The Moderating Effect of Gender on the Educational Gradient in Depressive Symptoms: A Further Analysis Based on Urban-Rural Stratification

The Moderating Effect of Gender on the Educational Gradient in Depressive Symptoms: A Further Analysis Based on Urban-Rural Stratification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Moderating Effect of Gender on the Educational Gradient in Depressive Symptoms: A Further Analysis Based on Urban-Rural Stratification shengchao liu, guangyao jia, chaoyang guo, shuyang zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9405307/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective This study aimed to investigate gender differences in the protective effect of education against depressive symptoms among older adults, while examining potential urban-rural disparities in this gender-moderated relationship. Methods Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) 2020, we analyzed 9,225 participants aged ≥ 60years. Depressive symptoms were assessed using the CESD-10 scale. Multiple linear regression models were constructed to examine education×gender interactions in the overall sample. Subsequent stratified analyses by urban/rural residence were performed to evaluate coefficient variations, complemented by comprehensive robustness checks. Results The analysis revealed a significant inverse association between education level and depressive symptoms (β=-0.697, P <0.001), with women demonstrating higher depressive scores than men (β = 0.669, P <0.001). A significant education×gender interaction was observed (β=-0.348, P =0.016), indicating a more pronounced protective effect of education among women: each additional education level reduced depressive scores by 0.697 points in men versus 1.045 points in women. Urban-rural stratification showed significant interaction in urban populations (β = -0.549, P = 0.020) but not in rural populations (β = -0.068, P = 0.750), though the urban-rural coefficient difference did not reach statistical significance ( P = 0.129). Conclusion Our findings demonstrate gender-specific protective effects of education against depressive symptoms in older adults, with women exhibiting greater benefits. This gender-moderation effect appears more substantial in urban settings compared to rural areas. These results underscore the importance of educational empowerment for women and suggest the need for targeted mental health interventions, particularly for rural female populations. education depressive symptoms gender differences urban-rural stratification elderly CHARLS Figures Figure 1 Introduction The rapid aging of China's population has brought increasing attention to mental health concerns among older adults. Depression, as a prevalent mental disorder, has emerged as a substantial global public health burden [ 1 – 3 ] . World Health Organization statistics indicate that approximately 400 million individuals worldwide experience depression, including over 50 million cases in China [ 4 – 7 ] . Educational attainment, serving as a fundamental social determinant, demonstrates a significant correlation with mental health status, a phenomenon commonly termed the "education gradient" [ 8 ] . Substantial empirical evidence suggests that individuals with lower educational qualifications exhibit elevated susceptibility to depressive symptoms [ 9 – 12 ] . However, the expression of this education gradient may display considerable heterogeneity across population subgroups, particularly when considering gender and urban-rural residence as potential moderators [ 13 ] . China's ongoing urbanization process has maintained the persistent urban-rural dichotomy, characterized by substantial disparities in resource distribution, cultural contexts, and social support systems. Notably, rural elderly populations demonstrate significantly higher prevalence rates of depressive symptoms compared to their urban counterparts [ 14 – 15 ] . Current research indicates distinct patterns of depression-related risk factors between urban and rural settings. Urban elderly populations appear influenced by a narrower range of factors (e.g., physical disability, sleep quality), whereas rural elderly face more diverse determinants (e.g., gender, education, marital status, chronic conditions) [ 16 ] . Consequently, the moderating effect of gender on the education-depression association may exhibit contextual variation based on urban or rural residence. This investigation employs data from the China Health and Retirement Longitudinal Study (CHARLS) to examine whether the protective effect of education against depressive symptoms in elderly populations demonstrates gender-specific variations, and whether these gender differences remain consistent across urban and rural contexts. The study's findings are expected to elucidate the complex interrelationships among these variables and contribute essential empirical evidence for developing targeted psychological interventions for aging populations. Data and methods Data Sources This study employs data from the 2020 China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey conducted by the National School of Development at Peking University. The survey encompasses 150 county-level and 450 village-level units across China, targeting individuals aged 45 years and older. CHARLS adopts a questionnaire design informed by internationally established aging studies, including the U.S. Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), and the Survey of Health, Aging, and Retirement in Europe (SHARE) [ 17 ] . The anonymized dataset is publicly accessible through the CHARLS official website ( https://charls.pku.edu.cn/index.htm ). In compliance with the Declaration of Helsinki, all rounds of the CHARLS survey were approved by the Biomedical Ethics Committee of Peking University. The fieldwork protocol for this survey was approved with the ethical approval number IRB00001052-11015, and written informed consent was obtained from each respondent. For this analysis, we included individuals aged ≥ 60 years with complete data on depressive symptoms, educational attainment, gender, and urban-rural residence, yielding a final analytical sample of 9,225 participants. Variables Dependent variable: Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), with total scores ranging from 0 to 30, where higher scores reflect greater symptom severity. For robustness analysis, depressive symptoms were dichotomized (present/absent) using a validated cutoff score (≥ 10). Independent variable: Educational attainment was categorized as follows: 0 = below primary education, 1 = primary education, and 2 = junior high school or higher. In regression models, this variable was treated as continuous and mean-centered to mitigate multicollinearity. Moderator variable: Gender was coded dichotomously (0 = male, 1 = female). Stratification variable: Residential type was initially classified as 1 = urban/town center, 2 = urban-rural/town-rural fringe, and 3 = rural. For analytical purposes, categories 1 and 2 were consolidated into an "urban" group (coded 1), while category 3 was designated as "rural" (coded 0), resulting in a binary classification. Control variables: Covariates included age (continuous), marital status (1 = partnered, 0 = unpartnered), personal income (1 = income-earning, 0 = no income), chronic disease count (continuous), smoking status (1 = current smoker, 0 = non-smoker), alcohol consumption (1 = current drinker, 0 = non-drinker), sleep duration (continuous, hours), social engagement (1 = participated in social activities in the past month, 0 = no participation), and activities of daily living (ADL) impairment (1 = presence of difficulties, 0 = no difficulties). Statistical Analysis Sample characteristics and depression scores were reported by urban-rural residence and gender. Categorical and continuous variables were presented as frequencies (%) and means ± standard deviations (X ± S), respectively. Group comparisons were conducted using F/χ² tests. A multiple linear regression model was employed in the full sample to examine the moderating effect of gender on the education-depression relationship, including education, gender, the education×gender interaction term, and all control variables. Robust standard errors were used to address heteroscedasticity. The regression was repeated with urban-rural stratification to separately estimate interaction effects in urban and rural subsamples, and independent samples t-tests were conducted to compare differences in interaction term coefficients between urban and rural areas. Finally, multiple robustness checks were performed: (1) dichotomizing depression scores and repeating the analysis using logistic regression; (2) treating education as a categorical variable (with "below primary school" as the reference) in the model; (3) conducting sensitivity analyses by excluding samples with ADL difficulties. All analyses were performed