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BMC Geriatrics

Erschienen in:

Open Access 01.12.2023 | Research

Prevalence and determinants of depressive symptoms among community-dwelling older adults in China based on differences in living arrangements: a cross-sectional study

verfasst von: Chang Fu, Lianmeng Cao, Fan Yang

Erschienen in: BMC Geriatrics | Ausgabe 1/2023

Abstract

Background

Older adults with different living arrangements may have different mental health statuses and different factors that influence their mental health. The aim of the present study is to investigate the prevalence and determinants of depressive symptoms among community-dwelling older adults in China based on differences in their living arrangements.

Methods

Participants were 6,055 older adults from the 2015 China Health and Retirement Longitudinal Study. Depressive symptoms and their determinants were evaluated using the 10-item Center for Epidemiologic Studies Depression Scale and multivariate logistic regression analysis, respectively.

Results

The prevalence of depressive symptoms among older adults living alone, as a couple, and with children was 47.8%, 33.2%, and 39.5%, respectively. The common risk factors for depressive symptoms were shorter sleep duration, poorer activities of daily living, and poorer self-rated health. Women, those with lower educational levels, and those suffering from chronic diseases had a higher risk of depressive symptoms among older adults living as a couple and those living with children. Smoking and participation in economic activities were also risk factors of depressive symptoms among older adults living with children and those living alone, respectively.

Conclusions

The findings suggest that older adults living as couples had the lowest prevalence of depressive symptoms, while those living alone had the highest prevalence of depressive symptoms. The determinants of depressive symptoms differed by living arrangement; hence, they should be considered in future interventions.

Supplementary Material 1

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12877-023-04339-6.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background

Depressive symptoms are one of the most common mental health problems among older adults [1]. Furthermore, it can significantly increase older adults’ risk of frailty [2] and decrease their quality of life [3]. China has the largest population of older adults in the world; specifically, by the end of 2021, its population aged 60 years and above reached 267.36 million, accounting for 18.9% of its total population [4]. A systematic review and meta-analysis showed that the pooled overall prevalence of depressive symptoms among older adults in China was 20.0% [5]. The high prevalence of depressive symptoms among older adults has significantly increased the degree of medical expenditure in the country [6]. A study from China reported that there were about $42.67 per person of annual medical spending which is induced by depressive symptoms and depression [7]. Therefore, exploring the determinants of depressive symptoms and reducing their prevalence are crucial for older adults’ mental health. Previous studies reported several determinants of depressive symptoms, such as chronic diseases [8], educational level [9], and sleep duration [10].

In Chinese culture, living with aging parents and taking care of them is a primary moral principle for the younger generations [11]. However, with social economic development, including reduced fertility, the migration of the younger labor force from less developed to more developed areas, and the younger generation’s preference to live independently after marriage [12]—the living arrangements of older adults have changed rapidly in China. Recent years have seen the number of empty-nest older adults, which refers to those living alone or only with a spouse/partner owing to their children having already left home, increases rapidly [12]. By 2030, empty-nest older adults will account for 90% of the total older adult population in the country [13]. Therefore, the living arrangements of most older adults have shifted to either living alone, as a couple, or with children.

Previous studies have found that living arrangements are associated with older adults’ mental health outcomes, such as loneliness [14], suicidal ideation [15], and depressive symptoms [16] and that a lack of family emotional support or financial support from children may be vital for the poor mental health of empty-nest older adults [17, 18]. Accordingly, the mental health status of this population group has been concerned by many researchers. Studies found that empty-nest older adults are more likely to have depressive symptoms than non-empty-nest ones [19‐21]. Some other studies have explored the determinants of depressive symptoms among empty-nest older adults, such as sex, education level, and chronic diseases [22, 23]. However, these studies did not divide the empty-nest older adults into those living alone or as part of a couple.

A previous study found that older adults living as a couple had better cognitive function than those living with children; the possible reason for this phenomenon could be related to these older adults having less burden related to caring for children or grandchildren and being able to enjoy their life as a couple [24]. Therefore, older adults living as a couple may have a better mental health status than those living alone or with children, and the determinants of mental health may differ by their living arrangement. By further classifying empty-nest older adults into those living alone and those living as a couple and exploring the determinants of depressive symptoms among the three groups, i.e., those living alone, as a couple, or with children, those being most in need of mental health improvement can be identified and a scientific basis for targeted community interventions and relevant health policies can be provided.

In this study, we investigate both the prevalence and determinants of depressive symptoms among older adults according to three different kinds of living arrangements. To obtain knowledge on this topic may allow invested stakeholders to ensure more precise decision-making regarding the implementation of interventions for depressive symptoms in the older adult population.

Methods

Study population

This study used data released by the National School of Development of Peking University from the 2015 China Health and Retirement Longitudinal Study, which comprised a nationally representative sample of middle-aged and older Chinese community-dwelling residents. This sample included 21,096 individuals, selected through multistage sampling from 12,400 families in 450 villages and 150 county-level units [25]. The inclusion criteria were individuals aged ≥ 60 years and who were able to communicate with the interviewers. The exclusion criteria were individuals with serious diseases, e.g., Alzheimer’s and serious psychiatric disorders other than depression and those who did not provide information on their living arrangements and/or were living in nursing homes [26]. Older adults living with others (except children or spouse/partners) were excluded, as they comprised less than 2% of the sample. Finally, data from 6,055 participants were analyzed.

The original CHARLS was approved by the Ethical Review Committee of Peking University. All participants were asked to write an informed consent for inclusion before participating in this survey. Secondary analysis of CHARLS data does not require ethics approval. All analyses in this study were performed in accordance with relevant guidelines and regulations, including the Helsinki Declaration revised in 1989.

Measurement of depressive symptoms

Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D 10), which has good reliability and validity among older Chinese respondents [27]. Its 10 items were used to ask the respondents to rate how often they experienced depressive symptoms in the past week [25]. Four choices were selected to answer each question: zero (rarely), 1 (1–2 days), 2 (3–4 days), and 3 (5–7 days). The answers from respondents were recorded as 0 to 3 and 3 to 0 for the negatively and positively scored questions, respectively [28]. The total scores ranged from 0 to 30 [29], and a cut-off point of 10 was used for the diagnosis of depressive symptoms [27]. Cronbach’s alpha values for the CES-D 10 in this study were as follows: older adults living alone group, α = 0.680; living as a couple group, α = 0.637; and living with children group, α = 0.649.

Assessment of living arrangements

The participants were asked the question: “Who do you live with now, or with whom have you lived for more than 11 months during the past year?” According to their answers, they were categorized as older adults living alone, as a couple, or with children [24].

Measurement of determinants

In this study, the determinants of older adults’ depressive symptoms were chosen based on previous studies [30‐34]. Participants’ demographic characteristics included age, sex, marital status, education level, and region of living (urban or rural). Their lifestyle factors included smoking (current/past or never), alcohol consumption (yes or no), and sleep duration (< 6 h, 6–8 h, and ≥ 9 h per day) [35].

