Exploring a risk predictive model for depression in self-care elderly people: a national cross-sectional study from CLHLS

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Strengthening the health management of the elderly is an important measure to achieve healthy aging, and the mental health of the elderly needs special attention. The aim of the present study is to develop a risk predictive model for depression among self-care elderly individuals, so as to efficiently identify high-risk groups for depression and implement targeted screening and mental health management. Methods From the data of China Longitudinal Healthy Longevity Survey (CLHLS) in 2018, a total of 5,592 valid samples of self-care elderly aged over 60 were selected. Depression was measured using the CES−10 depression scale, with a score of 10 or higher being considered to have depression. A binary forward stepwise regression analysis was used to construct a risk prediction model of depression among self-care elderly people, and the c-index was used to assess the predictive power. Results Among the 5,592 self-care elderly people, 9.8% suffered from depression. The risk predictive model for depression included four risk factors, namely, gender, marital status, self-rated quality of life, and self-rated health. Compared with the males, females were more likely to suffer from depression(OR = 1.481, P = 0.023); compared with those current married and living with spouse, the widowed were more likely to suffer from depression(OR = 1.513, P < 0.001); those with poorer self-rated quality of life (OR = 2.916, P < 0.001), poorer self-rated health status (OR = 3.080, P 0.05, and the ROC index was 0.774; which meant that the model has good fitting degree and good prediction effect. Conclusions The development of a risk prediction model of depression among self-care elderly is instrumental in accurately identifying high - risk groups of depression, which facilitate targeted depression screening and comprehensive mental health management. depression risk predictive model self-care elderly people cross-sectional study Figures Figure 1 Introduction The data from China's Seventh National Population Census reveals that the population aged 60 and above, as well as the population aged 65 and above, constituted 18.70% and 13.50% respectively, indicating a progressively serious situation of population aging. In 2021, the Central Committee of the Communist Party of China and the State Council issued a policy named Opinions on strengthening the work related to the elderly in the new era , which emphasized that the concept of healthy aging should be integrated into the entire process of economic and social development [1]. The data from the blue book named Mental Health in China in 2023 revealed that 19.05% of the elderly were in a state of mild depression, while 12.17% were in a state of moderate to severe depression. This indicated that the mental health of the elderly warrants significant attention, and mental health management should be an important part of health management for the elderly. Presently, researches pay more attention to the mental health of disabled elderly people [2–4]. However, the mental health problems of self-care elderly should not be ignored either. Researches have demonstrated that the CES-D scale is an effective depression screening tool for the elderly population [5–7]. Nevertheless, a large-scale mental health screening was both inefficient and impractical. Developing a depression risk prediction model for self-care elderly individuals can effectively identify the high- risk groups of depression, conduct targeted depression screening, and enhance the efficiency of mental health management for self-care elderly. Some studies have identified that self-rated health, personal basic characteristics, family factors, and social factors are the influencing factors of depression in the elderly. Self-rated health is a kind of subjective evaluation of health made by respondents according to their physical, psychological and social functions. It could effectively and reliably reflect the health status of the population[8–9]. The physical health of the elderly would affect their mental health, and those with poor self-rated health were more likely to be depressed [10]. In terms of personal factors, gender has a statistically significant effect on depression in the elderly[11]. In terms of family factors, marital status and living style have a statistically significant impact on the depression of the elderly[12–15]. In terms of social factors, the elderly with medical insurance has a lower risk of depression [16]. This study attempted to establish a risk prediction model of depression in self-care elderly people based on the national data of CLHLS in 2018, in order to effectively identify high-risk groups of depression, provide reference for targeted screening of depression in the elderly, and improve the efficiency of mental health intervention for the elderly. Materials and methods Data source and study population This study used the data from China Longitudinal Healthy Longevity Survey (CLHLS) database in 2018, which was conducted by the research center for healthy aging and development of Peking University [17]. The CLHLS is extensively utilized in the field of elderly health, and the authority and quality of the data are highly recognized. The 2018 data was released to the public on April 3rd, 2020, and it was the latest publicly available data, making it the most recent and pertinent data available for the research objectives of this paper. This large-scale, nationwide public social survey data on the elderly exhibits excellent representativeness and timeliness. The scope of the investigation covered the elderly aged 65 and above across 23 provinces, cities, and autonomous regions nationwide (Note: A small number of residents under 60 years old, as well as those aged between 60 and 65, were also surveyed). The survey content comprised the basic characteristics of the elderly, their family situations, socio-economic conditions, and health status (including self-rated health, mental health, and daily self-care ability). The inclusion criteria for valid samples in this study were as follow: the elderly aged 60 and above who were capable of self-care; and the data were complete and valid for the dependent variable (each item of the CES−10 Depression Scale) and independent variables (such as self-rated health and basic characteristics of the elderly). The exclusion criteria were as follow: the individuals aged under 60 years old or age data missing, and those with missing or invalid data for the dependent and independent variables. According to the above standards, 5592 valid samples were included. Assessment of depression The measurement of depression utilized the CES−10 Depression Scale. This scale comprises 10 items, which include 8 positively worded items (e.g., Do you get annoyed by trivial matters? Is it difficult for you to concentrate on tasks? Do you feel sad and depressed?) and 2 negatively worded items (e.g., Are you hopeful about your future life? Do you feel as happy as you did in your youth?). For positively worded items, the value of each option was as follows: Rarely or Never = 0, Sometimes = 1, Often = 2, Always = 3. For negatively worded items, it was as follows: Always or Often = 0, Sometimes = 1, Rarely = 2, Never = 3. The scores of the 10 questions were summed. Referring to the research by WANG W et al. and TENG Jiashan et al. [18, 19], a cut-off point of 10 was used; individuals with a score of 10 or higher were classified as having depression, while those with a score below 10 were classified as not having depression. In this study, the scale demonstrates good reliability, with a Cronbach's α value of 0.706. Definition of variables The dependent variable was