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Factors affecting the evolution of Chinese elderly depression: a cross-sectional study

BMC Geriatrics volume 22, Article number: 109 (2022) Cite this article

Abstract

Background

In the past decades, China’s rapid economic growth has been accompanied by rapid changes in lifestyle and an increasing prevalence of mental disorders. This study explored the changes and factors associated with depression among the elderly population of China from 2011 to 2018.

Method

Data were obtained from the China Health and Retirement Longitudinal Study. The total sample size consists of 21,484 individuals aged ≥60 years, and the sample sizes in 2011, 2013, 2015 and 2018 were 5081, 4853, 5207, 6343, respectively. Depressive symptoms were measured by the 10-item Short-Form developed by the Center for Epidemiological Studies Depression.

Result

We found that the tendency in depression rate of the Chinese elderly from 2011 (36.8%) to 2018 (44.5%). The results showed poor health (OR = 3.553), ADL damage (OR = 2.010), multiple chronic diseases (OR = 1.287), and western (OR = 1.777) are risk factors for depression.

Conclusion

The rate of depression of the elderly people in China has risen dramatically. Therefore, additional steps to prevent, treat and care for the affected population are needed, Mental health prevention and treatment strategies should be incorporated into China’s public health policies in a timely manner to mitigate the serious economic burden caused by the increase of depression.

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Introduction

Depression is defined as a multi-dimensional heterogeneous condition, that has a negative impact on the individual’s physical health, quality of life and psychosocial functioning, and may lead to disability and death [1]. Genetic dispositions, health behaviors, physical exercise, adequate sleep, chronic diseases and social support are related to depression according to considerable research [2,3,4,5], but the specific pathogenesis of depression is still being explored [6].

Nowadays, Depression has become the second major risk factor affecting people’s health. Approximately 350 million people worldwide suffer from depression [7]. The global economic burden of diseases caused by depression is one of the top five among all types of diseases [8], accounting for 4% of the global burden of disease [6] and the global economic burden of non-fatal diseases of 10% [9]. The difference in the prevalence of depression is caused by regional differences. In high-income countries, such as the proportion of elderly depression is 6.6% (the United States) [10] and 3.7% (Singapore) [11]. In contrast, the incidence of depression in the elderly in low-and middle-income countries is higher [12]. For example, the prevalence of depression in the elderly over 60 years old is 9% (Africa) [8] and 7.8 to 34.8% (Asia) [13]. The challenge of depression in the elderly in developing countries is even more serious.

China is transitioning from a developing to developed country with a large, aging population, and the situation of depression among in the elderly is particularly acute. In recent years, the detection rate of depression in the elderly has reached 33% of the total population [14]. On average, about $42.67 per capita annual medical expense is associated with treatment of symptoms and depression in China [15]. Mental health status has significant effects on individual medical expenses [16]. There are more than 264 million people over the age of 60 in China in 2020 [17], and by 2050, the elderly population is estimated to increase to 480 million [7]. In this sense, it’s necessary to carry out research on the depression of the elderly in China.

Most of the existing studies on depression in the elderly have explored the current prevalence of depression in the elderly on the basis of cross-sectional research [2, 7, 18]. However, the socio-economic status and disease spectrum have also undergone tremendous changes, which may be inferred the influencing factors and forms of the disease change [19, 20]. Therefore, the main goals of this study are: first, to explore the trend of changes in the rate of depression; second, to explore the effects of influencing factors on depression in the elderly over time; and finally, this research not only provides reference for other developing countries to prevent depression in the elderly, but also can provide advice for countries with severe aging.

Method

Data sources and samples

The data of this study were derived from The China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of persons in China 45 years of age or older. Samples were chosen through multistage probability sampling. Firstly, 150 county-level units were randomly chosen with a probability-proportional-to-size (PPS) sampling technique from a sampling frame containing all county-level units with the exception of Tibet. Secondly, the sample was stratified by region, by living in urban or rural counties, and by county-level GDP. Thirdly, as primary sampling units (PSUs), 3 PSUs within each county-level unit (including cun in rural) or (shequ or juweihui in urban) were selected by using PPS sampling. Finally, the sample from 28 provinces, 150 counties, and 450 communities were identified [21]. This study excluded the following types of respondents: under the age of 60; missing scores for depression; missing health-related conditions, such as ADL scores, and health self-assessments, and the total sample size of the study was 21,484.