The health status characteristics included chronic disease (had or did not have), activities of daily living (ADL), body mass index (BMI), and self-rated health. The abilities of eating, dressing, using the toilet, getting in and out of bed, defecating, and bathing were included to evaluate the participant’s ADL [36]. Cronbach’s alpha values for the ADL in this study were as follows: older adults living alone group, α = 0.798; living as a couple group, α = 0.764; and living with children group, α = 0.834. Participants who expressed difficulty with any of these activities were classified as having ADL functional decline. BMI was categorized as normal (18.5 ≤ BMI < 24 kg/m²), underweight (BMI < 18.5 kg/m²), overweight (24 ≤ BMI < 28 kg/m²), and obese (BMI ≥ 28 kg/m²) [37]. Self-rated health was divided into good, fair, and poor [38]. Participants were also asked whether they had participated in economic activities.

Statistical analysis

Data were analyzed using SPSS version 20.0 (IBM Corp. Armonk, NY, USA). The multiple imputation was carried out using chained equations with 10 iterations to address missing data [39]. Based on the previous study, multiple imputation is the most accurate technique for dealing with missing values when assessing depressive symptoms [40]. In this study, the proportions of missing values were region of living (0.4%), education level (6.4%), smoking (0.5%), alcohol consumption (0.5%), sleep duration (8.1%), chronic disease (10.8%), ADL (22.1%), BMI (17.8%), self-rated health (5.1%), economic activity (0.7%), depressive symptoms (12.7%). There were no missing values for living arrangements, age, sex, and marital status. The logistic model was used to identify the variables predicting missing responses, which showed that the missing data of variables were related to the respondents’ age and the result indicated that the data were missing at random [40]. Therefore, the multiple imputation was carried out using chained equations with 10 iterations to address missing data [39]. Participants’ characteristics were described using percentages for categorical data. The prevalence of depressive symptoms was compared between the three groups by their characteristics using chi-square tests. Multivariate logistic regression analysis was used to examine the association between living arrangement and depressive symptoms and examine the determinants of depressive symptoms. Before data analysis, residual analysis was performed with logistic regression to test the assumptions of the regression models [41]. The variance inflation factor (VIF) was used to assess the multicollinearity of the variables, which showed that there is no muticollinearity in this study (VIF < 10) [42] (STable 1). The logistic regression model was evaluated by Hosmer-Lemeshow goodness-of-fit test and p > 0.05 was considered a well-fitting logistic regression model [43] (STable 2). Statistical significance was set at p < 0.05.

Results

Sample characteristics

Table 1 shows participants’ sociodemographic characteristics, health status, and depressive symptoms. Of the 6,055 participants, 58.4% were 60–69 years old, 54.3% were men, 66.4% were married/cohabitating, 79.7% lived in rural areas, and 54.3% had no formal education. Furthermore, 52.7% had never smoked, 65.7% did not consume alcohol, 45.1% slept 6–8 h per day, 82.7% had at least one chronic disease, 64.0% had normal ADL function, 52.1% had normal BMI, 51.2% had fair self-rated health status, and 50.6% participated in economic activities. The prevalence of depressive symptoms among the total sample was 38.6%, whereas its prevalence among older adults living alone, as a couple, and with children was 47.8%, 33.2%, and 39.5%, respectively. There were statistically significant differences in age, sex, marital status, region of living, education level, alcohol consumption, sleep duration, BMI, participation in economic activities, and depressive symptoms between the three groups (p < 0.05). After controlling all confounders, the results revealed that, compared with older adults living alone, older adults living as a couple and living with children were both less likely to have depressive symptoms (STable 3).

Table 1

Characteristics of the study population

Characteristics	Total(n = 6055)	Living alone (n = 1191)	Living as a couple (n = 2288)	Living with children (n = 2576)	χ²	P
Age (%)					186.737	< 0.001
60 ~ 69	58.4	44.4	63.3	60.6
70 ~ 79	30.3	37.5	30.4	26.7
≥ 80	11.3	18.1	6.3	11.3
Sex (%)					200.850	< 0.001
Male	54.3	42.1	65.3	50.2
female	45.7	57.9	34.7	49.8
Marital status (%)					2504.712	< 0.001
Married/ Cohabitating	66.4	16.0	99.3	60.4
Never married/divorced/ widowed/ separated	33.6	84.0	0.7	39.6
Region of living (%)					13.749	0.001
Urban	20.3	16.5	21.2	21.4
Rural	79.7	83.5	78.8	78.6
Education level(%)					151.580	< 0.001
No formal education	54.3	67.0	45.5	56.4
Elementary school	23.3	18.9	27.0	22.0
Middle school	14.0	9.2	16.5	14.0
High school or above	8.4	4.9	11.0	7.7
Smoking (%)					49.009	0.456
Current/past	47.3	42.2	53.0	44.7
Never	52.7	57.8	47.0	55.3
Alcohol consumption(%)					47.212	< 0.001
Yes	34.3	28.4	39.3	32.6
No	65.7	71.6	60.7	67.4
Sleep duration (hours per day), (%)					40.533	< 0.001
< 6	27.8	35.1	25.2	26.9
6 ~ 8	45.1	38.5	47.6	45.8
≥ 9	27.1	26.4	27.2	27.3
Chronic disease(%)					0.382	0.826
No	17.3	17.3	17.0	17.7
Yes	82.7	82.7	83.0	82.3
ADL(%)					3.555	0.169
Completely normal	64.0	62.2	65.6	63.5
Functional decline	36.0	37.8	34.4	36.5
BMI(%)					25.492	< 0.001
Normal	52.1	56.1	50.1	52.1
Underweight	8.2	8.1	6.9	9.3
Overweight	29.2	25.9	30.9	29.3
Obese	10.5	9.9	12.2	9.3
Self-rated health (%)					6.680	0.154
Good	20.7	20.6	19.6	21.8
Fair	51.2	49.1	52.6	50.8
Poor	28.2	30.3	27.8	27.5
Economic activity (%)					54.167	< 0.001
Yes	49.4	42.9	55.1	47.3
No	50.6	57.1	44.9	52.7
Depressive symptoms (%)					62.574	< 0.001
Yes	38.6	47.8	33.2	39.5
No	61.4	52.2	66.8	60.5

Note: BMI, body mass index; ADL, activities of daily living; Percentages may not add up to 100% due to rounding

Prevalence of depressive symptoms based on different sociodemographic characteristics

As shown in Table 2, there were significant differences among older adults living alone in terms of sex, region of living, education level, smoking status, sleep duration, chronic diseases, ADL, self-rated health, and prevalence of depressive symptoms. There were also differences among those living as a couple in terms of sex, region of living, education level, smoking status, alcohol consumption status, sleep duration, chronic diseases, ADL, self-rated health, and prevalence of depressive symptoms. Furthermore, among those living with children, significant differences were noted in terms of sex, marital status, region of living, educational level, smoking status, alcohol consumption status, sleep duration, chronic diseases, ADL, BMI, self-rated health, and prevalence of depressive symptoms.

