Family economic disparities and the onset of depressive symptoms among middle-aged and elderly residents in urban and rural areas: A cross-sectional study based on CHARLS

doi:10.21203/rs.3.rs-4747734/v1

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Family economic disparities and the onset of depressive symptoms among middle-aged and elderly residents in urban and rural areas: A cross-sectional study based on CHARLS

https://doi.org/10.21203/rs.3.rs-4747734/v1

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Background: Comparisons in family economic differences and the risk of depression among middle-aged and older individuals in China have not been well investigated.

Methods: This cross-sectional study included 10,884 participants aged 45 or older from the fifth wave of the China Health and Retirement Longitudinal Study (CHARLS) in 2020, with 73.56% aged 45-64 and 26.44% aged ≥65. We measured depressive symptoms using the 10-item Centre for Epidemiologic Studies Depression Scale (CES-D-10) with a cut-off score ≥ 10. Family economic status was estimated using family consumption per capita monthly. Log-binomial models with relative risks (RR) and 95% confidence intervals were used to estimate the relationship between family economic situation and depressive symptoms. Two-way interaction analyses provide insights into the associations between levels of consumption expenditure per capita with covariates and depression in both urban and rural households.

Results: The prevalence of depression was higher in rural areas (42.74%) compared to urban areas (35.36%, P < 0.001). Significant differences were found in various factors between urban and rural areas, including monthly family consumption per capita, marital status, education level, chronic disease, age, self-reported health, satisfaction with children, social activities, and cigarette smoking (all P < 0.05). High household consumption per capita (Top 20%) was associated with depressive symptoms in both urban (aRR = 1.295, 95% CI: 1.091-1.538) and rural areas (aRR = 1.226, 95% CI: 1.127-1.335). Having a chronic disease increased the risk of depression in both urban and rural areas regardless of household consumption per capita. In urban areas, marital status and education level influenced the risk of depression, while in rural areas, health status, child satisfaction, and social activity were influential factors.

Conclusions: Association between excessive consumption and depression in urban and rural areas. Healthcare and relevant support organizations in China should emphasize the importance of family economic status in depression among middle-aged and older adults in both urban and rural areas, and offer treatment and preventative strategies to all families.

Middle-aged and elderly

Depressive Symptoms

Urban-rural disparities

Economic Status

Log-binomial regression

CHARLS

Middle-aged and elderly residents refer to the social group whose biological age is over 45 years old [1]. With the advancement of societal aging, the age structure of China's population is rapidly shifting towards this social group [2]. Due to the increasing risk of various diseases in these age groups, it will impose a significant burden on social development [3]. Therefore, it is a group that requires close attention. Numerous studies have highlighted the increased risk of physical and mental health conditions in adults aged 45 and above [4, 5], with depressive symptoms being one of the most common mental health issues. According to the World Health Organization (WHO) in 2023 [6], approximately 14% of residents aged 60 years or older suffer from at least one mental disorder, with depression and anxiety being the most common. Depressive symptoms are even more common among middle-aged and older residents in China, compared to global rates. A study of adults aged 45 and over found that 31.2% experienced varying degrees of depressive symptoms [7]. Several studies have identified factors such as educational level [8], economic status [9], and chronic diseases [10] as contributors to depressive symptoms.

China's dual urban-rural economic structure represents a significant demographic and socioeconomic divide that has historically influenced the health and well-being of its population. Originating from the household registration system, known as "Hukou," this system has traditionally categorized citizens into agricultural and non-agricultural (urban) residents, creating distinct disparities in access to resources, economic opportunities, and social services [11-13]. Urban areas have experienced rapid industrialization and modernization, leading to higher income levels, better job prospects, and improved living standards. In contrast, rural areas are characterized by lower incomes, limited employment opportunities, and fewer resources for education and healthcare. These economic differences are further exacerbated by the concentration of social services and infrastructure in urban centers, leaving rural residents with relatively fewer options for quality healthcare, education, and social support. Although this disparity still exists, it has been gradually narrowing in the past two decades through Hukou system reform and regional coordinated development strategy [14].

A longitudinal study in China has found a higher prevalence of depressive symptoms in rural areas compared to urban areas [15]. Another study shows the rate of depression among the rural disabled elderly is higher (58%) compared to urban disabled elderly (46%) [16]. The lower risk of depressive symptoms among middle-aged and older urban residents than rural residents may be attributed to significant differences between urban and rural areas regarding key factors in social participation, income, and living conditions [17]. Additionally, differences in access to social support and health care between urban and rural areas also contribute to the varying risk of depressive symptoms [18, 19]. From an economic perspective, national income inequality in China has decreased, but there are still disparities in incomes between different regions, especially between urban and rural areas [20, 21]. Economic disparity is one of the key factors contributing to the higher prevalence of depressive symptoms in rural areas [22]. Rural residents in China face significant economic burdens in accessing mental health services. At the same time, urban patients have longer, and more frequent hospital stays, as well as greater hospitalization expenditures than rural patients [23].

