Prevalence and factors associated with depression among older adults in the case of a low-income country, Ethiopia: a systematic review and meta-analysis

Protein-enriched and anti-inflammatory dietary patterns, healthy lifestyle index and depressive symptoms: A cross-sectional study of 287,945 adults in China

OBJECTIVES

Depressive symptoms have a considerable negative impact on mental health. This study aimed to understand the relationship between the protein-enriched and anti-inflammatory dietary index scores, modified healthy lifestyle index scores (Modified HLIS), and depressive symptoms.

METHODS

This study used convenience sampling to conduct a single-center cross-sectional survey. From January 1, 2015 to December 31, 2020, a total of 287,945 Chinese adults from a health management center of a general hospital completed an online self-reported health questionnaire, which included demographic characteristics, the Dietary Diversity Scale, the Modified Healthy Lifestyle Index Scores and the Patient Health Questionnaire-9.

RESULTS

The higher anti-inflammatory dietary index scores (POR = 0.87; 95 % CI: 0.86-0.87; p < 0.001), moderate modified healthy lifestyle index scores (POR = 0.76; 95 % CI: 0.75-0.78; p < 0.001) and sufficient modified healthy lifestyle index scores (POR = 0.53; 95 % CI: 0.52-0.54; p < 0.001) were negatively associated with depressive symptoms, while the higher protein-enriched dietary index scores (POR = 1.01; 95 % CI: 1.01-1.02; p < 0.001) was positively correlated with depressive symptoms.

CONCLUSIONS

This study demonstrated that protein-enriched and anti-inflammatory dietary index scores, and multiple healthy lifestyles are associated depressive symptoms in adults.

Prevalence of sarcopenia and depressive symptoms among older adults: a scoping review

Sarcopenia causes a loss of skeletal muscle mass and decreases muscle strength and function. Depressive symptoms are a common cause of distress among geriatrics, significantly affecting the quality of life of older adults. Recently, studies have shown that a correlation exists between sarcopenia and depression. To determine the prevalence of sarcopenia and depressive symptoms and identify the factors associated with sarcopenia, we systematically searched the SCOPUS, Science Direct, and PubMed databases for papers on sarcopenia and depressive symptoms published from 2012 to 2022. We reviewed the literature on sarcopenia, depressive symptom prevalence, the prevalence of subjects with both sarcopenia and depressive symptoms, and the factors associated with sarcopenia. Only cross-sectional studies were included. Nineteen articles met the inclusion criteria for review, with overall sarcopenia prevalence ranging from 3.9% to 41.7%. The prevalence of depressive symptoms was reported in seven studies, ranging from 8.09% to 40%. The most commonly used tools to diagnose sarcopenia and depressive symptoms were the European Working Group on Sarcopenia in Older People consensus and the Geriatric Depression Scale, respectively. Being aged, malnourished, obese, having comorbidities (hypertension and diabetes), having impaired cognitive function, and having polypharmacy were found to be the factors associated with sarcopenia. Sarcopenia and depressive symptoms have been found to cause adverse health outcomes among older people. Appropriate nutritional assessments and interventions should be taken to manage these two geriatric conditions. Further studies should be planned, considering multidomain intervention strategies to improve sarcopenia and older people's mental health.

Association between perceived neighborhood environment and depression among residents living in mega-communities in Guiyang, China: a cross-sectional study

BACKGROUND

Little was known about the relationship between perceived neighborhood environment and depression among residents living in mega-communities. Furthermore, the mediating effects of physical activity (PA) and anxiety in this relationship have not been investigated. Thus, this study aimed to comprehensively examine the association between perceived neighborhood environment and depression among residents living in mega-communities, and test whether PA and anxiety mediated the association.

METHODS

A cross-sectional study on perceived neighborhood environment and depression was conducted among individuals who lived in mega-communities (n = 665) in Guiyang, China from July to August 2022. Perceived neighborhood environment was assessed from the following six aspects: traffic, building quality, accessibility, neighborhood, indoor, and pollution. Depression was measured by the Patients Health Questionnaire-9. Structural equation model was used to evaluate the association between perceived neighborhood environment and depression, and test the mediating effect of PA and anxiety in this association.

RESULTS

We found that neighborhood (β = -0.144, p = 0.002) and PA (β = -0.074, p < 0.001) were both negatively associated with depression, while anxiety was positively associated with depression (β = 0.447, p < 0.001). Married residents were less likely to experience depression than residents of other marital status. PA played a mediator role in the relationship between accessibility and depression (β = 0.014, p = 0.033). PA mediated the relationship between neighborhood and depression (β = -0.032, p = 0.015). The mediating effect of anxiety in the relationship between perceived neighborhood environment and depression was not significant.