whether there is depression. The independent variables were gender, age, years of schooling, household registration type, current residential area, co-residence of interviewee, current marital status, main source of financial support, type of medical insurance, self-reported quality of life, and self-reported health. Among them, self-reported quality of life was measured by the question "How do you think of your current life?" (The options were very good, good, so so, bad, very bad). Self-reported health was measured by the question "How do you think of your current health status?" (The options were very good, good, so so, bad, very bad). Statistical analysis The data were analyzed using IBM SPSS Statistics 27.0. Categorical variables and ordinal variables were described using frequencies (percentages). A univariate logistic regression model was developed. The assignments of variables are shown in Table 1 . Univariate analysis variables with P < 0.25 were considered potential independent variables and designated as candidate variables for the multivariate logistic regression model. Stepwise logistic regression analysis was carried out to develop the risk predictive model. Variables were considered eligible for inclusion through a likelihood ratio test(LRT) P = 0.05 and removed at P = 0.10. Hosmer-Lemeshow test was applied to assess the goodness-of-fit of the model, where a good fit was indicated by P > 0.05. A c-index was determined to evaluate the predictive power of the logistic regression model. This index is the area under the ROC curve, which is a measure of predictive performance.The range from the least to the best predictive ability was 0.5−1.0. Table 1 Assignments of variables in regression analysis Variables Assignment Dependent variable (Y) Depression 0 no, 1 yes Independent variables (X) Gender 0 male, 1 female Age 1 = 60–69, 2 = 70–79,3 = 80–89,4 = 90 or above Years of schooling 1 = 0year,2 = 1-3years,3 = 4-6years,4 = 7-9years, 5 = 10years or above Household register type 0 registered urban residents,1 registered rural residents Current residential area 1 City, 2 Town, 3 Rural Co-residence of interviewee 1 with household member(s), 2 alone, 3 in an institution Current marital status 1 currently married and living with spouse, 2 divorced(or separated), 3 widowed, 4 never married Main source of financial support 1 retirement wages, 2 spouse, 3 child(ren), 4 grandchild(ren), 5 other relative(s), 6 local government or community, 7 work by self, 8 others Do you have public free medical services 0 no, 1 yes Do you have urban employee/resident medical insurance 0 no, 1 yes Do you have new rural cooperative medical insurance 0 no, 1 yes Do you have commercial medical insurance at present 0 no, 1 yes Self-reported quality of life 1 very good, 2 good, 3 so so, 4 bad, 5 very bad Self-reported health 1 very good, 2 good, 3 so so, 4 bad, 5 very bad Results Characteristics of study participants As shown in Table 2 , 9.8% of self-care elderly people suffer from depression. Among the sample, females account for 51.1%, slightly higher than males. The proportion of the elderly in the age group of 70–79 years old is the highest (34.1%). 71.1% of the elderly have rural household registration, far exceeding those with urban household registration (28.9%). In terms of co-residence of interviewee, the proportion of the elderly living with their families is the highest (80.0%). In terms of marital status, the proportion of the elderly who are married and living with their spouses is the highest (52.6%). The main sources of financial support for the elderly are their children (37.1%) and retirement wages (30.1%). The proportion of the elderly who have public free medical services, urban employee/resident medical insurance, new rural cooperative medical insurance and commercial medical insurance is 3.5%, 25.8%, 61.4%, and 0.6% respectively. The proportion of the elderly whose self-rated quality of life is bad and very bad is 1.9%. The proportion of the elderly whose self-rated health is bad and very bad is 8.1%. Table 2 Basic characteristics of the sample Variables N % Depression(Yes) 546 9.8 Gender(Female) 2860 51.1 Age 60–69 993 17.8 70–79 1905 34.1 80–89 1492 26.7 ≧ 90 1202 21.5 Years of schooling 0 year 2130 38.1 1−3years 758 13.6 4−6years 1353 24.2 7−9years 710 12.7 10years or above 641 11.5 Household register type Registered urban residents 1614 28.9 Registered rural residents 3978 71.1 Current residential area City 1307 23.4 Town 1875 33.5 Rural 2410 43.1 Co-residence of interviewee With household member(s) 4472 80.0 Alone 998 17.8 In an institution 122 2.2 Current marital status Currently married and living with spouse 2944 52.6 Separated or divorced 131 2.3 Widowed 2477 44.3 Never married 40 0.7 Main source of financial support Retirement wages 1685 30.1 Spouse 199 3.6 Child(ren) 2073 37.1 Grandchild(ren) 51 0.9 Other relative(s) 9 0.2 Local government or community 518 9.3 Work by self 711 12.7 Others 346 6.2 Public free medical services(yes) 197 3.5 Urban employee/resident medical insurance(yes) 1445 25.8 New rural cooperative medical insurance(yes) 3434 61.4 Commercial medical insurance(yes) 32 0.6 Self-reported quality of life Very good 1391 24.9 Good 2650 47.4 So so 1445 25.8 Bad 89 1.6 Very bad 17 0.3 Self-reported health Very good 787 14.1 Good 2256 40.3 So so 2094 37.4 Bad 432 7.7 Very bad 23 0.4 Univariate logistic regression analysis As shown in Table 3 , the univariate logistic regression analysis revealed that gender, years of schooling, co-residence of interviewee, current marital status, main source of financial support, self-rated quality of life, and self-rated health had statistically significant impacts on the depression for self-care elderly people (P < 0.05). Females, those living alone, widowed or unmarried individuals, those financially depend on their children or government relief, those with a poor self-rated quality of life, and those with a poor self-rated health were more likely to suffer from depression. The elderly with longer years of schooling were inclined to have lower risk of depression. Table 3 Univariate analysis of depression in self-care elderly people Variables β OR 95%CI P Gender(Female) 0.393 1.481 1.238–1.773 <0.001 Age 0.054 1.055 0.968–1.151 0.222 Years of schooling −0.121 0.886 0.830–0.946 < 0.001 Household register type (Registered rural residents as standard variable) 0.148 1.160 0.949–1.417 0.148 Current residential area(City as standard variable) Town 0.226 1.254 0.980–1.604 0.072 Rural 0.218 1.244 0.982–1.576 0.071 Co-residence of interviewee(With household member(s) as standard variable) Alone 0.574 1.775 1.446–2.179 < 0.001 In an institution 0.311 1.364 0.774–2.404 0.282 Current marital status(Current married and living with spouse as standard variable) Separated or divorced −0.448 0.639 0.295–1.384 0.256 Widowed 0.414 1.513 1.263–1.811 < 0.001 Never married 1.040 2.829 1.289–6.209 0.009 Main source of financial support(Retirement wages as standard variable) Spouse 0.249 1.283 0.782–2.103 0.323 Child(ren) 0.335 1.398 1.118–1.749 0.003 Grandchild(ren) 0.759 2.136 0.984–4.636 0.055 Other relative(s) 0.361 1.435 0.178–11.560 0.734 Local government or community 0.649 1.914 1.414–2.589 < 0.001 Work by self 0.001 1.001 0.725–1.382 0.997 Others −0.069 0.933 0.603–1.444 0.755 Public free medical services (yes) −0.281 0.755 0.443–1.288 0.302 Urban employee/resident medical insurance(yes) −0.098 0.906 0.738–1.114 0.350 New rural cooperative medical insurance(yes) 0.181 1.198 0.996–1.441 0.056 Commercial medical(yes) −0.487 0.615 0.147–2.579 0.506 Self-reported quality of life 1.070 2.916 2.577−3.300 <0.001 Self-reported health 1.125 3.080 2.731–3.474 < 0.001 Multivariate risk prediction model of depression in self-care elderly people As shown in Table 4 , Logistic stepwise regression analysis reveal that the