The Impact of Mental Health on Cognitive Functioning among Community-Dwelling Elderly and Its Mechanisms: A Large-Scale Cross-Sectional Study of 10,370 Participants

The Impact of Mental Health on Cognitive Functioning among Community-Dwelling Elderly and Its Mechanisms: A Large-Scale Cross-Sectional Study of 10,370 Participants | 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 Impact of Mental Health on Cognitive Functioning among Community-Dwelling Elderly and Its Mechanisms: A Large-Scale Cross-Sectional Study of 10,370 Participants Dongmei Huang, Caizhong Zhou, Caili Li, Huiqiao Huang, Xiao Pan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4358759/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background To delve deeply into the impact of depression and anxiety on cognitive function in the elderly, as well as the mediating mechanisms involved. Methods Data were derived from the baseline survey of a cross-sectional study on the health status of community-dwelling elderly individuals in Guangxi, China, conducted from July 2022 to July 2023. Valid data from 10,370 elderly individuals aged 60 years and older were analyzed. Cognitive function of the elderly was assessed using the Memory Impairment Screen (AD8), while depression symptoms and anxiety symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder Scale-7 (GAD-7), respectively. Univariate and multiple linear regression analyses were conducted to explore the influencing factors of cognitive function in the elderly. Pearson correlation analysis was used to investigate the correlations among depression symptoms, anxiety symptoms, cognitive function, and related factors. Chain mediation analysis was performed using AMOS 26.0 software to explore the mechanisms of the effects of age and mental health on cognitive function among community-dwelling elderly individuals. Results A total of 10,370 elderly individuals were surveyed in this study, including 4,590 males and 5,780 females, aged 65–103 years (mean age: 73.41 ± 6.67 years). The mean score on the AD8 scale was 1.05 ± 1.71, with 2,484 (23.95%) individuals identified as having mild cognitive impairment and 1,705 (16.44%) individuals with cognitive dysfunction. The mean score on the PHQ9 scale was 1.22 ± 2.30, with 580 individuals (5.59%) classified as having mild depression, 121 (1.17%) with moderate depression, 33 (0.32%) with moderately severe depression, and 7 (0.07%) with severe depression. The mean score on the GAD7 scale was 0.70 ± 1.89, with 368 individuals (3.55%) identified as having mild anxiety, 78 (0.75%) with moderate anxiety, and 22 (0.21%) with severe anxiety.Multivariate linear regression analysis showed statistically significant differences in the effects of gender, age, category, years of education, marital status, PHQ9, and GAD7 on cognitive function among the elderly (P < 0.05). There was a positive correlation between PHQ9 scores and AD8 scores (r = 0.361, P < 0.001) in the elderly, as well as a positive correlation between GAD7 scores and AD8 scores (r = 0.287, P < 0.001). Additionally, a strong positive correlation was observed between PHQ9 scores and GAD7 scores (r = 0.690, P < 0.001). Age was also positively correlated with AD8 scores (r = 0.213, P < 0.001).The study further revealed a chained mediating effect of age, mental health, and cognitive function among the elderly. The total effect estimate was 0.055, which was statistically significant (P < 0.001). The direct effect estimate was 0.04, indicating a significant positive and direct impact of age on AD8 scores (P < 0.001). Conclusions The finding that mental health plays a partial mediating role between age and cognitive function provides a new perspective for understanding the decline in cognitive function. This discovery holds significant theoretical and practical implications for improving the mental health and cognitive function of the elderly, which can contribute to the development of more effective intervention measures and enhance the quality of life for the elderly. Elderly Cognitive Function Mental Health Mediation Effect Influencing Factors Figures Figure 1 Background With the increasing trend of global population aging, the mental health and cognitive function of the elderly population have become one of the focuses of social attention. According to the latest statistics, the number of people aged 60 and above in China has exceeded 280 million, accounting for 19.80% of the total population [ 1 ] . Currently, "actively responding to population aging" has been considered a critical national strategy [ 2 ] . Healthy cognitive function is considered an essential aspect of successful aging. As people age, their cognitive function gradually deteriorates, manifesting as speech, apraxia, agnosia, and other functional impairments, and even dementia, which significantly affects the quality of life and satisfaction of the elderly. Studies have found that cognitive impairment is one of the most common mental illnesses among the elderly, with a prevalence rate of 24.4% [ 3 ] . The decline in cognitive function not only seriously impairs the quality of life of the elderly [ 4 ] but also increases the burden on families and society [ 5 ] . In recent years, China has become one of the countries with the fastest growth rate of patients with cognitive dysfunction globally. It is estimated that by 2060, the total number of patients with cognitive dysfunction in China will reach 48.68 million cases [ 6 ] . Cognitive impairment has become an important public health issue in China [ 7 ] . In 2022, the National Health Commission launched a national initiative for elderly psychological care, drawing widespread attention to the mental health issues of the elderly. Mental health generally refers to positive and normal psychological activities or states. For a long time, researchers have defined the criteria for mental health as the absence of psychopathology symptoms such as anxiety and depression [ 8 ] . The mental health status and cognitive function level of the elderly have a significant impact on their quality of life and social participation abilities. As they age, the elderly face the risk of health issues such as depression, anxiety, and cognitive decline, which can severely affect their lives and well-being. Currently, the relationship between mental health and cognitive function has also received significant attention. Previous studies have indicated that mental health issues may be closely related to the decline in cognitive function, and conversely, the decline in cognitive function may also serve as a predictor of mental health problems. Some research has also found a bidirectional relationship between cognitive dysfunction and depression in the elderly [ 9 ] . The impairment of prefrontal cortex function in patients with severe depression can lead to a decline in cognitive abilities [ 10 ] . Furthermore, the detection rate of depression among the elderly population with cognitive impairment is 32.3% [ 11 ] , and a decline in memory among the elderly can also trigger adverse psychological effects such as anxiety. However, there is still controversy regarding the complex relationship between mental health and cognitive function, and further research is needed to delve deeper into the mediating effects and influencing mechanisms between them. Therefore, this study aims to explore the impact and underlying mechanisms of depression and anxiety on cognitive function among the elderly, based on a large sample dataset of older adults. Through a comprehensive assessment of mental health and cognitive function, we hope to reveal the complex relationship between them and provide deeper understanding and guidance for promoting the health and well-being of the elderly. This will also provide theoretical and practical guidance for future interventions and treatments, aiming to enhance the quality of life and social functioning of the elderly. Methods Data Collection This study employed a combination of multi-stage stratified and convenience sampling methods to select a data sample of 11,582 elderly individuals from multiple regions. After excluding 1,212 invalid questionnaires, a total of 10,370 questionnaires were included in the final analysis, with a questionnaire efficiency rate of 89.54%. The data sources included medical institutions, community health centers, and sampling surveys. A multi-dimensional dataset was collected, encompassing personal basic information, mental health status, cognitive function assessments, and other relevant information. Participant Selection The criteria for sample selection included: 1) individuals aged 60 years and older; 2) no history of severe cognitive impairment or mental illness; 3) agreement to participate in the study and signing of the informed consent form. Measurement Tools 1.General Situation Survey The survey covered information such as gender, age, educational background, marital status, and living situation. 2.Alzheimer's Disease 8 (AD8) Developed by the University of Washington in 2005, this scale is a brief and sensitive measurement tool based on patient interviews and screenings. It consists of eight questions and can effectively and reliably distinguish between patients with dementia and those without [ 12 , 13 ] . The scale assesses cognitive functions such as memory and orientation by asking the patients themselves. Evaluation is conducted based on responses of "yes" or "no." In this study, a score of 0 indicated normal cognitive function, 1–2 points suggested the possible presence of mild cognitive impairment, indicating a borderline state, and a score of 3 or above indicated the possible presence of cognitive dysfunction, necessitating further assessment [ 14 ] . 