Association between multiple chronic conditions and depressive symptoms among the elderly: Based on the China Health and Retirement Longitudinal Study (CHARLS)

doi:10.21203/rs.3.rs-1740273/v1

Background: Multiple (two or more) chronic conditions (MCCs) is an important influencing factor on healthy ageing, which is associated with an increased risk of mortality, functional decline, mental health problems, especially depression in the elderly. However, evidence on associations between MCCs and depression among the elderly is not clear. The aim of this study was to explore the relationship between MCCs and depression in a large Chinese community study.

Methods: Cross-sectional data of 6360 elderly were screened from the China Health and Retirement Longitudinal Study (CHARLS, wave 4) database. Depression symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CES-D-10), and the elderly with MCCs is that who have been diagnosed at least two or more chronic diseases by the doctor. To test the relationship between MCCs and depression among the elderly, chi-square test and generalized linear model (GLM) were carried out.

Results: The prevalence of chronic diseases in the elderly was 50.0%, of which 60.5% suffered from depression. Among the elderly with chronic disease, 19.8% suffered from MCCs, and the probability of depression in the elderly with MCCs was 1.332 times higher than that in the elderly without MCCs (OR:1.332, OR95% CI: 1.152 to 1.541). Urban and rural source, elementary school and above, social activities, life satisfaction, health satisfaction and child relationship satisfaction were the influencing factors of depression in the elderly with MCCs, while gender, urban and rural source, age(≥75y), per capita household income, life satisfaction, health satisfaction and child relationship satisfaction were the influencing factors of depression in the elderly without MCCs.

Conclusion: The results of this study contributed to the exiting research gap on the relationship between MCCs and depression among the elderly, and can develop depression interventions targeting specific chronic disease for elderly.

Elderly

Depressive symptoms

Multiple chronic conditions (MCCs)

China

The grow number of people entering old age, which is a major social problem faced by the whole world, especially the Chinese mainland, and is expected to continue ^[1-2]. According to the data of the Seventh Census of China in 2021, there were 264.02 million elderly people aged 60 years and over in China, accounting for 18.70% of the total population ^[3]. And it is estimated that by 2050, China’s elderly population will increase substantially and reach 400.83 million ^[4].

With the continuous increase of the elderly population, the growth of age leads to the degradation of human function and the increase of chronic diseases. Chronic diseases, with many pathogenic factors and long course, have become the main disease burden and death cause of the elderly. Meanwhile, the patient with chronic disease often has the “accumulation” of chronic diseases with multiple chronic diseases at the same time, which is called multiple chronic conditions (MCCs). Approximately 70% of people over 65 years old have MCCs ^[5-6], and the elderly with MCCs has become one of the most serious public health problems in China ^[7-8]. The incidence of MCCs is closely related to age. The CHARLS cohort from 2011 to 2015 analyzed the physical function and psychological status of patients with MCCs, which found that the prevalence of MCCs and the severity of health loss in the cohort increased year by year from 2011 to 2015, and the prevalence rate of MCCs increased from 46.9% to 62.1%. The incidence rate and health loss of MCCs will continue to increase with age ^[9-10]. Compared with the cross-sectional data of 2013 and 2015, it was found that the prevalence and health loss of MCCs among the elderly in China were gradually increasing too ^[11-12]. The high prevalence of chronic diseases has become the main health problem faced by the elderly in China, which not only affects the physical and mental health of elderly, but also increases the disease burden and economic losses, thus hinders the realization of healthy aging and comprehensive health strategic objectives in China ^[13].

Chronic disease not only cause physical pain to the elderly, but also have great impact on the emotion and psychology of the elderly to a certain extent, especially MCCs, which are often interrelated, and required the whole time-cycle from screening to death, multidisciplinary integration, and cross medical institution linkage management. Studies have shown that patients with MCCs often have different degrees of negative emotions such as anxiety and depression ^[14-15]. The prevalence of MCCS among the elderly is expected to increase the resulting mental health problems, especially depression. The prevalence of depression in the patients with chronic disease is much higher than that in the general population, especially the elderly population with MCCs ^[16]. The government should pay more attention to this major public health problem of depression in the elderly with MCCs.