Table 2

Prevalence of depressive symptoms in different social-demographic characteristics of the study population

Characteristics	Living alone		χ²	P	Living as a couple		χ²	P	Living with children		χ²	P
Characteristics	Incidence	Non-case			Incidence	Non-case			Incidence	Non-case
Age			3.735	0.155			1.695	0.429			2.035	0.362
60 ~ 69	50.9	45.3			65.6	64.1			64.9	67.0
70 ~ 79	37.5	40.3			30.0	30.1			28.4	25.6
≥ 80	11.6	14.4			4.4	5.8			6.8	7.3
Sex			18.291	< 0.001			73.880	< 0.001			53.615	< 0.001
Male	36.6	50.0			52.4	71.4			43.3	59.3
female	63.4	50.0			47.6	28.6			56.7	40.7
Marital status			1.766	0.184			2.050	0.152			23.429	< 0.001
Married/ Cohabitating	15.9	19.1			98.9	99.4			58.8	69.0
Never married/divorced/ widowed	84.1	80.9			1.1	0.6			41.2	31.0
Region of living			16.718	< 0.001			34.889	< 0.001			24.117	0.001
Urban	11.7	21.3			14.0	25.3			16.7	25.7
Rural	88.3	78.7			86.0	74.7			83.3	74.3
Education level			24.675	< 0.001			76.182	< 0.001				< 0.001
No formal education	71.6	57.1			55.3	38.7			64.7	44.5
Elementary school	18.2	22.8			26.8	27.3			19.8	25.5
Middle school	6.7	13.0			13.0	18.9			10.8	19.3
High school or above	3.6	7.0			4.9	15.1			4.7	10.7
Smoking			12.226	< 0.001			23.141	< 0.001			11.008	0.001
Current/past	38.4	49.3			45.4	56.5			42.0	49.3
Never	50.7	50.7			54.6	43.5			58.0	50.7
Alcohol consumption			2.288	0.130			39.609	< 0.001			7.175	0.007
Yes	26.6	30.9			30.7	45.0			30.6	36.2
No	73.4	69.1			69.3	55.0			69.4	63.8
Sleep duration (hours per day)			38.280	< 0.001			100.624	< 0.001			82.444	< 0.001
< 6	45.2	26.4			38.6	18.3			37.0	19.9
6 ~ 8	32.3	44.8			40.3	51.6			43.2	49.0
≥ 9	22.5	28.9			21.1	30.1			19.9	31.1
Chronic disease			17.014	< 0.001			38.940	< 0.001			40.858	< 0.001
No	12.4	22.9			20.6	9.3			10.6	21.8
Yes	87.6	77.1			79.4	90.7			89.4	78.2
ADL			57.477	< 0.001			105.089	< 0.001			110.954	< 0.001
Completely normal	50.6	76.1			51.2	76.1			53.1	77.6
Functional decline	49.4	23.9			48.8	23.9			46.9	22.4
BMI			2.089	0.554			6.691	0.082			10.062	0.018
Normal	55.0	57.2			51.7	48.5			53.1	50.7
Underweight	6.9	8.5			8.2	5.9			10.3	7.1
Overweight	27.5	25.7			29.2	32.7			27.1	32.4
Obese	10.6	8.5			10.9	12.9			9.5	9.8
Self-rated health			110.651	< 0.001			272.589	< 0.001			285.551	0.033
Good	11.4	28.1			7.1	25.5			10.4	29.5
Fair	42.3	54.6			43.9	57.6			43.9	55.8
Poor	46.3	17.3			49.0	16.9			45.7	43.9
Economic activity			1.994	0.158			0.227	0.634			0.022	0.883
Yes	48.8	44.3			43.8	55.1			50.1	50.4
No	51.2	55.7			56.2	44.9			49.9	49.6

Note: BMI, body mass index; ADL, activities of daily living; Percentages may not add up to 100% due to rounding

Determinants of depressive symptoms among older adults with different living arrangements

Among the older adults living alone, the results of multivariate logistic regression analysis showed that living in rural areas (odds ratio [OR] = 1.571, 95% confidence interval [CI]: 1.035–2.386), poorer ADL (OR = 2.375, 95% CI: 1.715–3.288), poorer self-rated health (fair: OR = 1.637, 95% CI: 1.127–2.380; poor: OR = 4.487, 95% CI: 2.895–6.955), and participation in economic activities (OR = 1.386, 95% CI: 1.006–1.911) were associated with a higher risk of depressive symptoms. Conversely, middle school education levels (OR = 0.580, 95% CI: 0.345–0.973) and longer sleep duration (6–8 h: OR = 0.437, 95% CI: 0.304–0.628; ≥ 9 h: OR = 0.542, 95% CI: 0.374–0.784) were associated with a lower risk of depressive symptoms (Table 3).

Among older adults living as a couple, the results showed that women (OR = 1.811, 95% CI: 1.340–2.449), having chronic diseases (OR = 1.439, 95% CI: 1.039–1.994), poorer ADL (OR = 2.127, 95% CI: 1.672–2.706), and poorer self-rated health (fair: OR = 2.282, 95% CI: 1.617–3.221; poor: OR = 6.690, 95% CI: 5.575–9.782) were associated with a higher risk of depressive symptoms. By contrast, higher education levels (middle school: OR = 0.676, 95% CI: 0.489–0.935; high school and above: OR = 0.452, 95% CI: 0.293–0.697) and longer sleep duration (6–8 h: OR = 0.484, 95% CI: 0.378–0.619; ≥ 9 h: OR = 0.445, 95% CI: 0.329–0.602) were associated with a lower risk of depressive symptoms (Table 3).

Among older adults living with children, the results showed that women (OR = 2.191, 95% CI: 1.640–2.927), having chronic diseases (OR = 1.399, 95% CI: 1.047–1.869), poorer ADL (OR = 2.290, 95% CI: 1.793–2.926), and poorer self-rated health (fair: OR = 1.937, 95% CI: 1.455–2.597; poor: OR = 5.806, 95% CI: 4.184–8.059) were associated with a higher risk of depressive symptoms. Conversely, being ≥ 80 years old (OR = 0.565, 95% CI: 0.339–0.942), higher education levels (elementary school: OR = 0.678, 95% CI: 0.529–0.869; middle school: OR = 0.572, 95% CI: 0.414–0.791; high school and above: OR = 0.440, 95% CI: 0.283–0.685), never having smoked (OR = 0.710, 95% CI: 0.535–0.944), and longer sleep duration (6–8 h: OR = 0.700, 95% CI: 0.534–0.919; ≥ 9 h: OR = 0.475, 95% CI: 0.360–0.628) were associated with a lower risk of depressive symptoms (Table 3).