A study on Japanese adults examined the impact of family economic factors on depression in middle-aged and older residents [24]. They found that the level of family income affected the prevalence of depression even after adjusting for confounding factors. The study showed that lower family income is associated with a higher risk of depression in Japan. There are also several related studies exploring the complex relationship between the mental health of economically active older residents. These studies confirm that family interactions and financial support can prevent depressive symptoms, further emphasizing the critical role of family economic stability in alleviating depressive symptoms in an aging population [25, 26]. Conversely, an excessive burden of financial stress can also significantly increase the risk of depressive symptoms. A systematic review emphasized the addition of economic hardship exacerbating depressive symptoms among adults [27]. These findings effectively demonstrate the importance of financial stability and lifelong support as key factors in preventing depressive symptoms in an aging population.

Recently, research on factors related to depressive symptoms among middle-aged and elderly residents in urban and rural areas has made significant progress. Semachew Kasa et al. [28] found that lifestyle habits may be a major contributor to the differences in depressive symptoms among urban and rural middle-aged and older residents, while it is still unclear whether the economic conditions of urban and rural households also play a role in this difference. Despite significant progress in research on factors related to depressive symptoms among middle-aged and elderly residents in urban and rural areas, our understanding of the relationship between economic status and depressive symptoms in two areas remains incomplete. To address these research gaps, we conducted a cross-sectional study using data from the 2020 China Health and Retirement Longitudinal Study (CHARLS). In addition to demographic factors (e.g., age, gender, education level, etc.), we included more comprehensive economic indicators such as monthly family consumption per capita, medical insurance, endowment insurance, and health behavior indicators such as social activities, and the history of smoking or drinking. This allowed for a comprehensive and systematic assessment of the economic variables affecting urban and rural areas, thereby providing evidence for policymakers for policy decisions. This study was divided into two objectives: 1) to identify if there are differences in depressive symptoms among middle-aged and elderly adults living in urban versus rural areas, and 2) to determine whether the factors inducing depressive symptoms differ among populations with varying economic statuses in urban and rural settings.

2.1 Study population

This study used the publicly accessible data by the National School of Development of Peking University from the 2020 China Health and Retirement Longitudinal Study (CHARLS), which is an ongoing longitudinal investigation that focuses on middle-aged and elderly individuals in China [29]. Similar to the U.S. Health and Retirement Study (HRS), CHARLS has conducted an extensive survey of middle-aged and older Chinese residents aged 45 and older since 2011, providing data on age, health status, economic status, and social support. The study sample comprised households with members aged 45 years and older and employs a multi-stage stratified probability-proportional-to-size (PPS) sampling technique. The research team developed the CHARLS-GIS software package to establish village sampling frames. Trained investigators conducted data collection through in-person interviews. The data collected for CHARLS are managed by the Institute of Social Science Survey at Peking University and are publicly available on the CHARLS website. This sample involved 19,395 participants from 150 counties/districts and 450 villages/urban communities in 28 provinces [30]. The participants were middle-aged and older individuals, defined as those aged 45 years or older (middle-aged: 45 to 64 years old; elderly: 65 years old and above). Respondents with missing birth years were excluded. Figure 1 shows that 10,884 participants were retained after excluding those younger than 45 years and those with missing values for the main outcome variables.

The original CHARLS was approved by the Ethical Review Committee of Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015. All participants were asked to sign an informed consent form before the study commenced, and no ethical permission was required for the secondary analysis of CHARLS data.

2.2 Definition of Economic Situations

The CHARLS indicators on economic status are divided into household income and household consumption segments. The household income section covers respondents' income from work, public transfers received, work benefits, household income from agriculture, household income from self-employment or business, as well as household income from financial management and rental properties. The questions in the household income section questionnaire vary from one round of the survey to the next. Yuzhou Wang et al. from the official CHARLS team suggest using household consumption indicators rather than income to measure living standards [31]. They argue that consumption is a more reliable measure of long-term resources than current income, particularly in low-income rural areas where incomes fluctuate significantly from year to year. Moreover, consumption is considered to have a smaller margin of error in measurement compared to income [32]. We ranked the family consumption status of urban and rural participants separately, categorizing them into five categories from low to high (≤20%, 21-40%, 41-60%, 61-80%, >80%). The five categories are further divided into percentile ranks: Rural P₂₀= 300, P₄₀= 600, P₆₀= 1000, P₈₀= 2000, Urban P₂₀= 400, P₄₀= 800, P₆₀= 1500, P₈₀= 2750 (monetary unit: RMB).