risk prediction model of depression encompasses four risk factors: gender, marital status, self-rated quality of life, and self-rated health. Specifically, the elderly who were female (OR = 1.260, P = 0.023), widowed (OR = 1.456, P < 0.001), reported a poor self-rated quality of life (OR = 1.995, P < 0.001), and reported a poor self-rated health status (OR = 2.418, P < 0.001) were more clined to suffer from depression. Table 4 Multivariate logistic stepwise regression of depression in self-care elderly people Variables β OR 95%CI P Gender(Female) 0.231 1.260 1.032–1.537 0.023 Current marital status (Current married and living with spouse as standard variable) Separated or divorced -0.452 0.637 0.287–1.413 0.267 Widowed 0.376 1.456 1.194–1.775 < 0.001 Never married 0.696 2.005 0.851–4.724 0.112 Self-reported quality of life 0.691 1.995 1.743–2.284 < 0.001 Self-reported health 0.883 2.418 2.113–2.766 0.05), indicating good fit.The equation of the multivariate logistic regression risk adjustment model is as follows: logit(Pi) =−6.511 + 0.231Gender−0.452Current marital status1(1) + 0.376Current marital status(2) + 0.696Current marital status(3) + 0.691Self-reported quality of life + 0.883 Self-reported health. Note Current marital status(1) is separated or divorced; Current marital status(2) is widowed; Current marital status(3) is never married. Discussion This study finds that the risk prediction model of depression in self-care elderly people includes four influencing factors: gender, marital status, self-reported quality of life, and self-reported health. Elderly people who are female, widowed, report a poor self-reported quality of life, and report a poor self-reported health are more likely to suffer from depression. Compared with the male, the female are more susceptible to depression. This is consistent with the research results of Wang Yue et al.[20]. The female often have to undertake the tedious things such as housework and caring for their grandchildren. As a result, they are more likely to have negative emotions such as anxiety, irritability. Marital status also significantly influences the prevalence of depression among the elderly. Compared with the elderly married and living with spouse, the widowed are more likely to suffer from depression, which are similar to the research results of Liu Min et al. [21]. The elderly married and living with spouse can receive more social support, they can get emotional and financial support from their spouses [22]. Self-rated quality of life is an important influencing factor for depression in the elderly. The elderly have worse self-rated quality of life are more likely to suffer from depression, which is consistent with the research results of Yang Juan et al [23]. Self-rated quality of life is the subjective evaluation of the objective quality of life by the elderly, which not only reflects the actual quality of life of the elderly, but also reflects their mental state. The elderly with a poor self-rated quality of life may have a poor actual quality of life, or they may have overly high requirements for quality of life. Both of these situations may increase the negative emotions of the elderly. Self-rated health is the subjective evaluation of their current health status by the elderly, which can largely reflect the elderly’s objective health condition [22]. The elderly have their own evaluation criteria and expectations for health. Most of them tend to evaluate their own health status after comparing it with that of the elderly around them. According to the social comparison theory [24], after comparing themselves with others, the elderly will make positive or negative self-judgments. Elderly people with a good self-rated health level tend to achieve a better result when comparing with others and often maintain a more positive psychological state. On the contrary, if the elderly have a poor self-rated health, they usually have an unfavorable result in the comparison with others, and are prone to negative emotions such as frustration and depression, thus the risk of depression will increase. In conclusion, during the process of health management for the elderly, the elderly who are female, widowed, have a poor self-rated quality of life, have a poor self-rated health should be paid attention to as key groups of depression. Currently, the proportion of the elderly suffering from depression in China remains quite high. Identifying the risk factors for depression in the elderly is of great significance for improving their mental health and reducing the disease burden. By constructing a depression risk prediction model in self-care elderly people, this study has found four risk factors, namely: gender, marital status, self-rated quality of life, and self-rated health. The data collection process for these indicators is simple and convenient, without the need for complex professional instruments or cumbersome detection procedures. At present, the health records for the elderly are established in the primary healthcare institutions in China. In the process of health management for the elderly, the collection of the four indicators mentioned above can effectively identify the high - risk groups for depression, and carry out targeted depression screening and mental health interventions. The construction of a convenient and efficient depression risk prediction model for the elderly can also help community workers and even family members to timely detect the elderly's depressive tendencies. Then, through personalized psychological counseling, strengthening of social support networks, and guidance on healthy lifestyles and other intervention measures, the depression can be effectively prevented and alleviated. There are some limitations in this study.This study attempts to construct a risk prediction model for depression in self-care elderly people, and there are certain limitations in its applicability to disabled elderly people and semi-disabled elderly people. Conclusions The risk predictive model for depression in self-care elderly people included four risk factors, namely, gender, marital status, self-rated quality of life, and self-rated health. The elderly who are female, widowed, have a poor self-rated quality of life, have a poor self-rated health should be paid attention to as key groups of depression. In the process of health management for the elderly, the general practitioner in the primary healthcare institutions should collect the four indicators mentioned above, and identify high - risk groups of depression through the risk prediction model. And then implement targeted depression screening and comprehensive mental health management. Declarations Ethics approval and consent to participate Ethics approval was obtained from Ethics Committee of the Institute of Environmental and Health-Related Product Safety of China Center for Disease Control and Prevention. All methods were carried out in accordance with Helsinki Declaration. All participants provided informed written consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (72364016, 72574088). Author Contribution XW designed the research. XW and JL collected the article’s data and conducted data analysis. XW and HY has carried out the preliminary drafting of the article. LL participated in the supervision and editing of this study. All authors of this study have contributed to this article and have unanimously agreed to its publication. Acknowledgement The authors thank the research center for healthy aging and development of Peking University for granting access to the data. The authors are also grateful for the data management team for providing the data set. 