The association between education level, widowhood, and depression among elderly Chinese after controlling for demographic, health, and behavioral variables

Abstract

With the rapid advancement of China’s aging population, the mental health problems of the elderly, especially depression, have become an important concern in the field of public health. However, research on the combined effect of education level and widowhood on depression is still relatively limited. The aim of this study is to explore the association between education level, widowhood status, and depression symptoms among elderly people in China, and further analyze the moderating effects of health status, demographic characteristics, and behavioral variables in this association. Data from the 2018 China Health and Retirement Longitudinal Study were used. A total of 3243 participants aged 50 and above were analyzed. A simplified 10 item CES-D scale was used to assess depression. Logistic regression models were used to examine the independent and interactive effects of education level and widowhood on depression, while adjusting for demographic variables (age, gender, type of residence), health status (chronic disease, self-rated health), and behavioral factors (physical activity, smoking). Even after controlling for confounding factors, lower levels of education were significantly associated with a higher risk of depressive symptoms (odds ratio [OR] = 1.52, P < .01). Widowhood is also a strong predictor of depression (OR = 1.79, P < .01), especially in women. Interaction analysis indicates that poor self-rated health and a sedentary lifestyle exacerbate the negative impact of low education and widowhood on depression. In addition, rural residents and individuals with lower socioeconomic status face a higher risk of depression symptoms. Low education level and widowhood are independent factors contributing to the high incidence of depression among elderly people in China. Health status and lifestyle factors play a crucial mediating role in this association. These findings emphasize the necessity of targeted intervention measures, such as community-based education programs, sports promotion, and strengthening social support networks, to reduce the risk of depression among vulnerable elderly people in China.

1. Introduction

China’s population is aging gradually; about 260 million individuals are 60 years of age or older, making up over 18% of the country’s overall population.^[1] The older population is constantly growing, and as a result, mental health issues, particularly depression, are becoming a major social concern.^[2] Depression is directly linked to health, quality of life, and the capacity to live independently, in addition to having a major negative impact on brain health. Additionally, widows play a critical role in the development of successful public health policies and control strategies.^[3] One of the key markers of an older person’s health is their level of education. Research demonstrates that education level affects older adults’ physical health, independence, and social skills, all of which are strongly correlated with mental health.^[4] Maintaining a high level of education is positively correlated with greater mental health and a decreased risk of depression.^[5] Dysfunction, weakness, and social isolation are typically the results of declining education and have been shown to be associated with risks for depression.^[6] The relationship between education level and depression has been the subject of numerous studies in Western nations,^[7] but in the context of China, particularly when other demographic, health, and behavioral factors are taken into account, there are still few studies that examine the relationship between education level and depression in China. One significant social factor influencing depression is widowhood, particularly for older women.^[8] In addition to causing emotional distress, widowhood may result in social exclusion and a reduction in financial assistance for care, both of which raise the risk of depression.^[9] The topic of widowhood has a more nuanced social and cultural importance in Chinese culture. Social pressure and further depression may result from the customary demands of filial piety and family care.^[10] However, control variables such as socioeconomic level, social support, and health status may limit the impact of widowhood on depression.^[11] Research on the separate effects of widowhood and educational attainment on depression has started, but few studies have simultaneously examined how these factors combine, particularly in the Chinese follow-up group.^[12,13] Additionally, the majority of studies did not properly account for other variables that could be crucial in the relationship between education level, widowhood, and depression, such as behavioral factors, health status, and demographic features.^[7,14] For instance, chronic conditions including diabetes, arthritis, and high blood pressure are prevalent in China. The effect of widowhood on depression may be amplified by certain medical disorders. Similarly, the psychological effects of widowhood may be exacerbated by alcohol consumption, smoking, and inactivity.^[15,16] This study investigates the combined effects of widowhood and educational attainment on depression in older Chinese individuals. To this end, it controls for important behavioral, health, and demographic variables, reveals the ways in which these factors interact, and investigates the mechanisms by which these factors affect depression. In addition to offering a theoretical foundation and empirical backing for the development of more successful public health strategies, this offers a fresh theoretical viewpoint for the investigation of depression in the older population of China.