Depression is a mental health problem characterized by persistent sadness, a loss of interest in some enjoyable activities, disturbed sleep and appetite, poor concentration, and so on ^[17-18], which is a kind of psychiatric disorder associated with biological and social environment factors, especially the damage of functional ^[19-22]. The Global Health Estimates report shows that the prevalence of depression in the elderly has reached a peak among the elderly, and more than 10% of the elderly worldwide have experienced depression ^[23]. While in China, the prevalence of depression in the elderly is about 15% ^[24]. Meanwhile, some studies have found that depression not only increased the prevalence of physical diseases ^[25-27], but also reduces the quality of life, especially the elderly with MCCs, is expected to increase psychological problems. Some studies has confirmed that the elderly with MCCs is prone to have depressive symptoms ^[14-15,28^]. However, the effect of MCCs on depressive symptoms in the elderly is not clear. It is necessary to explore the relationship between MCCs and depressive symptoms in the elderly on the basis of clarifying the status of MCCs and depressive symptoms in the elderly using a nationally representative database.

In this study, we used the data of CHARLS (wave four), a large Chinese community study, to explore the relationship between MCCs and depression in the elderly. According to the purpose of our study, we selected the elderly aged 60 and over as the research object, and demographic variables including sex, education level, urban and rural sources, marital status, living alone or not, social activities and so on. Given the significance of understanding the association between MCCs and depression, we analyzed the status of chronic disease and depression in the elderly, focused on the relationship between MCCs and depression in the elderly.

Study design and sample

Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS, wave 4), which was a survey of middle-aged and elderly people in China, based on a sample of households with members aged 45 years or above, aimed to establish a high quality public micro-database that can provide a wide range of information from socioeconomic status to health conditions ^[9.29]. In order to ensure the representativeness of the sample, the survey followed strict randomization procedures and used a multi-stage sampling method to ensure sample representativeness. When sampling county and rural administrative units, a probability proportional to size (PPS) sampling method was adopted. In the first stage of sampling, 150 county-level units were randomly selected using the PPS method from a sampling frame containing all county-level units in China (excluding Tibet). In the second stage, three communities (urban resident committees or rural administrative villages) were randomly chose using the PPS method from a sampling frame containing all communities in the county-level units. In the third stage, to create a sampling frame, using the software developed by the CHARLS team which utilized Google Earth map images, all dwelling units in a community were listed following an extensive mapping and listing operation, and then a certain number of dwelling units were randomly chose. The ethical approval was granted by the Institutional Review Board (IRB) of Peking University. The IRB approval number for the main household survey including anthropometrics is IRB00001052-11015, and the IRB approval number for biomarker collection is IRB00001052-11014 ^[9.29-31]. All data collected in CHARLS were preserved by the Institute of Social Science Survey of Peking University and released on the CHARLS website (http://www. charls.pku.edu.cn/).

CHARLS is a cohort study. A baseline survey was conducted in 2011, and the followed three wave survey were conducted in 2013y,2015y and 2018y. In order to understand the status of multiple chronic diseases and depressive symptoms in the elderly, we used the latest data from the wave 4 (2018y) of the CHARLS, which was conducted between July 2018 and March 2019, involved 19816 respondents in 150 counties/districts and 450 villages/urban communities ^[9]. According to our purpose, the elderly aged 60 years and over were selected as the research objects. After excluding the data was incomplete (incomplete filling of relevant variables such as depression, chronic diseases and daily activity ability), the final sample included in the analysis was 6360.

Measures

Depressive symptoms

Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CES-D-10) which has been validated among elderly respondents in China using CHARLS data ^[32]. The CES-D-10 response scale includes 10 questions regarding the participant’s experience in the past week: feeling bothered, having trouble in concentrating, feeling depressed, feeling as though everything was effortful, feeling hopeful, feeling fearful, having restless sleep, feeling happy, feeling lonely and having difficulty getting going ^[33]. The total CES-D-10 score ranges from 0 to 30, with higher scores indicating more severe depressive symptoms. In this study, we used a cutoff score of ≥ 10 to distinguish participants with depressive depression from those who were relatively free of depression ^[34-35]. The CES-D-10 used in CHARLS exhibited good internal consistency, with α = 0.793 in the baseline investigation in 2011y, α = 0.787 in the follow-up investigation in 2013y, α = 0.779 in the second follow-up investigation in 2015y^[36], and α = 0.818 in our study.