Table 3

Multivariate logistic regression analysis on the influencing factors associated with depressive symptom among older adults with different living arrangements

Characteristics	Living alone		Living as a couple		Living with children
Characteristics	OR	95% CI	OR	95% CI	OR	95% CI
Age (ref. 60 ~ 69)
70 ~ 79	0.734	(0.525,1.028)	0.862	(0.681,1.091)	0.933	(0.736,1.183)
≥ 80	0.634	(0.388,1.036)	0.672	(0.407,1.110)	0.565*	(0.339,0.942)
Sex (ref. male)
Female	1.364	(0.897,2.073)	1.811***	(1.340,2.449)	2.191***	(1.640,2.927)
Marital status(ref. Married/Cohabitating)
Never married/ divorced/widowed	1.119	(0.748,1.675)	1.721	(0.564,5.256)	1.148	(0.914,1.442)
Region of living (ref. Urban)
Rural	1.571*	(1.035,2.386)	1.328	(0.962,1.833)	1.079	(0.795,1.464)
Education level(ref. No formal education)
Elementary school	0.736	(0.497,1.090)	0.847	(0.645,1.113)	0.678**	(0.529,0.869)
Middle school	0.580*	(0.345,0.973)	0.676*	(0.489,0.935)	0.572**	(0.414,0.791)
High school or above	0.650	(0.319,1.323)	0.452***	(0.293,0.697)	0.440***	(0.283,0.685)
Smoking (ref. Current/past)
Never	1.282	(0.873,1.882)	1.057	(0.807,1.386)	0.710*	(0.535,0.944)
Alcohol consumption(ref. Yes)
No	0.911	(0.644,1.289)	1.156	(0.908,1.472)	0.856	(0.676,1.085)
Sleep duration(ref. < 6)
6 ~ 8	0.437***	(0.304,0.628)	0.484***	(0.378,0.619)	0.700*	(0.534,0.919)
≥ 9	0.542**	(0.374,0.784)	0.445***	(0.329,0.602)	0.475***	(0.360,0.628)
Chronic disease(ref.No)
Yes	1.411	(0.918,2.170)	1.439*	(1.039,1.994)	1.399*	(1.047,1.869)
ADL(ref. Completely normal)
Functional decline	2.375***	(1.715,3.288)	2.127***	(1.672,2.706)	2.290***	(1.793,2.926)
BMI(ref. Normal)
Underweight	0.796	(0.414,1.531)	0.966	(0.612,1.526)	1.234	(0.838,1.818)
Overweight	0.973	(0.641,1.477)	0.795	(0.610,1.036)	0.843	(0.641,1.110)
Obese	0.892	(0.487,1.637)	0.710	(0.503,1.004)	0.735	(0.521,1.088)
Self-rated health(ref. Good)
Fair	1.637**	(1.127,2.380)	2.282***	(1.617,3.221)	1.937***	(1.455,2.597)
Poor	4.487***	(2.895,6.955)	6.690***	(5.575,9.782)	5.806***	(4.184,8.059)
Economic activity (ref. No)
Yes	1.386*	(1.006,1.911)	1.157	(0.912,1.466)	1.141	(0.896,1.454)

Note: * p<0.05; **p<0.01; ***p<0.001; BMI, body mass index; ADL, activities of daily living

Discussion

In this study, the prevalence of depressive symptoms and its determinants among older adults with different living arrangements were firstly compared. Our findings provide suggestions for interventions to improve the mental health of community-dwelling older adults.

Among the three evaluated groups, older adults living alone had the highest prevalence of depressive symptoms. One explanation is that older adults who live alone are more likely to suffer from loneliness [44], and higher loneliness levels are associated with higher depressive symptoms [45]. Another explanation is that the group of older adults living alone reported shorter sleep durations, a larger proportion of poor self-rated health, and poorer ADL than the two other groups. In our study, sleep duration, self-rated health, and ADL were the determinants of depressive symptoms among the three groups; this is consistent with evidence existed [46‐48].

Previous studies reported that empty-nest older adults (living alone and living as a couple) had a higher prevalence of depressive symptoms than older adults living with children [22, 23]. However, we found that older adults living as a couple had a lower prevalence of depressive symptoms than those living with children. Along with the progress in society and improvement in their living standards, older adults usually prefer to enjoy their lives [24]. Generally, unlike those living with children [49], older adults living as a couple need not bear the burden of caring for their children; in the meantime, they need not experience higher levels of loneliness compared to those living alone. In addition, spouse support is crucial for older adults’ mental health [50, 51]. In concordance with these prior findings, older adults living as a couple in our sample showed the lowest prevalence of depressive symptoms among the three groups.

According to previous studies [48, 52, 53], the evidence on the relationship between age and depressive symptoms among older adults remains mixed. We found that being ≥ 80 years old was the sole protective factor for depressive symptoms among older adults living with children. Several studies from Asia revealed that fewer older adults were reliant on their children for their daily living, and the net flow of inter-generational support is usually downward, namely from old to the young [49, 54]. Therefore, as older adults’ physical strength gradually decreases with age, the stress of supporting their children or grandchildren could also decrease, potentially having a protective effect on their mental health.

Our findings showed that women were more likely to have depressive symptoms than men among older adults living as a couple and with children, but not among those living alone. In traditional Chinese culture, women usually play a caregiver role and undertake more personal care tasks than men [55]. This care burden may increase depressive symptoms among women [56]. By contrast, as female older adults living alone need not to care for others, they may not experience care-related stress.

Our results also showed that, among older adults living alone, those living in rural areas were more likely to have depressive symptoms than their urban counterparts. Previous studies also showed a higher prevalence of depressive symptoms among rural than urban older adults owing to the gap in economic development, health service resources, and social welfare between rural and urban areas [57, 58]. In addition, family support, especially spousal support, is crucial to older adults’ mental health [50]. Thus, low levels of both social welfare and family support may be associated with vulnerability to depression among rural older adults living alone.

Our data showed that education levels were negatively associated with the occurrence of depressive symptoms among older adults living as a couple and with children. This is consistent with the results of previous studies [9, 59]. However, we found that only the middle school education level was a protective factor for depressive symptoms among those living alone, while the highest educational backgrounds (high school or above) were not. A possible explanation for this phenomenon is the high proportion of women with a lower socioeconomic condition among the older adults living alone in our sample. Cermakova [60] found that women or individuals with a poor socioeconomic condition may not gain a large mental health benefit from education. Several studies revealed that older adults living alone have poorer socioeconomic conditions than those living with others [61, 62]. In this study, 57.9% of older adults were women and 83.5% lived in rural areas. Therefore, the relationship between education levels and depressive symptoms among older adults living alone needs to be further investigated in future study.

Previous studies showed an association between smoking and depressive symptoms, but their findings are mixed [63, 64]. In our study, no smoking was negatively associated with depressive symptoms only among older adults living with children. In this group, as older adults usually live with their children as well as their grandchildren, they may have been greatly concerned about the harmful effects of smoking on the health of the younger generations [65]. This could have, in turn, led them to choose to quit smoking [66], potentially leading to a high risk of developing depressive symptoms [67]. In addition, current smokers reported lower levels of family harmony than non-smokers in prior research [68], and social isolation from family members was associated with more depressive symptoms [69].