3.Mental Health Assessment The Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) assessments can be used as screening tools as well as measures to evaluate the severity of depressive symptoms and various types of anxiety symptoms [ 15 , 16 ] .The PHQ-9 has been established as one of the most reliable screening tools for depression [ 17 , 18 ] . The total score of the PHQ-9 ranges from 0 to 27, with the following interpretations: Normal: 0–4 points; Mild depression: 5–9 points; Moderate depression: 10–14 points; Moderately severe depression:15–19 points; Severe depression: 20 points and above. In epidemiological surveys, this scale can serve as an effective tool for screening and evaluating depressive symptoms among the elderly population in China.The GAD-7 is also highly reliable and valid [ 19 ] . The score interpretation is as follows: Normal: 0–4 points; Mild anxiety: 5–9 points; Moderate anxiety: 10–14 points; Severe anxiety: 15–21 points. A higher score indicates more prominent anxiety symptoms. Statistical Methods Statistical analysis in this study was performed using SPSS 23.0 software. Univariate analysis was conducted using variance analysis and chi-square (χ²) test, while multivariate analysis employed multiple linear regression to explore the influencing factors of cognitive function among elderly individuals in the community. Pearson correlation analysis was utilized to investigate the correlation among age, years of education, PHQ-9, GAD-7, and AD-8. Additionally, the chain mediation effect model was constructed using AMOS 26.0, and the mediating effects of PHQ-9 and GAD-7 were analyzed using the Bootstrap method, with a test level of α = 0.05. Results General Information of Elderly Individuals A total of 10,370 elderly individuals were surveyed in this study, including 4,590 males and 5,780 females. The age range was 65–103 years old, with a mean age of 73.41 ± 6.67 years. The mean score on the AD8 scale was 1.05 ± 1.71, with 2,484 (23.95%) individuals identified as having preclinical cognitive impairment and 1,705 (16.44%) individuals with cognitive dysfunction. For the PHQ-9 scale, the mean score was 1.22 ± 2.30, with 580 (5.59%) individuals classified as having mild depression, 121 (1.17%) with moderate depression, 33 (0.32%) with moderately severe depression, and 7 (0.07%) with severe depression. For the GAD-7 scale, the mean score was 0.70 ± 1.89, with 368 (3.55%) individuals classified as having mild anxiety, 78 (0.75%) with moderate anxiety, and 22 (0.21%) with severe anxiety. Univariate Analysis of Factors Influencing Cognitive Function The AD8 scores were categorized into three groups: normal cognition, preclinical cognitive impairment, and cognitive dysfunction. When comparing the influencing factors across different groups, statistically significant differences were observed in gender, age, years of education, marital status, living arrangement, and type of elderly individual (P < 0.001). See Table 1 for details. Table 1 Univariate Analysis of Influencing Factors in Different Cognitive Groups Project Total Normal Cognitive Group Critical Cognitive Group Cognitive Disability Group F/χ² p Gender 173.44 < 0.001 Male 4590 3012 1047 531 Female 5780 3169 1437 1174 Age 73.41 ± 6.67 72.48 ± 6.17 73.93 ± 6.73 76.05 ± 7.47 209.07 < 0.001 Years of Education 5.41 ± 4.66 5.98 ± 4.52 5.06 ± 5.12 3.89 ± 4.00 147.62 < 0.001 Marital Status 180.86 < 0.001 Married 7196 4520 1699 977 Divorced 81 55 17 9 Widowed 2945 1520 735 690 Unmarried 106 65 26 15 Separated 42 21 7 14 Who Lives With 136.76 < 0.001 Spouse 3401 2281 869 497 Children 3526 1933 921 720 Spouse & Children 2156 1438 467 280 Living Alone 744 417 180 152 Others 214 112 47 56 Category 78.08 < 0.001 Poverty 1470 856 344 270 Empty Nest 1258 713 321 224 Disability 49 15 10 24 Dementia 9 4 3 Elderly Living Alone 313 152 91 70 Special Family of Family Planning 25 15 3 7 None of the Above 7246 4426 2484 1705 PHQ9 1.22 ± 2.30 0.67 ± 1.55 1.51 ± 2.38 2.77 ± 3.41 657.21 < 0.001 GAD7 0.70 ± 1.89 0.36 ± 1.26 0.86 ± 1.97 1.71 ± 2.95 384.02 < 0.001 Note: F represents the F-statistic in ANOVA for continuous variables; χ² represents the chi-square statistic for categorical variables. The p-value indicates the significance level of differences among different cognitive groups for each factor. Multivariate Analysis of Factors Influencing Cognitive Function The regression equation included factors that were statistically significant in the univariate analysis. The assignment of variables is presented in Table 2 . Multivariate linear regression analysis showed that there were statistically significant differences in the effects of gender, age, category, years of education, marital status, PHQ9, and GAD7 on cognitive function in the elderly (P < 0.05). See Table 3 for details. Table 2 Variable Assignment Variable Assignment Gender Male = 1, Female = 2 Age Continuous variable, original value recorded Years of Education Continuous variable, original value recorded Marital Status Dummy variables: X1 = Divorced (0,1), X2 = Widowed (0,1), X3 = Unmarried (0,1), X4 = Separated (0,1) (with married as the reference) Who Lives With Dummy variables: X1 = With children (0,1), X2 = With spouse and children (0,1), X3 = Living alone (0,1), X4 = Others (0,1) (with living with spouse as the reference) Category Dummy variables: X1 = Empty nest (0,1), X2 = Disability (0,1), X3 = Dementia (0,1), X4 = Elderly living alone (0,1), X5 = Special family of family planning (0,1), X6 = None of the above (0,1) (with poverty as the reference) PHQ9 Continuous variable, original value recorded GAD7 Continuous variable, original value recorded Table 3 Multiple Linear Regression Analysis of Factors Influencing Cognitive Function in the Elderly Item B Value S.E. β Value t P 95% Confidence Interval for B Constant -2.371 0.196 -12.104 < 0.001 (-2.755, -1.987) Gender 0.224 0.033 0.066 6.885 < 0.001 (0.161, 0.288) Age 0.040 0.002 0.155 16.185 < 0.001 (0.035, 0.045) Category -0.023 0.006 -0.033 -3.657 < 0.001 (-0.035, -0.011) Years of Education -0.032 0.003 -0.089 -9.373 < 0.001 (-0.039, -0.026) Marital Status 0.043 0.019 0.024 2.303 0.021 (0.006, 0.079) Who Lives With 0.020 0.016 0.012 1.261 0.207 (-0.011, 0.052) PHQ9 0.209 0.009 0.280 22.425 < 0.001 (0.191, 0.227) GAD7 0.070 0.011 0.078 6.258 < 0.001 (0.048, 0.092) Note: a. Dependent variable: AD8, R² = 0.184, Adjusted R² = 0.183, F = 283.95, P < 0.001. Correlation between Mental Health and Cognitive Function in the Elderly Using Pearson correlation analysis, this study examined the correlation between mental health, cognitive function, and related factors in the elderly. The results indicated a positive correlation between the PHQ9 scores and AD8 scores in the elderly (r = 0.361, P < 0.001), as well as a positive correlation between the GAD7 scores and AD8 scores (r = 0.287, P < 0.001). Furthermore, a strong positive correlation was observed between the PHQ9 scores and GAD7 scores in the elderly (r = 0.690, P < 0.001), suggesting a close association between depression and anxiety in this population. Additionally, age was positively correlated with AD8 scores (r = 0.213, P < 0.001). Detailed results are presented in Table 4 . Table 4 Pearson Correlation Analysis (r-values) between Mental Health, Cognitive Function, and Related Factors in the Elderly Variables AD8 PHQ9 GAD7 Age Years of Education AD8 1 PHQ9 0.361** 1 GAD7 0.287** 0.690** 1 Age 0.213** 0.086** 0.034** 1 Years of Education -0.173** -0.086** -0.050** -0.203** 1 Note: All P values < 0.001. Chain-Mediating Role of Age, Mental Health, and Cognitive Function in the Elderly From the perspective of the model path, age has a positive impact on PHQ9 (β'=0.09), PHQ9 has a positive impact on GAD7 (β'=0.69), GAD7 has a positive impact on AD8 (β'=0.08), age has a positive impact on AD8 (β'=0.19), PHQ9 has a positive impact on AD8 (β'=0.29), and age has a negative impact on GAD7 (β'=-0.03). All of these effects are statistically significant (P < 0.001), indicating significant indirect effects, as detailed in Table 5 . Using the Bootstrap method (with 5000 resamples), the mediating variables were tested. The results showed that the estimated value of the total effect was 0.055, indicating a significant total effect (P < 0.001). The estimated value of the direct effect (Direct Effect) was 0.04, indicating a significant positive direct impact of the independent variable on the dependent variable (P < 0.001). The indirect effect (Indirect Effect) was transmitted through three mediating variable paths. Among them, the indirect effects of the age→PHQ9→AD8 and age→PHQ9→GAD7→AD8 paths were positive and significant (P < 0.001). However, the estimated value of the indirect effect for the age→GAD7→AD8 path was − 0.001, indicating a significant negative impact through this path (P < 0.001). See Table 6 for details. A chain-mediating effect diagram is shown in Fig. 1 . Table 5 Path Verification Path Unstandardized Coefficients Standardized Coefficients SE CR P age→PHQ9 0.03 0.09 0.003 8.833 <0.001 PHQ9→GAD7 0.57 0.69 0.006 96.969 <0.001 age→GAD7 -0.01 -0.03 0.002 -3.653 <0.001 GAD7→AD8 0.07 0.08 0.011 6.629 <0.001 age→AD8 0.05 0.19 0.002 20.621 <0.001 PHQ9→AD8 0.21 0.29 0.009 23.202 <0.001 Table 6 Serial Mediating Test Results Effect Path Estimate SE Lower Upper P Total Effect 0.055 0.003 0.049 0.060 <0.001 Direct Effect 0.048 0.003 0.042 0.053 <0.001 Indirect Effect age→PHQ9→AD8 0.006 0.001 0.005 0.008 <0.001 age→GAD7→AD8 -0.001 0.000 -0.001 0.000 <0.001 age→PHQ9→GAD7→AD8 0.001 0.000 0.001 0.002 <0.001 Discussion In this study, we focused on the current status of cognitive and mental health among elderly individuals in the community, while also exploring potential influencing factors of cognitive function. According to our survey data, the AD8 scale score was (1.05 ± 1.71), which is higher than the score of (0.99 ± 1.68) reported by Jin Shan et al. [ 20 ] in Guangdong's Shenzhen city. This indicates that the cognitive level of elderly individuals in Guangxi community is lower than that of their counterparts in Shenzhen, which may be attributed to Guangxi's mountainous location and the lower economic and cultural levels of its elderly population compared to Shenzhen. In this study, 16.44% of the elderly population exhibited cognitive dysfunction, which is roughly consistent with the range of 9.9–35.2% reported internationally [ 21 ] . Furthermore, 23.95% of the elderly were diagnosed with borderline cognitive impairment. Although they have not been strictly diagnosed with cognitive dysfunction, these individuals face potential risks and may develop into cognitive dysfunction or even more severe cognitive disorders such as Alzheimer's disease in the future. This underscores the ubiquity and severity of cognitive health issues among the elderly population. Attention to elderly individuals with borderline cognitive impairment not only benefits their individual health and well-being but also contributes to the overall improvement of society's cognitive health level. Depression and anxiety