Influencing factors of depressive symptoms in the elderly in rural China: Based on a health ecological model

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Studies have found that social support is strongly associated with depression in rural older people, so it is important to explore the factors influencing depression in rural older people in a comprehensive manner and to analyze the association between social support and depression. Methods On the basis of a theoretical model of health ecological, data were obtained from The China Health and Retirement Longitudinal Study in the 2018, with a sample consisting of 5,660 rural individuals aged ≥ 60 years. Then, Chi-square test and logistic regression analyses were used for statistical description and inference. Results Results indicate that the prevalence of depressive symptoms amongst rural older adults in China is 41.18%. The logistic regression analysis reveals that being female ( OR = 1.406, 95% CI : 1.170–1.689), having ≥ 3 non-communicable diseases ( OR = 1.736, 95% CI : 1.447–2.082), being not satisfied with spouse ( OR = 2.978, 95% CI : 2.304–3.849), and being not at all satisfied with children ( OR = 3.640, 95% CI : 1.736–7.635) are significantly correlated with depression. Conclusions The prevalence of depression amongst rural Chinese older adults is obviously high. Women and the elderly with chronic diseases need to be focused on. Hence, this study suggests that promoting interactivity amongst family members, increasing their relationship satisfaction, and encouraging active participation in social activities are necessary to further reduce the risk of depression amongst rural Chinese older adults. The government should not only improve the social security system, but also provide financial support and assistance to the elderly in rural China. social support depressive symptoms elderly rural CHARLS Figures Figure 1 Introduction Population ageing is a global demographic trend that has been exacerbated in recent decades by increasing life expectancy and declining fertility rates. China is no exception to this trend, according to the 2021 data of China’s National Bureau of Statistics, with18.70% of its population being over 60 years of age. Previous reports also predicted that China could become a deeply ageing society by 2021. The increasing degree of ageing and the expanding size of the older population have brought great impact to society, resulting in the increased prominence of various problems related to older adults. Therefore, improving the quality of life of this population and achieving healthy ageing have become urgent issues in today’s society. Depression is one of the most common mental health disorders amongst older people and is expected to jump to the number one disease burden by 2030 [ 1 ]. Approximately 350 million people suffer from depression worldwide, and over 95 million people suffer from depression in China. Compared with the general population, people with depression have a 20-fold higher risk of suicide and a 60% increased risk of cardiovascular disease [ 2 ]. Depression has been identified as a risk factor for death, poor health, and disability in older adults [ 3 ]. There is a disparity in socioeconomic development between urban and rural China. For example, urban residents born between 1940 and 1989 are 4.9 times more likely to attend elementary school than rural residents. In 2013, government investment in health care in rural areas was only one-third of that in urban areas. Moreover, 54.8% of older adults in urban areas in China participate in social activities, and this percentage is nearly 10% higher than those in rural areas [ 4 ]. In addition, urbanization is affecting the health of the population by changing the social environment, and the imbalance in economic development between rural and urban areas has led to a much higher prevalence of depression among older people in rural areas than in urban areas, some studies have shown that the prevalence of depression among older people in rural areas is 1.88 times higher than in urban areas [ 5 ]. As such, it is important to focus on the mental health of rural older adults. Depression in older adults may be influenced by a variety of factors that can be categorised as biological, genetic, and abnormal receptor expression, sociodemographic factors and so on. In detail, general demographic characteristics include domicile, age, gender, marital status, and education level [ 6 ]. Health status includes the type and number of non-communicable diseases (NCDs), functional disability, and pain [ 7 ]. Health behaviours include physical activity, smoking, alcohol consumption, and sleep schedules [ 8 ]. In addition, a growing body of research has found that social support networks have a direct impact on the mental health of older adults [ 9 ]. Nan proposed social support as perceived or actual instrumental or expressive support provided by communities, social networks, and trusted