2. Research methods

2.1. Data

The National Development Research Institute of Peking University sponsors the extensive multidisciplinary survey project known as the China Health and Retirement Longitudinal Study (CHARLS).^[17] It seeks to assist research on China’s aging population by methodically gathering information on the social environment, economic circumstances, and health status of the country’s middle-aged and older citizens. Numerous topics are covered by CHARLS data, such as family structure, socioeconomic position, medical service consumption, and individual physical and mental health. It represents high-quality microdata on Chinese families and individuals who are 45 years of age or older. In 2011, 2013, 2015, 2018, and 2020, CHARLS conducted surveys and interviews in 450 communities (villages) and 150 counties in 28 provinces (autonomous regions, municipalities). 23,000 respondents from 12,400 homes participated in the two-year follow-up CHARLS national baseline survey, which was started in 2011. 2018 data were utilized in this investigation. 3243 individuals who were at least 50 years old were chosen for this study. The Peking University Biomedical Ethics Review Committee has granted ethical permission for the collection of CHARLS data. We have permission to utilize this data from Peking University’s Public Data Management Bureau.

2.2. Research variables

2.2.1. Demographic, health status, and behavioral variables

Among the demographic factors are the following: gender (male or female), age (50–59, 60–69, 70–79, 80–89, and 90 years and older), and type of registered habitation (rural or urban). The following health status characteristics were gathered from participant self-reports: hypertension, hyperlipidemia, memory loss, stroke, asthma, arthritis, negative emotions, and disability. Variables related to behavior: Determine the participants’ levels of physical activity (PA) based on the International Physical Activity Questionnaire. High PA is defined as more than 23 MET hours per week, whereas low PA is defined as <23 MET hours per week; smoking (yes/no) is regarded as low PA.

2.2.2. Widowed status

Confirm the widowhood status of participants is confirmed through their self-report (yes/no).

2.2.3. Educational level

Conduct a survey questionnaire is used to assess the educational level of participants, including those who have completed high school or above and those who have completed high school or below.

2.2.4. Depression

Assess participants’ levels of depression using the streamlined CES-D scale (Radloff, 1977). There are ten items on the CHARLS CES-D scale: I feel under strain from things that don’t seem important; I have trouble focusing while I’m doing things; I feel melancholy; I believe everything is really hard; I have a positive outlook on the future; I’m afraid; I have trouble sleeping; I’m glad; I feel lonely; I don’t think I can live. Questions 5 and 8 are scored in reverse, with each question having a value between 0 and 3, which represents the intensity of symptoms from low to high. Participants are classed as “depressed” if their total score is greater than or equal to 10, and as “not depressed” if their score is less than or equal to 10.^[18–21]

2.3. Statistical analysis

A multivariate logistic regression model was employed for analysis in order to investigate the relationship between depressive symptoms, widowhood status, and educational attainment. Their effect on depression risk was determined using education level and widowhood status as the primary independent factors and depressed symptoms as the dependent variable. In order to account for any confounding variables, the subsequent multi-layer regression models are progressively developed: Model 1: Analyzes the direct association between depressive symptoms, education level, and widowhood status; Model 2: On the basis of Model 1, demographic variables such as gender, age, registered residence type, and education level are included for adjustment; Model 3: Further adds health status variables (such as self-evaluation of health, asthma, stroke, diabetes, hyperlipidemia, hypertension, and negative emotions) to adjust; Model 4: Based on Model 3, behavioral variables such as PA and smoking are included. A two-sided test with a significance threshold of P < .05 was used for all statistical analyses. IBM SPSS Statistics 27.0 software (IBM, Chicago) was used to finish the analysis.