are among the top ten causes of global disability [ 22 ] , and over 80% of individuals with mental disorders reside in low- and middle-income countries (LMICs) [ 23 ] . In this study, the PHQ9 scale score was (1.22 ± 2.30), with 7.15% of participants being diagnosed with depression. Similarly, the GAD7 scale score was (0.70 ± 1.89), with 4.51% exhibiting symptoms of anxiety. In most studies, the prevalence of depression among elderly patients is high, but there are significant variations in reported rates due to differences in methods and populations, ranging from 1–32% [ 24 – 26 ] . According to some research, the prevalence of depression among elderly individuals in Guangdong, China, is 2.79%, while the prevalence of anxiety is 1.39% [ 27 ] . In India, the crude prevalence of both depression and anxiety is 3.3% [ 28 ] . This study delved into the impacts of gender, age, category, years of education, marital status, PHQ9, and GAD7 on the cognitive function of community-based elderly individuals. The results indicate a positive correlation between age, PHQ9, and GAD7 scores with AD8 scores. This suggests that as individuals age, they experience increasing levels of emotional distress and anxiety, which correlate with a decline in cognitive function. Conversely, there is a negative correlation between years of education and AD8 scores, indicating that a higher level of education can help maintain cognitive function in older adults. During the aging process, the brain inevitably undergoes various structural and functional changes [ 29 ] . Macroscopically, brain atrophy is a prominent feature of aging, and its occurrence rate increases with age. However, while brain atrophy is an inevitable consequence of aging, it is still possible to delay its progression and protect cognitive function through certain intervention measures. A higher level of education has a positive impact on the cognitive function of older adults. Education not only enhances an individual's knowledge base but also exercises advanced cognitive functions such as abstract thinking and logical reasoning. Through long-term learning and thinking, the brain of older adults can remain active, thus delaying the process of cognitive decline. Additionally, education can help older adults better cope with life challenges and stress, improving their psychological resilience and further protecting their cognitive function. Studies have shown that both childhood education and lifelong learning are closely associated with a lower risk of dementia [ 30 ] . This may be because education promotes the growth and connectivity of neurons, enhancing the plasticity and adaptability of the brain. Therefore, for older adults, maintaining habits of continuous learning and thinking is an important pathway to improve their cognitive function and prevent dementia. By constructing a structural equation model, this study delved into the complex relationships among age, depressive symptoms (PHQ9), anxiety symptoms (GAD7), and cognitive function (AD8). It further revealed the mediating role of mental health between age and cognitive function. According to the coefficients of the model paths, we found that age had a significant positive impact on PHQ9, PHQ9 on GAD7, GAD7 on AD8, and age on AD8, which was consistent with our expectations. These positive effects indicate that as individuals age, they are more likely to experience depressive and anxious emotional issues, and these emotional problems further affect their cognitive function. It's noteworthy that the influence of age on GAD7 is negative, which seems to contradict common sense. However, this could be due to changes in the pressure sources and coping strategies faced by older adults as they age. With the increase in age, older adults may gradually adapt to various challenges in life, or the sources of anxiety they face may decrease due to a narrowing social circle. Nevertheless, this negative effect cannot fully explain the underlying mechanism, and further research is needed to explore it. Through the application of the Bootstrap method to test mediation effects, we found that the total effect, direct effect, and indirect effects were all significant. This indicates that mental health plays a crucial mediating role between age and cognitive function. Specifically, PHQ9 and GAD7, as mediating variables, not only have significant individual impacts on AD8 but also form complex indirect effect paths through their mutual influence. Among them, the indirect effects of the two paths, age→PHQ9→AD8 and age→PHQ9→GAD7→AD8, are positive and significant, suggesting that age affects cognitive function in older adults through its influence on depressive symptoms, and anxiety symptoms amplify this process. However, the indirect effect of the age→GAD7→AD8 path is negative, possibly due to an "offsetting" effect between the negative influence of age on GAD7 and the positive influence of GAD7 on AD8. This again reminds us that the relationship between mental health and cognitive function in older adults is highly complex, requiring comprehensive consideration of multiple factors for a full understanding. In summary, this study not only reveals the complex relationships among age, depression, anxiety, and cognitive function but also emphasizes the mediating role of mental health in these relationships. These results are significant for understanding the mechanisms underlying cognitive decline in older adults and for developing effective intervention measures. Future research can further explore other potential mediating variables and influencing factors to construct a more comprehensive model to guide health management and cognitive function improvement in older adults. Conclusion Through a cross-sectional design, this study explored the relationship between age, mental health, and cognitive function among the elderly population in a wide range of communities. With a large and diverse sample size, the results of this study are representative to a certain extent. The study found that mental health plays a partial mediating role between age and cognitive function, providing a new perspective for understanding the decline in cognitive function. However, there are also limitations in this study: the cross-sectional design cannot determine causality, there may be sample selection bias, and other potential influencing factors have not been considered. Future studies need to further optimize the design, expand the scope of research, and adopt multiple data collection methods to more accurately reveal the relationships between variables and guide the health management of the elderly. Declarations Acknowledgements We would like to express our gratitude to all the participants in our study, and we sincerely thank the Health Commission of Guangxi Zhuang Autonomous Region for their valuable assistance in this research. Author contributions DH participated in the development of the questionnaire, cross-sectional survey, data collection, result analysis and manuscript writing. CZ and CL participated in literature research, questionnaire survey and data collection. XP and YP designed the study and participated in the questionnaire survey. QP, LL participated in the questionnaire. HH coordinates and oversees all phases of the project. All authors have read and approved the final version of the manuscript. Funding This study was supported by three research grant projects:Investigation and Countermeasure Study on Oral Health of the Elderly in Guangxi Zhuang Autonomous Region (2022004).Self-Funded Research Project by the Health Commission of Guangxi Zhuang Autonomous Region: Analysis of the Correlation and Predictive Value of Anthropometric Indicators with Mild Cognitive Impairment in the Elderly (Z-A20230629).Key Disciplines and Cultivation Disciplines in Medical and Health of Guangxi Zhuang Autonomous Region (GuWeiKeJiaoFa [2022] No. 4). Availability of data and materials All data and materials used in this study have been properly preserved and anonymized. Due to ethical constraints, they cannot be publicly shared but can be provided to academic peers and research institutions upon reasonable request. The anonymized data for this study are held by Dr. DH. Those interested in obtaining the data and study materials should contact Dr. DH to request appropriate approval for access. Ethics approval and consent to participate This study has obtained ethical approval from the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University. Participants provided informed consent before participating. We confirm that all methods were performed in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests References China Government Network. (2023, January 17). Press conference on the economic performance of 2022 held by the Information Office of the State Council [EB/OL]. Retrieved from https://www.gov.cn/xinwen/2023-01/17/content_5737627.htm China Government Network. (2019, November 21). The CPC Central Committee and the State Council issued the Medium- and Long-Term Plan for Actively Responding to Population Aging [EB/OL]. Retrieved from http://www.gov.cn/xinwen/2019-11/21/content_5454347.htm Nakasujja, N., et al. (2007). Psychiatric disorders among the elderly on non-psychiatric wards in an african setting. International Psychogeriatrics , 19 (4), 691–704. Zhang, Q. L., Wu, Y. L., Han, T. K., et al. (2019). Changes in cognitive function and risk factors for cognitive impairment of the elderly in China: 2005-2014. International Journal of Environmental Research and Public Health , 16 (16), 2847. Lara, E., Koyanagi, A., Caballero, F., et al. (2017). Cognitive reserve is associated with quality of life: A population-based study. Experimental Gerontology , 87 (Pt A), 67-73. Prince, M., Ali, G. C., Guerchet, M., et al. (2016). Recent global trends in the prevalence and incidence of dementia, and survival with dementia. Alzheimers Research & Therapy , 8 (1), 23. Jia, L. F., Du, Y. F., Chu, L., et al. (2020). Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study. The Lancet Public Health , 5 (12), e661-e671. Wang, X. Q., & Zhang, D. J. (2012). Construction of a relationship model between psychological quality and mental health: Surpassing PTH and DFM. Journal of Southwest University (Social Science Edition) , 38 (6), 67-74 +174.