CONCLUSIONS

This study demonstrated that neighborhood, which was assessed by satisfaction with safety, hygiene, parking, greening, lighting, and building shape, was negatively associated with depression, and PA mediated the relationship.

Factors affecting depression among married adults: a gender-based household cross-sectional study

BACKGROUND

Depression is a common mental health problem all over the world including Bangladesh. World Health Organization included it in the Mental Health Gap Action Programme as one of its priority conditions. Research on this issue is scanty in Bangladesh. Therefore, we designed to a gender-based household study on associated factors of depression among married adults in Rajshahi City of Bangladesh.

METHODS

We conducted this household cross-sectional study from August 01 to October 31, 2019. A total of 708 married adults currently living together in Rajshahi City were recruited for this study. We applied a multi-stage random sampling technique for selecting samples and used a semi-structured questionnaire to collect necessary information from them. The Patient Health Questionnaire-9 was used for measuring depression and frequency distribution and binary logistic regression model were used for data analysis.

RESULTS

The prevalence of depression (moderate to severe) was 14.4% (95% CI: 11.9-16.9) among married adults, and females (21.2%, 95% CI: 17.2-25.4) suffered more than males (7.6%, 95% CI: 4.8-10.5). A multiple binary logistic regression model established four main factors of depression among married females: (i) multiple marriage [AOR = 19.982; 95% CI: 10.081-39.610; p < 0.01]; (ii) poor relationship with spouse [AOR = 2.175; 95%CI: 1.068-4.428; p < 0.05]; (iii) chronic medical comorbidity [AOR = 1.876; 95%CI: 1.009-2.626; p < 0.05]; and (iv) 7-12 years duration of conjugal life [AOR = 2.091, 1.009-4.334; p < 0.05]. Two main factors of depression among married males were (i) multiple marriage [AOR = 24.605; 95% CI: 20.228-40.402; p < 0.01] and hard work [AOR = 4.358; 95%CI: 1.109-7.132; p < 0.05].

CONCLUSION

The prevalence of depression was significantly high among the study population, and females were the most vulnerable group. The concerned authorities and stakeholders should take appropriate measures to manage the problem with special focus on the risk factors and the vulnerable groups.

Depressive Disorders, Cognitive and Physical Function of Older People in Early Dementia Detection

BACKGROUND

Aging is associated with cognitive decline, leading to cognitive and physical impairments, which are risk factors for loss of independence and dementia development. Early diagnosis is beneficial for both, the patient and their family, to avoid long-term consequences. The aim of this study was to analyze the frequency of depressive disorders and their influence on cognitive and physical function of older people in early dementia detection.

METHODS

There were 852 patients, aged at least 60 years, from the Central Teaching Hospital. The study was conducted between September 2022 and June 2023. The qualified participants were examined using four tools: Geriatric Depression Scale (GDS), Instrumental Activities of Daily Living (IADL), Timed Up and Go (TUG) and Schulman's Clock-Drawing Test.

RESULTS

Over one-third had depressive disorders. A relationship with p < 0.05 was observed between GDS and IADL: r = -0.61. A relationship with p > 0.05 was observed between GDS and TUG: r = -024. A relationship with p < 0.05 was observed between GDS and CDT: r = 0.74.

CONCLUSIONS

The first signs of depressive disorders in older people may be considered an indication for further diagnosis of dementia.

Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest

Korea is showing the fastest trend in the world in population aging; there is a high interest in the elderly population nationwide. Among the common chronic diseases, the elderly tends to have a high incidence of depression. That said, it has been vital to focus on preventing depression in the elderly in advance. Hence, this study aims to select the factors related to depression in low-income seniors identified in previous studies and to develop a prediction model. In this study, 2975 elderly people from low-income families were extracted using the 13th-year data of the Korea Welfare Panel Study (2018). Decision trees, logistic regression, neural networks, and random forest were applied to develop a predictive model among the numerous data mining techniques. In addition, the wrapper's stepwise backward elimination, which finds the optimal model by removing the least relevant factors, was applied. The evaluation of the model was confirmed via accuracy. It was verified that the final prediction model, in the case of a decision tree, showed the highest predictive power with an accuracy of 97.3%. Second, psychological factors, leisure life satisfaction, social support, subjective health awareness, and family support ranked higher than demographic factors influencing depression. Based on the results, an approach focused on psychological support is much needed to manage depression in low-income seniors. As predicting depression in the elderly varies on numerous influencing factors, using a decision tree may be beneficial to establish a firm prediction model to identify vital factors causing depression in the elderly population.