Previous findings have shown that those who suffer from chronic diseases have a high risk of depressive symptoms [70, 71]. However, in this study, chronic diseases were associated with the occurrence of depressive symptoms only among older adults living as a couple and with children. As older adults with chronic diseases often need more care, chronic diseases impose a burden on older adults, their families, and society [72, 73]. Then, when older adults living with family members contract chronic diseases, they usually receive more care from their family members than those living alone [24]. However, from the viewpoint of older adults, they often feel fear of being a burden to their families [74]. The guilt of perceiving oneself as a burden could lead older adults to have more depressive symptoms [75]. As older adults who live alone often take care of themselves or request the care of professional caregivers, they may show less guilt in terms of increasing family members’ burden. This may be the mechanism behind our results for this population; however, this finding remains to be further assessed in future research.

We also found that participation in economic activities was a risk factor of depressive symptoms among older adults living alone. Compared with the groups of older adults living as a couple and with children, those living alone may tend to have less financial support, which results in them experiencing more stress from economic pressures and participating in economic activities passively, which in turn impacts their mental health negatively [76].

This study has several limitations. First, as it is a cross-sectional study, the causal relationship between depressive symptoms and its determinants could not be determined. Second, as a survey based on self-reporting, there is a risk of recall bias and inaccurate answers from participants. Third, the CES-D 10 was used to assess depressive symptoms; however, it serves only as a screening tool and cannot be used to diagnose depression [48]. Fourth, this study did not include older adults changed starting their current living arrangement less than 11 months prior to the survey. As such, the short-term effects of living arrangement on depressive symptoms need to be considered in future study [16]. Fifth, due to different reasons for living alone (such as never married or bereavement) may affect older adults’ depressive symptom status, which also need to be considered in future research. Finally, nearly 80% of the sample in this study were rural Chinese older adults, which may limit the generalizability of the findings to all older adults.

Conclusions

Among the participants, those living as a couple had the lowest prevalence of depressive symptoms, while those living alone had the highest prevalence. The determinants of depressive symptoms differed by living arrangement. Shorter sleep duration, poorer ADL, and poorer self-rated health were the main risk factors for depressive symptoms among participants. Women, those with lower education levels, and those with chronic diseases had a higher risk of depressive symptoms among older adults living as a couple and with children. Smoking was a risk factor of depressive symptoms among older adults living with children; participation in economic activities was a risk factor of depressive symptoms among older adults living alone. We suggest that community health workers, especially those who work in rural areas, should pay increased attention to mental health of older adults living alone. Implementing targeted interventions should be considered not only their living arrangements but also the factors affecting their depressive symptoms.

Acknowledgements

We thank the CHARLS research team and the field team for their collecting data. We also thank all participants who had been involved and contributed to the procedure of data collection.

Declarations

Competing interests

The author(s) declared no conflict of interest.

Ethical approval for this study was not required because it was based exclusively on the public available data, CHARLS, and the study subjects were not directly approached. All study participants gave their informed consent for inclusion before they participated in the study. All methods in the present study were carried out in accordance with relevant guidelines and regulations.

Not applicable.

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Supplementary Material 1

Cai Y, Qiu P, He Y, Wang C, Wu Y, Yang Y. Age-varying relationships between family support and depressive symptoms in chinese community-dwelling older adults. J Affect Disord. 2023;333:94–101. https://doi.org/10.1016/j.jad.2023.04.045.CrossRefPubMed

Zhao W, Zhang Y, Liu X, Yue J, Hou L, Xia X, Zuo Z, Liu Y, Jia S, Dong B, Ge N. Comorbid depressive and anxiety symptoms and frailty among older adults: findings from the West China health and aging trend study. J Affect Disord. 2020;277:970–6. https://doi.org/10.1016/j.jad.2020.08.070.CrossRefPubMed

Bai W, Zhang J, Smith RD, Cheung T, Su Z, Ng CH, Zhang Q, Xiang YT. Inter-relationship between cognitive performance and depressive symptoms and their association with quality of life in older adults: a network analysis based on the 2017–2018 wave of chinese longitudinal healthy longevity survey (CLHLS). J Affect Disord. 2023;320:621–7. https://doi.org/10.1016/j.jad.2022.09.159.CrossRefPubMed

National Bureau of Statistics of the People’s Republic of China. Statistical Communique on National Economic and Social Development. 2021. Available from: http://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202201/t20220117_1826436.html. Accessed January 17, 2022.

Tang T, Jiang J, Tang X. Prevalence of depressive symptoms among older adults in mainland China: a systematic review and meta-analysis. J Affect Disord. 2021;293:379–90. https://doi.org/10.1016/j.jad.2021.06.050.CrossRefPubMed

Sun X, Zhou M, Huang L, Nuse B. Depressive costs: medical expenditures on depression and depressive symptoms among rural elderly in China. Public Health. 2020;181:141–50. https://doi.org/10.1016/j.puhe.2019.12.011.CrossRefPubMed

Hsieh CR, Qin X. Depression hurts, depression costs: the medical spending attributable to depression and depressive symptoms in China. Health Econ. 2018;27(3):525–44. https://doi.org/10.1002/hec.3604.CrossRefPubMed

You L, Yu Z, Zhang X, Wu M, Lin S, Zhu Y, Xu Z, Lu J, Wei F, Tang M, Wang J, Jin M, Chen K. Association between Multimorbidity and Depressive Symptom among Community-Dwelling Elders in Eastern China. Clin Interv Aging. 2019;14:2273–80. https://doi.org/10.2147/CIA.S221917.CrossRefPubMedPubMedCentral

Li L, Sun W, Luo J, Huang H. Associations between education levels and prevalence of depressive symptoms: NHANES (2005–2018). J Affect Disord. 2022;301:360–7. https://doi.org/10.1016/j.jad.2022.01.010.CrossRefPubMed

10.

Zhang XF, Liu F, Liu WP, Ye XM, Cui BY, Wang HJ. [Relationship between sleep duration and depressive symptoms in middle-aged and elderly people in four provinces of China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2021;42(11):1955–61. https://doi.org/10.3760/cma.j.cn112338-20200930-01210. Chinese.CrossRefPubMed

11.

Zeng Y, Que S, Lin C, Fang Y. The Expected demand for Elderly Care Services and anticipated living arrangements among the Oldest Old in China based on the Andersen Model. Front Public Health. 2021;9:715586. https://doi.org/10.3389/fpubh.2021.715586.CrossRefPubMedPubMedCentral

12.

Wang Z, Shu D, Dong B, Luo L, Hao Q. Anxiety disorders and its risk factors among the Sichuan empty-nest older adults: a cross-sectional study. Arch Gerontol Geriatr. 2013;56(2):298–302. https://doi.org/10.1016/j.archger.2012.08.016.CrossRefPubMed

13.