2.3 Assessment of depressive symptoms

Depressive symptoms were assessed using the Centre for Epidemiologic Studies Depression Scale (CES-D-10) [33], which has been validated among older participants in China using CHARLS data [34, 35]. The CES-D-10 includes 10 questions with 4 response options: 1) None or almost none (less than 1 day); 2) Rare (1-2 days); 3) Frequent (3-4 days); 4) Almost always (5-7 days), reflecting the participant's experiences over the past week, such as feeling bothered, having trouble concentrating, feeling depressed, and feeling hopeful about the future [36]. The total score ranges from 0 to 30, with a score of 10 or above indicating the presence of depressive symptoms (0 = Normal, 1 = Depressive Symptoms). A cutoff score ≥ 10 was used to identify the respondents who had significant depressive symptoms [33, 37].

2.4 Measurement of determinants

In this study, the determinants of depressive symptoms among middle-aged and elderly residents in urban and rural areas include four key outcome measurements: depressive symptoms, demographic variables, health-related variables, and health behaviour variables. (Table S1).

Demographic variables include urban/rural areas, gender, age, marital status, and education level. In the context of urban/rural areas, the Hukou system in China serves as a population registration mechanism that designates an individual's residency status as either rural or urban. Specifically, individuals holding agricultural Hukou are categorized as rural residents, while those with non-agricultural or unified residence Hukou are classified as urban residents. The Hukou system, a longstanding feature of China's administrative framework, mandates that all Chinese citizens register within the system, designating their residency as either agricultural or non-agricultural (commonly referred to as rural or urban).

Health-related variables include self-reported health, satisfaction with their children, and life satisfaction. This category of variables can indicate the participants' physical and mental health status.

Health behaviour variables include chronic diseases, social activities, duration of sleep, alcohol consumption, and cigarette smoking. This group of variables can provide insight into the types of behaviours that may impact the health of participants and how frequently they occur.

Economy-related variables include medical insurance, endowment insurance and monthly family consumption per capita. These variables indicate the participants’ economic status. In China, "Medical Insurance" and "Endowment Insurance" are important components of the social security system. Medical insurance is a social security system established by the Chinese government. Its primary purpose is to ensure that citizens can receive appropriate medical treatment when they are sick or injured, thus reducing the financial burden caused by illness. Depending on the insured population, medical insurance can be divided into categories such as Basic Medical Insurance for Urban Employees, Basic Medical Insurance for Urban Residents, and New Rural Cooperative Medical Insurance. Endowment insurance, on the other hand, is to ensure that people have a certain source of income after retirement to sustain a basic standard of living. Furthermore, Basic Pension Insurance can be subdivided into Basic Pension Insurance for urban workers and Basic Pension Insurance for urban and rural residents.

2.5 Statistical analysis

All statistical analyses were performed using Stata 17.0 software (StataCorp LLC, Texas). First, the characteristics of the participants were presented as frequencies and percentages. The chi-square test was utilized to compare the differences in factors influencing the prevalence of depression among middle-aged and older adults in urban and rural areas. The variance inflation factor (VIF) tests for possible covariance between the variables. As shown in Table S2, all the values are less than 10. This indicates that there is no covariance between the variables, and the selection of the variables is meaningful [38]. Then, an adjusted log-binomial regression model was used to calculate the risk ratio (RR) of the covariates to analyze the correlates of depressive symptoms among urban and rural middle-aged and elderly individuals. Two-way interaction log-binomial models provide additional insight into the interaction factors that influence the occurrence of depressive symptoms based on varying household expenditures per capita in urban and rural areas.

The general form of the log-binomial model was:

P indicates the incidence of the event outcome, usually referred to as prevalence. Log-binomial models are a valuable tool for exploring associations between exposure factors and dichotomous outcomes (e.g., presence or absence of disease) in epidemiological studies [39]. This model is a specific type of Generalized Linear Model (GLM) used to analyze associations in cross-sectional studies. It directly estimates the prevalence ratio (PR) or relative risk (RR) rather than using the odds ratio (OR) to characterize the association between exposure and outcome. When the incidence of event outcomes is high (>10%), using OR to describe the strength of association will overestimate the association between exposure and outcome [40]. This approach can help prevent the issue of overestimating the OR. The log-binomial model for the categorical variables calculated relative risks (RR) with 95% CIs.