partners, a concept that provides information about the types and sources of social support that has been well applied in social support research in China [ 10 ]. There is substantial evidence explaining that high-quality social support can improve the mental health of older adults [ 11 ]. Some studies have found that older people who live alone and are widowed are more likely to experience depressive symptoms and that an older spouse can provide not only mutual help in daily care but also more emotional support [ 12 ], moreover, emotional support, as an important part of social support, can effectively reduce the level of depression in older adults [ 13 ]. As for social activities, research studies conducted in Korea and China have shown that participation in two or three social activities, donations, volunteer work, and physical exercise can effectively prevent and alleviate the development of depression in older adults [14; 15; 16]. In terms of community support, Yan-Yan Chen suggested that increasing the accessibility of community infrastructure could help improve common depressive symptoms in older adults [ 17 ]. Hu suggested that the government provision of free or discounted community services for older adults and the design of community-based preventive mental health service centres would promote recreation, social interaction, and social contact for older adults to alleviate depression [ 18 ]. Although numerous studies have concluded that differences in social support have been identified as important determinants of depressive symptoms, they have mainly focused on specific populations in a particular region or examined the association between single dimensions, such as socioeconomic well-being, childcare, and depression [19; 20]. Little is known about the relationship between different dimensions of social support and depression in older adults in rural China. Theory And Hypotheses At present, there are many theories about the influencing factors of depression, such as social determinants of health, social ecology theory proposed by Bronfenbrenne, Grossman's theory of health production, Anderson theory, health ecological model and so on [21; 22]. Among them, the health ecological model proposed by Collins in 2002, which incorporates ecological theory, argues that the factors affecting the health of a population are multidimensional, it can be divided into five main dimensions: personal characteristics, health behaviours, interpersonal networks, living and working conditions and policy environment (Fig. 1 ) [ 23 ]. Based on this theoretical model, Yao Liu et al. explored the factors affecting the adherence to eye screening in patients with diabetes in rural communities [ 24 ]. Lei and colleagues took middle-aged and elderly patients with chronic diseases as research objects, to explore the influencing factors of depression [ 25 ]. Different from previous studies, based on this theoretical model and using a nationally representative dataset from the 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS), this study aims to understand the prevalence of depression among older adults in rural China, conduct a comprehensive and systematic analysis of the factors influencing depression in this population, with a focus on the associations between different dimensions of social support and depressive symptoms. To provide empirical evidence for improving the mental health of the elderly in rural China, and to provide reference for other countries facing similar situations. Based on the above, our main hypothesis is that high-quality social support can reduce depression amongst rural older adults. By communicating with children, increasing couple satisfaction, and actively participating in social activities, rural older adults are likely to relieve their depression. Methods Data Data were derived from CHARLS organised by the Academy of Social Sciences of Peking University. A baseline survey of these data was conducted in 2011, and a follow-up was conducted every 2 ~ 3 years. CHARLS, employing a multistage stratification with a probability proportional to size sampling method, collected high-quality data from a nationally representative sample of Chinese residents aged 45 years and above from 28 provinces, municipal cities, and autonomous regions. This study used the latest follow-up data from 2018. The study design and sampling procedure of CHARLS can be found through the Open Research Data Platform of Peking University ( http://charls.pku.edu.cn/ ) [ 26 ]. The inclusion criteria covered 7,538 rural Chinese aged 60 years and above; those who had deleted key variables and reported incomplete social support and depression related programs during the survey, emotional or mental problems, and memory-related diseases (Alzheimer’s disease, brain atrophy, and Parkinson’s disease) were excluded, resulting in the removal of 1,878 cases. Censored data were supplemented with the 2013 and 2015 and wave data. Ultimately, 5,660 participants (2,856 men and 2,804 women) were enrolled in the study. Variables Dependent Variable Depressive symptoms were measured using the short form of the Centre for Epidemiological Studies Depression Scale (CES-D), which is a useful mental health measure for Chinese older people [27; 28]. It contains 10 items: (1) troubled by trivial matters, (2) troubled by inattention, (3) feeling frustrated, (4) laborious to do anything, (5) full of hope, (6) feeling fear, (7) poor sleep, (8) feeling happy, (9) feeling lonely, and (10) unable to continue living. Depressive symptoms over the past week were measured from 0 (little or no time [< 1 d]) to 3 (most or all the time [5–7 days]). Before summing up the item scores, the scores for questions 5 and 8 were converted. Thus, the total score of the scale ranged from 0 to 30, with a score of 10 or more indicating reasonable levels of sensitivity and specificity amongst Chinese older adults [ 13 ]. Thus, respondents with scores ≥ 10 or those who were taking antidepressant medication were considered to have depressive symptoms [ 29 ]. Independent variable This study selected appropriate variables for each layer under the guidance of a model of health ecological. Personal characteristics This variable included age (60–69, 70–79, and ≥ 80 years), gender (male or female), health satisfaction (completely satisfied, very satisfied, somewhat satisfied, not very satisfied, not at all satisfied), and number of NCDs (0, 1–2, or ≥ 3). Activities of daily living (ADL) were measured using a six-item scale in CHARLS that included shopping, doing household work, making phone calls, taking medications, preparing hot meals, and managing money. The scores were 0 for no difficulty and 1 for having difficulty in each activity. A total score greater than 6 was classified as having difficulty in daily activities, and a score less than or equal to 6 was classified as not having such difficulty. Health behaviour This variable included smoking status (yes or no), alcohol consumption (yes or no), and sleep duration (< 6 hours or ≥ 6 hours). Interpersonal networks : This study uses different dimensions of social support: family support, participation in social activities and community support to measure access to interpersonal networks [ 30 ]. (1) Family support This includes whether the children visit their parents monthly (yes or no), whether the children contact their parents by phone/message/on wechat /by mail/ by email monthly (yes or no), whether the participants take care of grandchildren, whether the participants receive financial support from children (yes or no), whether the participants offer financial support for children (yes or no), their satisfaction with their marriage (very satisfied, somewhat satisfied, not satisfied, no spouse), and their satisfaction with their children (very satisfied, somewhat satisfied, not satisfied, no child). (2) Social activities This includes whether the participants visit a community club, engage in sports, social, use the internet, and join other social activities (yes or no). (3) Community support : This includes community infrastructure and resources, which was measured by the following items: whether the village/community has outside exercising facilities (yes or no), whether the village/community has a dancing team or other exercise groups (yes or no), whether the village/community has organisations to help the older adults and those with disabilities (yes or no), and whether the village/community has an association for older adults (yes or no). Living and working conditions The combined concept of socio-economic status can be used to measure living and working conditions, and existing studies typically use education levels. income and expenditure to measure socio-economic status [ 31 ]. Education level was classified as illiterate, primary school and below, or junior school and above. Monthly household expenditures were < 500, 500–2,000, and ≥ 2,000. Policy environment Public support was proxied by social security and welfare for older people, referring to whether the participants have pensions (yes or no) and health insurance (yes or no). Analysis The data were expressed as a percentage of the classification value. Firstly, a chi-square test was used to explore the differences in depressive symptoms among rural older people with different characteristics. Secondly, based on the results of the univariate analysis, model 1,2,3,4 was constructed using logistic regression based on the health ecological model to analyse the relationship between personal characteristics, health behaviour, interpersonal networks, living and working environment and depressive symptoms. The level of statistical significance was P < 0.05 (two-sided). All analyses were conducted using SPSS 26.0 software. Results The Characteristic of the Samples The sociodemographic characteristics of the 5,660 older adult respondents were presented in table 1. The average age was 67.90 years (range = 