Chang Y, Guo X, Guo L, Li Z, Yang H, Yu S, Sun G, Sun Y. Comprehensive comparison between empty nest and Non-Empty Nest Elderly: a cross-sectional study among rural populations in Northeast China. Int J Environ Res Public Health. 2016;13(9):857. https://doi.org/10.3390/ijerph13090857.CrossRefPubMedPubMedCentral

14.

Kim HJ, Fredriksen-Goldsen KI. Living arrangement and loneliness among Lesbian, Gay, and bisexual older adults. Gerontologist. 2016;56(3):548–58. https://doi.org/10.1093/geront/gnu083.CrossRefPubMed

15.

Kim D, Kim D, Lee K, Choi N, Roh S. Suicidal ideation among the elderly living in the community: correlation with living arrangement, subjective memory complaints, and depression. J Affect Disord. 2022;298(Pt A):160–5. https://doi.org/10.1016/j.jad.2021.10.066.CrossRefPubMed

16.

Kobayashi E, Harada K, Okamoto S, Liang J. Living alone and depressive symptoms among older Japanese: do urbanization and Time Period Matter? J Gerontol B Psychol Sci Soc Sci. 2023;78(4):718–29. https://doi.org/10.1093/geronb/gbac195.CrossRefPubMed

17.

Wang L, Liu W, Liang Y, Wei Y. Mental Health and Depressive feeling of empty-nest Elderly People in China. Am J Health Behav. 2019;43(6):1171–85. https://doi.org/10.5993/AJHB.43.6.14.CrossRefPubMed

18.

Su D, Wu XN, Zhang YX, Li HP, Wang WL, Zhang JP, Zhou LS. Depression and social support between China’ rural and urban empty-nest elderly. Arch Gerontol Geriatr. 2012;55(3):564–9. https://doi.org/10.1016/j.archger.2012.06.006.CrossRefPubMed

19.

Zhang HH, Jiang YY, Rao WW, Zhang QE, Qin MZ, Ng CH, Ungvari GS, Xiang YT. Prevalence of Depression among empty-nest Elderly in China: a Meta-analysis of Observational Studies. Front Psychiatry. 2020;11:608. https://doi.org/10.3389/fpsyt.2020.00608.CrossRefPubMedPubMedCentral

20.

Zhai Y, Yi H, Shen W, Xiao Y, Fan H, He F, Li F, Wang X, Shang X, Lin J. Association of empty nest with depressive symptom in a chinese elderly population: a cross-sectional study. J Affect Disord. 2015;187:218–23. https://doi.org/10.1016/j.jad.2015.08.031.CrossRefPubMed

21.

Hu N, Shi T, Xu L, Pan G, Hu Z. The influence of empty-nest living on the health of the older people living in the rural areas of Lishui, China: a cross-sectional study. Med (Baltim). 2022;101(4):e28691. https://doi.org/10.1097/MD.0000000000028691.CrossRef

22.

Zhang C, Xue Y, Zhao H, Zheng X, Zhu R, Du Y, Zheng J, Yang T. Prevalence and related influencing factors of depressive symptoms among empty-nest elderly in Shanxi, China. J Affect Disord. 2019;245:750–6. https://doi.org/10.1016/j.jad.2018.11.045.CrossRefPubMed

23.

Ma Y, Fu H, Wang JJ, Fan LH, Zheng JZ, Chen RL, Qin X, Hu Z. Study on the prevalence and risk factors of depressive symptoms among ‘empty-nest’ and ‘non-empty-nest’ elderly in four provinces and cities in China. Zhonghua Liu Xing Bing Xue Za Zhi. 2012;33(5):478–82. Chinese.PubMed

24.

Yang F, Li Z, Wang GW, Shi XX, Fu C. Cognitive function and its influencing factors in empty-nest elderly and non-empty-nest elderly adults in China. Aging. 2021;13(3):4552–63. https://doi.org/10.18632/aging.202416.CrossRefPubMedPubMedCentral

25.

Nikoloski Z, Zhang A, Hopkin G, Mossialos E. Self-reported symptoms of Depression among Chinese Rural-to-Urban Migrants and left-behind Family Members. JAMA Netw Open. 2019;2(5):e193355. https://doi.org/10.1001/jamanetworkopen.2019.3355.CrossRefPubMedPubMedCentral

26.

Guo YQ, Zhang CC, Huang H, Zheng X, Pan XJ, Zheng JZ. Mental health and related influencing factors among the empty-nest elderly and the non-empty-nest elderly in Taiyuan, China: a cross-sectional study. Public Health. 2016;141:210–7. https://doi.org/10.1016/j.puhe.2016.09.005.CrossRefPubMed

27.

Boey KW. Cross-validation of a short form of the CES-D in chinese elderly. Int J Geriatr Psychiatry. 1999;14(8):608–17. https://doi.org/10.1002/(sici)1099-1166(199908)14:8%3C608::aid-gps991%3E3.0.co;2-z.CrossRefPubMed

28.

Fu C, Wang G, Shi X, Cao F. Social support and depressive symptoms among physicians in tertiary hospitals in China: a cross-sectional study. BMC Psychiatry. 2021;21(1):217. https://doi.org/10.1186/s12888-021-03219-w.CrossRefPubMedPubMedCentral

29.

Luo Y, Zhu D, Nicholas S, He P. Depressive symptoms, health behaviors and risk of diabetes in chinese mid-aged and older adults. J Affect Disord. 2019;246:783–8. https://doi.org/10.1016/j.jad.2018.12.131.CrossRefPubMed

30.

Kim H, Kwon S, Hong S, Lee S. Health behaviors influencing depressive symptoms in older Koreans living alone: secondary data analysis of the 2014 korean longitudinal study of aging. BMC Geriatr. 2018;18(1):186. https://doi.org/10.1186/s12877-018-0882-4.CrossRefPubMedPubMedCentral

31.

Zare H, Meyerson NS, Nwankwo CA, Thorpe RJ Jr. How Income and Income Inequality Drive depressive symptoms in U.S. adults, does sex matter: 2005–2016. Int J Environ Res Public Health. 2022;19(10):6227. https://doi.org/10.3390/ijerph19106227.CrossRefPubMedPubMedCentral

32.

Chung SS, Joung KH. Demographics and Health Profiles of depressive symptoms in korean older adults. Arch Psychiatr Nurs. 2017;31(2):164–70. https://doi.org/10.1016/j.apnu.2016.09.009.CrossRefPubMed

33.

Tang X, Qi S, Zhang H, Wang Z. Prevalence of depressive symptoms and its related factors among China’s older adults in 2016. J Affect Disord. 2021;292:95–101. https://doi.org/10.1016/j.jad.2021.04.041.CrossRefPubMed

34.

Chunnan L, Shaomei S, Wannian L. The association between sleep and depressive symptoms in US adults: data from the NHANES (2007–2014). Epidemiol Psychiatr Sci. 2022;31:e63. https://doi.org/10.1017/S2045796022000452.CrossRefPubMedPubMedCentral

35.

