Associations of depressive symptoms and chronic diseases with activities of daily living among middle-aged and older population in China: A population-based cohort study

Effects of spatial accessibility of community health services on the activities of daily living among older adults in China: a propensity score matching study

Background

The Chinese government proposes to establish a hierarchical diagnosis and treatment system, and attaches great importance to community health services. Under the background of population aging and the increase of older adults with disability, this study aimed to analyze the effect of spatial accessibility of community health services on the activities of daily living (ADL) among older adults in China.

Methods

A research sample of 7,922 older adults from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data in 2018 was adopted. There were 2,806 participants in the treatment group and 5,116 participants in the control group. The propensity score matching method was adopted to match the treatment and control groups to calculate the values of average treatment effects on treated (ATT).

Results

The results of kernel density matching method showed that the factual ADL score of the treatment group was 10.912, the counterfactual ADL score of the control group was 10.694, and the ATT value was 0.218 (p < 0.01). The spatial accessibility of community health services could significantly improve the activities of daily living among older adults in China. Meanwhile, there was urban-rural heterogeneity in the impact of spatial accessibility of community health services on the activities of daily living of older adults in China. The effect value in urban samples (ATT = 0.371, p < 0.01) was higher than that in rural samples (ATT = 0.180, p < 0.01).

Conclusion

Spatial accessibility of community health services could improve the activities of daily living among older adults in China. The Chinese government should take actions to improve the distribution of community health service resources.

Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health

Background

EPs pose significant challenges to individual health and quality of life, attracting attention in public health as a risk factor for diminished quality of life and healthy life expectancy in middle-aged and older adult populations. Therefore, in the context of global aging, meticulous exploration of the factors behind emotional issues becomes paramount. Whether ADL can serve as a potential marker for EPs remains unclear. This study aims to provide new evidence for ADL as an early predictor of EPs through statistical analysis and validation using machine learning algorithms.

Methods

Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) national baseline survey, comprising 9,766 samples aged 45 and above, were utilized. ADL was assessed using the BI, while the presence of EPs was evaluated based on the record of "Diagnosed with Emotional Problems by a Doctor" in CHARLS data. Statistical analyses including independent samples t-test, chi-square test, Pearson correlation analysis, and multiple linear regression were conducted using SPSS 25.0. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.

Results

Population demographic analysis revealed a significantly lower average BI score of 65.044 in the "Diagnosed with Emotional Problems by a Doctor" group compared to 85.128 in the "Not diagnosed with Emotional Problems by a Doctor" group. Pearson correlation analysis indicated a significant negative correlation between ADL and EPs (r = -0.165, p < 0.001). Iterative analysis using stratified multiple linear regression across three different models demonstrated the persistent statistical significance of the negative correlation between ADL and EPs (B = -0.002, β = -0.186, t = -16.476, 95% CI = -0.002, -0.001, p = 0.000), confirming its stability. Machine learning algorithms validated our findings from statistical analysis, confirming the predictive accuracy of ADL for EPs. The area under the curve (AUC) for the three models were SVM-AUC = 0.700, DT-AUC = 0.742, and LR-AUC = 0.711. In experiments using other covariates and other covariates + BI, the overall prediction level of machine learning algorithms improved after adding BI, emphasizing the positive effect of ADL on EPs prediction.

Conclusion

This study, employing various statistical methods, identified a negative correlation between ADL and EPs, with machine learning algorithms confirming this finding. Impaired ADL increases susceptibility to EPs.

Gender-specific association of the accumulation of chronic conditions and disability in activities of daily living with depressive symptoms

BACKGROUND

In the era of rapid aging with a rising prevalence of multimorbidity, complex interactions between physical and psychological conditions have challenged the health care system. However, little is known about the association of the accumulation of chronic conditions and disability in activities of daily living with depressive symptoms, especially in developed countries.

METHODS

This population-based cohort study used data from the Health and Retirement Study. A total of 22,335 middle-aged and older adults participated in the 2014 (T1), 2016 (T2), and 2018 (T3) waves of the cohort were included. The accumulation of chronic conditions and disability were defined as the number of chronic diseases and the five activities of daily living. Depressive symptoms were measured by the Center for Epidemiologic Studies Depression Scale. A longitudinal mediation model with a cross-lagged panel model was run. As robust check, the models were applied with a longer follow-up period (from 2012 to 2018). Additionally, results were estimated in China.

RESULTS

Bidirectional associations have been found among the accumulation of chronic conditions, disability, and depressive symptoms, especially between disability and depression. Disability (T2) mediated 11.11 % and 16.87 % of the association between the accumulation of chronic conditions (T1) and depression (T3) for men and women in the United States. The results were consistent in robust analysis.

CONCLUSIONS

This study found that men and women routinely experienced disability and depressive symptoms because of the accumulation of chronic conditions. In terms of depressive symptoms, women were more sensitive to the accumulation of chronic conditions through disability.

Chronic disease and multimorbidity in the Chinese older adults' population and their impact on daily living ability: a cross-sectional study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

BACKGROUND

Owing to an increase in life expectancy, it is common for the older adults to suffer from chronic diseases that can result in disability and a low quality of life. This study aimed to explore the influence of chronic diseases and multimorbidities on activities of daily living (ADLs) and instrumental ADLs (IADLs) in an older Chinese population.

METHODS

Based on the Chinese Longitudinal Healthy Longevity Survey (2018), 9,155 older adults aged 65 years and above were included in the study. A self-administered questionnaire was used to collect information on demographic characteristics, chronic diseases, ADLs, and IADLs. The impact of factors affecting ADL and IADL impairment in older adults was analysed using binary logistic regression.