Chronic disease and multiple chronic conditions (MCCs)

In CHARLS, the elderly with chronic disease is those who had been diagnosed chronic disease by the doctor up to the time of investigation. A total of 14 chronic diseases was investigated, including hypertension, dyslipidemia (high or low), diabetes or hyperglycemia, cancer, malignant tumor, chronic lung disease, liver disease, heart disease (heart infarction, coronary heart disease, angina pectoris, congestive heart failure, etc.), stroke (including cerebral infarction and cerebral hemorrhage), kidney disease (excluding cancer or cancer), diseases of stomach or digestive system (excluding tumor or cancer), memory related diseases (Alzheimer's disease, brain atrophy, Parkinson's disease), arthritis or rheumatism, and asthma. The relevant variables of chronic diseases in this study were respectively defined as: ①Having chronic diseases or not: as long as you have been diagnosed by a doctor with any kind of chronic diseases(14 chronic diseases in CHARLS), you are regarded as a patient with chronic diseases, and the value is 1, otherwise the value is 0; ②Having multiple chronic conditions (MCCs) or not: MCCs are defined as having two or more chronic diseases, and if you have MCCs , it is assigned to 1; otherwise it is assigned to 0; ③The number of MCCs: having two chronic diseases are assigned to 1, having three chronic diseases are assigned to 2, and having four or more chronic diseases are assigned to 3.

Statistical analysis

All the data in our study were obtained from the CHARLS database, which was converted into XLS format by StataV13.0 and analyzed using IBM SPSS Statistics V22.0. Demographic characteristics of the elderly were described using frequencies and percentages (the categorical variables were n (%)), and Chi-square test was used to compare the prevalence of depression between the elderly with MCCs and without MCCs, and OR_-unadjusted was calculated. Then a generalized linear model (GLM) was employed to estimate the association between MCCs and depression after controlling other confounding factors, the results were expressed as adjusted Odds ratio (OR_-adjusted) and it’s 95% confidence interval(95%CI).

GLM is an extension of the traditional linear model that allows the overall average to depend on a linear predictor through a nonlinear link function and allows the response probability distribution to be any member of the exponential distribution family, including logistic regression model. In our study, a binary logistic regression model was used to analyze the influence of MCCs on depression in the elderly when controlling for other confounding factors. A GLM always includes three components:

In order to explore the association between MCCs and depression in the elderly, three logistic regression models were built, one model for estimating the influence of MCCs on depression after controlling all the confounding factors, and two other models stratified by having MCCs or not to estimate the risk factors for depression in each group. Then binary logistic regression model were used to analyze influencing factors of the occurrence of depression among the elderly with MCCs or not. All statistical analyses were performed using IBM SPSS StatisticsV22.0, and P-value < 0.05 was considered statistically significant.

The characteristics of the participants (6360 elderly people) were displayed in Table 1, including 1736(27.3%) urban elderly and 4624(72.7%) rural elderly, and including 2910(45.8%) male elderly and 3450(54.2%) female elderly. Among the participants, 23.4% elderly lived alone and 76.6% lived with other family members.

Table 1

The characteristics of participants (N, %)

Variables	Options	Percentage	Variables	Options	Percentage
Gender	Male	2910(45.8)	Duration of sleep	≤5h	1922（30.2）
Gender	Female	3450(54.2)		6-9h	4096（64.4）
Urban or rural areas	Urban	1736（27.3）		≥10h	342（5.4）
Urban or rural areas	Rural	4624（72.7）	Living alone or not	Yes	1487（23.4）
Age	60-64	1953（30.7）	Living alone or not	No	4873（76.6）
	65-74	3195（50.2）	Social activities	Yes	3118（49.03）
	≥75	1212（19.1）	Social activities	No	3242（50.97）
Marital status	Married and live with spouse	4874(76.6)	Per capita household income	Low income	1443（22.7）
	Married, not live with spouse	218（3.4）		Middle income	3172（49.9）
	Divorced	63（1.0）		High income	1745（27.4）
	Widowered	1169（18.4）	Life satisfaction	Satisfied Not Satisfied	5612（88.2） 748（11.8）
	Never married	36（0.6）	Health satisfaction	Satisfied	4286（67.4）
Education level	Illiterate	1820（28.6）	Health satisfaction	Dissatisfied	2074（32.6）
	Primary school and below	2978（46.8）	Marital satisfaction	Satisfied	5981（94.0）
	Elementary school and above	1562（24.6）	Marital satisfaction	Dissatisfied	241（3.8）
Self-reported health	Very good	456（7.2）	Child relationship satisfaction	Satisfied	5987（94.1）
	Good	566（8.9）	Child relationship satisfaction	Dissatisfied	373（5.9）
	Fair	3071（48.3）
	Poor	1726（8.5）
	Very poor	541（8.5）