using R 4.3.3 software, with P < 0.05 considered statistically significant. Results Basic Characteristics The study enrolled a total of 9,225 elderly participants aged ≥ 60 years, consisting of 3,021 urban residents (32.7%) and 6,204 rural residents (67.3%). The cohort included 4,503 males (48.8%) and 4,722 females (51.2%). Significant intergroup differences were detected across all measured variables (P < 0.05). Notably, depression scores were elevated in rural participants compared to urban counterparts (rural males: 14.6 ± 6.15, rural females: 17.1 ± 6.78; urban males: 12.6 ± 5.41, urban females: 14.8 ± 6.45). Furthermore, within each subgroup, females exhibited higher depression scores than males. Educational attainment displayed marked disparities, with urban males demonstrating the highest level (1.37 ± 0.78) and rural females the lowest (0.32 ± 0.63), reflecting pronounced urban-rural and gender-based educational inequalities. Additional findings revealed a higher proportion of married males compared to females, with negligible urban-rural variation. The prevalence of chronic diseases was relatively uniform across groups. Urban residents exhibited marginally greater engagement in social activities than rural residents. Sleep duration was slightly prolonged in males relative to females and modestly reduced in urban areas compared to rural areas. Comprehensive sample characteristics, stratified by urban-rural residence and gender, are presented in Table 1 . Table 1 Sample Characteristics by Urban-Rural Residence and Gender Variable Rural Urban F/χ² P Male (n = 3074) Female (n = 3130) Male (n = 1429) Female (n = 1592) Depression scores 14.6 ± 6.15 17.1 ± 6.78 12.6 ± 5.41 14.8 ± 6.45 189.5 < 0.001 Education 0.89 ± 0.84 0.32 ± 0.63 1.37 ± 0.78 0.90 ± 0.89 673.0 < 0.001 Age 69.5 ± 6.46 69.4 ± 6.49 68.9 ± 6.46 69.0 ± 6.51 3.72 0.011 Chronic 0.69 ± 1.02 0.75 ± 1.04 0.77 ± 1.08 0.83 ± 1.12 6.42 < 0.001 Sleep 6.30 ± 1.93 5.69 ± 2.20 6.05 ± 1.64 5.79 ± 1.80 54.7 < 0.001 Married, n (%) 2664 (86.7) 2289 (73.1) 1272 (89.0) 1126 (70.7) 331.0 < 0.001 Social activity, n (%) 1328 (43.2) 1377 (44.0) 728 (50.9) 838 (52.6) 56.7 < 0.001 Smoke, n (%) 1547 (50.3) 156 (5.0) 600 (42.0) 75 (4.7) 2241.0 < 0.001 Drink, n (%) 1304 (42.4) 260 (8.3) 627 (43.9) 139 (8.7) 1449.0 < 0.001 ADL, n (%) 1061 (34.5) 1602 (51.2) 344 (24.1) 580 (36.4) 360.0 < 0.001 Income, n (%) 621 (20.2) 302 (9.6) 315 (22.0) 125 (7.9) 258.0 < 0.001 Gender Moderation Effect in Full Sample The results of the multiple linear regression analysis for the complete sample are summarized in Table 2 . In the main effects model, education demonstrated a significant negative association with depression scores (β = -0.697, 95% CI: -0.898 to -0.496, P < 0.001), indicating that higher educational attainment was linked to lower levels of depressive symptoms. Conversely, gender exhibited a significant positive association (β = 0.669, 95% CI: 0.370 to 0.968, P < 0.001), with female participants reporting higher depression scores compared to males. The interaction model revealed a statistically significant education × gender interaction (β = -0.348, 95% CI: -0.631 to -0.065, P = 0.016), suggesting that the protective effect of education against depression was more pronounced among females. Marginal effect analysis further indicated that each incremental increase in education level was associated with an average reduction of 0.70 points in depression scores for males ( P < 0.001), compared to a more substantial reduction of 1.05 points for females ( P < 0.001). Among the covariates, age, marital status (having a spouse), engagement in social activities, and longer sleep duration were inversely associated with depression scores, whereas the number of chronic diseases, smoking status, and limitations in activities of daily living (ADL) showed positive correlations (all P < 0.05). Table 2 Full-sample linear regression: The moderating effect of gender on the education-depression relationship Variable depression Main effects model Interaction model Education -0.859 *** ( -0.076 ) -0.697 *** ( -0.103 ) Gender 0.670 *** ( -0.156 ) 0.669 *** ( -0.156 ) Age -0.068 *** ( -0.01 ) -0.068 *** ( -0.01 ) Married -1.152 *** ( -0.16 ) -1.154 *** ( -0.16 ) Chronic 0.448 *** ( -0.058 ) 0.447 *** ( -0.058 ) Social activity -0.458 *** ( -0.123 ) -0.461 *** ( -0.123 ) Sleep -0.647 *** ( -0.031 ) -0.645 *** ( -0.031 ) Smoke 0.399 ** ( -0.16 ) 0.406 ** ( -0.16 ) Drink -0.790 *** ( -0.153 ) -0.792 *** ( -0.153 ) ADL 3.810 *** ( -0.131 ) 3.805 *** ( -0.131 ) Income -0.526 *** ( -0.178 ) -0.536 *** ( -0.178 ) education × gender -0.348 ** ( -0.148 ) Constant 22.871 *** ( -0.815 ) 22.808 *** ( -0.816 ) Observations 9,101 9,101 R 2 0.214 0.214 Adjusted R 2 0.213 0.213 Residual Std. Error 5.768 (df = 9089) 5.767 (df = 9088) F Statistic 224.345 *** (df = 11; 9089) 206.209 *** (df = 12; 9088) Note : * P <0.1; ** P <0.05; *** P <0.01 Urban-Rural Stratified Analysis The stratified regression analyses by urban-rural residence are summarized in Table 3 . For the urban cohort, while the main effect of education was not statistically significant (β = -0.308, P = 0.095), gender demonstrated a significant main effect (β = 0.882, P < 0.001). Notably, the education × gender interaction term reached statistical significance (β=-0.549, P = 0.020), suggesting that urban females derived substantially greater benefits from education compared to their male counterparts. In contrast, the rural sample exhibited significant main effects for both education (β = -0.581, P < 0.001) and gender (β = 0.899, P < 0.001), but the interaction term was non-significant (β = -0.068, P = 0.750), indicating comparable educational benefits between rural males and females. A comparative analysis of interaction coefficients between urban (-0.549) and rural (-0.068) samples yielded a difference of -0.481 (SE = 0.317), with Z = -1.516, P = 0.129, which did not achieve statistical significance. Table 3 Urban-Rural Stratified Linear Regression: Heterogeneity of Gender Moderation Effects Variable Depression Urban Rural Education -0.308 * (0.185) -0.581 *** (0.129) Gender 0.882 *** (0.268) 0.899 *** (0.205) Age -0.041 ** (0.017) -0.076 *** (0.013) Married -1.011 *** (0.261) -1.286 *** (0.200) Chronic 0.320 *** (0.090) 0.557 *** (0.075) Social activity -0.321 (0.198) -0.448 *** (0.154) Sleep -0.754 *** (0.058) -0.623 *** (0.037) Smoke 0.063 (0.266) 0.462 ** (0.198) Drink -0.864 *** (0.248) -0.727 *** (0.191) ADL 3.920 *** (0.225) 3.576 *** (0.161) Income -0.260 (0.294) -0.670 *** (0.221) education × gender -0.549 ** (0.237) -0.068 (0.212) Constant 20.429 *** (1.342) 23.754 *** (1.015) Observations 2,998 6,103 R2 0.226 0.198 Adjusted R2 0.223 0.197 Residual Std. Error 5.347 (df = 2985) 5.909 (df = 6090) F Statistic 72.490*** (df = 12; 2985) 125.646*** (df = 12; 6090) Note: * P < 0.1;** P < 0.05༛ *** P < 0.01 Figure 1 visually presents the education-predicted depression score curves stratified by gender in urban and rural areas. In urban settings, the female curve exhibits a significantly steeper slope compared to males, whereas in rural areas, the two curves remain nearly parallel. Robustness Tests Following dichotomization of depression scores (with scores ≥ 10 classified as depression), logistic regression analysis revealed that the coefficient for the education×gender interaction term in the full sample was nonsignificant (β=-0.035, P = 0.578), potentially attributable to information loss from dichotomization. However, stratified analyses by urban-rural residence yielded findings consistent with the primary analysis: the interaction odds ratio (OR) was 0.79 ( P = 0.048) in urban areas and 0.98 ( P = 0.85) in rural settings. When education was modeled as a categorical variable (using "below primary school" as the reference), a significant interaction emerged between junior high school or higher education and female gender in the full sample (β=-0.78, P = 0.010), whereas the interaction between primary school education and female gender remained nonsignificant (β=-0.35, P = 0.275). These results suggest that the protective effect of education for women is primarily evident at the junior high school level or above. Furthermore, sensitivity analyses excluding individuals with activities of daily living (ADL) difficulties confirmed the robustness of the education×gender interaction (β=-0.40, P = 0.018). Discussion Utilizing data from the CHARLS 2020 survey, this study conducted a systematic investigation into gender disparities in the protective effect of educational attainment against depressive symptoms among older adults, while further exploring the urban-rural heterogeneity of this gender-moderated relationship. The analysis demonstrated a statistically significant gender difference in the depression-protective effect of education in the overall sample, with female participants exhibiting greater benefits compared to their male counterparts. Urban-rural stratification revealed