60–108, SD = 6.00), with those aged between 60 and 69 years totalling 3,775 (66.70%). The number of male respondents was 2,856 (50.46%), most of the participants were illiterate at 3,267 (57.72%), former smoking was 2,532(44.73%), or drinking in the past was1,855 (32.77%), 3,711(65.57%) participants slept ≥6 hours at night, many of them had one or two NCDs (49.06%), self-rated health satisfaction was 2,533(44.75%) for somewhat satisfied, a total of 2,133 (37.33%) subjects scored more than 6 in ADL. Overall, 50.02% of the respondents have a monthly household expenditure of 500-2000 yuan. Association Between Sociodemographic Characteristics and Depressive Symptoms The mean CESD-10 score was 9.21 (SD = 6.722) and ranged from 0 to 30. Specifically, 2,331 participants (41.18%) demonstrated depressive symptoms. This included 398 (7.0%) with a score of 0 and 12 (0.2%) with a score of 30. Statistically significant differences were observed in the incidence of depressive symptoms amongst the subgroups. For example, the rate was higher in females (49.47%) than in males (33.05%) ( P ＜0.001), in participants with a lower level of education than in those with a high level of education ( P ＜0.001), in those with three or more NCDs (53.92%) than in those with no chronic disease (26.94%) ( P ＜0.001), in those with more difficulties in daily living activities (58.21%) than in those without (31.04%) ( P ＜0.001), , and in participants who had <500 monthly household expenditures (47.45%) than in those with ≥2,000 monthly household expenditures (41.18%) ( P ＜0.001). However, the age( P =0.086), sleep hours( P =0.879), health insurance ( P =0.076) and pension( P =0.680) were not significantly different between the two groups of participants. The results are presented in Table 2. Association Between Social Support and Depressive Symptoms Table 3 illustrates the relationship between participants’ social support and depressive symptoms. There were statistically significant differences in the prevalence of depressive symptoms between the different social support dimensions: family support, social activities, and community support subgroups ( P < 0.05). For example, the prevalence of depressive symptoms amongst parents whose children visited or contracted monthly both were significantly lower than amongst those who did not receive such visit or contract (39.94% vs. 47.78%; 40.48% vs.50.77%, all P < 0.001). Participants who were very satisfied with their marital status had a significantly lower risk of developing depressive symptoms than those who did not satisfied (30.78% vs. 74.36%, P < 0.001). Participants who participated in some social activities were less likely to suffer from depression than those who did not participate( P ＜0.05). Older people living with community support are less likely to experience depressive symptoms than those without some community support ( P < 0.001). Regression analysis on influencing factors of the key variables Variables that were statistically significant in univariate analysis and had an impact on depressive symptoms in previous studies were included in the regression analysis. Based on the health ecological model, model 1,2,3,4 was constructed and after adjusting all the variables in model 4, the results showed that: participants were at higher risk of depressive symptoms among females ( OR =1.406, 95% CI : 1.170– 1.689）, with NCDS ≥3 ( OR =1.736, 95% CI :1.447–2.082), and with limited ability to living daily activities ( OR = 1.968, 95% CI : 1.726–2.244). The elderly giving financial support to their children was significantly associated with a decrease in the prevalence of depressive symptoms ( OR =0.792, 95% CI : 0.657–0.954). Participants who were not satisfied ( OR =2.978, 95% CI : 2.304−3.849) or did not have a spouse ( OR =1.423, 95% CI : 1.150−1.761) were significantly more likely to have depressive symptoms than those who were very satisfied with their marital status. Compared to those who were not participated in sport or social activities, subjects who were participated had a significant lower to have depressive symptoms ( OR =0.662, 95% CI : 0.480−0.913). The degree of fit of the regression model improved gradually with the addition of each factor at each level. Model 4 accounted for 27.7% of the total variation, as shown in Table 4. Disscussion The aim of this study was to investigate the prevalence of depressive symptoms in rural older people in China, to clarify the factors influencing depressive symptoms in this population based on health ecological theory, and to focus on the association between different social support dimensions and depressive symptoms. The findings here may provide a theoretical basis for intervention strategies and measures to improve depressive symptoms in rural older people. The prevalence of depression in the rural older adults was 41.18%, which is consistent with that in previous population-based studies that reported a 38.47–46.15% prevalence of depression in rural