[In Chinese] Song, Y. L., Song, P. P., Wang, X. C., et al. (2014). Longitudinal study of the relationship between cognitive function and depression in the elderly using a multilevel model. Chinese Journal of Health Statistics , 31 (1), 27-30.[In Chinese] Zhang, J., & Yu, L. (2020). Depression and cognitive decline in the elderly. Chinese Journal of Health Psychology , 28 (11), 1754-1760.[In Chinese] Qian, S., Liu, Y., & Zhang, Y. W. (2022). Meta-analysis of the detection rate of depressive symptoms in elderly patients with mild cognitive impairment in Chinese communities from 2011 to 2021. Chinese Journal of General Practice , 21 (3), 231-236.[In Chinese] Galvin, J. E., Roe, C. M., Xiong, C., et al. (2006). Validity and reliability of the AD8 informant interview in dementia. Neurology , 67 (11), 1942–1948. Razavi, M., Tolea, M. I., Margrett, J., et al. (2014). Comparison of 2 informant questionnaire screening tools for dementia and mild cognitive impairment: AD8 and IQCODE. Alzheimer disease and associated disorders , 28 (2), 156–161. Chen, H. H., Sun, F. J., Yeh, T. L., et al. (2018). The diagnostic accuracy of the Ascertain Dementia 8 Questionnaire for detecting cognitive impairment in primary care in the community, clinics and hospitals: A systematic review and meta-analysis. Family Practice , 35 (3), 239-246. Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9. Journal of General Internal Medicine , 16 (9), 606–613. Kroenke, K., Spitzer, R. L., Williams, J. B. W., et al. (2010). The patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. General Hospital Psychiatry , 32 (4), 345–359. El-Den, S., Chen, T. F., Gan, Y. L., et al. (2018). The psychometric properties of depression screening tools in primary healthcare settings: A systematic review. Journal of Affective Disorders , 225 , 503-522. Costantini, L., Pasquarella, C., Odone, A., et al. (2021). Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. Journal of Affective Disorders , 279 , 473-483. Stocker, R., Tran, T., Hammarberg, K., et al. (2021). Patient Health Questionnaire 9 (PHQ-9) and General Anxiety Disorder 7 (GAD-7) data contributed by 13,829 respondents to a national survey about COVID-19 restrictions in Australia. Psychiatry research , 298 , 113792. Jin, S., Hu, W. X., Zhang, S. M., et al. (2022). Status and influencing factors of cognitive dysfunction among the elderly aged ≥65 years in Shenzhen community. Chinese Primary Health Care , 36 (11), 52-55.[In Chinese] Pottie, K., Rahal, R., Jaramillo, A., et al. (2016). Recommendations on screening for cognitive impairment in older adults. CMAJ: Canadian Medical Association Journal , 188 (1), 37-46. Vos, T., Barber, R. M., Bell, B., et al.(2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet , 386 (9995), 743–800. World Health Organization. (2008). The Global Burden of Disease:2004 Update. Available online at: https://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf (accessed May 23, 2020) Zenebe, Y., Akele, B., W/Selassie, M., et al. (2021). Prevalence and determinants of depression among old age: a systematic review and meta-analysis. Annals of general psychiatry , 20 (1), 55. Kvalbein-Olsen, L. C., Aakhus, E., Haavet, O. R., et al. (2023). Unrecognised depression among older people: a cross-sectional study from Norwegian general practice. BJGP open, 7 (1), BJGPO.2022.0135. Forlani, C., Morri, M., Ferrari, B., et al. (2014). Prevalence and gender differences in late-life depression: a population-based study. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, 22 (4), 370–380. He, Z. F., Tan, W. Y., Ma, H.,et al. (2024). Prevalence and factors associated with depression and anxiety among older adults: A large-scale cross-sectional study in China. Journal of affective disorders, 346 , 135–143. De Man, J., Absetz, P., Sathish, T., et al. (2021). Are the PHQ-9 and GAD-7 Suitable for Use in India? A Psychometric Analysis. Frontiers in psychology, 12 , 676398. Connell, E., Le Gall, G., Pontifex, M. G., et al. (2022). Microbial-derived metabolites as a risk factor of age-related cognitive decline and dementia. Molecular neurodegeneration, 17 (1), 43. Larsson, S. C., Traylor, M., Malik, R., et al. on behalf of the International Genomics of Alzheimer’s Project (2017). Modifiable pathways in Alzheimer's disease: Mendelian randomisation analysis. BMJ (Clinical research ed.), 359 , j5375. Additional Declarations No competing interests reported. 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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-4358759","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299597043,"identity":"d9973ee5-fa99-4d43-ac07-be1c85e058dc","order_by":0,"name":"Dongmei Huang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Huang","suffix":""},{"id":299597044,"identity":"f01aceb2-4ec5-4f65-bf52-6f06f5f082a0","order_by":1,"name":"Caizhong Zhou","email":"","orcid":"","institution":"The Second People's Hospital of Teng County","correspondingAuthor":false,"prefix":"","firstName":"Caizhong","middleName":"","lastName":"Zhou","suffix":""},{"id":299597045,"identity":"e17e5c25-541c-4c26-ad04-9690a141aeba","order_by":2,"name":"Caili Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Caili","middleName":"","lastName":"Li","suffix":""},{"id":299597047,"identity":"3ca57417-439a-4a04-9eae-1074c333719d","order_by":3,"name":"Huiqiao Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACAwkGxgcILhtxWpgNSNbCJkGaFnPp5m2VP/cw2G24dsaA4UPZYQb+2Q34tVjOOVZ2m+cZQ/LM2TkGjDPOHWaQuHOAgMNu5JjdZjjAkMwvnWPAzNt2GOjUBMJaCn8AtbCBtPwlVgsDzwEGO7AtjMRpSSuW5jkgkSA5O63gYM+5dB6JGwS1JG/8+OOAjb3B7eSND36UWcvxzyCgBaQLiCUSG4DkASDmIageqoXBnhiVo2AUjIJRMEIBAOfSP7O6/nnqAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Huiqiao","middleName":"","lastName":"Huang","suffix":""},{"id":299597049,"identity":"6763c0cb-80a9-46f4-8a63-51148db0b804","order_by":4,"name":"Xiao Pan","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Pan","suffix":""},{"id":299597051,"identity":"6e2fcc83-efc5-4d87-96d5-4d9a78023328","order_by":5,"name":"Yanfei Pan","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanfei","middleName":"","lastName":"Pan","suffix":""},{"id":299597052,"identity":"849fac87-0b67-4799-8364-1b6bec77ebbc","order_by":6,"name":"Qini Pan","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qini","middleName":"","lastName":"Pan","suffix":""},{"id":299597053,"identity":"e9ac8510-cc34-4e61-b9c5-83e3c300a496","order_by":7,"name":"Lichong Lai","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lichong","middleName":"","lastName":"Lai","suffix":""}],"badges":[],"createdAt":"2024-05-02 11:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4358759/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4358759/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-026-26196-9","type":"published","date":"2026-01-12T16:29:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56282247,"identity":"39ed471d-fe49-46cb-9b14-9c3ae3110f0d","added_by":"auto","created_at":"2024-05-10 21:27:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57099,"visible":true,"origin":"","legend":"\u003cp\u003eChain Mediation Effect Diagram\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4358759/v1/aeb3a398f1e08d60e0158fff.png"},{"id":100618957,"identity":"eda39766-6784-441b-bc41-3bb04c1d56e1","added_by":"auto","created_at":"2026-01-19 18:04:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1137200,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4358759/v1/3993fe38-24b4-4f20-96e0-947b5ba65340.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Mental Health on Cognitive Functioning among Community-Dwelling Elderly and Its Mechanisms: A Large-Scale Cross-Sectional Study of 10,370 Participants","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the increasing trend of global population aging, the mental health and cognitive function of the elderly population have become one of the focuses of social attention. According to the latest statistics, the number of people aged 60 and above in China has exceeded 280\u0026nbsp;million, accounting for 19.80% of the total population \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Currently, \"actively responding to population aging\" has been considered a critical national strategy \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Healthy cognitive function is considered an essential aspect of successful aging. As people age, their cognitive function gradually deteriorates, manifesting as speech, apraxia, agnosia, and other functional impairments, and even dementia, which significantly affects the quality of life and satisfaction of the elderly. Studies have found that cognitive impairment is one of the most common mental illnesses among the elderly, with a prevalence rate of 24.4% \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The decline in cognitive function not only seriously impairs the quality of life of the elderly \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e but also increases the burden on families and society \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In recent years, China has become one of the countries with the fastest growth rate of patients with cognitive dysfunction globally. It is estimated that by 2060, the total number of patients with cognitive dysfunction in China will reach 48.68\u0026nbsp;million cases \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Cognitive impairment has become an important public health issue in China \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In 2022, the National Health Commission launched a national initiative for elderly psychological care, drawing widespread attention to the mental health issues of the elderly. Mental health generally refers to positive and normal psychological activities or states. For a long time, researchers have defined the criteria for mental health as the absence of psychopathology symptoms such as anxiety and depression \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The mental health status and cognitive function level of the elderly have a significant impact on their quality of life and social participation abilities. As they age, the elderly face the risk of health issues such as depression, anxiety, and cognitive decline, which can severely affect their lives and well-being.