). Our results showed that for urban respondents, providing help to others (not regularly. OR = 0.753, 95% CI: 0.579–0.980, P = 0.035), going to a sport (not regularly. OR = 0.685, 95% CI: 0.508–0.924, P = 0.013), and using the Internet (not regular. OR = 0.613, 95% CI: 0.477–0.789, P < 0.001; almost weekly. OR = 0.196, 95% CI: 0.060–0.645, P = 0.007) were all significantly and negatively associated with depressive symptoms, while for rural respondents, interacting with friends (not regularly. OR = 1.205, 95% CI: 1.028–01.412, P = 0.021) and using the Internet (not regularly. OR = 0.441, 95% CI: 0.278–0.698, P < 0.001) were significantly and negatively associated with depressive symptoms.ConclusionsAccording to our research, there is a cross-sectional relationship between participating in a specific social activity and depressed symptoms in Chinese older adults, and this relationship varies across urban and rural older adults. This suggests that taking part in specific social activities may be crucial for reducing depression symptoms in older persons, developing more focused interventions that might support healthy aging, and offering a guide for policymakers and activists working to improve the mental health of seniors.

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Section: Discussion Risk Factors a Ecting Depressive Symptoms In Th...supporting

confidence: 80%

Association between specific social activities and depressive symptoms among older adults: A study of urban-rural differences in China

Han

Moreira

et al. 2023

Front. Public Health

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“…The KHPS is an official database carried out by the Korea Institute for Health and Social Affairs and the National Health Insurance Service since 2008. More detailed descriptions of the study design and profile have been published elsewhere 16 . Briefly, the purpose of KHPS is “to generate basic data on individual healthcare behaviors, health level, usage of health services, and healthcare expenditures”.…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of machine learning approaches for predicting frequent emergency department visits

Safaripour

Lim

2022

Health Informatics J

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Background Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users. Method Korean Health Panel data from 2008 to 2015 were used for this study. Individuals with four or more visits per year were considered frequent ED users. Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) as well as two ensemble models, namely Bagging and Voting, were trained and tested to examine their predictive performance. Results The ML classification algorithms identified frequent ED users with high precision (90%–98%) and sensitivity (87%–91%), whereas LR showed fair precision (65%) and sensitivity (67%). The ML algorithms showed a high area under the curve (AUC) values from 89% for SVM to 96% for Random Forest, while LR showed the lowest AUC (65%). The classification error varied among algorithms; LR had the highest classification error (24.07%) while RF had the least (3.8%). Conclusions Results show that ML classification algorithms are robust techniques to predict frequent ED users, and the variables in administrative health panels are reliable indicators for this purpose.

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“…More detailed descriptions of the study design and profile have been published elsewhere [ 48 ] . Briefly, KHPS used a stratified sampling frame taken from the Korean Population and Housing Census in 2005.…”

Section: Methodsmentioning

confidence: 99%

“…Covariates included sex, age, education, marital status, residential area, number of members in the household, household composition type, housing type, current chronic disease status, private health insurance, household income quantile, and household expense [ 48 ]. Age was categorized as 65–69, 70–74, 75–79, and 80 years and older.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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Trajectories of Depression and Their Predictors in a Population-Based Study of Korean Older Adults

Cited by 7 publications

References 26 publications

Association between specific social activities and depressive symptoms among older adults: A study of urban-rural differences in China

Association between specific social activities and depressive symptoms among older adults: A study of urban-rural differences in China

Comparative analysis of machine learning approaches for predicting frequent emergency department visits

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

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