3. Results

3.1. Demographic characteristics

In this study, we analyzed the demographic, health status, and lifestyle characteristics of the elderly population in China. In the overall sample, there were 1597 male participants, accounting for 49.2%, and 1646 female participants, accounting for 50.8%. The gender distribution difference was not significant (P = .757). The age distribution of participants showed significant differences, with the 70 to 79 age group having the highest number of participants (34.4%), while the 50 to 59 age group had the lowest number of participants (6.4%) (P = .000). In terms of residence, rural residents accounted for the vast majority (76.3%), while urban residents only accounted for 23.7%, and this difference was statistically significant (P = .000). In terms of educational level, the majority of participants had a junior high school education or below (80.8%), with fewer participants having a high school education or above (19.2%) (P = .000). The widowed status also showed significant statistical differences, with only 12.0% of participants being widowed (P = .000). In terms of health status, participants generally reported poor self-rated health, with 75.3% of respondents reporting poor self-rated health (P = .000). The prevalence of chronic diseases was high: 12.2% of participants had hypertension, 14.0% had hyperlipidemia, and 25.9% had diabetes. The incidence of these health problems was significantly related to health status (P = .000). The incidence of asthma and stroke was relatively low, at 2.9% and 6.3% respectively (P = .000). Regarding depressive symptoms, 35.5% of participants exhibited depressive symptoms, and this proportion was significantly higher than that of the non-depressed group (P = .000). In terms of lifestyle variables, the distribution of participants’ PA levels is relatively uniform, with 48.5% reporting high-level PA and 51.5% reporting low-level PA, and the difference was not statistically significant (P = .46). In addition, 26.7% of participants smoked, while 73.3% of participants did not smoke, indicating a significant association between smoking and depression (P = .000). These results reveal significant characteristics of the elderly population in China in terms of gender, age, education level, health status, lifestyle, etc, providing important background information for further analysis of the association between education level, widowhood status, and depression symptoms (Fig. 1 and Table 1).

Figure 1.

The China Health and Retirement Longitudinal Study (CHARLS) had 19,752 participants in 2018. 16,206 individuals remained after 3546 were eliminated due to missing data on depression, education level, and widowhood. 12,963 individuals were disqualified based on behavioral, health, and demographic factors. In the end, 3243 people participated in the survey.

Table 1.

Characteristics of elderly participants in the CHARLS in 2018.

	Number of participants	%	P
Gender
Male	1597	49.2%	.757
Female	1646	50.8%
Age (yr)
50–59	207	6.4%	.000
60–69	636	19.6%
70–79	1135	34.4%
80–89	1054	32.9%
≥90	216	6.7%
Location of residence
City	768	23.7%	.000
Rural	2475	76.3%
Degree of education
Junior high school and below	2619	80.8%	.000
High school and above	624	19.2%
Widowed
Yes	388	12.0%	.000
No	2855	88.0%
Self-rated health status
Good	801	24.7%	.000
Bad	2442	75.3%
Hypertension
Yes	396	12.2%	.000
No	2847	87.8%
Hyperlipidemia
Yes	455	14.0%	.000
No	2789	86.0%
Diabetes
Yes	254	25.9%	.000
No	2989	74.1%
Asthma
Yes	93	2.9%	.000
No	3150	97.1%
Stroke
Yes	205	6.3%	.000
No	3038	93.7%
Walk one kilometre
Yes	2708	83.5.4%	.000
No	535	16.5.6%
Depression
Yes	1151	35.5%	.000
No	2092	64.5%
Physical activity
High	1574	48.5%	.46
Low	1669	51.5%
Smoking
Yes	867	26.7%	.000
No	2376	73.3%

CHARLS = China Health and Retirement Longitudinal Study.