\u003c/p\u003e \u003cp\u003eCurrently, the relationship between mental health and cognitive function has also received significant attention. Previous studies have indicated that mental health issues may be closely related to the decline in cognitive function, and conversely, the decline in cognitive function may also serve as a predictor of mental health problems. Some research has also found a bidirectional relationship between cognitive dysfunction and depression in the elderly \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The impairment of prefrontal cortex function in patients with severe depression can lead to a decline in cognitive abilities \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the detection rate of depression among the elderly population with cognitive impairment is 32.3% \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and a decline in memory among the elderly can also trigger adverse psychological effects such as anxiety. However, there is still controversy regarding the complex relationship between mental health and cognitive function, and further research is needed to delve deeper into the mediating effects and influencing mechanisms between them.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to explore the impact and underlying mechanisms of depression and anxiety on cognitive function among the elderly, based on a large sample dataset of older adults. Through a comprehensive assessment of mental health and cognitive function, we hope to reveal the complex relationship between them and provide deeper understanding and guidance for promoting the health and well-being of the elderly. This will also provide theoretical and practical guidance for future interventions and treatments, aiming to enhance the quality of life and social functioning of the elderly.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eThis study employed a combination of multi-stage stratified and convenience sampling methods to select a data sample of 11,582 elderly individuals from multiple regions. After excluding 1,212 invalid questionnaires, a total of 10,370 questionnaires were included in the final analysis, with a questionnaire efficiency rate of 89.54%. The data sources included medical institutions, community health centers, and sampling surveys. A multi-dimensional dataset was collected, encompassing personal basic information, mental health status, cognitive function assessments, and other relevant information.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eParticipant Selection\u003c/h2\u003e \u003cp\u003eThe criteria for sample selection included: 1) individuals aged 60 years and older; 2) no history of severe cognitive impairment or mental illness; 3) agreement to participate in the study and signing of the informed consent form.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Tools\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e1.General Situation Survey\u003c/h2\u003e \u003cp\u003eThe survey covered information such as gender, age, educational background, marital status, and living situation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.Alzheimer's Disease 8 (AD8)\u003c/h2\u003e \u003cp\u003eDeveloped by the University of Washington in 2005, this scale is a brief and sensitive measurement tool based on patient interviews and screenings. It consists of eight questions and can effectively and reliably distinguish between patients with dementia and those without \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The scale assesses cognitive functions such as memory and orientation by asking the patients themselves. Evaluation is conducted based on responses of \"yes\" or \"no.\" In this study, a score of 0 indicated normal cognitive function, 1\u0026ndash;2 points suggested the possible presence of mild cognitive impairment, indicating a borderline state, and a score of 3 or above indicated the possible presence of cognitive dysfunction, necessitating further assessment \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.Mental Health Assessment\u003c/h2\u003e \u003cp\u003eThe Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) assessments can be used as screening tools as well as measures to evaluate the severity of depressive symptoms and various types of anxiety symptoms \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.The PHQ-9 has been established as one of the most reliable screening tools for depression \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The total score of the PHQ-9 ranges from 0 to 27, with the following interpretations: Normal: 0\u0026ndash;4 points; Mild depression: 5\u0026ndash;9 points; Moderate depression: 10\u0026ndash;14 points; Moderately severe depression:15\u0026ndash;19 points; Severe depression: 20 points and above. In epidemiological surveys, this scale can serve as an effective tool for screening and evaluating depressive symptoms among the elderly population in China.The GAD-7 is also highly reliable and valid \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The score interpretation is as follows: Normal: 0\u0026ndash;4 points; Mild anxiety: 5\u0026ndash;9 points; Moderate anxiety: 10\u0026ndash;14 points; Severe anxiety: 15\u0026ndash;21 points. A higher score indicates more prominent anxiety symptoms.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eStatistical analysis in this study was performed using SPSS 23.0 software. Univariate analysis was conducted using variance analysis and chi-square (χ\u0026sup2;) test, while multivariate analysis employed multiple linear regression to explore the influencing factors of cognitive function among elderly individuals in the community. Pearson correlation analysis was utilized to investigate the correlation among age, years of education, PHQ-9, GAD-7, and AD-8. Additionally, the chain mediation effect model was constructed using AMOS 26.0, and the mediating effects of PHQ-9 and GAD-7 were analyzed using the Bootstrap method, with a test level of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneral Information of Elderly Individuals\u003c/h2\u003e\n \u003cp\u003eA total of 10,370 elderly individuals were surveyed in this study, including 4,590 males and 5,780 females. The age range was 65\u0026ndash;103 years old, with a mean age of 73.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67 years. The mean score on the AD8 scale was 1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71, with 2,484 (23.95%) individuals identified as having preclinical cognitive impairment and 1,705 (16.44%) individuals with cognitive dysfunction. For the PHQ-9 scale, the mean score was 1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30, with 580 (5.59%) individuals classified as having mild depression, 121 (1.17%) with moderate depression, 33 (0.32%) with moderately severe depression, and 7 (0.07%) with severe depression. For the GAD-7 scale, the mean score was 0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89, with 368 (3.55%) individuals classified as having mild anxiety, 78 (0.75%) with moderate anxiety, and 22 (0.21%) with severe anxiety.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eUnivariate Analysis of Factors Influencing Cognitive Function\u003c/h2\u003e\n \u003cp\u003eThe AD8 scores were categorized into three groups: normal cognition, preclinical cognitive impairment, and cognitive dysfunction. When comparing the influencing factors across different groups, statistically significant differences were observed in gender, age, years of education, marital status, living arrangement, and type of elderly individual (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate Analysis of Influencing Factors in Different Cognitive Groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProject\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal Cognitive Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCritical Cognitive Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCognitive Disability Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF/\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.93\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.05\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWho Lives With\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpouse \u0026amp; Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving Alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmpty Nest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElderly Living Alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecial Family of Family Planning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone of the Above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e657.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e384.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: F represents the F-statistic in ANOVA for continuous variables; \u0026chi;\u0026sup2; represents the chi-square statistic for categorical variables. The p-value indicates the significance level of differences among different cognitive groups for each factor.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariate Analysis of Factors Influencing Cognitive Function\u003c/h2\u003e\n \u003cp\u003eThe regression equation included factors that were statistically significant in the univariate analysis. The assignment of variables is presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Multivariate linear regression analysis showed that there were statistically significant differences in the effects of gender, age, category, years of education, marital status, PHQ9, and GAD7 on cognitive function in the elderly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariable Assignment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssignment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;1, Female\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous variable, original value recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous variable, original value recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDummy variables: X1\u0026thinsp;=\u0026thinsp;Divorced (0,1), X2\u0026thinsp;=\u0026thinsp;Widowed (0,1), X3\u0026thinsp;=\u0026thinsp;Unmarried (0,1), X4\u0026thinsp;=\u0026thinsp;Separated (0,1) (with married as the reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWho Lives With\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDummy variables: X1\u0026thinsp;=\u0026thinsp;With children (0,1), X2\u0026thinsp;=\u0026thinsp;With spouse and children (0,1), X3\u0026thinsp;=\u0026thinsp;Living alone (0,1), X4\u0026thinsp;=\u0026thinsp;Others (0,1) (with living with spouse as the reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDummy variables: X1\u0026thinsp;=\u0026thinsp;Empty nest (0,1), X2\u0026thinsp;=\u0026thinsp;Disability (0,1), X3\u0026thinsp;=\u0026thinsp;Dementia (0,1), X4\u0026thinsp;=\u0026thinsp;Elderly living alone (0,1), X5\u0026thinsp;=\u0026thinsp;Special family of family planning (0,1), X6\u0026thinsp;=\u0026thinsp;None of the above (0,1) (with poverty as the reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous variable, original value recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous variable, original value recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultiple Linear Regression Analysis of Factors Influencing Cognitive Function in the Elderly\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta; Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% Confidence Interval for B\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.755, -1.