Study variables

Outcome variable: depressive symptoms

Depressive symptom was measured through a 10-item Brief Epidemiological Research Center Depression (CES-D) [22]. Existing studies have shown that the scale have highly effective and reliability in Chinese elderly [23, 24]. The Cronbach’s alpha in our research is 0.784 (Table 1). The scale’s score is between 0 and 30 by using the four-point Likert scoring method. In addition, there were two key items of reverse score in this scale. With reference to previous studies, we set a critical value of 10 points or higher as the Cut-off points [25]. Depressive symptoms were divided into (0 = no depressive symptoms; 1 = depressive symptoms).

Table 1 The distribution of CSED-10 items
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Independent variable

The core view of most contemporary depression theories is that stress can increase the risk of depression by stimulating individual cognition and possible biological processes. Relevant studies have also shown that major stressful life events are powerful factors that make depression [25,26,27]. For example, ADL, chronic diseases, social activities and night sleep have been demonstrated in previous studies. Therefore, these factors were also considered in our research.

The socio-demographic variables considered in the analysis were age (60–69, 70–79,≥80), gender (male, female), marriage (married or unmarried (divorced, widowed, never married)), residence (urban, rural) and geographical region (East, Middle, West). The socio-economic status was determined as the educational level (elementary school and below, junior high school, high school or above). Individual annual income (RMB) were divided into “≤10,000”, “10,000–20,000” and “≥20,000” [7, 18].

Social activities were represented as 0 = no social activities, 1 = participants who participated in at least one social activity. Nighttime sleep were defined as 1 = < 7 h, 2 = ≥7 h [28]. ADL disability (lack of the ability to carry out daily activities) was described as difficulties related to the following six items: dressing, bathing, eating, going to or getting out of bed, going to the toilet, and abstinence. If they cannot complete one of them independently, they were judged to be disabled (0 was defined as = not disabled, 1 = disabled). Participants first performed a five-level assessment of their self-reported health status (very good, good, fair, poor, and very poor). Based on this report, “very good” or “good” was defined as “good”; “fair”; “poor” or “very bad” was expressed as “poor”. Chronic diseases were divided into two groups:(1 = no disease, 2 = one or more chronic diseases) [7].

Statistical analysis

All statistical analyses were performed using SPSS25. The data was expressed as a percentage of the classification value. χ2 (chi-square) analysis was used to explore the differences in the prevalence of depressive symptoms; on the basis of univariate analysis, binary logistic regression analysis was used to determine the potential risk factors of depressive symptoms. Data were expressed as OR and 95% CI. The test level was 0.05, and the p-value < 0.05 was considered statistically significant.

Result

The characteristics of respondents and the detection rate of depression

Overall, more than half of the respondents were women (54.1%); 61.8% were 60–69 years old; 79.2% were married. The majority of the respondents had an education level of elementary school and below (79.9%) and the annual income is below 10,000 yuan (70.9%) (Table 2).

Table 2 The basic information (n(%))
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The detection rates of depression in 2011, 2013, 2015, and 2018 were (36.8%), (38.7%), (42.7%), (44.5%), respectively. The detection rates of newly-added elderly people with depression in 2013, 2015, and 2018 were (37.7%), (40.8%), (41.1%), respectively. In addition, the rates of depression of the elderly who have been repeatedly investigated in the four waves were (37.4%), (40.8%), (45.0%), (49.8%). There are differences in the detection rate of depression among the elderly (including overall, newly added, and repeated) in different years (P < 0.05), and the detection rate of depression shows an upward trend (Table 3).

Table 3 The detection rate of depression in the elderly in different years
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Analysis of depression in the elderly with different characteristics

The results showed that there were differences in the rate of depression in the elderly with different chronic diseases, ADL status, self-reported health, social activities, sleep time and other characteristics (P < 0.05). The rate of depressive symptoms in the elderly in the western region, female, 60–69 years old, elementary school education and below, unmarried, suffering from a variety of chronic diseases, ADL disability, and poor health is even higher. However, it is worth noting that in the chi-square analysis in 2018, it was found that there was no difference in the rate of depressive symptoms among elderly people of different ages (P = 0.123) (Table 4).

Table 4 Chi-square analysis of depression in the elderly with different characteristics
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Logistic regression model

Binary logistic regression analysis was used to explore the influencing factors of depression in the elderly, and the results of each period section show that there is an association between region, gender, marriage, residence, sleep, social activities, self-reported heath, chronic disease, ADL and depression (Table 5).