3.1 Participant characteristics

As shown in Table S3, among the 10,884 participants, 52% were female. The gender ratio was roughly equal. The proportion of ages between 45 and 54 years was 65.76%. 78.64% of participants were married and residing permanently with a spouse, and 42.77% of participants education level of elementary school and below. For economy-related variables, 95.33% of participants have medical insurance, and 85.36% of participants have endowment insurance, There is little difference in the frequency of people in various ranked subgroups of per capita household consumption (≤20%: 17.06%, 21~40%: 17.92%, 41~60%: 21.58%, 61~80%: 21.11%, >80%: 22.33%). Regarding health-related variables, 50.46% of the participants self-reported their health status as fair. 45.17% of participants expressed "very satisfied" with their children, while 42.82% reported "somewhat satisfied". Approximately 54.98% of individuals reported "somewhat satisfied" with their life satisfaction. Among the health behavior variables, 75.9% of participants reported having at least one type of chronic disease. 59.10% of participants' duration of sleep time was less than or equal to 6 hours. Additionally, 68.46% of participants do not consume alcohol, and 60.19% do not smoke.

3.2 Prevalence and correlates of depression

The estimated prevalence of depressive symptoms in groups from urban and rural areas is presented in Table 1. In Urban areas, there were statistically significant differences in Marital status, Education level and Chronic disease (all P＜0.05). In rural areas, the prevalence estimates were different in Age, Self-reported health, Satisfaction with their children, Chronic disease, Social activities and Cigarette smoking (all P＜0.05). The presence of a chronic disease is a common factor influencing the difference in prevalence estimates between urban and rural areas (P＜0.001). The result of the Chi-square test showed that there was a significant difference in the estimated prevalence of depressive symptoms between urban and rural areas.

Grouped by family consumption per capita (monthly), depression occurred in both urban and rural households of different economic statuses. There was a significant difference in the prevalence of depression when comparing within regions (Urban: χ²= 28.965, P＜0.001, Rural: χ²=34.318, P＜0.01). The highest incidence of depression in urban and rural areas was in the family consumption per capita >80% group (Q5). The prevalence of depression ranged from 40.47% to 50.21% in rural families with different family economic statuses and from 30.03% to 40.10% in urban families with different economic statuses. Depression was higher in rural families with varying economic statuses compared to urban families with different economic statuses.

TABLE 1 Prevalence and Correlates of Depressive Symptoms among Participants in Urban and Rural Areas