Table 2 shows that 55.2% of the elderly having depression, 50.04% of the elderly suffered from chronic diseases, and 19.83% suffer from MCCs, of which 61.70% suffered from two chronic diseases, 23.39% suffered from three chronic diseases and 14.91% suffered from four or more chronic diseases. There were significant differences in the prevalence of MCCs between urban and rural areas, different ages, different educational levels, life satisfaction or not, health satisfaction or not, marriage satisfaction or not, child relationship satisfaction or not (P < 0.05). The prevalence of MCCs in urban elderly was higher than that in rural elderly. The prevalence of MCCs in 65-74 years old elderly was the highest, and the prevalence of MCCs in elderly who were dissatisfied with life, self-health, marriage and relationship was higher than that in the satisfied elderly (Table 3).

Table 2

Prevalence of chronic diseases and depression in the elderly

Variables	Options	Percentage（N/%）
Chronic disease or not	Yes	3184（50.1）
Chronic disease or not	No	3176（49.4）
MCCs or not	Yes	1261（19.8）
MCCs or not	No	5099（80.2）
Number of chronic diseases	Two types	778（61.7）
	Three types	295（23.4）
	Four or more types	188（14.9）

Table 3

Demographic characteristics stratified by MCCs or not（N/%）

Variables	Option	MCCs	Non-MCCs	χ²
Gender	Male	578（19.9）	2332（80.1）	0.004
Gender	Female	683（19.8）	2767（80.2）	0.004
Urban or rural areas	Urban	423（24.2）	1313（75.6）	30.95**
Urban or rural areas	Rural	838（18.1）	3786（81.9）	30.95**
Age	60-64	386（19.8）	1567（80.2）	8.11*
	65-74	668（20.9）	2527（79.1）
	≥75	207（17.1）	1005（82.9）
Marital status	Married and live with spouse	978（20.1）	3896（79.9）	3.65
	Married, not live with spouse	46（21.1）	172（78.9）
	Divorced	16（25.4）	47（74.6）
	Widowered	213（18.2）	956（81.8）
	Never married	8（22.2）	28（77.8）
Education level	Illiterate	332（18.2）	1488（81.8）	17.24**
	Primary school and below	563（18.9）	2415（81.1）
	Elementary school and above	366（23.4）	1196（76.6）
Per capita household income	Low income	264（18.3）	1179（81.7）	5.50
	Middle income	621（19.6）	2551（80.4）
	High income	376（21.5）	1369（78.5）
Social activities	Yes	629（20.2）	2489（79.8）	0.46
Social activities	No	632（19.5）	2610（80.5）	0.46
Duration of sleep	≤5h	381（19.8）	1541（80.2）	2.47
	6-9h	800（19.5）	3296（80.5）
	≥10h	75（23.1）	249（76.9）
Living alone or not	Yes	282（19.0）	1205（81.0）	0.91
Living alone or not	No	979（20.1）	3894（79.9）	0.91
Life satisfaction	Satisfied Dissatisfied	1057(18.8)	4555(81.2)	29.56**
Life satisfaction	Satisfied Dissatisfied	204(27.3)	544(72.7)	29.56**
Health satisfaction	Satisfied	686(16.0)	3600(84.0)	120.75**
Health satisfaction	Dissatisfied	575(27.7)	1499(72.3)	120.75**
Marital satisfaction	Satisfied	1141(19.1)	4840(80.9)	35.64**
Marital satisfaction	Dissatisfied	75(31.1)	166(68.9)	35.64**
Child relationship satisfaction	Satisfied	1148(19.2)	4839(80.0)	27.31**
Child relationship satisfaction	Dissatisfied	113(30.3)	260(69.7)	27.31**