Table 5 Binary logistic regression analysis of depressive symptoms (OR, 95% CI)
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Then, time (the year of the survey) was also used as a factor, and its effect in depression in the elderly was discussed after adjusting for variables such as socioeconomic characteristics and health status. The results show that poor health (OR = 3.553), ADL disability (OR = 2.010), respondents in 2018 (OR = 2.213), Western (OR = 1.777), women (OR = 1.521); multiple chronic diseases (OR = 1.287) and (OR = 1.294) are risk factors for depression. Sleeping time at night≥7(OR = 0.549), living in town (OR = 0.635), annual income≥20000yuan (OR = 0.741), and social activities (OR = 0.825) are protective factors for depression in the elderly (Table 6).

Table 6 Binary logistics regression analysis of overall depression in the elderly (OR, 95% CI)
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Table 7 Chi-square analysis of factors affecting depression (N = 21,484)
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Discussion

The study focused on the trend of changes in the rate of depression among the elderly in China. The results show that the rate of depression in the elderly in China has reached 44.5% (in 2018), which is higher than the results of a similar domestic study [7, 14]. Our research also demonstrated that the rate of depressive symptoms showed an upward trend in both the newly-added elderly interviewed and the elderly who have always been in this survey cohort. This finding indicated that the increase in the detection rate of depression caused by time changes is indeed an issue in China, and steps must be taken to improve the mental health condition of elderly.

Research results show that elderly people with poor health (OR = 3.553), ADL impairment (OR = 2.010) and multiple chronic diseases (OR = 1.287) have a higher risk of depression, which is consistent with previous research conclusions [3, 7, 18]. Chronic diseases will cause the health of the elderly to deteriorate, and long-term medication after illness will increase the economic burden of the disease. In addition, due to the damage of ADL, the majority of the elderly rely on the care of family members, which increases the financial burden of the family, and the elderly will also have psychological burden problems. Moreover, it is inconvenient for them to participate in social activities and this prevents them from being able to talk about their inner emotions and may exacerbate depression [29,30,31].

Sleep is a very important factor affecting health, which has been found to cause many diseases and even all-cause mortality [32]. Our research also indicate that elderly people who sleep for more than 7 h (OR = 0.549) have a lower risk of depression, which is consistent with the study of Gehrman P [33]. Daytime physical fatigue or mental fatigue caused by poor night sleep quality may disrupt the circadian rhythm or cause hormonal changes. Furthermore, lack of sleep at night may be one of the causes of mental disorders or the sequelae of previous mental disorders, and these factors can cause depression produce [32, 34,35,36].

There is consensus that SES plays an important role in health [37]. Our research found that elderly people with higher personal annual income (OR = 0.741) have a lower risk of depression. It has been suggested that poor SES may lead to poor access to mental health services, and further affect the diagnosis and treatment of depression. It is difficult for low-income populations to attend to healthcare needs and to be screened for depression symptoms [38]. Divorce (OR = 1.294) was found to be a risk factor for depression, which is consistent with the findings of Zhang Y and Ouyang P [7, 39]. As a special group, the elderly may rely more on social support, especially support from family members [40]. The absence of a spouse not only means that the financial support from the spouse is weakened, but also they cannot share the inner feelings with others, This may increase the possibility of depression in the divorced elderly [7, 41].

Social activity (OR = 0.825) was also confirmed as a meaningful factor for depression. The review of empirical literature by Adams et al. reveals that socializing (e.g., spending time with friends, family, or neighbors) has the strongest effect on well-being in late life [42]. By comparison, infrequent participation in social activities is an indicator of social isolation [43].What’s more, events involving interpersonal stress and social rejection are the strongest risk factors for depressive symptoms [44]. Hence social activities should be promoted as an important means of preventing depression in the elderly.

In addition to the factors discussed above, we also found that the West region (OR = 1.777), women (OR = 1.521), higher education (OR = 0.771), and living in city (OR = 0.635) are associated with depression. Considerable research demonstrates that these factors are very important in terms of the potential harm associated with depression [9, 18, 38, 45]. In areas with better economic conditions, the health-related policies, living surroundings and community infrastructure always show better conditions, which are effective in promoting mental health [45, 46]. Experience with rejection, criticism and separation are the factors leading to depression. Women are more likely to perceive these emotions, which leads to a higher likelihood of them suffering from depression [47].