that this gender-moderated effect remained significant in urban settings (favoring females) but was non-significant in rural areas, where both genders derived comparable benefits. Notably, the coefficient difference for the urban-rural interaction term failed to achieve statistical significance. These findings indicate that females experience enhanced mental health benefits from education, a result consistent with prior studies [ 13 , 18 ] yet inconsistent with the traditional gender role hypothesis positing diminished educational returns for women. Potential mechanisms underlying this observation include: (1) education enhances female autonomy, decision-making capacity, and psychological resilience, thereby improving stress-coping mechanisms and amplifying mental health protection [ 19 ] ; (2) within China's rapidly evolving socioeconomic landscape, educated women may attain greater upward mobility and access to support networks, mitigating depression risk; and (3) education may promote health literacy and healthcare utilization, optimizing resource allocation for mental well-being. Additionally, the study identified a threshold effect, wherein the protective influence of education became particularly pronounced at or above junior high school attainment, suggesting a critical educational threshold for mental health improvement in women. While the urban-rural interaction term coefficients did not achieve statistical significance, the observed point estimates revealed a notable disparity in gender moderation effects between urban (-0.549) and rural (-0.068) areas, with only the urban interaction term reaching significance. This pattern potentially reflects fundamental differences in socio-cultural contexts. Urban environments, characterized by greater gender equality awareness, may facilitate women's ability to translate educational attainment into employment opportunities, economic autonomy, and enhanced social standing, thereby yielding mental health benefits. Furthermore, urban areas' more robust social support infrastructure could amplify the protective effects of education for women. Conversely, rural regions often maintain traditional gender role divisions, where educated women may face constraints in converting educational resources into mental health advantages due to familial obligations and societal expectations [ 20 ] . Additionally, potential disparities in educational quality and relevance to mental health promotion between urban and rural settings may contribute to these observed differences. The current study's limitations, including a modest urban female sample size (n = 1,592) and potential urban-rural classification ambiguities (particularly regarding peri-urban areas), may have constrained statistical power to detect significant differences. Future investigations employing larger cohorts or more precise geographic delineations would be valuable for substantiating these findings. The present study demonstrates that rural elderly populations exhibit significantly elevated depression levels, consistent with prior findings on urban-rural disparities in depressive symptoms [ 21 – 22 ] . Notably, this investigation advances the examination of gender differences by revealing that gender not only predicts depression severity but also moderates the education-health association. Distinct from previous studies examining gender or urban-rural differences in isolation, this research innovatively integrates both factors, demonstrating that gender-moderating effects manifest differently across distinct sociostructural contexts, thereby offering an intersectional framework for understanding health disparities. These findings yield critical implications for designing mental health interventions for elderly populations. First, the more pronounced protective effect of education among female participants underscores the necessity of enhancing educational opportunities for women, particularly for elderly women with limited formal education. Community-based education programs and health literacy initiatives may help mitigate their educational disadvantages. Second, rural women emerge as a high-priority subgroup for mental health interventions, as they not only exhibit the highest depression scores (mean = 17.1) but also derive fewer mental health benefits from education. Thus, rural regions should implement gender-sensitive psychological support services while promoting gender equity to facilitate the translation of education into measurable mental health improvements. Finally, urban-rural differences suggest the need for context-specific intervention strategies: urban programs should address workplace stress and work-family conflict, whereas rural initiatives must target structural barriers, including resource limitations and traditional belief systems. This study has several limitations that warrant consideration. First, the cross-sectional design inherently limits causal inference, as the observed associations between education and outcomes may be influenced by reverse causality or unmeasured confounding factors. Longitudinal study designs or instrumental variable approaches would be valuable for future verification. Second, reliance on self-reported depression symptoms may introduce reporting bias. Third, the relatively low educational attainment among elderly participants resulted in a small sample size in the high-education group, potentially compromising the precision of effect estimates. Fourth, the urban-rural classification based on administrative boundaries might not accurately reflect participants' actual living environments or subjective identities, particularly for peri-urban areas, which could lead to measurement error. Fifth, the absence of potential mediating variables (e.g., social support, health behaviors) limited the investigation of underlying mechanisms. Future research should explore the pathways through which education influences mental health outcomes, as well as the moderating effects of gender and urban-rural status on these pathways. Conclusions Drawing upon nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS) 2020, this investigation demonstrates significant gender-based variations in the protective role of education against depressive symptoms among older adults, with women exhibiting more pronounced benefits. The observed gender-modifying effect is particularly evident in urban settings, while remaining statistically insignificant in rural areas. These findings underscore the imperative of incorporating both gender-specific and urban-rural contextual factors in the development of mental health interventions for the elderly population. Special consideration should be given to enhancing educational opportunities and psychological support for older women in rural communities. Consequently, advancing educational equity, promoting gender parity, and reducing urban-rural disparities emerge as crucial strategies for optimizing mental health outcomes in aging populations. Declarations Funding None to declare. References Abdoli N, Salari N, Darvishi N, et al. The global prevalence of major depressive disorder (MDD) among the elderly: A systematic review and meta-analysis[J]. Neurosci Biobehav Rev. 2022;132:1067–73. Liu J, Liu Y, Ma W, et al. Temporal and spatial trend analysis of all-cause depression burden based on Global Burden of Disease (GBD) 2019 study[J]. Sci Rep. 2024;14(1):12346. World Health Organization. Depression: global burden and projections[EB/OL]. https://www.who.int/mental_health/management/depression/en/ HERRMAN, H,KIELING C,MCGORRY, P et al. Reducing the global burden of depression:a Lancet-World Psychiatric Association Commission[J].Lancet,2019,393(10189):e42–43. HERRMAN, H,PATEL V,KIELING, C et al. Time for united action on depression:a Lancet-World Psychiatric Association Commission[J].Lancet,2022,399(10328):957–1022. LI Y J,WU Y L,ZHAI, L, et al. Longitudinal association of sleep duration with depressive symptoms among middle-aged and older. Chinese[J] Sci Rep. 2017;7(1):11794. WU Y Y,LEI P C,YE R, X et al. Prevalence and risk factors of depression in middle-aged and older adults in urban and rural areas in China:a cross-sectional study[J].Lancet,2019,394:S53. van de Weijer MP, Demange PA, Pelt DHM et al. Disentangling potential causal effects of educational duration on well-being, and mental and physical health outcomes. Psychol Med. 2024; 54 Psychol Med. Minh A, Bültmann U, Reijneveld SA, et al. Depressive symptom trajectories and early adult education and employment:comparing longitudinal cohorts in Canada and the United States[J]. Int J Environ Res Public Health. 