RESULTS

In total, 66.3% participants had chronic diseases. Hypertension, heart disease, arthritis, diabetes and cerebrovascular disease were among the top chronic diseases. Of these, 33.7% participants had multimorbidities. The most common combination of the two chronic diseases was hypertension and heart disease (11.2%), whereas the most common combination of the three chronic diseases was hypertension, heart disease, and diabetes (3.18%). After categorising the older adults into four age groups, dementia, visual impairment, and hearing impairment were found to be more prevalent with increasing age. The prevalence of hypertension, heart disease, cerebrovascular disease, gastrointestinal ulcers, arthritis and chronic nephritis gradually increased with age until the age of 75 years, peaked in the 75-84 years age group, and then showed a decreasing trend with age. Multimorbidity prevalence followed a similar pattern. Regression analysis indicated that the increase in age group and the number of chronic diseases independently correlated with impairments in ADL as well as IADL. Additionally, gender, physical activity, educational background, obesity, depressive symptoms, and falls also had an impact on ADLs or IADLs.

CONCLUSION

Chronic diseases and multimorbidities are common in older adults, and it is important to note that aging, multimorbidity, obesity, and unhealthy lifestyle choices may interfere with ADLs or IADLs in older adults. Therefore, it is imperative that primary healthcare providers pay special attention to older adults and improve screening for multimorbidity and follow-up needs.

Taking precautions in advance: a lower level of activities of daily living may be associated with a higher likelihood of memory-related diseases

Introduction

Memory-related diseases (MDs) pose a significant healthcare challenge globally, and early detection is essential for effective intervention. This study investigates the potential of Activities of Daily Living (ADL) as a clinical diagnostic indicator for MDs. Utilizing data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS), encompassing 10,062 Chinese individuals aged 45 or older, we assessed ADL using the Barthel Index (BI) and correlated it with the presence of MDs. Statistical analysis, supplemented by machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression), was employed to elucidate the relationship between ADL and MDs.

Background

MDs represent a significant public health concern, necessitating early detection and intervention to mitigate their impact on individuals and society. Identifying reliable clinical diagnostic signs for MDs is imperative. ADL have garnered attention as a potential marker. This study aims to rigorously analyze clinical data and validate machine learning algorithms to ascertain if ADL can serve as an indicator of MDs.

Methods

Data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) were employed, encompassing responses from 10,062 Chinese individuals aged 45 or older. ADL was assessed using the BI, while the presence of MDs was determined through health report questions. Statistical analysis was executed using SPSS 25.0, and machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Learning (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.

Results

Population characteristics analysis revealed that the average BI score for individuals with MDs was 70.88, significantly lower than the average score of 87.77 in the control group. Pearson's correlation analysis demonstrated a robust negative association (r = -0.188, p < 0.001) between ADL and MDs. After adjusting for covariates such as gender, age, smoking status, drinking status, hypertension, diabetes, and dyslipidemia, the negative relationship between ADL and MDs remained statistically significant (B = -0.002, β = -0.142, t = -14.393, 95% CI = -0.002, -0.001, p = 0.000). The application of machine learning models further confirmed the predictive accuracy of ADL for MDs, with area under the curve (AUC) values as follows: SVM-AUC = 0.69, DT-AUC = 0.715, LR-AUC = 0.7. Comparative analysis of machine learning outcomes with and without the BI underscored the BI's role in enhancing predictive abilities, with the DT model demonstrating superior performance.

Conclusion

This study establishes a robust negative correlation between ADL and MDs through comprehensive statistical analysis and machine learning algorithms. The results validate ADL as a promising diagnostic indicator for MDs, with enhanced predictive accuracy when coupled with the Barthel Index. Lower levels of ADL are associated with an increased likelihood of developing memory-related diseases, underscoring the clinical relevance of ADL assessment in early disease detection.

Association between chronic diseases and depression in the middle-aged and older adult Chinese population-a seven-year follow-up study based on CHARLS

Background

With the aging of the Chinese population, the prevalence of depression and chronic diseases is continually growing among middle-aged and older adult people. This study aimed to investigate the association between chronic diseases and depression in this population.

Methods

Data from the China Health and Retirement Longitudinal Study (CHARLS) 2011-2018 longitudinal survey, a 7-years follow-up of 7,163 participants over 45 years old, with no depression at baseline (2011). The chronic disease status in our study was based on the self-report of the participants, and depression was defined by the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). The relationship between baseline chronic disease and depression was assessed by the Kaplan-Meier method and Cox proportional hazards regression models.

Results

After 7-years follow-up, 41.2% (2,951/7163, 95% CI:40.1, 42.3%) of the participants reported depression. The analysis showed that participants with chronic diseases at baseline had a higher risk of depression and that such risk increased significantly with the number of chronic diseases suffered (1 chronic disease: HR = 1.197; 2 chronic diseases: HR = 1.310; 3 and more chronic diseases: HR = 1.397). Diabetes or high blood sugar (HR = 1.185), kidney disease (HR = 1.252), stomach or other digestive diseases (HR = 1.128), and arthritis or rheumatism (HR = 1.221) all significantly increased the risk of depression in middle-aged and older adult Chinese.

Conclusion

The present study found that suffering from different degrees of chronic diseases increased the risk of depression in middle-aged and older adult people, and these findings may benefit preventing depression and improving the quality of mental health in this group.