Variables	Urban (n=3891, n (%))				Rural (n=6993, n (%))
Variables	Depression	Normal	χ²	P-value	Depression	Normal	χ²	P-value
Gender
Male	651(34.72)	1224(65.28)	0.656	0.418	1451(43.31)	1899(56.69)	0.856	0.355
Female	725(35.96)	1291(64.04)	0.656	0.418	1538(42.22)	2105(57.78)	0.856	0.355
Age (years)
45~64	918(35.27)	1685(64.73)	0.032	0.858	1823(41.78)	2540(58.22)	4.365	0.037*
≥ 65	458(35.56)	830(64.44)	0.032	0.858	1166(44.35)	1464(55.65)	4.365	0.037*
Marital status
Married with spouse present	1014(34.44)	1930(65.56)	11.170	0.048*	2333(42.35)	3176(57.65)	7.942	0.159
Married but not living with spouse temporarily for reasons such as work	159(42.18)	218(57.82)			207(43.67)	267(56.33)
Separated	5(27.78)	13(72.22)			6(24.00)	19(76.00)
Divorced	35(37.23)	59(62.77)			23(41.07)	33(58.93)
Widowed	162(35.45)	295(64.55)			417(45.42)	501(54.58)
Never Married	1(100)	0(0)			3(27.27)	8(72.73)
Education level
Illiterate	145(34.77)	272(65.23)	9.125	0.028*	800(44.20)	1010(55.80)	5.966	0.113
Elementary school and below	534(38.42)	856(61.58)			1397(42.79)	1868(57.21)
Middle school	383(33.63)	756(66.37)			574(40.20)	854(59.80)
High/vocational school and above	314(33.23)	631(66.77)			218(44.49)	272(55.51)
Medical insurance
No	46(35.94)	82(64.06)	0.019	0.890	144(41.62)	202(58.38)	0.188	0.665
Yes	1330(35.34)	2433(64.66)	0.019	0.890	2845(42.80)	3802(57.20)	0.188	0.665
Endowment insurance
No	211(36.95)	360(63.05)	0.739	0.390	444(43.44)	578(56.56)	0.241	0.624
Yes	1165(35.09)	2155(64.91)	0.739	0.390	2545(42.62)	3426(57.38)	0.241	0.624
Family consumption per capita
≤ 20%	109(30.53)	248(69.47)	28.965	<0.001	607(40.47)	893(59.53)	34.318	<0.01
21~40%	173(30.03)	403(69.97)			556(40.47)	818(59.53)
41~60%	305(32.24)	641(67.76)			589(41.98)	814(58.02)
61~80%	297(37.83)	488(62.17)			633(41.84)	880(58.16)
> 80%	492(40.10)	735(59.90)			604(50.21)	599(49.79)
Self-reported health
Very good	157(33.12)	317(66.88)	6.639	0.156	295(37.72)	487(62.28)	15.941	0.003
Good	191(32.26)	401(67.74)			339(40.79)	492(59.21)
Fair	745(35.80)	1336(64.20)			1457(42.71)	1954(57.29)
Poor	220(38.80)	347(61.20)			645(45.58)	770(54.42)
Very poor	63(35.59)	114(64.41)			253(45.67)	301(54.33)
Satisfaction with their children
Completely satisfied	98(34.15)	189(65.85)	5.552	0.352	188(37.83)	309(62.17)	21.468	0.001
Very satisfied	589(35.27)	1081(64.73)			1402(43.19)	1844(56.81)
Somewhat satisfied	615(35.22)	1131(64.78)			1230(42.20)	1685(57.80)
Not very satisfied	60(39.47)	92(60.53)			127(48.29)	136(51.71)
Not satisfied at all	9(31.03)	20(68.97)			40(63.49)	23(36.51)
Childless	5(71.43)	2(28.57)			2(22.22)	7(77.78)
Life satisfaction
Completely satisfied	56(32.37)	117(67.63)	4.522	0.340	151(41.60)	212(58.40)	7.640	0.106
Very satisfied	401(34.24)	770(65.76)			903(43.16)	1189(56.84)
Somewhat satisfied	777(35.50)	1412(64.50)			1586(41.79)	2209(58.21)
Not very satisfied	115(40.35)	170(59.65)			261(46.28)	303(53.72)
Not satisfied at all	27(36.99)	46(63.01)			88(49.16)	91(50.84)
Chronic disease
Having none of these chronic disease	46(5.90)	734(94.10)	370.570	<0.001	508(30.90)	1136(69.10)	123.165	<0.001
Having one of these chronic disease	1330(42.75)	1781(57.25)	370.570	<0.001	2481(46.38)	2868(53.62)	123.165	<0.001
Social activities
Having none of these social activities	628(35.68)	1132(64.32)	0.142	0.706	1646(45.23)	1993(54.77)	19.213	<0.001
Having one of these social activities	748(35.10)	1383(64.90)	0.142	0.706	1343(40.04)	2011(59.96)	19.213	<0.001
Duration of sleep
≤ 6 h	875(35.96)	1558(64.04)	2.148	0.342	1718(42.96)	2281(57.04)	0.185	0.912
6~9 h	449(33.94)	874(66.06)			1039(42.48)	1407(57.52)
≥ 9 h	52(38.52)	83(61.48)			232(42.34)	316(57.66)
Drinking wine
Drink more than once a month	391(34.54)	741(65.46)	0.867	0.648	796(42.75)	1066(57.25)	1.168	0.558
Drink but less than once a month	158(34.42)	301(65.58)			267(44.80)	329(55.20)	1.168	0.558
None of these	827(35.96)	1473(64.04)			1926(42.47)	2609(57.53)
Cigarette smoking
Still smoke	327(34.49)	621(65.51)	0.581	0.748	822(42.95)	1092(57.05)	21.278	<0.001
Cessation	199(34.91)	371(65.09)			393(43.62)	508(56.38)
Never smoked	850(35.82)	1523(64.18)			1774(42.46)	2404(57.54)
Note: P＜0.05,P＜0.01,**P＜0.001

3.3 Correlates of Depression in Different Economic Situations

Due to the dichotomous nature of China's urban-rural economic development, we grouped urban and rural populations, adjusted covariates, and analyzed the associations of economic situations with depressive symptoms separately. Table 2 shows the results of log-binomial models assessing the risk of depressive symptom onset during the cross-sectional study in 2020 based on family economic situations. The association between very high household consumption per capita (those with a consumption ranking of more than 80% of the sample) and depressive symptoms is significant in both rural and urban areas. Household consumption per capita >80% group was a risk factor for depression (Urban: aRR=1.295, 95% CI: 1.091-1.538, Rural: aRR=1.226, 95% CI: 1.127-1.335). There was a significant association with the occurrence of depressive symptoms in higher family consumption per capita levels in urban areas (61% - 80%) compared to rural areas, where this factor was also a risk factor (aRR=1.228, 95% CI: 1.025-1.471). However, there was no statistically significant relationship between other household consumption per capita groups (low and medium consumption groups) and depressive symptoms. To analyze in-depth the factors influencing depression across different economic statuses of households, we used an interaction term analysis to discover significant associations with depressive symptoms in potentially different economic statuses across urban and rural conditions in the middle-aged and older age groups.