3.2. Analysis of influencing factors of educational level

The results show that the key factors affecting the education level of the elderly in China include age, registered residence type, and education level, all of which are statistically significant. Age is significantly negatively correlated with education level; as age increases, education level significantly decreases (odds ratio [OR] = 0.441, P = .037), indicating that aging has a more significant limiting effect on physical function. In addition, the type of registered residence also shows a significant impact. The education level of the rural elderly is lower than that of the urban elderly (OR = 0.584, P = .004), which may be related to the relative lack of medical resources and social support in rural areas. Similarly, older adults with higher levels of education exhibit stronger levels of education (OR = 0.584, P = .012), reflecting the important role of education in promoting healthy behavior and improving quality of life. Although gender and other health status factors such as hypertension and diabetes may theoretically affect the level of education, no statistical significance was found in this study. These findings highlight the comprehensive impact of multidimensional socioeconomic and demographic factors on the physical function of the elderly, providing important evidence for promoting health interventions for the elderly (Fig. 2).

Figure 2.

Forest plot showing the odds ratio (OR) and 95% confidence interval (CI) of factors related to the educational level of Chinese elderly. Important predictors included age, type of household registration, and education level itself.

3.3. Analysis of influencing factors of widowhood

By analyzing various factors that affect the state of widowhood, the results show that gender, age, registered residence type, education level, and self-assessment health status have significant effects on the state of widowhood. The forest plot shows that age has the most significant impact on widowhood status (OR = 13.447, 95% confidence interval [CI]: 4.686–38.582, P < .001), indicating that increasing age significantly increases the likelihood of widowhood, which may be related to natural changes in lifespan. The risk of widowhood in women is significantly higher than that in men (OR = 0.444, 95% CI: 0.346–0.568, P < .001), which may be related to differences in lifespan and sociocultural factors. In terms of registered residence type, the risk of widowhood for rural residents was significantly higher than that for urban residents (OR = 2.475, 95% CI: 1.855–3.303, P < .001), reflecting the difference in social support and living conditions between urban and rural areas. Individuals with lower education levels also exhibit a higher risk of widowhood (OR = 2.382, 95% CI: 1.575–3.602, P < .001), which may be due to the association between low education levels and lower socioeconomic status, thereby affecting individual family stability and health support. In addition, individuals with poor self-rated health also significantly increased the risk of widowhood (OR = 1.546, 95% CI: 1.191–2.007, P = .001). Other variables, such as hypertension, hyperlipidemia, diabetes, stroke, and emotional status, did not have a statistically significant impact on widowhood, but smoking showed a significant impact. The risk of widowhood for smokers was significantly higher than that for nonsmokers (OR = 1.556, 95% CI: 1.211–2.001, P = .001). These findings reveal the comprehensive impact of multiple sociodemographic and health factors on widowhood status, emphasizing the importance of interventions targeting these factors in the elderly population (Fig. 3).

Figure 3.

Forest plots show odds ratios (ORs) and 95% confidence intervals (CIs) for factors affecting widowhood status in older adults. Age, gender, household registration type, education level, self-rated health status, and smoking showed significant correlation.

3.4. Analysis of influencing factors of depression

The forest plot shows the OR and 95% CI of the effects of different factors on depression, revealing the relative contribution of each factor to depression. Physical activity level showed the most significant protective effect, with an OR significantly lower than 1 and a narrow CI range, indicating a strong and consistent association with depression. Similarly, the absence of hypertension and the ability to walk 1 km also showed a protective effect, with an OR lower than 1 and a CI that did not cross the reference line of 1, which was statistically significant. In contrast, asthma and age were significantly positively associated with depression, with an OR >1 and a relatively wide CI, reflecting the variability of their effects. The CIs of factors such as household registration type, absence of widowhood, stroke, high education level, and smoking all crossed 1, indicating that they had no statistically significant association with depression. Overall, these results highlight the importance of PA and activity ability in reducing the risk of depression, while also indicating that factors such as widowhood, asthma, low education level, and aging may increase the susceptibility to depression. These results provide a comprehensive perspective for understanding the multifactorial characteristics of depression and provide guidance for the development of intervention measures (Fig. 4).