2021;18(8):4279. Magakwe TSS, John EE, Daniel-Nwosu E et al. Association between educational attainment and mental health conditions among Africans working and studying in selected African countries. Sci Rep. 2025; 15 Sci Rep. Weng AX. Education and Mental Health in Young Adulthood: New Evidence From Genetic Markers. Health Econ. 2025; 34 Health Econ. Kersjes C, Junker S, Mauz E et al. Income, Educational Level, and Depressive Symptoms in a Time of Multiple Crises: Trends Revealed by High-Frequency Mental Health Surveillance in Germany, 2019–2024. DTSCH ARZTEBL INT. 2025; 122 DTSCH ARZTEBL INT. Vonneilich N, Becher H, Berger K et al. Depressive symptoms, education, gender and history of migration - an intersectional analysis using data from the German National Cohort (NAKO). Int J Equity Health. 2025; 24 Int J Equity Health. Wang C, Wang N, Shen W et al. Exploring disparities in depressive symptoms between rural and urban middle-aged and elderly adults in China: evidence from CHARLS. PSYCHOL HEALTH MED. 2026; 31 PSYCHOL HEALTH MED. Hu C, Jiang Q, Yuan Y et al. Depressive symptoms among the oldest-old in China: a study on rural-urban differences. BMC Public Health. 2024; 24 BMC Public Health. Wang J, Wang Y, Chen S et al. Urban-rural differences in key factors of depressive symptoms among Chinese older adults based on random forest model. J AFFECT DISORDERS. 2024; 344 J AFFECT DISORDERS. Zhao Y, Yisong Hu JP, Smith J, Strauss G, Yang. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. Gibson PA, Baker EH, Milner AN. The role of sex, gender, and education on depressive symptoms among young adults in the United States. J AFFECT DISORDERS. 2016; 189 J AFFECT DISORDERS. Ross CE, Mirowsky J. Sex differences in the effect of education on depression: resource multiplication or resource substitution? SOC SCI MED. 2006; 63 SOC SCI MED. Liu S, Heshmati A. Relationship between education and well-being in China. J SOCIAL EC DEV. 2023; 25 J SOCIAL EC DEV. Purtle J, Nelson KL, Yang Y et al. Urban-Rural Differences in Older Adult Depression: A Systematic Review and Meta-analysis of Comparative Studies. AM J PREV MED. 2019; 56 AM J PREV MED. Liu H, Fan X, Luo H et al. Comparison of Depressive Symptoms and Its Influencing Factors among the Elderly in Urban and Rural Areas: Evidence from the China Health and Retirement Longitudinal Study (CHARLS). Int J Environ Res Public Health. 2021; 18 Int J Environ Res Public Health. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 17 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9405307","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635231165,"identity":"6f91469c-39f3-4490-a5a8-d3d852ce991b","order_by":0,"name":"shengchao liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIie3RsQqCQBzH8bODf8uZ6z+ikx4gUA5EF59F8SWa4sTBRegFfIvAWfEBXF1DaHJxc2iosSavLei++wf+P/6E6HQ/GFDaDI8ZuU1prUY2a0gcs/CFm0OkRrjFPGRwimXHHNXDiIfIME4zNvUjCflRLpPEd3wUOTWvQUkS4dXLpOkjhvs0M6sdI3VcLRNDYg1oyJbdVQldbSXg4UVAlQAVRoHCzUAEpaOwxb5000DmM7et9taPp5Avko9Q9TXv5Fuh0+l0f9ETiPA23jFv+X0AAAAASUVORK5CYII=","orcid":"","institution":"The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital)","correspondingAuthor":true,"prefix":"","firstName":"shengchao","middleName":"","lastName":"liu","suffix":""},{"id":635231168,"identity":"1113ad5a-c543-4f11-bddd-51524c397a8b","order_by":1,"name":"guangyao jia","email":"","orcid":"","institution":"The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"guangyao","middleName":"","lastName":"jia","suffix":""},{"id":635231170,"identity":"2e313406-557d-4b40-904d-8fd08178d9bf","order_by":2,"name":"chaoyang guo","email":"","orcid":"","institution":"The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"chaoyang","middleName":"","lastName":"guo","suffix":""},{"id":635231171,"identity":"f44f9604-1874-4c66-845a-5754d0f5b40c","order_by":3,"name":"shuyang zhang","email":"","orcid":"","institution":"The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"shuyang","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2026-04-13 14:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9405307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9405307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108974354,"identity":"0a31920e-7826-4688-99ee-1ebc5c278f15","added_by":"auto","created_at":"2026-05-11 10:50:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":658669,"visible":true,"origin":"","legend":"\u003cp\u003eGender-specific educational prediction curves of depression scores under urban-rural stratification\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9405307/v1/f92815b7b31d43b6a4fd7014.png"},{"id":108974402,"identity":"bd424081-e7be-45c4-a980-b1a7a802d133","added_by":"auto","created_at":"2026-05-11 10:51:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1035609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9405307/v1/a35b47b4-9967-4410-af3b-c852fc34dafc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Moderating Effect of Gender on the Educational Gradient in Depressive Symptoms: A Further Analysis Based on Urban-Rural Stratification","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid aging of China's population has brought increasing attention to mental health concerns among older adults. Depression, as a prevalent mental disorder, has emerged as a substantial global public health burden \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. World Health Organization statistics indicate that approximately 400\u0026nbsp;million individuals worldwide experience depression, including over 50\u0026nbsp;million cases in China \u003csup\u003e[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Educational attainment, serving as a fundamental social determinant, demonstrates a significant correlation with mental health status, a phenomenon commonly termed the \"education gradient\" \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Substantial empirical evidence suggests that individuals with lower educational qualifications exhibit elevated susceptibility to depressive symptoms \u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. However, the expression of this education gradient may display considerable heterogeneity across population subgroups, particularly when considering gender and urban-rural residence as potential moderators \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChina's ongoing urbanization process has maintained the persistent urban-rural dichotomy, characterized by substantial disparities in resource distribution, cultural contexts, and social support systems. Notably, rural elderly populations demonstrate significantly higher prevalence rates of depressive symptoms compared to their urban counterparts \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Current research indicates distinct patterns of depression-related risk factors between urban and rural settings. Urban elderly populations appear influenced by a narrower range of factors (e.g., physical disability, sleep quality), whereas rural elderly face more diverse determinants (e.g., gender, education, marital status, chronic conditions) \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Consequently, the moderating effect of gender on the education-depression association may exhibit contextual variation based on urban or rural residence.\u003c/p\u003e \u003cp\u003eThis investigation employs data from the China Health and Retirement Longitudinal Study (CHARLS) to examine whether the protective effect of education against depressive symptoms in elderly populations demonstrates gender-specific variations, and whether these gender differences remain consistent across urban and rural contexts. The study's findings are expected to elucidate the complex interrelationships among these variables and contribute essential empirical evidence for developing targeted psychological interventions for aging populations.\u003c/p\u003e"},{"header":"Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eThis study employs data from the 2020 China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey conducted by the National School of Development at Peking University. The survey encompasses 150 county-level and 450 village-level units across China, targeting individuals aged 45 years and older. CHARLS adopts a questionnaire design informed by internationally established aging studies, including the U.S. Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), and the Survey of Health, Aging, and Retirement in Europe (SHARE) \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The anonymized dataset is publicly accessible through the CHARLS official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://charls.pku.edu.cn/index.htm\u003c/span\u003e\u003cspan address=\"https://charls.pku.edu.cn/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In compliance with the Declaration of Helsinki, all rounds of the CHARLS survey were approved by the Biomedical Ethics Committee of Peking University. The fieldwork protocol for this survey was approved with the ethical approval number IRB00001052-11015, and written informed consent was obtained from each respondent.