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Prevalence of depression and its influencing factors among elderly people in rural areas of Sichuan Province. Mod Prev Med. 2024;51(16):2892–2897,2910. Li TY, Bing JY, Li X, Xue QD, Zhang Y, Li RN, Wang Y, Sun H. Study on self-rated health status and its influencing factors of elderly people living alone in China. Modern Preventive Medicine. 2021; 48(11): 2027–2031. Yang J, Lü XZ, Shang L, Li HZ, Lu C, Zhang M, Zhang DM, Lin XH, Wang HL, Guan T. Detection rate of depressive and anxious emotions among the elderly in Shenzhen and related factors. Chinese Mental Health Journal. 2023; 37(5): 373–379. Festinger L. A theory of social comparison processes. Hum Relat. 1954;7(2):117–140. 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-8101254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583090569,"identity":"dcd47b0b-cddc-4c21-b821-764c2935f806","order_by":0,"name":"Xuan Wang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Wang","suffix":""},{"id":583090570,"identity":"642b63d3-f878-42fd-83e8-67ea49f4d3f3","order_by":1,"name":"Huiyuan Yang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Huiyuan","middleName":"","lastName":"Yang","suffix":""},{"id":583090571,"identity":"198d9fae-3bf5-49a0-b734-80c3619c6ae4","order_by":2,"name":"Jiajunni Li","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jiajunni","middleName":"","lastName":"Li","suffix":""},{"id":583090572,"identity":"1144f836-da2a-4314-aba3-5007a883f7f1","order_by":3,"name":"Liqing Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACPmbmhgMMBkAWO2PjAYYKCTl5QlrYmBmhWsCMMxbGhg2EtDAwQpUwMzAcYGyrSGQ4QEgLyD1vCu7YNQBdeJh3nkQCYwPzw0c3CDjs4ByDZ8kNQMZh3m0SeewMbMbGOQS0HOYxOJzMANVSzNjAwyZNgpY5EokNB4jUYgfR0kCkFqBfDicwgBnHJIwNmwn4hZ//8OEPb/4ctmdgb3/44E1NnZw8e/PDx/i0gAEPA0Pi/gMMDEw8IB4zIeVQLfYgmvEHMapHwSgYBaNgxAEAYXtIbNjxow0AAAAASUVORK5CYII=","orcid":"","institution":"Jiangxi Science and Technology Normal University","correspondingAuthor":true,"prefix":"","firstName":"Liqing","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-11-13 04:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8101254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8101254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101674064,"identity":"0fc87ece-9e94-4b2d-96ed-4e1de2e78b02","added_by":"auto","created_at":"2026-02-02 13:16:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72089,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the multivariate risk prediction model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8101254/v1/301a84c9ffb1a966d1583988.png"},{"id":101753833,"identity":"77a68a8b-4faf-400a-8139-8e72e1c1d6c0","added_by":"auto","created_at":"2026-02-03 10:40:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":845180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8101254/v1/775c87c1-1be6-4701-9c7a-f22d8c564c67.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring a risk predictive model for depression in self-care elderly people: a national cross-sectional study from CLHLS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe data from China's Seventh National Population Census reveals that the population aged 60 and above, as well as the population aged 65 and above, constituted 18.70% and 13.50% respectively, indicating a progressively serious situation of population aging. In 2021, the Central Committee of the Communist Party of China and the State Council issued a policy named \u003cem\u003eOpinions on strengthening the work related to the elderly in the new era\u003c/em\u003e, which emphasized that the concept of healthy aging should be integrated into the entire process of economic and social development [1].\u003c/p\u003e \u003cp\u003eThe data from the blue book named \u003cem\u003eMental Health in China in 2023\u003c/em\u003e revealed that 19.05% of the elderly were in a state of mild depression, while 12.17% were in a state of moderate to severe depression. This indicated that the mental health of the elderly warrants significant attention, and mental health management should be an important part of health management for the elderly. Presently, researches pay more attention to the mental health of disabled elderly people [2\u0026ndash;4]. However, the mental health problems of self-care elderly should not be ignored either.\u003c/p\u003e \u003cp\u003eResearches have demonstrated that the CES-D scale is an effective depression screening tool for the elderly population [5\u0026ndash;7]. Nevertheless, a large-scale mental health screening was both inefficient and impractical. Developing a depression risk prediction model for self-care elderly individuals can effectively identify the high- risk groups of depression, conduct targeted depression screening, and enhance the efficiency of mental health management for self-care elderly.\u003c/p\u003e \u003cp\u003eSome studies have identified that self-rated health, personal basic characteristics, family factors, and social factors are the influencing factors of depression in the elderly. Self-rated health is a kind of subjective evaluation of health made by respondents according to their physical, psychological and social functions. It could effectively and reliably reflect the health status of the population[8\u0026ndash;9]. The physical health of the elderly would affect their mental health, and those with poor self-rated health were more likely to be depressed [10]. In terms of personal factors, gender has a statistically significant effect on depression in the elderly[11]. In terms of family factors, marital status and living style have a statistically significant impact on the depression of the elderly[12\u0026ndash;15]. In terms of social factors, the elderly with medical insurance has a lower risk of depression [16].\u003c/p\u003e \u003cp\u003eThis study attempted to establish a risk prediction model of depression in self-care elderly people based on the national data of CLHLS in 2018, in order to effectively identify high-risk groups of depression, provide reference for targeted screening of depression in the elderly, and improve the efficiency of mental health intervention for the elderly.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study population\u003c/h2\u003e \u003cp\u003eThis study used the data from China Longitudinal Healthy Longevity Survey (CLHLS) database in 2018, which was conducted by the research center for healthy aging and development of Peking University [17]. The CLHLS is extensively utilized in the field of elderly health, and the authority and quality of the data are highly recognized. The 2018 data was released to the public on April 3rd, 2020, and it was the latest publicly available data, making it the most recent and pertinent data available for the research objectives of this paper. This large-scale, nationwide public social survey data on the elderly exhibits excellent representativeness and timeliness.\u003c/p\u003e \u003cp\u003eThe scope of the investigation covered the elderly aged 65 and above across 23 provinces, cities, and autonomous regions nationwide (Note: A small number of residents under 60 years old, as well as those aged between 60 and 65, were also surveyed). The survey content comprised the basic characteristics of the elderly, their family situations, socio-economic conditions, and health status (including self-rated health, mental health, and daily self-care ability). The inclusion criteria for valid samples in this study were as follow: the elderly aged 60 and above who were capable of self-care; and the data were complete and valid for the dependent variable (each item of the CES\u0026minus;10 Depression Scale) and independent variables (such as self-rated health and basic characteristics of the elderly). The exclusion criteria were as follow: the individuals aged under 60 years old or age data missing, and those with missing or invalid data for the dependent and independent variables. According to the above standards, 5592 valid samples were included.