TABLE 2 RR with 95%CI of Depressive Symptoms Based on Household Consumption per Capita by Urban and Rural

Level of family consumption per capita	Urban				Rural
Level of family consumption per capita	Crude RR (95% CI)	P-value	Adjusted RR (95% CI)^b	P-value	Crude RR(95% CI)	P-value	Adjusted RR (95%CI)^c	P-value
≤ 20%	1.000 (Ref.)	-	1.000 (Ref.)	-	1.000 (Ref.)	-	1.000 (Ref.)	-
21% - 40%	0.984 (0.805, 1.202)	0.872	0.973 (0.796, 1.189)	0.788	1.000 (0.915, 1.093)	1.000	0.990 (0.905, 1.083)	0.828
41% - 60%	1.056 (0.881, 1.266)	0.557	1.047 (0.873, 1.256)	0.621	1.037 (0.951, 1.132)	0.407	1.029 (0.943, 1.122)	0.524
61% - 80%	1.239 (1.035, 1.484)*	0.020	1.228 (1.025, 1.471)*	0.026	1.034 (0.949, 1.126)	0.445	1.019 (0.935, 1.111)	0.664
＞80%	1.313 (1.107, 1.558)**	0.002	1.295 (1.091, 1.538)**	0.003	1.241 (1.142, 1.348)***	＜0.001	1.226 (1.127, 1.335)***	＜0.001
Note: ^aAssociations were analyzed using log-binomial models, treating Family consumption per capita as categorical variables, respectively ^bAdjusted for gender, age, self-reported health, duration of sleep, social activities, life satisfaction, cigarette smoking and drinking wine ^cAdjusted for gemder, age, marital status, education level, duration of sleep, life satisfaction and drinking wine P＜0.05; * P＜0.01; *** P＜0.001

Figure 2 uses a forest plot to visualize the relationship between factor interactions and depression at different levels of household consumption per capita. The study revealed that having a chronic disease was a risk factor for depression across household consumption levels per capita in both urban and rural areas (Urban: Q1-RR=1.69, 95% CI: 1.07-2.67, Q2-RR=1.86, 95% CI: 1.24-2.81, Q3-RR=1.38, 95% CI: 1.09-1.73, Q4-RR=1.50, 95% CI: 1.16-1.93, Q5-RR=1.64, 95% CI: 1.35-1.98; Rural: Q1-RR=1.39, 95% CI: 1.17-1.65, Q2-RR=1.55, 95% CI: 1.30-1.84, Q3-RR=1.74, 95% CI: 1.43-2.12, Q4-RR=1.53, 95% CI: 1.29-1.81, Q5-RR=1.32, 95% CI: 1.12-1.55, all P<0.05).

In urban areas, middle-aged and older adults who were married but not living with their spouse temporarily due to reasons such as work, and whose household consumption expenditures per capita were in the top or bottom 20%, were statistically significant as risk factors for depression (Q1-RR=1.59, 95% CI: 1.00-2.53; Q5-RR=1.23, 95% CI: 1.02-1.48). The middle-aged and elderly group with moderate household consumption expenditure per capita (41-60%) and with high school or vocational school education were more likely to have depressive symptoms (Q3-RR=1.48, 95% CI: 1.08-2.03). However, higher household consumption expenditure per capita (61-80%) and higher education levels were protective factors against depression (Middle school: Q4-RR=0.68, 95% CI: 0.50-1.47). Individuals in the top 20% of households with consumption expenditure per capita and only primary education were more likely to be depressed (Q5-RR=1.15, 95% CI: 0.90-1.47). We also found that higher household consumption expenditure per capita (61-80%) and self-reported good health were protective factors against the emergence of depressive symptoms (Q4-RR=0.66, 95% CI: 0.47-0.94). Middle-aged and older adults with moderate household consumption expenditure per capita (41-60%) and no children were more likely to be depressed (Q3-RR=2.36, 95% CI: 1.21-4.59). Middle-aged and older adults with the highest level of household consumption per capita (top 20%) and who were socially active were protective factors against the emergence of depression (Q5-RR=0.83, 95% CI: 0.73-0.95).