\u003c/p\u003e \u003cp\u003eFor this analysis, we included individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years with complete data on depressive symptoms, educational attainment, gender, and urban-rural residence, yielding a final analytical sample of 9,225 participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eDependent variable: Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), with total scores ranging from 0 to 30, where higher scores reflect greater symptom severity. For robustness analysis, depressive symptoms were dichotomized (present/absent) using a validated cutoff score (\u0026ge;\u0026thinsp;10).\u003c/p\u003e \u003cp\u003eIndependent variable: Educational attainment was categorized as follows: 0\u0026thinsp;=\u0026thinsp;below primary education, 1\u0026thinsp;=\u0026thinsp;primary education, and 2\u0026thinsp;=\u0026thinsp;junior high school or higher. In regression models, this variable was treated as continuous and mean-centered to mitigate multicollinearity.\u003c/p\u003e \u003cp\u003eModerator variable: Gender was coded dichotomously (0\u0026thinsp;=\u0026thinsp;male, 1\u0026thinsp;=\u0026thinsp;female).\u003c/p\u003e \u003cp\u003eStratification variable: Residential type was initially classified as 1\u0026thinsp;=\u0026thinsp;urban/town center, 2\u0026thinsp;=\u0026thinsp;urban-rural/town-rural fringe, and 3\u0026thinsp;=\u0026thinsp;rural. For analytical purposes, categories 1 and 2 were consolidated into an \"urban\" group (coded 1), while category 3 was designated as \"rural\" (coded 0), resulting in a binary classification.\u003c/p\u003e \u003cp\u003eControl variables: Covariates included age (continuous), marital status (1\u0026thinsp;=\u0026thinsp;partnered, 0\u0026thinsp;=\u0026thinsp;unpartnered), personal income (1\u0026thinsp;=\u0026thinsp;income-earning, 0\u0026thinsp;=\u0026thinsp;no income), chronic disease count (continuous), smoking status (1\u0026thinsp;=\u0026thinsp;current smoker, 0\u0026thinsp;=\u0026thinsp;non-smoker), alcohol consumption (1\u0026thinsp;=\u0026thinsp;current drinker, 0\u0026thinsp;=\u0026thinsp;non-drinker), sleep duration (continuous, hours), social engagement (1\u0026thinsp;=\u0026thinsp;participated in social activities in the past month, 0\u0026thinsp;=\u0026thinsp;no participation), and activities of daily living (ADL) impairment (1\u0026thinsp;=\u0026thinsp;presence of difficulties, 0\u0026thinsp;=\u0026thinsp;no difficulties).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSample characteristics and depression scores were reported by urban-rural residence and gender. Categorical and continuous variables were presented as frequencies (%) and means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (X\u0026thinsp;\u0026plusmn;\u0026thinsp;S), respectively. Group comparisons were conducted using F/χ\u0026sup2; tests. A multiple linear regression model was employed in the full sample to examine the moderating effect of gender on the education-depression relationship, including education, gender, the education\u0026times;gender interaction term, and all control variables. Robust standard errors were used to address heteroscedasticity. The regression was repeated with urban-rural stratification to separately estimate interaction effects in urban and rural subsamples, and independent samples t-tests were conducted to compare differences in interaction term coefficients between urban and rural areas. Finally, multiple robustness checks were performed: (1) dichotomizing depression scores and repeating the analysis using logistic regression; (2) treating education as a categorical variable (with \"below primary school\" as the reference) in the model; (3) conducting sensitivity analyses by excluding samples with ADL difficulties. All analyses were performed using R 4.3.3 software, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBasic Characteristics\u003c/h2\u003e \u003cp\u003eThe study enrolled a total of 9,225 elderly participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, consisting of 3,021 urban residents (32.7%) and 6,204 rural residents (67.3%). The cohort included 4,503 males (48.8%) and 4,722 females (51.2%).\u003c/p\u003e \u003cp\u003eSignificant intergroup differences were detected across all measured variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, depression scores were elevated in rural participants compared to urban counterparts (rural males: 14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15, rural females: 17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78; urban males: 12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41, urban females: 14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45). Furthermore, within each subgroup, females exhibited higher depression scores than males. Educational attainment displayed marked disparities, with urban males demonstrating the highest level (1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78) and rural females the lowest (0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63), reflecting pronounced urban-rural and gender-based educational inequalities.\u003c/p\u003e \u003cp\u003eAdditional findings revealed a higher proportion of married males compared to females, with negligible urban-rural variation. The prevalence of chronic diseases was relatively uniform across groups. Urban residents exhibited marginally greater engagement in social activities than rural residents. Sleep duration was slightly prolonged in males relative to females and modestly reduced in urban areas compared to rural areas. Comprehensive sample characteristics, stratified by urban-rural residence and gender, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Characteristics by Urban-Rural Residence and Gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF/χ\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3074)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;3130)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1429)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;1592)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e189.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e673.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2664 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2289 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1272 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1126 (70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e331.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1328 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1377 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e728 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e838 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1547 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e600 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2241.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1304 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e627 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1449.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1061 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1602 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e580 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e360.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e621 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e315 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e258.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGender Moderation Effect in Full Sample\u003c/h2\u003e \u003cp\u003eThe results of the multiple linear regression analysis for the complete sample are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the main effects model, education demonstrated a significant negative association with depression scores (β = -0.697, 95% CI: -0.898 to -0.496, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher educational attainment was linked to lower levels of depressive symptoms. Conversely, gender exhibited a significant positive association (β\u0026thinsp;=\u0026thinsp;0.669, 95% CI: 0.370 to 0.968, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with female participants reporting higher depression scores compared to males.