Figure 4.

The forest plot shows the odds ratio (OR) and 95% confidence interval (CI) of the factors affecting depression in Chinese elderly. Physical activity level and walking ability were protective, while asthma and age were positively associated with depression.

3.5. Analysis of linear hierarchical regression models between participants’ educational level, widowhood, and depressive symptoms

The association between widowhood and depressive symptoms was analyzed using a linear stratified regression model. The research results showed that the explanatory power of the model for depressive symptoms significantly improved after gradually incorporating demographic, health status, emotional, and lifestyle variables. The model that only includes widowhood status (R² = 0.010, adjusted R² = 0.009, P < .001) indicates a certain association between widowhood and depressive symptoms, but its individual effect is relatively limited. After further incorporating demographic variables such as education level, household registration type, gender, and age, the explanatory power of the model significantly improved (R² = 0.063, adjusted R² = 0.062, P < .001), indicating that social demographic characteristics have a greater impact on depressive symptoms. Subsequently, after adding health status variables (such as diabetes, stroke, asthma, self-assessment health status, etc), the R² of the model increased slightly (R² = 0.070, adjusted R² = 0.067, P < .001), but the increase was relatively small, indicating that the explanatory power of health factors for depressive symptoms was limited. Finally, after incorporating emotional states (such as poor mood), lifestyle (such as PA and smoking), and education level, the explanatory power of the model reached the highest level (R² = 0.197, adjusted R² = 0.194, P < .001), indicating that these variables have important predictive effects on depressive symptoms. The research results indicate that the association between widowhood and depressive symptoms is influenced by multiple factors, among which emotional state and behavioral factors have the most significant impact on depression, emphasizing the necessity of comprehensively considering social, health, and psychological factors when intervening in depressive symptoms (Table 2).

Table 2.

Analysis of linear hierarchical regression models between participants’ educational level, widowhood, and depressive symptoms.

Model	R	R2	Adjusted R2	Variation statistics
				F variation	P
1	.101*	.010	.010	16.692	.000
2	.251†	.063	.062	43.504	.000
3	.266‡	.071	.067	20.382	.000
4	.444§	.197	.194	52.770	.000

^*Predictive variables: (constant), widowhood, educational level.

^†Predictive variables: (constant), widowhood, education level, household registration type, gender, age.

^‡Predictor variables: (constant), widowhood, education level, household type, gender, age, diabetes, stroke, asthma, self-assessment health status, hypertension, hyperlipidemia.

^§Predictors: (constant), widowhood, education level, household type, gender, age, diabetes, stroke, asthma, self-assessment of health, hypertension, hyperlipidemia, poor mood, physical activity, smoking, walking 1 km.

4. Discussion

This study is based on a nationally representative sample, explores the complex association between education level, widowhood status, and depressive symptoms, revealing the moderating effects of health status, demographic characteristics, and behavioral variables. The research results provide a new perspective for understanding the multidimensional impact mechanism of the mental health of the elderly and provide empirical evidence for developing targeted intervention measures.