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of depression\u003c/h3\u003e\n\u003cp\u003eThe measurement of depression utilized the CES\u0026minus;10 Depression Scale. This scale comprises 10 items, which include 8 positively worded items (e.g., Do you get annoyed by trivial matters? Is it difficult for you to concentrate on tasks? Do you feel sad and depressed?) and 2 negatively worded items (e.g., Are you hopeful about your future life? Do you feel as happy as you did in your youth?).\u003c/p\u003e \u003cp\u003eFor positively worded items, the value of each option was as follows: Rarely or Never\u0026thinsp;=\u0026thinsp;0, Sometimes\u0026thinsp;=\u0026thinsp;1, Often\u0026thinsp;=\u0026thinsp;2, Always\u0026thinsp;=\u0026thinsp;3. For negatively worded items, it was as follows: Always or Often\u0026thinsp;=\u0026thinsp;0, Sometimes\u0026thinsp;=\u0026thinsp;1, Rarely\u0026thinsp;=\u0026thinsp;2, Never\u0026thinsp;=\u0026thinsp;3. The scores of the 10 questions were summed. Referring to the research by WANG W et al. and TENG Jiashan et al. [18, 19], a cut-off point of 10 was used; individuals with a score of 10 or higher were classified as having depression, while those with a score below 10 were classified as not having depression. In this study, the scale demonstrates good reliability, with a Cronbach's α value of 0.706.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinition of variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe dependent variable was whether there is depression. The independent variables were gender, age, years of schooling, household registration type, current residential area, co-residence of interviewee, current marital status, main source of financial support, type of medical insurance, self-reported quality of life, and self-reported health. Among them, self-reported quality of life was measured by the question \"How do you think of your current life?\" (The options were very good, good, so so, bad, very bad). Self-reported health was measured by the question \"How do you think of your current health status?\" (The options were very good, good, so so, bad, very bad).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were analyzed using IBM SPSS Statistics 27.0. Categorical variables and ordinal variables were described using frequencies (percentages).\u003c/p\u003e \u003cp\u003eA univariate logistic regression model was developed. The assignments of variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Univariate analysis variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered potential independent variables and designated as candidate variables for the multivariate logistic regression model. Stepwise logistic regression analysis was carried out to develop the risk predictive model. Variables were considered eligible for inclusion through a likelihood ratio test(LRT) P\u0026thinsp;=\u0026thinsp;0.05 and removed at P\u0026thinsp;=\u0026thinsp;0.10. Hosmer-Lemeshow test was applied to assess the goodness-of-fit of the model, where a good fit was indicated by P\u0026thinsp;\u0026gt;\u0026thinsp;0.05. A c-index was determined to evaluate the predictive power of the logistic regression model. This index is the area under the ROC curve, which is a measure of predictive performance.The range from the least to the best predictive ability was 0.5\u0026minus;1.0.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssignments of variables in regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssignment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable (Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 no, 1 yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variables (X)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 male, 1 female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;60\u0026ndash;69, 2\u0026thinsp;=\u0026thinsp;70\u0026ndash;79,3\u0026thinsp;=\u0026thinsp;80\u0026ndash;89,4\u0026thinsp;=\u0026thinsp;90 or above\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;0year,2\u0026thinsp;=\u0026thinsp;1-3years,3\u0026thinsp;=\u0026thinsp;4-6years,4\u0026thinsp;=\u0026thinsp;7-9years,\u003c/p\u003e \u003cp\u003e5\u0026thinsp;=\u0026thinsp;10years or above\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold register type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 registered urban residents,1 registered rural residents\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent residential area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 City, 2 Town, 3 Rural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-residence of interviewee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 with household member(s), 2 alone, 3 in an institution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 currently married and living with spouse, 2 divorced(or separated), 3 widowed, 4 never married\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain source of financial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 retirement wages, 2 spouse, 3 child(ren),\u003c/p\u003e \u003cp\u003e4 grandchild(ren), 5 other relative(s), 6 local government or community, 7 work by self, 8 others\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you have public free medical services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 no, 1 yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you have urban employee/resident medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 no, 1 yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you have new rural cooperative medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 no, 1 yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you have commercial medical insurance at present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 no, 1 yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported quality of life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 very good, 2 good, 3 so so, 4 bad, 5 very bad\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 very good, 2 good, 3 so so, 4 bad, 5 very bad\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 9.8% of self-care elderly people suffer from depression. Among the sample, females account for 51.1%, slightly higher than males. The proportion of the elderly in the age group of 70–79 years old is the highest (34.1%). 71.1% of the elderly have rural household registration, far exceeding those with urban household registration (28.9%). In terms of co-residence of interviewee, the proportion of the elderly living with their families is the highest (80.0%). In terms of marital status, the proportion of the elderly who are married and living with their spouses is the highest (52.6%). The main sources of financial support for the elderly are their children (37.1%) and retirement wages (30.1%). The proportion of the elderly who have public free medical services, urban employee/resident medical insurance, new rural cooperative medical insurance and commercial medical insurance is 3.5%, 25.8%, 61.4%, and 0.6% respectively. The proportion of the elderly whose self-rated quality of life is bad and very bad is 1.9%. The proportion of the elderly whose self-rated health is bad and very bad is 8.1%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of the sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression(Yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(Female)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2860\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60–69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e993\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70–79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1905\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80–89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1492\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≧ 90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1202\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of