The situation of middle-aged and older adults in rural areas differed significantly from that in urban areas. Those with the lowest and highest household consumption expenditure per capita (bottom 20% and top 20%) and who were widowed were more likely to be depressed (Q1-RR=1.20, 95% CI: 1.01-1.43; Q5-RR=1.16, 95% CI: 1.01-1.34). There was no association between people in rural areas with different household consumption per capita and their level of education and the occurrence of depressive symptoms. Individuals with lower levels of household consumption per capita (bottom 20% and 21-40%) and self-reported poor health were more likely to be depressed (Poor: Q1-RR=1.31, 95% CI: 1.06-1.62; Q2-RR=1.29, 95% CI: 1.03-1.62; Very Poor: Q1-RR=1.35, 95% CI: 1.03-1.77). Different household consumption expenditure per capita groups and dissatisfaction with children were risk factors for depression in rural areas, with lower household consumption expenditure per capita (21-40%) being more likely to be depressed about their children's situation (either satisfied or dissatisfied) (Very satisfied: RR=1.27, 95% CI: 0.96-1.68; Not satisfied at all: RR=1.98, 95% CI: 1.08-2.57). Engaging in social activities was a protective factor against depression in the middle-aged and older age groups (Q2-RR=0.88, 95% CI: 0.77-0.99; Q4-RR=0.88, 95% CI: 0.78-0.99; Q5-RR=0.88, 95% CI: 0.78-0.99).

This study utilized the 2020 CHARLS data. After controlling for various variables in urban and rural areas separately and considering urban-rural disparities, a log-binomial regression model was applied to 10,884 participants to estimate the association between household economic status and depression. Log-binomial models enable the direct estimation of the relative risk (RR), which is highly interpretable in epidemiological studies. Additionally, log-binomial regression is preferred for analyzing binary outcomes in cross-sectional studies over logistic regression, as prevalence is more straightforward to interpret than odds ratios and easier to convey to non-specialists [41]. Our findings indicate that middle-aged and older adults with high family consumption per capita (monthly) in both urban and rural areas were more susceptible to depression. Notably, we observed an interaction between different levels of family consumption per capita and the presence of chronic disease, which heightened the risk of depression among middle-aged and elderly individuals in both urban and rural settings.

We found that the prevalence of depressive symptoms was higher in rural areas than in urban areas, which is consistent with the findings of many studies [42, 43]. This phenomenon may be related to the relative lack of medical resources, weaker social support systems, and poorer living conditions in rural areas [44]. In addition, middle-aged and older adults in rural areas may face more economic pressures and difficulties in life, which also increases their risk of depression [43]. Based on the results of the univariate analysis, we found that per capita household consumption expenditures and the presence of chronic diseases acted as common influences on the presence or absence of depression in both urban and rural areas [45]. This finding suggests that economic and physical conditions should be necessary considerations in the formulation of public health policies by the relevant authorities.

According to the results of the main regression analyses, the risk of depressive symptoms was higher in the group with the highest per capita household consumption, both in urban and rural areas. This result may reflect other stressors present in high-consumption households, such as high expectations and high-stress lifestyles [43]. In addition, high-consumption households may be more likely to neglect mental health issues, leading to the development of depressive symptoms [44]. Shopping addiction, also known as "compulsive buying disorder," is a mental illness that can cause significant psychological, financial, and family problems, including depression, substantial debt, and breakdown of intimate relationships/families [46]. Related studies have also found that chronic psychosocial stress induces increased expression of MMP-8 (matrix metalloproteinase-8) in peripheral immune cells, leading to alterations in the extracellular space (ECS) of the nucleus ambiguus (NAc), a reward region of the brain, which correlates with altered neurophysiology and social avoidance in the NAc [47]. This finding explains both physiopathological aspects of having social activities as an important factor in avoiding depression in families with different levels of consumption in urban and rural areas. The health sector should give due consideration to the impact of family economic status on mental health. Especially for high-consumption families, mental health education and support should be strengthened to help them recognize and cope with depressive symptoms. Psychological interventions can be provided where necessary to encourage socialization to reduce chronic psychological stress and thus avoid depression.

We found that chronic illness is an important risk factor for depressive symptoms [48]. Regardless of family economic status, middle-aged and older adults with chronic illnesses are more likely to experience depressive symptoms. This may be due to the long-term physical discomfort and reduced quality of life associated with chronic illnesses, which increase the psychological burden on patients [49]. Meanwhile, in urban areas, marital status and education level have an impact on the risk of depression. This may be related to urban lifestyle, social networks, and psychological stress; in rural areas, health status, child satisfaction, and socialization activities have an impact on depression risk. This may be related to social support, family relationships, and lifestyle in rural areas. Health care providers and related support organizations in China should pay attention to the impact of urban-rural differences on depression in middle-aged and older adults. They can provide family-specific treatment and prevention strategies to reduce the risk of depression.