4.1. The association between educational level and depressive symptoms

This study found that the risk of depressive symptoms in elderly people with a low education level was significantly higher than that in elderly people with a high education level, which is consistent with the results of previous studies. Si Miaomiao et al^[22] explored the relationship between health-related quality of life and health service utilization in rural middle-aged and elderly hypertensive patients and analyzed the moderating effect of education level. The results showed that a higher education level had a stronger moderating effect on the relationship between health-related quality of life and health service utilization, and the slope of health service utilization in rural middle-aged and elderly hypertensive patients with a high education level was smaller than that in those with a low education level. Song Longjie^[23] established a model to explore the direct impact of education level on the mental health of the elderly and then conducted a mediation effect analysis on the sample to further explore the indirect impact of education level on the mental health of the elderly. The results showed that the improvement of education level can significantly reduce the risk of psychological problems in the elderly, and education level can indirectly affect the mental health of the elderly by affecting self-evaluation of health. Wu Jingyi et al^[24] analyzed data from 15,860 elderly people aged 65 and above in the 2005 to 2008 China Elderly Health Survey and found that cognitive impairment significantly increased the risk of all-cause mortality (HR = 1.32), and its harmful effect weakened with decreasing education level (HR = 1.47 for education > 6 years, HR = 1.32 for education 0 years). At the same time, education level only showed a protective effect on death in people with normal cognition (HR = 0.83), and this protective association was not observed in people with cognitive impairment (HR = 1.09), suggesting that cognitive management strategies for the elderly should be formulated in combination with education level stratification. Ren Jiaqing et al found that a decline in education level may increase loneliness and psychological distress by reducing social opportunities and limiting the elderly’s participation in community and family activities.^[25] After controlling for demographic characteristics, health status, and behavioral variables, this study found that the association between low education level and depressive symptoms in the elderly was still significant, indicating that education level is an important independent factor affecting the mental health of the elderly. Improving education level may be an important strategy to alleviate depressive symptoms in the elderly. The promotion of PA programs such as walking training and functional exercise in the community can not only improve the educational level but also enhance the social interaction and mental health of the elderly.^[26–28] In addition, these interventions can be combined with health management to provide comprehensive physical and psychological support for the elderly.^[29]

4.2. The association between widowhood and depressive symptoms

This study showed that widowed elderly people have a higher risk of depression than those who are not widowed, which is consistent with previous studies. Jiang C et al^[3] found that the emotional loss and support system collapse caused by widowhood not only increase the psychological burden of the elderly, but also may lead to the deterioration of their individual social functions. This is particularly evident in the Chinese cultural context, where the traditional family structure emphasizes mutual support and dependence between spouses. You H et al^[30] and Domènech-Abella J et al^[31] found that widowhood may lead to the repositioning of the elderly’s role in the family, exacerbating isolation and social pressure. Tan Cuilian et al^[32] found that Chinese elderly widowed women may have a higher risk of depression. This may be related to the fact that elderly women traditionally bear more emotional and social responsibilities in the family, and they are more likely to feel lonely and helpless after widowhood. Liu Wenjun^[33] found that widowhood may further deteriorate mental health by changing an individual’s lifestyle, such as reducing PA and increasing unhealthy behaviors. Li Yueying^[34] found that the impact of widowhood on the mental health of the elderly is not only the direct loss of social support, but also involves deeper psychological and behavioral mechanisms. Notably, the psychological toll of widowhood may begin long before the loss itself. Research has shown that spouses of patients with chronic illnesses often experience significant declines in quality of life, anxiety, and sleep disturbances, suggesting that prolonged caregiving burden erodes psychological resilience and may predispose individuals to depression upon widowhood.^[35] Comprehensive measures should be taken to intervene in the mental health of widowed elderly people, including strengthening social support networks and providing targeted psychological interventions. Promoting social participation through community activities and helping widowed women establish new social connections may also effectively alleviate depressive symptoms.^[36,37]