Note: *:P<0.05; **:P<0.001

The result of Chi-square test (Table 4) showed that the possibility of depression in the elderly with MCCs is 1.65 times higher than that in the elderly without MCCs (OR_-unadjusted: 1.65, 95% CI: 1.45 to 1.88). However, there was no significant difference in the prevalence of depression in the elderly with different numbers of MCCs (P > 0.05), the possibility of depression in the elderly with chronic diseases is 1.53 times higher than that in the elderly without chronic diseases (OR_-unadjusted: 1.53, 95% CI: 1.39 to 1.69). And the result of binary logistic regression showed that after controlling gender, urban and rural areas, education level, per capita household income, social activities, duration of sleep, living alone or not, life satisfaction, health satisfaction, marital satisfaction and child relationship satisfaction, the probability of depression in the elderly with MCCs was 1.332 times higher than that in the elderly without MCCs (OR_-adjusted: 1.332, OR95% CI: 1.152 to 1.541).

In addition, the binary logistic regression was used to analysis the influencing factors of depression in the elderly with MCCs and without MCCs. Urban and rural source, elementary school and above, social activities, life satisfaction, health satisfaction and child relationship satisfaction were the influencing factors of depression in the elderly with MCCs, while gender, urban and rural source, age(≥75y), per capita household income, life satisfaction, health satisfaction and child relationship satisfaction were the influencing factors of depression in the elderly without MCCs. Compared with the urban elderly with MCCs, the OR (95%CI) of the rural elderly with MCCs was 1.644 (1.197-2.257), and the OR (95%CI) of the rural elderly without MCCs was 1.549(1.329-1.806) compared with the urban elderly without MCCs. Compared with the male elderly without MCCs, the OR (95%CI) of the female elderly without MCCs was 1.376(1.204-1.572), while for the elderly with MCCs, there was no significant difference between male and female. And whether there were MCCs or not, the elderly who were satisfied with their life, health and children relationship would be less likely to suffer from depression (Table 5).

Table 4

Comparison of depression among the elderly with chronic diseases (N/%)

Variables	Options	Depression	Not depression	χ²	OR_-unadjusted(95%CI)
Chronic disease or not	Yes	1925（60.5）	1259（39.5）	78.18**	1.53（1.39,1.69）
Chronic disease or not	No	1586（49.9）	1590（50.1）	78.18**	1.53（1.39,1.69）
MCCs or not	Yes	818（64.9）	443（35.1）	59.41**	1.65（1.45,1.88）
MCCs or not	No	2693（52.8）	2406（47.2）	59.41**	1.65（1.45,1.88）
Number of MCCs	Two types	493（63.4）	285（36.3）	2.24	1.09（0.93,1.28）
	Three types	201（68.1）	94（31.9）
	Four or more types	124（66.0）	64（34.0）

Note: (1) *: P < 0.05, **: P < 0.001.

(2) OR_-unadjusted: odds ratio without other variables controlled; 95% CI: 95% confidence interval.

Table 5

Influencing factors of depression in the elderly with MCCs and that without MCCs

Variables	Options	Elderly with MCCs			Elderly without MCCs
Variables	Options	OR		OR (95%CI)	OR	OR (95%CI)
Gender	Male	1	-		1	-
Gender	Female	1.195	0.900,1.587		1.376**	1.204,1.572
Urban or rural areas	Urban	1	-		1	-
Urban or rural areas	Rural	1.644**	1.197,2.257		1.549**	1.329,1.806
Age	60-64	1	-		1	-
	65-74	1.099	0.806,1.498		0.941	0.816,1.085
	≥75	1.474	0.971,2.238		0.809*	0.673,0.973
Marital status	Married and live with spouse	1	-		1	-
	Married, not live with spouse	3.351	0.477,23.553		1.246	0.642,2.418
	Divorced	4.977	0.502,49.388		1.466	0.611,3.517
	Widowered	3.600	0.534,24.253		1.319	0.727,2.391
	Never married	0.920	0.070,12.093		0.701	0.241,2.033
Education level	Illiterate	1	-		1	-
	Primary school and below	0.780	0.546,1.114		1.113	0.956,1.295
	Elementary school and above	0.610*	0.401,0.928		0.861	0.715,1.059
Per capita household income	Low income	1	-		1	-
	Middle income	1.090	0.760,1.563		0.777**	0.666,0.906
	High income	0.823	0.539,1.258		*0.4808**	0.398,0.578
Social activities	No	1	-		1	-
Social activities	Yes	0.728*	0.556,0.953		0.896	0.792,1.013
Duration of sleep	≤5h	1	-		1	-
	6-9h	0.810	0.605,1.085		0.897	0.786,1.024
	≥10h	0.691	0.380,1.257		1.310	0.970,1.769
Living alone or not	No	0.455	0.069,2.984		0.937	0.520,1.698
Living alone or not	Yes	1	-		1	-
Life satisfaction	Dissatisfied	0.081**	0.034,0.193		0.223**	0.167,0.298
Life satisfaction	Satisfied	1	-		1	-
Health satisfaction	Dissatisfied	1	-		1	-
Health satisfaction	Satisfied	0.340**	0.257,0.451		0.333**	0.288,0.385
Marital satisfaction	Dissatisfied	1	-		1	-
Marital satisfaction	Satisfied	0.479	0.133,1.724		1.058	0.676,1.655
Child relationship satisfaction	Dissatisfied	1	-		1	-
Child relationship satisfaction	Satisfied	0.203**	0.085,0.489		0.287**	0.268,0.558