Ji A, Lou H, Lou P, Xu C, Zhang P, Qiao C, Yang Q. Interactive effect of sleep duration and sleep quality on risk of stroke: an 8-year follow-up study in China. Sci Rep. 2020;10(1):8690. https://doi.org/10.1038/s41598-020-65611-y.CrossRefPubMedPubMedCentral

36.

Tang S, Liu M, Yang T, Ye C, Gong Y, Yao L, Xu Y, Bai Y. Association between falls in elderly and the number of chronic diseases and health-related behaviors based on CHARLS 2018: health status as a mediating variable. BMC Geriatr. 2022;22(1):374. https://doi.org/10.1186/s12877-022-03055-x.CrossRefPubMedPubMedCentral

37.

Chen C, Lu FC, Department of Disease Control Ministry of Health, PR China. The guidelines for prevention and control of overweight and obesity in chinese adults. Biomed Environ Sci. 2004;17 Suppl:1–36.PubMed

38.

Falk H, Skoog I, Johansson L, Guerchet M, Mayston R, Hörder H, Prince M, Prina AM. Self-rated health and its association with mortality in older adults in China, India and Latin America-a 10/66 Dementia Research Group study. Age Ageing. 2017;46(6):932–9. https://doi.org/10.1093/ageing/afx126.CrossRefPubMedPubMedCentral

39.

Dufournet M, Moutet C, Achi S, Delphin-Combe F, Krolak-Salmon P, Dauphinot V, MEMORA group. Proposition of a corrected measure of the Lawton instrumental activities of daily living score. BMC Geriatr. 2021;21(1):39. https://doi.org/10.1186/s12877-020-01995-w.CrossRefPubMedPubMedCentral

40.

Du X, Wu R, Kang L, Zhao L, Li C. Tobacco smoking and depressive symptoms in chinese middle-aged and older adults: handling missing values in panel data with multiple imputation. Front Public Health. 2022;10:913636. https://doi.org/10.3389/fpubh.2022.913636.CrossRefPubMedPubMedCentral

41.

Zhang F, Yu Y, Wang H, Zhang Y, Bai Y, Huang L, Zhang H. Association between handgrip strength and depression among chinese older adults: a cross-sectional study from the China Health and Retirement Longitudinal Study. BMC Geriatr. 2023;23(1):299. https://doi.org/10.1186/s12877-023-04034-6.CrossRefPubMedPubMedCentral

42.

Ghimire S, Paudel G, Mistry SK, Parvez M, Rayamajhee B, Paudel P, Tamang MK, Yadav UN. Functional status and its associated factors among community-dwelling older adults in rural Nepal: findings from a cross-sectional study. BMC Geriatr. 2021;21(1):335. https://doi.org/10.1186/s12877-021-02286-8.CrossRefPubMedPubMedCentral

43.

Igbokwe CC, Ejeh VJ, Agbaje OS, Umoke PIC, Iweama CN, Ozoemena EL. Prevalence of loneliness and association with depressive and anxiety symptoms among retirees in northcentral Nigeria: a cross-sectional study. BMC Geriatr. 2020;20(1):153. https://doi.org/10.1186/s12877-020-01561-4.CrossRefPubMedPubMedCentral

44.

United Nations Population Division. Living arrangements of older persons around the World. New York: United Nations Department of Economic and Social Affairs/Population Division; 2005.

45.

Lee SL, Pearce E, Ajnakina O, Johnson S, Lewis G, Mann F, Pitman A, Solmi F, Sommerlad A, Steptoe A, Tymoszuk U, Lewis G. The association between loneliness and depressive symptoms among adults aged 50 years and older: a 12-year population-based cohort study. Lancet Psychiatry. 2021;8(1):48–57. https://doi.org/10.1016/S2215-0366(20)30383-7.CrossRefPubMedPubMedCentral

46.

Sun Y, Shi L, Bao Y, Sun Y, Shi J, Lu L. The bidirectional relationship between sleep duration and depression in community-dwelling middle-aged and elderly individuals: evidence from a longitudinal study. Sleep Med. 2018;52:221–9. https://doi.org/10.1016/j.sleep.2018.03.011.CrossRefPubMed

47.

Mulsant BH, Ganguli M, Seaberg EC. The relationship between self-rated health and depressive symptoms in an epidemiological sample of community-dwelling older adults. J Am Geriatr Soc. 1997;45(8):954–8. https://doi.org/10.1111/j.1532-5415.1997.tb02966.x.CrossRefPubMed

48.

Fan X, Guo X, Ren Z, Li X, He M, Shi H, Zha S, Qiao S, Zhao H, Li Y, Pu Y, Liu H, Zhang X. The prevalence of depressive symptoms and associated factors in middle-aged and elderly chinese people. J Affect Disord. 2021;293:222–8. https://doi.org/10.1016/j.jad.2021.06.044.CrossRefPubMed

49.

Wang B, Zhou JTO. Intergenerational economic support patterns of chinese Elderly families. J Yunnan Minzu University (Social Sciences). 2020;37:88–95. [in Chinese with English abstract].

50.

Shang Q. Social support, rural/urban residence, and depressive symptoms among chinese adults. J Community Psychol. 2020;48(3):849–61. https://doi.org/10.1002/jcop.22302.CrossRefPubMed

51.

Bai Y, Bian F, Zhang L, Cao Y. The impact of Social Support on the Health of the Rural Elderly in China. Int J Environ Res Public Health. 2020;17(6):2004. https://doi.org/10.3390/ijerph17062004.CrossRefPubMedPubMedCentral

52.

Dao ATM, Nguyen VT, Nguyen HV, Nguyen LTK. Factors Associated with Depression among the Elderly Living in Urban Vietnam. Biomed Res Int. 2018;2018:2370284. https://doi.org/10.1155/2018/2370284.CrossRefPubMedPubMedCentral

53.

Lin H, Jin M, Liu Q, Du Y, Fu J, Sun C, Ma F, Li W, Liu H, Zhang X, Zhu Y, Chen Y, Sun Z, Wang G, Huang G, Yan J. Gender-specific prevalence and influencing factors of depression in elderly in rural China: a cross-sectional study. J Affect Disord. 2021;288:99–106. https://doi.org/10.1016/j.jad.2021.03.078.CrossRefPubMed

54.

Schröder-Butterfill E. Inter-generational family support provided by older people in Indonesia. Ageing Soc. 2004;24:497–530.CrossRefPubMedPubMedCentral

55.

Miller B, Cafasso L. Gender differences in caregiving: fact or artifact? Gerontologist. 1992;32(4):498–507. https://doi.org/10.1093/geront/32.4.498.CrossRefPubMed

56.

Tabler J, Geist C. Do gender differences in housework performance and informal adult caregiving explain the gender gap in depressive symptoms of older adults? J Women Aging. 2021;33(1):41–56. https://doi.org/10.1080/08952841.2019.1681243.CrossRefPubMed

57.

Wang Y, Li Z, Fu C. Urban-rural differences in the association between social activities and depressive symptoms among older adults in China: a cross-sectional study. BMC Geriatr. 2021;21(1):569. https://doi.org/10.1186/s12877-021-02541-y.CrossRefPubMedPubMedCentral

58.