\u003c/p\u003e \u003cp\u003eThe interaction model revealed a statistically significant education \u0026times; gender interaction (β = -0.348, 95% CI: -0.631 to -0.065, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), suggesting that the protective effect of education against depression was more pronounced among females. Marginal effect analysis further indicated that each incremental increase in education level was associated with an average reduction of 0.70 points in depression scores for males (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared to a more substantial reduction of 1.05 points for females (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAmong the covariates, age, marital status (having a spouse), engagement in social activities, and longer sleep duration were inversely associated with depression scores, whereas the number of chronic diseases, smoking status, and limitations in activities of daily living (ADL) showed positive correlations (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFull-sample linear regression: The moderating effect of gender on the education-depression relationship\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003edepression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain effects model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteraction model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.859\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.076\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.697\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.103\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.670\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.156\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.156\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.01\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.068\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.01\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.152\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.16\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.154\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.16\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.448\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.058\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.058\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.458\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.123\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.461\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.123\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.647\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.031\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.645\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.031\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.399\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.16\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.16\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.790\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.153\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.792\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.153\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.810\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.131\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.805\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.131\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.526\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.178\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.536\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.178\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeducation \u0026times; gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.348\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.148\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.871\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.815\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.808\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e(\u003c/sup\u003e-0.816\u003csup\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Std. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.768 (df\u0026thinsp;=\u0026thinsp;9089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.767 (df\u0026thinsp;=\u0026thinsp;9088)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224.345\u003csup\u003e***\u003c/sup\u003e (df\u0026thinsp;=\u0026thinsp;11; 9089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206.209\u003csup\u003e***\u003c/sup\u003e (df\u0026thinsp;=\u0026thinsp;12; 9088)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.1; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUrban-Rural Stratified Analysis\u003c/h3\u003e\n\u003cp\u003eThe stratified regression analyses by urban-rural residence are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For the urban cohort, while the main effect of education was not statistically significant (β = -0.308, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.095), gender demonstrated a significant main effect (β\u0026thinsp;=\u0026thinsp;0.882, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the education \u0026times; gender interaction term reached statistical significance (β=-0.549, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), suggesting that urban females derived substantially greater benefits from education compared to their male counterparts. In contrast, the rural sample exhibited significant main effects for both education (β = -0.581, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and gender (β\u0026thinsp;=\u0026thinsp;0.899, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but the interaction term was non-significant (β = -0.068, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.750), indicating comparable educational benefits between rural males and females. A comparative analysis of interaction coefficients between urban (-0.549) and rural (-0.068) samples yielded a difference of -0.481 (SE\u0026thinsp;=\u0026thinsp;0.317), with Z = -1.516, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.129, which did not achieve statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUrban-Rural Stratified Linear Regression: Heterogeneity of Gender Moderation Effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.308\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.581\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.129)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.882\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.205)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.041\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.076\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.286\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.320\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.557\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.075)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.321\u003c/p\u003e \u003cp\u003e(0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.448\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.754\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.623\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.037)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003cp\u003e(0.266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.462\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.198)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.864\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.727\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.191)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.920\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.576\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.260\u003c/p\u003e \u003cp\u003e(0.294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.670\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.221)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeducation \u0026times; gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.549\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003cp\u003e(0.212)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.429\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.754\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Std. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.347 (df\u0026thinsp;=\u0026thinsp;2985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.909 (df\u0026thinsp;=\u0026thinsp;6090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.490*** (df\u0026thinsp;=\u0026thinsp;12; 2985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.646*** (df\u0026thinsp;=\u0026thinsp;12; 6090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNote:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1;**\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05༛ ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e visually presents the education-predicted depression score curves stratified by gender in urban and rural areas. In urban settings, the female curve exhibits a significantly steeper slope compared to males, whereas in rural areas, the two curves remain nearly parallel.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRobustness Tests\u003c/h3\u003e\n\u003cp\u003eFollowing dichotomization of depression scores (with scores\u0026thinsp;\u0026ge;\u0026thinsp;10 classified as depression), logistic regression analysis revealed that the coefficient for the education\u0026times;gender interaction term in the full sample was nonsignificant (β=-0.035, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.578), potentially attributable to information loss from dichotomization. However, stratified analyses by urban-rural residence yielded findings consistent with the primary analysis: the interaction odds ratio (OR) was 0.79 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) in urban areas and 0.98 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85) in rural settings.