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

Long-term depressive symptom trajectories and related baseline characteristics in primary care patients: Analysis of the PsicAP clinical trial

BACKGROUND

There is heterogeneity in the long-term trajectories of depressive symptoms among patients. To date, there has been little effort to inform the long-term trajectory of symptom change and the factors associated with different trajectories. Such knowledge is key to treatment decision-making in primary care, where depression is a common reason for consultation. We aimed to identify distinct long-term trajectories of depressive symptoms and explore pre-treatment characteristics associated with them.

METHODS

A total of 483 patients from the PsicAP clinical trial were included. Growth mixture modeling was used to identify long-term distinct trajectories of depressive symptoms, and multinomial logistic regression models to explore associations between pre-treatment characteristics and trajectories.

RESULTS

Four trajectories were identified that best explained the observed response patterns: "recovery" (64.18%), "late recovery" (10.15%), "relapse" (13.67%), and "chronicity" (12%). There was a higher likelihood of following the recovery trajectory for patients who had received psychological treatment in addition to the treatment as usual. Chronicity was associated with higher depressive severity, comorbidity (generalized anxiety, panic, and somatic symptoms), taking antidepressants, higher emotional suppression, lower levels on life quality, and being older. Relapse was associated with higher depressive severity, somatic symptoms, and having basic education, and late recovery was associated with higher depressive severity, generalized anxiety symptoms, greater disability, and rumination.

CONCLUSIONS

There were different trajectories of depressive course and related prognostic factors among the patients. However, further research is needed before these findings can significantly influence care decisions.

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

Background

Engaging 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.

Methods

A 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.

Results

In 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.

Conclusions

According 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.

Physical activity, sedentary behavior, and cardiovascular disease risk in Korea: a trajectory analysis

OBJECTIVES

To identify the distinct trajectories of sedentary behavior (SB) and explore whether reduced cardiovascular disease (CVD) risk was associated with a distinct trajectory of physical activity (PA).

METHODS

We analyzed data from 6,425 people who participated in the Korean Health Panel Survey over a period of 10 years. The participants' self-reported SB and PA were assessed annually, and trajectory groups were identified using a group-based trajectory model for longitudinal data analysis. Logistic regression analysis was performed to assess the association between CVD risk (10-year cumulative incidence) and the trajectories of SB and PA. The adjusted variables included socio-demographic factors, the predisposing diseases of CVD, and baseline health behaviors.

RESULTS

Trajectory analysis identified 4 SB trajectory groups: SB group 1 (low and slightly increasing trend, 53.1%), SB group 2 (high and rapidly decreasing trend, 14.7%), SB group 3 (high and slightly decreasing trend, 9.9%), and SB group 4 (low and rapidly increasing trend, 22.2%). The 3 PA trajectory groups were PA group 1 (moderate and slightly decreasing trend, 32.1%), PA group 2 (low and slightly decreasing trend, 57.3%), and PA group 3 (maintained inactivity, 10.7%). By the 10-year follow-up, 577 cases of incident CVD had occurred. We also noted a 50% reduction in the risk of CVD when SB group 4 was accompanied by PA group 1 (odds ratio, 0.50; 95% confidence interval, 0.28 to 0.90).

CONCLUSIONS

Despite increased time spent in SB, maintaining PA about 2 days to 3 days per week reduced the occurrence of CVD.

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

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.

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 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.