Trajectories of Depressive Symptoms Among Older Adults and in Adults With Hip Fracture: Analysis From the English Longitudinal Study of Ageing

Interactive changes in depression and loneliness symptoms prior to and during the COVID-19 pandemic: A longitudinal network analysis

OBJECTIVES

Depression and loneliness co-occur frequently. This study examined interactive changes between depression and loneliness among older adults prior to and during the COVID-19 pandemic from a longitudinal network perspective.

METHODS

This network study was based on data from three waves (2016-2017, 2018-2019, and 2020) of the English Longitudinal Study of Ageing (ELSA). Depression and loneliness were measured with the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8) and three item version of the University of California Los Angeles (UCLA) Loneliness Scale, respectively. A network model was constructed using an Ising Model while network differences were assessed using a Network Comparison Test. Central symptoms were identified via Expected Influence (EI).

RESULTS

A total of 4,293 older adults were included in this study. The prevalence and network of depression and loneliness did not change significantly between the baseline and pre-pandemic assessments but increased significantly from the pre-pandemic assessment to during COVID-19 assessment. The central symptom with the strongest increase from pre-pandemic to pandemic assessments was "Inability to get going" (CESD8) and the edge with the highest increase across depression-loneliness symptom communities was "Lack companionship" (UCLA1) - "Inability to get going" (CESD8). Finally, "Feeling depressed" (CESD1) and "Everything was an effort" (CESD2) were the most central symptoms over the three assessment periods.

CONCLUSIONS

The COVID-19 pandemic was associated with significant changes in the depression-loneliness network model. The most changed symptoms and edges could be treatment targets for reducing the risk of depression and loneliness in older adults.

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 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.

Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults:Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

BACKGROUND

Falls are the most common adverse outcome of depression in older adults, yet a accurate risk prediction model for falls stratified by distinct long-term trajectories of depressive symptoms is still lacking.

METHODS

We collected the data of 1617 participants from the China Health and Retirement Longitudinal Study register, spanning between 2011 and 2018. The 36 input variables included in the baseline survey were regarded as candidate features. The trajectories of depressive symptoms were classified by the latent class growth model and growth mixture model. Three data balancing technologies and four machine learning algorithms were utilized to develop predictive models for fall classification of depressive prognosis.

RESULTS

Depressive symptom trajectories were divided into four categories, i.e., non-symptoms, new-onset increasing symptoms, slowly decreasing symptoms, and persistent high symptoms. The random forest-TomekLinks model achieved the best performance among the case and incident models with an AUC-ROC of 0.844 and 0.731, respectively. In the chronic model, the gradient boosting decision tree-synthetic minority oversampling technique obtained an AUC-ROC of 0.783. In the three models, the depressive symptom score was the most crucial component. The lung function was a common and significant feature in both the case and the chronic models.

CONCLUSIONS

This study suggests that the ideal model has a good chance of identifying older persons with a high risk of falling stratified by long-term trajectories of depressive symptoms. Baseline depressive symptom score, lung function, income, and injury experience are influential factors associated with falls of depression evolution.