Note: (1) Taking the first category as the reference category (the one with the minimum value).

(2) OR: OR_-_adjusted_,odds ratio under other variables controlled; 95% CI: 95% confidence interval.

(3) *:P<0.05, **:P<0.001

Our study found that the prevalence of chronic diseases in the elderly was 50.0% and 19.8% of the elderly suffered from MCCs, which have become one of the most serious public health problems in China. The prevalence of chronic diseases and MCCs in the urban elderly was higher than that in rural elderly, and the prevalence of chronic disease and MCCs in the elderly aged 65–74 was higher than that in the elderly aged 60–64, 75 and above. And the prevalence of MCCs in elderly with un-satisfactory life, un-satisfactory health, un-satisfactory marriage and un-satisfactory child relationship was higher than that in the satisfied elderly.

The course of chronic diseases in the elderly is long, the clinical cure is difficult, and the condition is easy to repeat. Some patients among elderly need long-term or even lifelong treatment, which increases the physical and mental pressure of patients and reduces the quality of life of patients. Chronic diseases not only seriously endanger the physical and mental health of elderly and reduce the quality of life, but also bring heavy family burden and adverse impact on family members. This low quality of life and heavy family burden directly lead to the occurrence of depression ^[37]. Our study found that there was significant difference in the prevalence of depression between the elderly with MCCs and the elderly without MCCs. The depression estimate in the elderly with MCCs (64.9%) was significantly higher than that of the elderly without MCCs (52.8%), which was higher than that in Ji HQ’s study using the data of CHARLS in 2015 ^[38]. The result of binary logistic regression showed that after controlling other variables, the probability of depression in the elderly with MCCs was 1.332 times higher than that in the elderly without MCCs, which answered Hypothesis 2(MCCs is an important influencing factor for depression in the elderly, the prevalence of depression in the elderly with MCCs is higher than that in the elderly without MCCs.). This study preliminary prompted depressive symptoms associated with MCCs in a Chinese elderly community population. MCCs as an important factor affecting the health and healthy aging of the elderly, not only cause the physical pain of the elderly, but also have a great impact on the emotion and psychology of the elderly to a great extent, and then affect their mental health level. Studies have shown that patients with chronic diseases often have different degrees of negative emotions such as anxiety and depression ^[27–28.39]. And depression may prove particularly troubling for individuals managing MCCs that require high levels of attention and monitoring (e.g., diabetes, pulmonary disease). Those high in depressive symptoms may be less likely to self-manage illness, leading to additional costs and burdens to individuals and society ^[40–41]. These analyses add important information to our understanding of the relationship between chronic diseases and depression in the elderly, which indicated that we should pay more attention to the elderly with chronic diseases, especially MCCs. The link between depression and chronic disease, such as cognitive decline in elderly adults, multimorbidity has been well documented ^[42–43].