schooling\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2130\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1−3years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4−6years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1353\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7−9years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10years or above\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e641\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold register type\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegistered urban residents\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1614\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegistered rural residents\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3978\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent residential area\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1307\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1875\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2410\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-residence of interviewee\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith household member(s)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4472\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e998\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn an institution\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent marital status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married and living with spouse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2944\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated or divorced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2477\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain source of financial support\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetirement wages\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1685\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild(ren)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2073\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandchild(ren)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther relative(s)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal government or community\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork by self\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e711\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic free medical services(yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban employee/resident medical insurance(yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1445\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew rural cooperative medical insurance(yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3434\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial medical insurance(yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported quality of life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1391\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2650\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSo so\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1445\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery bad\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported health\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e787\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSo so\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2094\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery bad\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e \u003cb\u003eUnivariate logistic regression analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the univariate logistic regression analysis revealed that gender, years of schooling, co-residence of interviewee, current marital status, main source of financial support, self-rated quality of life, and self-rated health had statistically significant impacts on the depression for self-care elderly people (P \u0026lt; 0.05). Females, those living alone, widowed or unmarried individuals, those financially depend on their children or government relief, those with a poor self-rated quality of life, and those with a poor self-rated health were more likely to suffer from depression. The elderly with longer years of schooling were inclined to have lower risk of depression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of depression in self-care elderly people\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(Female)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.481\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.238–1.773\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968–1.151\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of schooling\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e−0.121\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.830–0.946\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold register type (Registered rural residents as standard variable)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949–1.417\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent residential area(City as standard variable)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.254\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980–1.604\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.244\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.982–1.576\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-residence of interviewee(With household member(s) as standard variable)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.775\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.446–2.179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn an institution\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.364\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.774–2.404\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent marital status(Current married and living with spouse as standard variable)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated or divorced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e−0.448\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295–1.384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.513\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.263–1.811\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.829\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.289–6.209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain source of financial support(Retirement wages as standard variable)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.283\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.782–2.103\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild(ren)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.398\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.118–1.749\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandchild(ren)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.136\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984–4.636\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther relative(s)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.435\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.178–11.560\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal government or community\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.914\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.414–2.589\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork by self\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.725–1.382\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e−0.069\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.603–1.444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic free medical services (yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e−0.281\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.443–1.288\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban employee/resident medical insurance(yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e−0.098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.738–1.114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew rural cooperative medical insurance(yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.198\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996–1.441\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial medical(yes)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e−0.487\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147–2.579\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported quality of life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.916\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.577−3.300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported health\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.080\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.731–3.474\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultivariate risk prediction model of depression in self-care elderly people\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Logistic stepwise regression analysis reveal that the risk prediction model of depression encompasses four risk factors: gender, marital status, self-rated quality of life, and self-rated health. Specifically, the elderly who were female (OR = 1.260, P = 0.023), widowed (OR = 1.456, P \u0026lt; 0.001), reported a poor self-rated quality of life (OR = 1.995, P \u0026lt; 0.001), and reported a poor self-rated health status (OR = 2.418, P \u0026lt; 0.001) were more clined to suffer from depression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic stepwise regression of depression in self-care elderly people\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(Female)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.260\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.032–1.537\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent marital status (Current married and living with spouse as standard variable)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated or divorced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.452\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.287–1.413\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.456\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.194–1.775\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.851–4.724\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported quality of life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.995\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.743–2.284\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported health\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.418\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.113–2.766\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.511\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e—\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e—\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e—\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe c-index was 0.774(95% CI:0.754–0.795). ROC curve is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Hosmer-Lemeshow test showed that the goodness-of-fit of the model was 0.187(\u0026gt; 0.05), indicating good fit.The equation of the multivariate logistic regression risk adjustment model is as follows:\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003elogit(Pi) =−6.511 + 0.231Gender−0.452Current marital status1(1) + 0.376Current marital status(2) + 0.696Current marital status(3) + 0.691Self-reported quality of life + 0.883 Self-reported health.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eCurrent marital status(1) is separated or divorced; Current marital status(2) is widowed; Current marital status(3) is never married.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study finds that the risk prediction model of depression in self-care elderly people includes four influencing factors: gender, marital status, self-reported quality of life, and self-reported health. Elderly people who are female, widowed, report a poor self-reported quality of life, and report a poor self-reported health are more likely to suffer from depression.\u003c/p\u003e\u003cp\u003eCompared with the male, the female are more susceptible to depression. This is consistent with the research results of Wang Yue et al.[20]. The female often have to undertake the tedious things such as housework and caring for their grandchildren. As a result, they are more likely to have negative emotions such as anxiety, irritability. Marital status also significantly influences the prevalence of depression among the elderly. Compared with the elderly married and living with spouse, the widowed are more likely to suffer from depression, which are similar to the research results of Liu Min et al. [21]. The elderly married and living with spouse can receive more social support, they can get emotional and financial support from their spouses [22].\u003c/p\u003e\u003cp\u003eSelf-rated quality of life is an important influencing factor for depression in the elderly. The elderly have worse self-rated quality of life are more likely to suffer from depression, which is consistent with the research results of Yang Juan et al [23]. Self-rated quality of life is the subjective evaluation of the objective quality of life by the elderly, which not only reflects the actual quality of life of the elderly, but also reflects their mental state. The elderly with a poor self-rated quality of life may have a poor actual quality of life, or they may have overly high requirements for quality of life. Both of these situations may increase the negative emotions of the elderly.\u003c/p\u003e\u003cp\u003eSelf-rated health is the subjective evaluation of their current health status by the elderly, which can largely reflect the elderly’s objective health condition [22]. The elderly have their own evaluation criteria and expectations for health. Most of them tend to evaluate their own health status after comparing it with that of the elderly around them. According to the social comparison theory [24], after comparing themselves with others, the elderly will make positive or negative self-judgments. Elderly people with a good self-rated health level tend to achieve a better result when comparing with others and often maintain a more positive psychological state. On the contrary, if the elderly have a poor self-rated health, they usually have an unfavorable result in the comparison with others, and are prone to negative emotions such as frustration and depression, thus the risk of depression will increase. In conclusion, during the process of health management for the elderly, the elderly who are female, widowed, have a poor self-rated quality of life, have a poor self-rated health should be paid attention to as key groups of depression.\u003c/p\u003e \u003cp\u003eCurrently, the proportion of the elderly suffering from depression in China remains quite high. Identifying the risk factors for depression in the elderly is of great significance for improving their mental health and reducing the disease burden. By constructing a depression risk prediction model in self-care elderly people, this study has found four risk factors, namely: gender, marital status, self-rated quality of life, and self-rated health. The data collection process for these indicators is simple and convenient, without the need for complex professional instruments or cumbersome detection procedures.