\u003c/p\u003e \u003cp\u003eWhen education was modeled as a categorical variable (using \"below primary school\" as the reference), a significant interaction emerged between junior high school or higher education and female gender in the full sample (β=-0.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), whereas the interaction between primary school education and female gender remained nonsignificant (β=-0.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.275). These results suggest that the protective effect of education for women is primarily evident at the junior high school level or above. Furthermore, sensitivity analyses excluding individuals with activities of daily living (ADL) difficulties confirmed the robustness of the education\u0026times;gender interaction (β=-0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUtilizing data from the CHARLS 2020 survey, this study conducted a systematic investigation into gender disparities in the protective effect of educational attainment against depressive symptoms among older adults, while further exploring the urban-rural heterogeneity of this gender-moderated relationship. The analysis demonstrated a statistically significant gender difference in the depression-protective effect of education in the overall sample, with female participants exhibiting greater benefits compared to their male counterparts. Urban-rural stratification revealed that this gender-moderated effect remained significant in urban settings (favoring females) but was non-significant in rural areas, where both genders derived comparable benefits. Notably, the coefficient difference for the urban-rural interaction term failed to achieve statistical significance.\u003c/p\u003e \u003cp\u003eThese findings indicate that females experience enhanced mental health benefits from education, a result consistent with prior studies \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e yet inconsistent with the traditional gender role hypothesis positing diminished educational returns for women. Potential mechanisms underlying this observation include: (1) education enhances female autonomy, decision-making capacity, and psychological resilience, thereby improving stress-coping mechanisms and amplifying mental health protection \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e; (2) within China's rapidly evolving socioeconomic landscape, educated women may attain greater upward mobility and access to support networks, mitigating depression risk; and (3) education may promote health literacy and healthcare utilization, optimizing resource allocation for mental well-being. Additionally, the study identified a threshold effect, wherein the protective influence of education became particularly pronounced at or above junior high school attainment, suggesting a critical educational threshold for mental health improvement in women.\u003c/p\u003e \u003cp\u003eWhile the urban-rural interaction term coefficients did not achieve statistical significance, the observed point estimates revealed a notable disparity in gender moderation effects between urban (-0.549) and rural (-0.068) areas, with only the urban interaction term reaching significance. This pattern potentially reflects fundamental differences in socio-cultural contexts. Urban environments, characterized by greater gender equality awareness, may facilitate women's ability to translate educational attainment into employment opportunities, economic autonomy, and enhanced social standing, thereby yielding mental health benefits. Furthermore, urban areas' more robust social support infrastructure could amplify the protective effects of education for women. Conversely, rural regions often maintain traditional gender role divisions, where educated women may face constraints in converting educational resources into mental health advantages due to familial obligations and societal expectations\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Additionally, potential disparities in educational quality and relevance to mental health promotion between urban and rural settings may contribute to these observed differences. The current study's limitations, including a modest urban female sample size (n\u0026thinsp;=\u0026thinsp;1,592) and potential urban-rural classification ambiguities (particularly regarding peri-urban areas), may have constrained statistical power to detect significant differences. Future investigations employing larger cohorts or more precise geographic delineations would be valuable for substantiating these findings.\u003c/p\u003e \u003cp\u003eThe present study demonstrates that rural elderly populations exhibit significantly elevated depression levels, consistent with prior findings on urban-rural disparities in depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Notably, this investigation advances the examination of gender differences by revealing that gender not only predicts depression severity but also moderates the education-health association. Distinct from previous studies examining gender or urban-rural differences in isolation, this research innovatively integrates both factors, demonstrating that gender-moderating effects manifest differently across distinct sociostructural contexts, thereby offering an intersectional framework for understanding health disparities.\u003c/p\u003e \u003cp\u003eThese findings yield critical implications for designing mental health interventions for elderly populations. First, the more pronounced protective effect of education among female participants underscores the necessity of enhancing educational opportunities for women, particularly for elderly women with limited formal education. Community-based education programs and health literacy initiatives may help mitigate their educational disadvantages. Second, rural women emerge as a high-priority subgroup for mental health interventions, as they not only exhibit the highest depression scores (mean\u0026thinsp;=\u0026thinsp;17.1) but also derive fewer mental health benefits from education. Thus, rural regions should implement gender-sensitive psychological support services while promoting gender equity to facilitate the translation of education into measurable mental health improvements. Finally, urban-rural differences suggest the need for context-specific intervention strategies: urban programs should address workplace stress and work-family conflict, whereas rural initiatives must target structural barriers, including resource limitations and traditional belief systems.\u003c/p\u003e \u003cp\u003eThis study has several limitations that warrant consideration. First, the cross-sectional design inherently limits causal inference, as the observed associations between education and outcomes may be influenced by reverse causality or unmeasured confounding factors. Longitudinal study designs or instrumental variable approaches would be valuable for future verification. Second, reliance on self-reported depression symptoms may introduce reporting bias. Third, the relatively low educational attainment among elderly participants resulted in a small sample size in the high-education group, potentially compromising the precision of effect estimates. Fourth, the urban-rural classification based on administrative boundaries might not accurately reflect participants' actual living environments or subjective identities, particularly for peri-urban areas, which could lead to measurement error. Fifth, the absence of potential mediating variables (e.g., social support, health behaviors) limited the investigation of underlying mechanisms. Future research should explore the pathways through which education influences mental health outcomes, as well as the moderating effects of gender and urban-rural status on these pathways.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDrawing upon nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS) 2020, this investigation demonstrates significant gender-based variations in the protective role of education against depressive symptoms among older adults, with women exhibiting more pronounced benefits. The observed gender-modifying effect is particularly evident in urban settings, while remaining statistically insignificant in rural areas. These findings underscore the imperative of incorporating both gender-specific and urban-rural contextual factors in the development of mental health interventions for the elderly population. Special consideration should be given to enhancing educational opportunities and psychological support for older women in rural communities. Consequently, advancing educational equity, promoting gender parity, and reducing urban-rural disparities emerge as crucial strategies for optimizing mental health outcomes in aging populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdoli N, Salari N, Darvishi N, et al. The global prevalence of major depressive disorder (MDD) among the elderly: A systematic review and meta-analysis[J]. Neurosci Biobehav Rev. 2022;132:1067\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Liu Y, Ma W, et al. Temporal and spatial trend analysis of all-cause depression burden based on Global Burden of Disease (GBD) 2019 study[J]. Sci Rep. 2024;14(1):12346.