Besides, we also explored the effect of the passage of time on depression in the elderly. This study confirmed that the rate of depression among the elderly in China has increased, and the rate of depressive symptoms increased by nearly 10% from 2011 (36.8%) to 2018 (44.5%). Compared with studies in similar time periods, for example, 32.6% ~ 41.6% (Hu bei, 2011–2015) [48], 39.86% (Jing Yu, 2012) [2], 46.15% (Fang M, 2015) [18]. Our research are roughly consistent with their research results. In general, the level of depression among the elderly in China is gradually increasing. The reasons for this phenomenon are as follows: Firstly, According to the 6th Chinese Census, the elderly living alone or with their spouse accounted for more than 50% of the total elderly population; this number (the 5th Census was 38%) has risen Nearly 12% in decades [49]. The problem of urbanization and empty nesting accompanied by rapid social and economic development is not conducive to the mental health of the elderly [50]. Secondly, the deterioration of physical function will cause the elderly to suffer from chronic diseases and impaired ability of daily living. The heavy economic burden of the disease and the pressure of daily care will lead to depression. Finally, mental health services for the elderly were added to existing public health policies. In addition, the health administration has also implemented mental care projects for the elderly (including monitoring of diseases such as depression or anxiety, and exploring comprehensive community interventions). However, the effects of mental health interventions for the elderly are affected in many ways, such as the level of regional economy, medical resources (professional institutions, caregivers), and the degree of self-esteem of the elderly. Therefore, it’s urgent to improve the mental health intervention measures for the elderly to avoid other public health problems caused by the deterioration of mental health problems.

Strengths and limitations

Interpretation of these findings should take into consideration several limitations. Firstly, since this study used cross-sectional analysis, we are unable to draw conclusions about whether there is a clear causal relationship between depressive symptoms and research factors. Secondly, the depression detection scale in this study can only be used as a reference opinion, and the diagnosis requires further clinical diagnosis. Finally, there were limited considerations about the factors that affect the occurrence of depression in the elderly, and subsequent research could further explore the role of depression and other factors under the influence of time.

Conclusion

These above conclusions shed light on the urgent need for reforming the current mental health system in China, and further government involvement is required to improve the treatment and prevention of the mental health conditions. The following suggestions are put forward for the depression of the elderly:(1) The government should use the power of the social organizations to deal with the various needs of the elderly, improve the quality of life of older people. For example, the establishment of a community mental health care center for the elderly. (2) Make full use of the various resources in the community to promote various elderly care services in the community, and provide paid family services (such as nannies and hourly labor caregivers) for the elderly who are inconvenient or difficult to live with. This would include purchasing daily necessities, sick care, providing company, etc. They might assist the elderly to establish cultural organizations and activities for the elderly according to their own interests, which enriches the spiritual life of the elderly. (3) Family members should play an irreplaceable function of spiritual support for the elderly and pay attention to the changes in the mental health of the elderly in time. As well as advocating the elderly to maintain an optimistic attitude towards life.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

SES:

Socioeconomic Status

ADL:

Activities of Daily Living

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Acknowledgements

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Funding

This research was supported by Liaoning Provincial Social Science Planning Fund Project (L20ARK001).

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Authors and Affiliations

  1. College of Public Health, Dalian Medical University, Dalian, 116044, China

    Longyan Cui, Ding Ding, Junfeng Chen, Minghui Wang & Fanrong He

  2. Dalian Municipal Center of Disease prevention and control, Dalian, 116044, China

    Shiyang Yu

Authors
  1. Longyan Cui
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  2. Ding Ding
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  3. Junfeng Chen
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  4. Minghui Wang
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  5. Fanrong He
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Contributions

L.Y.C Mainly analyzed the data and wrote the manuscript; D. D designed the study and reviewed the paper; J.F.CH provided advice on the writing of the paper; M.H.W, SYY and F.R.HE responsible for collecting and cleansing the data. The author(s) read and approved the final manuscript.

Corresponding authors

Correspondence to Ding Ding or Junfeng Chen.

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This research is based on an open database-China Health and Retirement Longitudinal Survey (CHARLS), which is hosted by the National Development Research Institute of Peking University and jointly executed by the Chinese Social Science Survey Center of Peking University and the Youth League Committee of Peking University. The large-scale interdisciplinary survey project is a major project funded by the National Natural Science Foundation of China. All participants in the project signed written informed consent.

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Cui, L., Ding, D., Chen, J. et al. Factors affecting the evolution of Chinese elderly depression: a cross-sectional study. BMC Geriatr 22, 109 (2022). https://doi.org/10.1186/s12877-021-02675-z

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Keywords

BMC Geriatrics

ISSN: 1471-2318

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