Our study used the latest data from CHARLS, which is based on a nationally representative cohort, representing Chinese middle-aged and older people. The large sample size of the data we processed, the use of two-way interaction analysis, and the use of RR values that are more precise than ORs make the results persuasive [40]; the study reveals the relationship between depression and household economic status, which can provide a reference for relevant health policymakers, who should pay more attention to those with higher per capita expenditures in urban and rural areas, to reduce the risk of depression. However, we must recognize some limitations in the paper. First, this is a cross-sectional study based on the year 2020, so it is not possible to determine a causal relationship between household economic status and depression. We did not use a more rigorous cohort study due to the complexity of dealing with data from economically relevant indicators. Nevertheless, we employed statistically rigorous methods and analyzed them in the context of China to ensure the validity and reasonableness of our findings. In future studies, standardized data processing procedures for economic indicators will be our first consideration. Secondly, the results regarding the presence of chronic diseases, participation in social activities, and the CES-D-10 questionnaire were obtained from self-reports of the sample population, which may introduce a risk of recall bias and inaccurate responses. The CES-D-10 questionnaire is a commonly used tool for assessing clinically significant depressive symptoms. However, it is only a screening tool and cannot be used to diagnose depression [50]. Finally, 64.25% of the sample in this study consisted of middle-aged and elderly individuals aged 45 years and above in rural China, which may limit the generalizability of the findings.

The study utilized data from the 2020 China Health and Retirement Longitudinal Study (CHARLS) conducted by the National School of Development of Peking University, focusing on middle-aged and elderly individuals in China. The research sample included 19,395 participants aged 45 years and older from various regions in China. The study assessed depressive symptoms using the CES-D-10 scale and analyzed determinants such as demographic, health-related, health behavior, and economy-related variables. The findings revealed that economic status, health-related factors, and health behavior variables were significant determinants of depressive symptoms. The results indicated that household consumption per capita was significantly associated with depressive symptoms in both urban and rural areas, with higher consumption levels posing a higher risk. Additionally, having a chronic disease was identified as a common risk factor for depression across different economic statuses. The research also indicated that various demographic and health-related factors influenced the prevalence of depressive symptoms differently in urban and rural settings. The study's results provide valuable insights into the factors contributing to depressive symptoms among middle-aged and elderly individuals in China, emphasizing the importance of economic status and health-related variables in understanding mental health outcomes in different regions. Addressing economic disparities and health-related factors could help mitigate depressive symptoms in these populations.

Ethics approval and consent to participate

All methods concerning human participants in our study were conducted in accordance with the ethical standards laid out in the 1964 Declaration of Helsinki and its subsequent amendments. Ethical approval for all waves of the CHARLS study was obtained from the Institutional Review Board (IRB) at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015. Written signed informed consent was obtained at recruitment from all participants, including legal representatives for illiterate participants.

Consent for Publication

All authors approved the final manuscript and the submission to this journal.

Availability of data and materials

The dataset that support the findings of this study are available on the Charls website (http://charls.pku.edu.cn/en/).

Competing interests

The authors declare no competing interests.

Funding

Not applicable

Authors' contributions

TM conceived the study, acquired, and cleaned the data. YZ and HM designed the study and performed the statistical analyses. XS plotted Figure 1, while YZ and HM prepared Figure 2. TM, YZ, and HM co-wrote the manuscript. XS, BW, RD, LY, and YB substantially revised the manuscript. All authors contributed to the interpretation of data. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors read and approved the final manuscript.

Acknowledgements

We greatly appreciate to the National School of Development, Peking University for providing us with the CHARLS data.

We express our sincere gratitude to Professor Hui Chen from the Department of Biomedical Informatics at Capital Medical University for providing guidance on the analytical methods utilized in this paper.

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Family economic disparities and the onset of depressive symptoms among middle-aged and elderly residents in urban and rural areas: A cross-sectional study based on CHARLS

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Abstract

Figures

1. Introduction

2. Methods

2.1 Study population

2.2 Definition of Economic Situations

2.3 Assessment of depressive symptoms

2.4 Measurement of determinants

2.5 Statistical analysis

3. Results

3.1 Participant characteristics

3.2 Prevalence and correlates of depression

3.3 Correlates of Depression in Different Economic Situations

4. Discussion

5. Conclusion

Declarations

Ethics approval and consent to participate

Consent for Publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1