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Depression: global burden and projections[EB/OL]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/mental_health/management/depression/en/\u003c/span\u003e\u003cspan address=\"https://www.who.int/mental_health/management/depression/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHERRMAN, H,KIELING C,MCGORRY, P et al. Reducing the global burden of depression:a Lancet-World Psychiatric Association Commission[J].Lancet,2019,393(10189):e42\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHERRMAN, H,PATEL V,KIELING, C et al. Time for united action on depression:a Lancet-World Psychiatric Association Commission[J].Lancet,2022,399(10328):957\u0026ndash;1022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI Y J,WU Y L,ZHAI, L, et al. Longitudinal association of sleep duration with depressive symptoms among middle-aged and older. Chinese[J] Sci Rep. 2017;7(1):11794.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWU Y Y,LEI P C,YE R, X et al. Prevalence and risk factors of depression in middle-aged and older adults in urban and rural areas in China:a cross-sectional study[J].Lancet,2019,394:S53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan de Weijer MP, Demange PA, Pelt DHM et al. Disentangling potential causal effects of educational duration on well-being, and mental and physical health outcomes. Psychol Med. 2024; 54 Psychol Med.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinh A, B\u0026uuml;ltmann U, Reijneveld SA, et al. Depressive symptom trajectories and early adult education and employment:comparing longitudinal cohorts in Canada and the United States[J]. Int J Environ Res Public Health. 2021;18(8):4279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagakwe TSS, John EE, Daniel-Nwosu E et al. Association between educational attainment and mental health conditions among Africans working and studying in selected African countries. Sci Rep. 2025; 15 Sci Rep.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng AX. Education and Mental Health in Young Adulthood: New Evidence From Genetic Markers. Health Econ. 2025; 34 Health Econ.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKersjes C, Junker S, Mauz E et al. Income, Educational Level, and Depressive Symptoms in a Time of Multiple Crises: Trends Revealed by High-Frequency Mental Health Surveillance in Germany, 2019\u0026ndash;2024. DTSCH ARZTEBL INT. 2025; 122 DTSCH ARZTEBL INT.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVonneilich N, Becher H, Berger K et al. Depressive symptoms, education, gender and history of migration - an intersectional analysis using data from the German National Cohort (NAKO). Int J Equity Health. 2025; 24 Int J Equity Health.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Wang N, Shen W et al. Exploring disparities in depressive symptoms between rural and urban middle-aged and elderly adults in China: evidence from CHARLS. PSYCHOL HEALTH MED. 2026; 31 PSYCHOL HEALTH MED.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu C, Jiang Q, Yuan Y et al. Depressive symptoms among the oldest-old in China: a study on rural-urban differences. BMC Public Health. 2024; 24 BMC Public Health.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Wang Y, Chen S et al. Urban-rural differences in key factors of depressive symptoms among Chinese older adults based on random forest model. J AFFECT DISORDERS. 2024; 344 J AFFECT DISORDERS.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Yisong Hu JP, Smith J, Strauss G, Yang. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibson PA, Baker EH, Milner AN. The role of sex, gender, and education on depressive symptoms among young adults in the United States. J AFFECT DISORDERS. 2016; 189 J AFFECT DISORDERS.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss CE, Mirowsky J. Sex differences in the effect of education on depression: resource multiplication or resource substitution? SOC SCI MED. 2006; 63 SOC SCI MED.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Heshmati A. Relationship between education and well-being in China. J SOCIAL EC DEV. 2023; 25 J SOCIAL EC DEV.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurtle J, Nelson KL, Yang Y et al. Urban-Rural Differences in Older Adult Depression: A Systematic Review and Meta-analysis of Comparative Studies. AM J PREV MED. 2019; 56 AM J PREV MED.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Fan X, Luo H et al. Comparison of Depressive Symptoms and Its Influencing Factors among the Elderly in Urban and Rural Areas: Evidence from the China Health and Retirement Longitudinal Study (CHARLS). Int J Environ Res Public Health. 2021; 18 Int J Environ Res Public Health.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"education, depressive symptoms, gender differences, urban-rural stratification, elderly, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-9405307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9405307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e This study aimed to investigate gender differences in the protective effect of education against depressive symptoms among older adults, while examining potential urban-rural disparities in this gender-moderated relationship.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eUtilizing data from the China Health and Retirement Longitudinal Study (CHARLS) 2020, we analyzed 9,225 participants aged ≥ 60years. Depressive symptoms were assessed using the CESD-10 scale. Multiple linear regression models were constructed to examine education×gender interactions in the overall sample. Subsequent stratified analyses by urban/rural residence were performed to evaluate coefficient variations, complemented by comprehensive robustness checks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults The analysis revealed a significant inverse association between education level and depressive symptoms (β=-0.697, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), with women demonstrating higher depressive scores than men (β = 0.669, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). A significant education×gender interaction was observed (β=-0.348, \u003cem\u003eP\u003c/em\u003e=0.016), indicating a more pronounced protective effect of education among women: each additional education level reduced depressive scores by 0.697 points in men versus 1.045 points in women. Urban-rural stratification showed significant interaction in urban populations (β = -0.549, \u003cem\u003eP\u003c/em\u003e = 0.020) but not in rural populations (β = -0.068, \u003cem\u003eP\u003c/em\u003e= 0.750), though the urban-rural coefficient difference did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e = 0.129).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion Our findings demonstrate gender-specific protective effects of education against depressive symptoms in older adults, with women exhibiting greater benefits. This gender-moderation effect appears more substantial in urban settings compared to rural areas. These results underscore the importance of educational empowerment for women and suggest the need for targeted mental health interventions, particularly for rural female populations.\u003c/p\u003e","manuscriptTitle":"The Moderating Effect of Gender on the Educational Gradient in Depressive Symptoms: A Further Analysis Based on Urban-Rural Stratification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:50:45","doi":"10.21203/rs.3.rs-9405307/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"114672420481427261637412853806485601565","date":"2026-05-05T07:35:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-01T00:44:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-17T15:29:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T00:28:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T00:28:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-13T14:10:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35e021cc-4c46-45f9-9089-2ac482959d25","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"114672420481427261637412853806485601565","date":"2026-05-05T07:35:46+00:00","index":36,"fulltext":""},{"type":"reviewersInvited","content":"15","date":"2026-05-01T00:44:47+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:50:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:50:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9405307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9405307","identity":"rs-9405307","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}