4.3. The moderating effect of health status, behavioral variables, and demographic characteristics

This study found that after adjusting for health status variables, behavioral variables, and demographic characteristics, the association between education level and widowhood and depression symptoms in older adults remained statistically significant. Health status (self-rated health status, asthma, hypertension) and behavioral patterns (walking ability, PA level) significantly affect the incidence of depression symptoms in elderly people. This is consistent with previous research. Yaqun et al^[38] found that elderly people with poor self-rated health have a high risk of developing depression. After adjusting for demographic variables, health status, and lifestyle variables, there is still a significant correlation between self-rated health status and depression. Furthermore, elderly individuals with chronic conditions may develop maladaptive cognitive patterns such as somatosensory amplification, which has been shown to increase the risk of depression by magnifying the perception of physical symptoms.^[39] Many studies have shown that behavioral patterns have a particularly significant impact on depressive symptoms. Behaviors such as low PA and poor walking ability may further exacerbate depressive symptoms by restricting social participation and exacerbating physical health problems.^[40–42] A lack of PA not only increases the likelihood of depression symptoms, but also increases feelings of loneliness and social isolation by restricting social interactions among older adults. A study shows that elderly people with good walking ability have a significantly lower risk of depression compared to those with poor walking ability. The reason may be that elderly people with good walking ability have broader social support and participation. These findings emphasize the importance of health behavior interventions in preventing and alleviating depressive symptoms in older adults. This study also emphasizes the important moderating role of demographic characteristics between education level, widowhood, and depressive symptoms. Variables such as age, gender, and education level can affect the occurrence of depressive symptoms and indirectly impact mental health by altering health status and behavioral patterns. The study by Xu X et al^[43] suggests that older adults with lower levels of education may have a lower sense of control over their health status due to a lack of health knowledge, which may further exacerbate depressive symptoms. Henning Smith et al^[44] found that due to the increase in medical resources and community support, urban elderly people may have lower levels of depression, while rural elderly people face higher mental health risks due to a lack of resources and insufficient social support. Gender differences are also worth noting. The study by Tian Yinghan et al suggests that elderly women may face a greater risk of depression after losing their spouse, which is related to their roles in the family and social expectations.^[45] The above research indicates that health status and behavioral patterns play an important mediating role in the formation of depression symptoms in the elderly, and demographic characteristics further affect the psychological health of the elderly by regulating health and behavioral variables. Future mental health interventions for the elderly should focus on the specific needs of different subgroups, such as providing more social support for the rural elderly, designing concise and feasible health intervention plans for elderly people with lower education levels, and providing emotional support and psychological services for widowed elderly women. Only by comprehensively considering these multidimensional factors can we effectively alleviate the burden of depression symptoms in the elderly and improve their overall quality of life.

5. Conclusions and limitations

The multifaceted influence mechanism of these factors on mental health was shown by this study’s comprehensive analysis of the intricate relationship between senior Chinese people’s depressed symptoms, widowhood status, and educational attainment. The research results indicate that education level and widowhood status are important risk factors for depressive symptoms, while health status and behavioral variables play important moderating roles in these associations. The study emphasizes the importance of comprehensive interventions, including improving physical function, enhancing social support, and promoting healthy lifestyles. Future policies and intervention measures should promote PA programs at the community level, provide psychological support services for widowed elderly people, and strengthen the screening and management of mental health. This study not only expands our understanding of the mental health of the elderly but also provides empirical support for policymaking and practice.

Although this study reveals an important association between education level, widowhood status, and depressive symptoms, there are still some limitations. Firstly, this study adopts a cross-sectional design, and the causal association is not yet clear. Future research can use a longitudinal design to further verify the temporal dynamics of these associations. Secondly, the assessment of depressive symptoms relies on self-report and may have some subjective bias. It is recommended to combine clinical diagnostic tools for verification in the future. In addition, this study did not fully explore the impact of cultural background on the mental health of elderly people, especially in the context of urban-rural differences and changes in family structure, which may have important supplementary significance for the research results.

Acknowledgments

The authors thank all the participants in CHARLS team for their time and effort devoted to the project.

Author contributions

Conceptualization: Qingdong Ling, Yaqun Zhang.

Data curation: Qingdong Ling, Yaqun Zhang.

Formal analysis: Qingdong Ling, Yaqun Zhang.

Funding acquisition: Qingdong Ling, Yaqun Zhang.

Investigation: Qingdong Ling, Yaqun Zhang.

Methodology: Qingdong Ling, Yaqun Zhang.

Project administration: Yaqun Zhang.

Software: Qingdong Ling, Yaqun Zhang.

Supervision: Yaqun Zhang.

Writing – original draft: Qingdong Ling, Yaqun Zhang.

Writing – review & editing: Qingdong Ling, Yaqun Zhang.