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.161, 0.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035, 0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.035, -0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.039, -0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006, 0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWho Lives With\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.011, 0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.191, 0.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.048, 0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: a. Dependent variable: AD8, R\u0026sup2; = 0.184, Adjusted R\u0026sup2; = 0.183, F\u0026thinsp;=\u0026thinsp;283.95, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation between Mental Health and Cognitive Function in the Elderly\u003c/h2\u003e\n \u003cp\u003eUsing Pearson correlation analysis, this study examined the correlation between mental health, cognitive function, and related factors in the elderly. The results indicated a positive correlation between the PHQ9 scores and AD8 scores in the elderly (r\u0026thinsp;=\u0026thinsp;0.361, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as a positive correlation between the GAD7 scores and AD8 scores (r\u0026thinsp;=\u0026thinsp;0.287, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, a strong positive correlation was observed between the PHQ9 scores and GAD7 scores in the elderly (r\u0026thinsp;=\u0026thinsp;0.690, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a close association between depression and anxiety in this population. Additionally, age was positively correlated with AD8 scores (r\u0026thinsp;=\u0026thinsp;0.213, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed results are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePearson Correlation Analysis (r-values) between Mental Health, Cognitive Function, and Related Factors in the Elderly\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAD8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePHQ9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGAD7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.287**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.690**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.173**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.086**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.050**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.203**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: All P values\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eChain-Mediating Role of Age, Mental Health, and Cognitive Function in the Elderly\u003c/h2\u003e\n \u003cp\u003eFrom the perspective of the model path, age has a positive impact on PHQ9 (\u0026beta;\u0026apos;=0.09), PHQ9 has a positive impact on GAD7 (\u0026beta;\u0026apos;=0.69), GAD7 has a positive impact on AD8 (\u0026beta;\u0026apos;=0.08), age has a positive impact on AD8 (\u0026beta;\u0026apos;=0.19), PHQ9 has a positive impact on AD8 (\u0026beta;\u0026apos;=0.29), and age has a negative impact on GAD7 (\u0026beta;\u0026apos;=-0.03). All of these effects are statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating significant indirect effects, as detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Using the Bootstrap method (with 5000 resamples), the mediating variables were tested. The results showed that the estimated value of the total effect was 0.055, indicating a significant total effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The estimated value of the direct effect (Direct Effect) was 0.04, indicating a significant positive direct impact of the independent variable on the dependent variable (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The indirect effect (Indirect Effect) was transmitted through three mediating variable paths. Among them, the indirect effects of the age\u0026rarr;PHQ9\u0026rarr;AD8 and age\u0026rarr;PHQ9\u0026rarr;GAD7\u0026rarr;AD8 paths were positive and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the estimated value of the indirect effect for the age\u0026rarr;GAD7\u0026rarr;AD8 path was \u0026minus;\u0026thinsp;0.001, indicating a significant negative impact through this path (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e for details. A chain-mediating effect diagram is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePath Verification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u0026rarr;PHQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9\u0026rarr;GAD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u0026rarr;GAD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD7\u0026rarr;AD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u0026rarr;AD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9\u0026rarr;AD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSerial Mediating Test Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect Path\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u0026rarr;PHQ9\u0026rarr;AD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u0026rarr;GAD7\u0026rarr;AD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u0026rarr;PHQ9\u0026rarr;GAD7\u0026rarr;AD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we focused on the current status of cognitive and mental health among elderly individuals in the community, while also exploring potential influencing factors of cognitive function. According to our survey data, the AD8 scale score was (1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71), which is higher than the score of (0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68) reported by Jin Shan et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e in Guangdong\u0026apos;s Shenzhen city. This indicates that the cognitive level of elderly individuals in Guangxi community is lower than that of their counterparts in Shenzhen, which may be attributed to Guangxi\u0026apos;s mountainous location and the lower economic and cultural levels of its elderly population compared to Shenzhen. In this study, 16.44% of the elderly population exhibited cognitive dysfunction, which is roughly consistent with the range of 9.9\u0026ndash;35.2% reported internationally \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Furthermore, 23.95% of the elderly were diagnosed with borderline cognitive impairment. Although they have not been strictly diagnosed with cognitive dysfunction, these individuals face potential risks and may develop into cognitive dysfunction or even more severe cognitive disorders such as Alzheimer\u0026apos;s disease in the future. This underscores the ubiquity and severity of cognitive health issues among the elderly population. Attention to elderly individuals with borderline cognitive impairment not only benefits their individual health and well-being but also contributes to the overall improvement of society\u0026apos;s cognitive health level.\u003c/p\u003e\n\u003cp\u003eDepression and anxiety are among the top ten causes of global disability \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, and over 80% of individuals with mental disorders reside in low- and middle-income countries (LMICs) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In this study, the PHQ9 scale score was (1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30), with 7.15% of participants being diagnosed with depression. Similarly, the GAD7 scale score was (0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89), with 4.51% exhibiting symptoms of anxiety. In most studies, the prevalence of depression among elderly patients is high, but there are significant variations in reported rates due to differences in methods and populations, ranging from 1\u0026ndash;32% \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. According to some research, the prevalence of depression among elderly individuals in Guangdong, China, is 2.79%, while the prevalence of anxiety is 1.39% \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In India, the crude prevalence of both depression and anxiety is 3.3% \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study delved into the impacts of gender, age, category, years of education, marital status, PHQ9, and GAD7 on the cognitive function of community-based elderly individuals. The results indicate a positive correlation between age, PHQ9, and GAD7 scores with AD8 scores. This suggests that as individuals age, they experience increasing levels of emotional distress and anxiety, which correlate with a decline in cognitive function. Conversely, there is a negative correlation between years of education and AD8 scores, indicating that a higher level of education can help maintain cognitive function in older adults.\u003c/p\u003e\n\u003cp\u003eDuring the aging process, the brain inevitably undergoes various structural and functional changes \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Macroscopically, brain atrophy is a prominent feature of aging, and its occurrence rate increases with age. However, while brain atrophy is an inevitable consequence of aging, it is still possible to delay its progression and protect cognitive function through certain intervention measures. A higher level of education has a positive impact on the cognitive function of older adults. Education not only enhances an individual\u0026apos;s knowledge base but also exercises advanced cognitive functions such as abstract thinking and logical reasoning. Through long-term learning and thinking, the brain of older adults can remain active, thus delaying the process of cognitive decline. Additionally, education can help older adults better cope with life challenges and stress, improving their psychological resilience and further protecting their cognitive function.