In addition, gender, urban and rural areas, educational level, self-rated health, life satisfaction, health satisfaction, child relationship satisfaction, living alone or not, and per capita family income are important influencing factors of depression in the elderly, which is similar to the results of previous reports ^[36,44−46]. Urban and rural source, elementary school and above, social activities, life satisfaction, health satisfaction and child relationship satisfaction were the influencing factors of depression in the elderly with MCCs, while gender, urban and rural source, age(≥ 75y), per capita household income, life satisfaction, health satisfaction and child relationship satisfaction were the influencing factors of depression in the elderly without MCCs. And the prevalence of depression in the female elderly without MCCs was higher than that in the male elderly without MCCs. Whether there were MCCs, the prevalence rate of the elderly in rural areas were higher than that of elderly in urban areas. We found that duration of sleep had no effect on depression of the elderly with MCCs or without MCCs, which was inconsistent with Zeo’s study to some extent. The quality of sleep may affect the prevalence of depression in the elderly, and moderate the relationship between pain interference and depression among the elderly with chronic pain such that good sleep quality attenuates the effect of pain interference on depression, and poor sleep quality amplifies the effect of pain interference on depression ^[47]. Meanwhile, our study found that marital status and marital satisfaction had no effect on depression in the elderly, while Mieke’s study found that more chronic diseases in one spouse were associated with depression in the other spouse. And one spouse’s MCCs and the other spouse’s mental health was a dynamic process ^[14]. And whether there were MCCs or not, the elderly who were satisfied with their life, health and children relationship would be less likely to suffer from depression, which indicated that good parent-child relationship, social support and high life satisfaction were the protective factors of depression in the elderly whether they having MCCs or not.

Based on the data of a large Chinese community study, this study found that the probability of depression in the elderly with MCCs was 1.332 times higher than that in the elderly without MCCs. In addition, gender, urban and rural areas, educational level, self-rated health, life satisfaction, health satisfaction, child relationship satisfaction, living alone or not and per capita household income are important influencing factors of depression in the elderly. Future secondary prevention and health management of the elderly with MCCs will be an important work in the process of mental health intervention for the elderly.

However, there are also some shortcomings of this study. First, our study was based on a cross-sectional analysis of the fourth wave of the CHARLS database, so it was difficult to draw conclusion about causation and we couldn’t make a causal inference. Second, although CESD-10 has been widely used in the investigation of depression and had good reliability, it was based on subjective feelings of the participant, and there may still be a certain gap with the real diagnosis of depression.

MCCs: Multiple Chronic Conditions; OR: Odds Ratio; CI: Confidence Interval.

Acknowledgments

We thank the Institute of Social Science Survey of Peking University for their organizing of CHARLS, and all the participants, investigators and assistants of CHARLS (Wave 4), especially Zhao YH, John S, Chen XX, Wang YF, Gong JQ, Meng QQ, Wang GW and Wang HL. We would like to thank Zhou ZL, Fan XJ and Rui Li for their excellent paper writing suggestions; Sun HW and Xu ZY for providing data management and analysis assistance.

Authors’ contributions

Conceptualization: Liu HX and Zhou ZL; methodology: Sun HW and Fan XJ; software: Sun HW and Liu HX; formal analysis and writing the original draft preparation: Liu HX; review and modification: Liu HX and Zhou ZL. All authors have read and agreed to the published version of the manuscript and have no competing interests.

Founding

This work was supported by Shandong Province Natural Science Foundation [grant number: ZR2021QG050] and Social Science Planning Project of Shandong Province [grant number:21DRKJ02].

Ethics approval and Consent to participate

Ethical approval of the China Health and Retirement Longitudinal Study (CHARLS) was granted by the Institutional Review Board (IRB) of Peking University. The IRB approval number for the main household survey including anthropometrics is IRB00001052-11015, and the IRB approval number for biomarker collection is IRB00001052-11014. All methods in our study were carried out in accordance with relevant guidelines and regulations, and informed consent was obtained from all subjects and/or their legal guardian(s).

Competing interests

All the authors declare they have no conflicts of interest.

Author details

¹ School of Public Health and Management, Binzhou Medical University, No. 346, Guanhai Road, Laishan District, Yantai, Shandong, 264003, China. ²School of Public Health, Xi’an Jiaotong University Health Science Center, No. 76, Yantai West Road, Xi’an, Shaanxi, 710061, China. ³School of Public Policy and Administration, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi, 710049, China.

Supplementary Information

No supplemental files. All the data we used have been publicly released on the CHARLS website (http://www. charls.pku.edu.cn/).

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No competing interests reported.

Association between multiple chronic conditions and depressive symptoms among the elderly: Based on the China Health and Retirement Longitudinal Study (CHARLS)

Status:

Version 1

Abstract

Background

Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1