Li LW, Liu J, Xu H, Zhang Z. Understanding rural-urban differences in depressive symptoms among older adults in China. J Aging Health. 2016;28(2):341–62. https://doi.org/10.1177/0898264315591003.CrossRefPubMed

59.

Luo Y, Zhu D, Shi X, Nicholas S, He P. Education as a moderator in the effect of diabetes on depressive symptoms in chinese middle-aged and older adults: a population-based longitudinal study. J Affect Disord. 2018;240:41–7. https://doi.org/10.1016/j.jad.2018.07.026.CrossRefPubMed

60.

Cermakova P, Pikhart H, Kubinova R, Bobak M. Education as inefficient resource against depressive symptoms in the Czech Republic: cross-sectional analysis of the HAPIEE study. Eur J Public Health. 2020;30(5):948–52. https://doi.org/10.1093/eurpub/ckaa059.CrossRefPubMedPubMedCentral

61.

Evans IEM, Llewellyn DJ, Matthews FE, Woods RT, Brayne C, Clare L. CFAS-Wales research team. Living alone and cognitive function in later life. Arch Gerontol Geriatr. 2019;81:222–33. https://doi.org/10.1016/j.archger.2018.12.014.CrossRefPubMed

62.

Mudrazija S, Angel JL, Cipin I, Smolic S. Living alone in the United States and Europe: the impact of public support on the independence of older adults. Res Aging. 2020;42(5–6):150–62. https://doi.org/10.1177/0164027520907332.CrossRefPubMed

63.

Lam TH, Li ZB, Ho SY, Chan WM, Ho KS, Li MP, Leung GM. Smoking and depressive symptoms in chinese elderly in Hong Kong. Acta Psychiatr Scand. 2004;110(3):195–200. https://doi.org/10.1111/j.1600-0447.2004.00342.x.CrossRefPubMed

64.

Furihata R, Konno C, Suzuki M, Takahashi S, Kaneita Y, Ohida T, Uchiyama M. Unhealthy lifestyle factors and depressive symptoms: a japanese general adult population survey. J Affect Disord. 2018;234:156–61. https://doi.org/10.1016/j.jad.2018.02.093.CrossRefPubMed

65.

Rutagumirwa SK, Hutter I, Bailey A. We never Graduate from Care giving roles; Cultural Schemas for Intergenerational Care Role among older adults in Tanzania. J Cross Cult Gerontol. 2020;35(4):409–31. https://doi.org/10.1007/s10823-020-09412-w.CrossRefPubMed

66.

Huang K, Chen H, Liao J, Nong G, Yang L, Winickoff JP, Zhang Z, Abdullah AS. Factors Associated with Complete Home Smoking Ban among chinese parents of Young Children. Int J Environ Res Public Health. 2016;13(2):161. https://doi.org/10.3390/ijerph13020161.CrossRefPubMedPubMedCentral

67.

Cooper J, Borland R, Yong HH, Fotuhi O. The impact of quitting smoking on depressive symptoms: findings from the International Tobacco Control Four-Country Survey. Addiction. 2016;111(8):1448–56. https://doi.org/10.1111/add.13367.CrossRefPubMedPubMedCentral

68.

Wang X, Wang MP, Viswanath K, Wan A, Lam TH, Chan SS. Smoking and secondhand smoke exposure at Home were Associated with Poor Perceived Family Well-Being: findings of FAMILY project. PLoS ONE. 2016;11(8):e0161761. https://doi.org/10.1371/journal.pone.0161761.CrossRefPubMedPubMedCentral

69.

Taylor HO, Taylor RJ, Nguyen AW, Chatters L. Social isolation, Depression, and psychological distress among older adults. J Aging Health. 2018;30(2):229–46. https://doi.org/10.1177/0898264316673511.CrossRefPubMed

70.

Parajuli J, Berish D, Jao YL. Chronic conditions and depressive symptoms in older adults: the mediating role of functional limitations. Aging Ment Health. 2021;25(2):243–9. https://doi.org/10.1080/13607863.2019.1693971.CrossRefPubMed

71.

Jiang CH, Zhu F, Qin TT. Relationships between Chronic Diseases and Depression among Middle-aged and Elderly People in China: a prospective study from CHARLS. Curr Med Sci. 2020;40(5):858–70. https://doi.org/10.1007/s11596-020-2270-5.CrossRefPubMed

72.

Adelman RD, Tmanova LL, Delgado D, Dion S, Lachs MS. Caregiver burden: a clinical review. JAMA. 2014;311(10):1052–60. https://doi.org/10.1001/jama.2014.304.CrossRefPubMed

73.

Wang LM, Chen ZH, Zhang M, Zhao ZP, Huang ZJ, Zhang X, Li C, Guan YQ, Wang X, Wang ZH, Zhou MG. [Study of the prevalence and disease burden of chronic disease in the elderly in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2019;40(3):277–83. https://doi.org/10.3760/cma.j.issn.0254-6450.2019.03.005. Chinese.CrossRefPubMed

74.

Minichiello V, Browne J, Kendig H. Perceptions and consequences of ageism: views of older people. Aging Soc. 2000;20(3):253–78. https://doi.org/10.1017/S0144686X99007710.CrossRef

75.

Pedroso-Chaparro MDS, Márquez-González M, Vara-García C, Cabrera I, Romero-Moreno R, Barrera-Caballero S, Losada A. Guilt for perceiving oneself as a burden in healthy older adults. Associated factors. Aging Ment Health. 2021;25(12):2330–6. https://doi.org/10.1080/13607863.2020.1822291.CrossRefPubMed

76.

Xu Y, Yang J, Gao J, Zhou Z, Zhang T, Ren J, Li Y, Qian Y, Lai S, Chen G. Decomposing socioeconomic inequalities in depressive symptoms among the elderly in China. BMC Public Health. 2016;16(1):1214. https://doi.org/10.1186/s12889-016-3876-1.CrossRefPubMedPubMedCentral

Titel: Prevalence and determinants of depressive symptoms among community-dwelling older adults in China based on differences in living arrangements: a cross-sectional study
verfasst von: Chang Fu
Lianmeng Cao
Fan Yang
Publikationsdatum: 01.12.2023
Verlag: BioMed Central
Erschienen in: BMC Geriatrics / Ausgabe 1/2023
Elektronische ISSN: 1471-2318
DOI: https://doi.org/10.1186/s12877-023-04339-6

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Publisher’s Note

Background

Methods

Study population

Measurement of depressive symptoms

Assessment of living arrangements

Measurement of determinants

Statistical analysis

Results

Sample characteristics

Prevalence of depressive symptoms based on different sociodemographic characteristics

Determinants of depressive symptoms among older adults with different living arrangements

Discussion

Conclusions

Acknowledgements

Declarations

Competing interests

Ethics approval and consent to participate

Consent for publication

Publisher’s Note

Electronic supplementary material

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