\u003c/p\u003e\n\u003cp\u003eStudies have shown that both childhood education and lifelong learning are closely associated with a lower risk of dementia \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. This may be because education promotes the growth and connectivity of neurons, enhancing the plasticity and adaptability of the brain. Therefore, for older adults, maintaining habits of continuous learning and thinking is an important pathway to improve their cognitive function and prevent dementia.\u003c/p\u003e\n\u003cp\u003eBy constructing a structural equation model, this study delved into the complex relationships among age, depressive symptoms (PHQ9), anxiety symptoms (GAD7), and cognitive function (AD8). It further revealed the mediating role of mental health between age and cognitive function. According to the coefficients of the model paths, we found that age had a significant positive impact on PHQ9, PHQ9 on GAD7, GAD7 on AD8, and age on AD8, which was consistent with our expectations. These positive effects indicate that as individuals age, they are more likely to experience depressive and anxious emotional issues, and these emotional problems further affect their cognitive function.\u003c/p\u003e\n\u003cp\u003eIt\u0026apos;s noteworthy that the influence of age on GAD7 is negative, which seems to contradict common sense. However, this could be due to changes in the pressure sources and coping strategies faced by older adults as they age. With the increase in age, older adults may gradually adapt to various challenges in life, or the sources of anxiety they face may decrease due to a narrowing social circle. Nevertheless, this negative effect cannot fully explain the underlying mechanism, and further research is needed to explore it.\u003c/p\u003e\n\u003cp\u003eThrough the application of the Bootstrap method to test mediation effects, we found that the total effect, direct effect, and indirect effects were all significant. This indicates that mental health plays a crucial mediating role between age and cognitive function. Specifically, PHQ9 and GAD7, as mediating variables, not only have significant individual impacts on AD8 but also form complex indirect effect paths through their mutual influence. Among them, the indirect effects of the two paths, age\u0026rarr;PHQ9\u0026rarr;AD8 and age\u0026rarr;PHQ9\u0026rarr;GAD7\u0026rarr;AD8, are positive and significant, suggesting that age affects cognitive function in older adults through its influence on depressive symptoms, and anxiety symptoms amplify this process. However, the indirect effect of the age\u0026rarr;GAD7\u0026rarr;AD8 path is negative, possibly due to an \u0026quot;offsetting\u0026quot; effect between the negative influence of age on GAD7 and the positive influence of GAD7 on AD8. This again reminds us that the relationship between mental health and cognitive function in older adults is highly complex, requiring comprehensive consideration of multiple factors for a full understanding.\u003c/p\u003e\n\u003cp\u003eIn summary, this study not only reveals the complex relationships among age, depression, anxiety, and cognitive function but also emphasizes the mediating role of mental health in these relationships. These results are significant for understanding the mechanisms underlying cognitive decline in older adults and for developing effective intervention measures. Future research can further explore other potential mediating variables and influencing factors to construct a more comprehensive model to guide health management and cognitive function improvement in older adults.\u003c/p\u003e"},{"header":"Conclusion","content":" \u003cp\u003eThrough a cross-sectional design, this study explored the relationship between age, mental health, and cognitive function among the elderly population in a wide range of communities. With a large and diverse sample size, the results of this study are representative to a certain extent. The study found that mental health plays a partial mediating role between age and cognitive function, providing a new perspective for understanding the decline in cognitive function. However, there are also limitations in this study: the cross-sectional design cannot determine causality, there may be sample selection bias, and other potential influencing factors have not been considered. Future studies need to further optimize the design, expand the scope of research, and adopt multiple data collection methods to more accurately reveal the relationships between variables and guide the health management of the elderly.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all the participants in our study, and we sincerely thank the Health Commission of Guangxi Zhuang Autonomous Region for their valuable assistance in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDH participated in the development of the questionnaire, cross-sectional survey, data collection, result analysis and manuscript writing. CZ and CL participated in literature research, questionnaire survey and data collection. XP and YP designed the study and participated in the questionnaire survey. QP, LL participated in the questionnaire. HH coordinates and oversees all phases of the project. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by three research grant projects:Investigation and Countermeasure Study on Oral Health of the Elderly in Guangxi Zhuang Autonomous Region (2022004).Self-Funded Research Project by the Health Commission of Guangxi Zhuang Autonomous Region: Analysis of the Correlation and Predictive Value of Anthropometric Indicators with Mild Cognitive Impairment in the Elderly (Z-A20230629).Key Disciplines and Cultivation Disciplines in Medical and Health of Guangxi Zhuang Autonomous Region (GuWeiKeJiaoFa [2022] No. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and materials used in this study have been properly preserved and anonymized. Due to ethical constraints, they cannot be publicly shared but can be provided to academic peers and research institutions upon reasonable request. The anonymized data for this study are held by Dr. DH. Those interested in obtaining the data and study materials should contact Dr. DH to request appropriate approval for access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has obtained ethical approval from the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University. Participants provided informed consent before participating. We confirm that all methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChina Government Network. (2023, January 17). Press conference on the economic performance of 2022 held by the Information Office of the State Council [EB/OL]. Retrieved from https://www.gov.cn/xinwen/2023-01/17/content_5737627.htm\u003c/li\u003e\n\u003cli\u003eChina Government Network. (2019, November 21). The CPC Central Committee and the State Council issued the Medium- and Long-Term Plan for Actively Responding to Population Aging [EB/OL]. Retrieved from http://www.gov.cn/xinwen/2019-11/21/content_5454347.htm\u003c/li\u003e\n\u003cli\u003eNakasujja, N., et al. (2007). Psychiatric disorders among the elderly on non-psychiatric wards in an african setting. \u003cem\u003eInternational Psychogeriatrics\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(4), 691\u0026ndash;704.\u003c/li\u003e\n\u003cli\u003eZhang, Q. L., Wu, Y. L., Han, T. K., et al. (2019). Changes in cognitive function and risk factors for cognitive impairment of the elderly in China: 2005-2014. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(16), 2847.\u003c/li\u003e\n\u003cli\u003eLara, E., Koyanagi, A., Caballero, F., et al. (2017). Cognitive reserve is associated with quality of life: A population-based study. \u003cem\u003eExperimental Gerontology\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e(Pt A), 67-73.\u003c/li\u003e\n\u003cli\u003ePrince, M., Ali, G. C., Guerchet, M., et al. (2016). Recent global trends in the prevalence and incidence of dementia, and survival with dementia. \u003cem\u003eAlzheimers Research \u0026amp; Therapy\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 23.\u003c/li\u003e\n\u003cli\u003eJia, L. F., Du, Y. F., Chu, L., et al. (2020). Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study. \u003cem\u003eThe Lancet Public Health\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(12), e661-e671.\u003c/li\u003e\n\u003cli\u003eWang, X. Q., \u0026amp; Zhang, D. J. (2012). Construction of a relationship model between psychological quality and mental health: Surpassing PTH and DFM. \u003cem\u003eJournal of Southwest University (Social Science Edition)\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(6), 67-74 +174.[In Chinese]\u003c/li\u003e\n\u003cli\u003eSong, Y. L., Song, P. P., Wang, X. C., et al. (2014). Longitudinal study of the relationship between cognitive function and depression in the elderly using a multilevel model. \u003cem\u003eChinese Journal of Health Statistics\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(1), 27-30.[In Chinese]\u003c/li\u003e\n\u003cli\u003eZhang, J., \u0026amp; Yu, L. (2020). Depression and cognitive decline in the elderly. \u003cem\u003eChinese Journal of Health Psychology\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(11), 1754-1760.[In Chinese]\u003c/li\u003e\n\u003cli\u003eQian, S., Liu, Y., \u0026amp; Zhang, Y. W. (2022). Meta-analysis of the detection rate of depressive symptoms in elderly patients with mild cognitive impairment in Chinese communities from 2011 to 2021. \u003cem\u003eChinese Journal of General Practice\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 231-236.[In Chinese]\u003c/li\u003e\n\u003cli\u003eGalvin, J. E., Roe, C. M., Xiong, C., et al. (2006). Validity and reliability of the AD8 informant interview in dementia. \u003cem\u003eNeurology\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(11), 1942\u0026ndash;1948.