\u003c/p\u003e \u003cp\u003eAt present, the health records for the elderly are established in the primary healthcare institutions in China. In the process of health management for the elderly, the collection of the four indicators mentioned above can effectively identify the high - risk groups for depression, and carry out targeted depression screening and mental health interventions. The construction of a convenient and efficient depression risk prediction model for the elderly can also help community workers and even family members to timely detect the elderly's depressive tendencies. Then, through personalized psychological counseling, strengthening of social support networks, and guidance on healthy lifestyles and other intervention measures, the depression can be effectively prevented and alleviated.\u003c/p\u003e\u003cp\u003eThere are some limitations in this study.This study attempts to construct a risk prediction model for depression in self-care elderly people, and there are certain limitations in its applicability to disabled elderly people and semi-disabled elderly people.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe risk predictive model for depression in self-care elderly people included four risk factors, namely, gender, marital status, self-rated quality of life, and self-rated health. The elderly who are female, widowed, have a poor self-rated quality of life, have a poor self-rated health should be paid attention to as key groups of depression. In the process of health management for the elderly, the general practitioner in the primary healthcare institutions should collect the four indicators mentioned above, and identify high - risk groups of depression through the risk prediction model. And then implement targeted depression screening and comprehensive mental health management.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethics approval was obtained from Ethics Committee of the Institute of Environmental and Health-Related Product Safety of China Center for Disease Control and Prevention. All methods were carried out in accordance with Helsinki Declaration. All participants provided informed written consent.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China\u003c/p\u003e \u003cp\u003e(72364016, 72574088).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXW designed the research. XW and JL collected the article\u0026rsquo;s data and conducted data analysis. XW and HY has carried out the preliminary drafting of the article. LL participated in the supervision and editing of this study. All authors of this study have contributed to this article and have unanimously agreed to its publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the research center for healthy aging and development of Peking University for granting access to the data. The authors are also grateful for the data management team for providing the data set.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data involved in our study can be found in the CLHLS database (https://doi.org/10.18170/DVN/WBO7LK), For additional inquiries, please contact the data management team of research center for healthy aging and development of Peking University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu YS, Zhao XY. An interpretation of The Opinions of the CPC Central Committee and the State Council on Strengthening the Work on Aging in the New Era: A programmatic document for promoting the high-quality development of elderly work in the new era. Admin Reform. 2022;(4):9\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang YJ, Zhang T, Zhao PW, Liu J, Li J. Investigation on mental health and care dependence of semi-disabled elderly and their relationship with health rights empowerment of primary caregivers. China Journal of Health Psychology. 2023; 31(10): 1484\u0026ndash;1488.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Liu ZJ, Hu YL. Supply-demand matching and satisfaction of home care services for disabled elderly in rural areas and their impact on mental health: An analysis of survey data from Shandong and Zhejiang. Popul Dev. 2022;28(5):70\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen SZ, Zong ZH. 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A theory of social comparison processes. Hum Relat. 1954;7(2):117\u0026ndash;140.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"depression, risk predictive model, self-care elderly people, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-8101254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8101254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e In order to cope with the problem of population aging, China has put forward the strategy of healthy aging. Strengthening the health management of the elderly is an important measure to achieve healthy aging, and the mental health of the elderly needs special attention. The aim of the present study is to develop a risk predictive model for depression among self-care elderly individuals, so as to efficiently identify high-risk groups for depression and implement targeted screening and mental health management.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e From the data of China Longitudinal Healthy Longevity Survey (CLHLS) in 2018, a total of 5,592 valid samples of self-care elderly aged over 60 were selected. Depression was measured using the CES\u0026minus;10 depression scale, with a score of 10 or higher being considered to have depression. A binary forward stepwise regression analysis was used to construct a risk prediction model of depression among self-care elderly people, and the c-index was used to assess the predictive power.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e Among the 5,592 self-care elderly people, 9.8% suffered from depression. The risk predictive model for depression included four risk factors, namely, gender, marital status, self-rated quality of life, and self-rated health. Compared with the males, females were more likely to suffer from depression(OR\u0026thinsp;=\u0026thinsp;1.481, P\u0026thinsp;=\u0026thinsp;0.023); compared with those current married and living with spouse, the widowed were more likely to suffer from depression(OR\u0026thinsp;=\u0026thinsp;1.513, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); those with poorer self-rated quality of life (OR\u0026thinsp;=\u0026thinsp;2.916, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), poorer self-rated health status (OR\u0026thinsp;=\u0026thinsp;3.080, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were more likely to suffer from depression. The Hosmer-Lemeshow test showed that P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, and the ROC index was 0.774; which meant that the model has good fitting degree and good prediction effect.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e The development of a risk prediction model of depression among self-care elderly is instrumental in accurately identifying high - risk groups of depression, which facilitate targeted depression screening and comprehensive mental health management.\u003c/p\u003e","manuscriptTitle":"Exploring a risk predictive model for depression in self-care elderly people: a national cross-sectional study from CLHLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 13:16:13","doi":"10.21203/rs.3.rs-8101254/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-30T04:58:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T10:03:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-14T07:42:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-14T07:41:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-13T04:06:27+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":"ae2c8e09-948e-40d2-9f2b-262348ea3f1e","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T13:16:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 13:16:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8101254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8101254","identity":"rs-8101254","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}