\u003c/li\u003e\n\u003cli\u003eRazavi, M., Tolea, M. I., Margrett, J., et al. (2014). Comparison of 2 informant questionnaire screening tools for dementia and mild cognitive impairment: AD8 and IQCODE. \u003cem\u003eAlzheimer disease and associated disorders\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 156\u0026ndash;161.\u003c/li\u003e\n\u003cli\u003eChen, H. H., Sun, F. J., Yeh, T. L., et al. (2018). The diagnostic accuracy of the Ascertain Dementia 8 Questionnaire for detecting cognitive impairment in primary care in the community, clinics and hospitals: A systematic review and meta-analysis. \u003cem\u003eFamily Practice\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(3), 239-246.\u003c/li\u003e\n\u003cli\u003eKroenke, K., Spitzer, R. L., \u0026amp; Williams, J. B. W. (2001). The PHQ-9. \u003cem\u003eJournal of General Internal Medicine\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(9), 606\u0026ndash;613.\u003c/li\u003e\n\u003cli\u003eKroenke, K., Spitzer, R. L., Williams, J. B. W., et al. (2010). The patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. \u003cem\u003eGeneral Hospital Psychiatry\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4), 345\u0026ndash;359.\u003c/li\u003e\n\u003cli\u003eEl-Den, S., Chen, T. F., Gan, Y. L., et al. (2018). The psychometric properties of depression screening tools in primary healthcare settings: A systematic review. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e225\u003c/em\u003e, 503-522.\u003c/li\u003e\n\u003cli\u003eCostantini, L., Pasquarella, C., Odone, A., et al. (2021). Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e279\u003c/em\u003e, 473-483.\u003c/li\u003e\n\u003cli\u003eStocker, R., Tran, T., Hammarberg, K., et al. (2021). Patient Health Questionnaire 9 (PHQ-9) and General Anxiety Disorder 7 (GAD-7) data contributed by 13,829 respondents to a national survey about COVID-19 restrictions in Australia. \u003cem\u003ePsychiatry research\u003c/em\u003e, \u003cem\u003e298\u003c/em\u003e, 113792.\u003c/li\u003e\n\u003cli\u003eJin, S., Hu, W. X., Zhang, S. M., et al. (2022). Status and influencing factors of cognitive dysfunction among the elderly aged \u0026ge;65 years in Shenzhen community. \u003cem\u003eChinese Primary Health Care\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(11), 52-55.[In Chinese]\u003c/li\u003e\n\u003cli\u003ePottie, K., Rahal, R., Jaramillo, A., et al. (2016). Recommendations on screening for cognitive impairment in older adults. \u003cem\u003eCMAJ: Canadian Medical Association Journal\u003c/em\u003e, \u003cem\u003e188\u003c/em\u003e(1), 37-46.\u003c/li\u003e\n\u003cli\u003eVos, T., Barber, R. M., Bell, B., et al.(2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990\u0026ndash;2013: A systematic analysis for the Global Burden of Disease Study 2013. \u003cem\u003eLancet\u003c/em\u003e, \u003cem\u003e386\u003c/em\u003e(9995), 743\u0026ndash;800.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2008). The Global Burden of Disease:2004 Update. Available online at: https://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf (accessed May 23, 2020)\u003c/li\u003e\n\u003cli\u003eZenebe, Y., Akele, B., W/Selassie, M., et al. (2021). Prevalence and determinants of depression among old age: a systematic review and meta-analysis. \u003cem\u003eAnnals of general psychiatry\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 55.\u003c/li\u003e\n\u003cli\u003eKvalbein-Olsen, L. C., Aakhus, E., Haavet, O. R., et al. (2023). Unrecognised depression among older people: a cross-sectional study from Norwegian general practice. \u003cem\u003eBJGP open, 7\u003c/em\u003e(1), BJGPO.2022.0135.\u003c/li\u003e\n\u003cli\u003eForlani, C., Morri, M., Ferrari, B., et al. (2014). Prevalence and gender differences in late-life depression: a population-based study. \u003cem\u003eThe American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, 22\u003c/em\u003e(4), 370\u0026ndash;380. \u003c/li\u003e\n\u003cli\u003eHe, Z. F., Tan, W. Y., Ma, H.,et al. (2024). Prevalence and factors associated with depression and anxiety among older adults: A large-scale cross-sectional study in China. \u003cem\u003eJournal of affective disorders, 346\u003c/em\u003e, 135\u0026ndash;143.\u003c/li\u003e\n\u003cli\u003eDe Man, J., Absetz, P., Sathish, T., et al. (2021). Are the PHQ-9 and GAD-7 Suitable for Use in India? A Psychometric Analysis. \u003cem\u003eFrontiers in psychology, 12\u003c/em\u003e, 676398.\u003c/li\u003e\n\u003cli\u003eConnell, E., Le Gall, G., Pontifex, M. G., et al. (2022). Microbial-derived metabolites as a risk factor of age-related cognitive decline and dementia. \u003cem\u003eMolecular neurodegeneration, 17\u003c/em\u003e(1), 43.\u003c/li\u003e\n\u003cli\u003eLarsson, S. C., Traylor, M., Malik, R., et al. on behalf of the International Genomics of Alzheimer\u0026rsquo;s Project (2017). Modifiable pathways in Alzheimer\u0026apos;s disease: Mendelian randomisation analysis. \u003cem\u003eBMJ (Clinical research ed.), 359\u003c/em\u003e, j5375.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Elderly, Cognitive Function, Mental Health, Mediation Effect, Influencing Factors","lastPublishedDoi":"10.21203/rs.3.rs-4358759/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4358759/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e To delve deeply into the impact of depression and anxiety on cognitive function in the elderly, as well as the mediating mechanisms involved.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e Data were derived from the baseline survey of a cross-sectional study on the health status of community-dwelling elderly individuals in Guangxi, China, conducted from July 2022 to July 2023. Valid data from 10,370 elderly individuals aged 60 years and older were analyzed. Cognitive function of the elderly was assessed using the Memory Impairment Screen (AD8), while depression symptoms and anxiety symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder Scale-7 (GAD-7), respectively. Univariate and multiple linear regression analyses were conducted to explore the influencing factors of cognitive function in the elderly. Pearson correlation analysis was used to investigate the correlations among depression symptoms, anxiety symptoms, cognitive function, and related factors. Chain mediation analysis was performed using AMOS 26.0 software to explore the mechanisms of the effects of age and mental health on cognitive function among community-dwelling elderly individuals.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e A total of 10,370 elderly individuals were surveyed in this study, including 4,590 males and 5,780 females, aged 65\u0026ndash;103 years (mean age: 73.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67 years). The mean score on the AD8 scale was 1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71, with 2,484 (23.95%) individuals identified as having mild cognitive impairment and 1,705 (16.44%) individuals with cognitive dysfunction. The mean score on the PHQ9 scale was 1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30, with 580 individuals (5.59%) classified as having mild depression, 121 (1.17%) with moderate depression, 33 (0.32%) with moderately severe depression, and 7 (0.07%) with severe depression. The mean score on the GAD7 scale was 0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89, with 368 individuals (3.55%) identified as having mild anxiety, 78 (0.75%) with moderate anxiety, and 22 (0.21%) with severe anxiety.Multivariate linear regression analysis showed statistically significant differences in the effects of gender, age, category, years of education, marital status, PHQ9, and GAD7 on cognitive function among the elderly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was a positive correlation between PHQ9 scores and AD8 scores (r\u0026thinsp;=\u0026thinsp;0.361, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the elderly, as well as a positive correlation between GAD7 scores and AD8 scores (r\u0026thinsp;=\u0026thinsp;0.287, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, a strong positive correlation was observed between PHQ9 scores and GAD7 scores (r\u0026thinsp;=\u0026thinsp;0.690, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age was also positively correlated with AD8 scores (r\u0026thinsp;=\u0026thinsp;0.213, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).The study further revealed a chained mediating effect of age, mental health, and cognitive function among the elderly. The total effect estimate was 0.055, which was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The direct effect estimate was 0.04, indicating a significant positive and direct impact of age on AD8 scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e The finding that mental health plays a partial mediating role between age and cognitive function provides a new perspective for understanding the decline in cognitive function. This discovery holds significant theoretical and practical implications for improving the mental health and cognitive function of the elderly, which can contribute to the development of more effective intervention measures and enhance the quality of life for the elderly.\u003c/p\u003e","manuscriptTitle":"The Impact of Mental Health on Cognitive Functioning among Community-Dwelling Elderly and Its Mechanisms: A Large-Scale Cross-Sectional Study of 10,370 Participants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-10 21:06:29","doi":"10.21203/rs.3.rs-4358759/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-07T08:28:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-06T06:44:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-06T06:44:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-05-02T11:06:24+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":"2b8a24dd-e3a3-45c6-8fbd-9565d65bf138","owner":[],"postedDate":"May 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:38:34+00:00","versionOfRecord":{"articleIdentity":"rs-4358759","link":"https://doi.org/10.1186/s12889-026-26196-9","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-01-12 16:29:02","publishedOnDateReadable":"January 12th, 2026"},"versionCreatedAt":"2024-05-10 21:06:29","video":"","vorDoi":"10.1186/s12889-026-26196-9","vorDoiUrl":"https://doi.org/10.1186/s12889-026-26196-9","workflowStages":[]},"version":"v1","identity":"rs-4358759","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4358759","identity":"rs-4358759","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}