older adults, including the works by Fang (CHARLS, 2015) [ 32 ] and Li (CHARLS, 2011) in the context of China [ 33 ]. From a cross-sectional comparison perspective, the prevalence of depression amongst rural older adults in the study was higher than that in the studies conducted by Abdin [ 34 ] on older adults in Singapore (3.7%), Hong Kong, China (17.8%), and urban China (23.5%). This may be related to the fact that developed areas have better infrastructure, advanced medical technology, and better social support and welfare than rural areas. The impact of personal characteristics on depressive symptoms in the rural elderly The results of this study showed that rural older adults aged over 80 years were less likely than rural older adults aged 60–70 years to have depression ( OR = 0.710, 95% CI : 0.534–0.943). By contrast, a study by Tazelaar showed that age is positively associated with depression symptoms and that symptoms increase with age [ 35 ]. Meanwhile, the study by El-Gilany revealed that people over 85 years of age are less likely to have depression than those aged 60–75 years ( OR = 0.2, 95% CI : 0.1–0.9) [ 36 ]. The present study also showed that depression is significantly higher in women than in men ( OR = 1.406, 95% CI :1.170–1.689). This result was comparable to various other studies. For India, Pilania noted a significant correlation between depression in rural older adults and depression in women ( OR = 2.7, 95% CI : 1.4–5.0) [ 37 ]. A study by Altun for Turkish older adults showed that older female adults are 2.53 times more likely to suffer from depression than older male adults ( OR = 2.53, 95% CI : 1.17–5.50) [ 38 ]. Similarly, El-Gilany (2018) concluded that the prevalence of depression is significantly higher in women than in men (51.3% and 38.6%, respectively) [ 36 ]. These gender differences are likely a result of a myriad of factors, including biological, social, demographic, and psychological effects [ 39 ]. Older women have a longer life expectancy, are more vulnerable to health problems and negative life events, and are prone to feelings of loneliness and helplessness [ 40 ]. Moreover, differences in the perceived level and availability of social support tend to contribute to differences in depression levels between men and women [ 41 ]. Our study found that (1) participants with difficulties in daily activities and rural older adults with two or more NCDs were more likely to have depressive symptoms and that (2) older adults who rated their health as ‘not at all satisfied’ were much more likely to have depressive symptoms than those who rated their health as “completely satisfied” ( OR = 6.333; 95% CI : 4.050–9.902). Similar results were reported by Yu-Han Bi in a 2021 study that found a dose-response relationship between the incidence of depression and the number of chronic diseases [ 42 ]. Furthermore, this finding is consistent with a meta-analysis by Read based on data from 40 studies, which revealed that people with multiple disorders have twice the risk of developing depression [ 43 ]. Impaired physical health reduces the quality of life of older adults and limits their social reach. At the same time, high medical costs increase their psychological burden and likelihood of developing depressive symptoms. Therefore, for the elderly with physical diseases, the government should improve the medical security system, encourage patients to actively seek medical treatment, organize volunteers to provide them with instrumental support, and improve their daily activities to alleviate their negative emotions. The impact of interpersonal networks on depressive symptoms in the rural elderly Emotional support and nice parent–child relationships provided by visiting parents help alleviate depression in older adults [ 44 ]. Previous studies have used the number of children and grandchildren to measure children's support for their parents [ 18 ]. Both objective and subjective perspectives were used in this investigation: whether children visit their parents monthly and parents' ratings of their satisfaction with their children. The results of this study showed that parents who received monthly child visits were 0.769 times less likely to have depressive symptoms than those who did not receive such visits (95% CI : 0.649–0.913). Depression was significantly lower amongst older adults who had good relationships with their children than amongst those who had bad relationships with their children. This may be due to the fact that older adults in rural areas are limited by socioeconomic conditions and their own circumstances; they have a single way to obtain emotional support and are less involved in entertainment and interactive activities. Resulting in few sources of social support in daily life, other than visits from children [ 45 ]. Hence, it is important to appropriately increase the frequency of children’s meetings with older adults and encourage more communication channels and opportunities [ 46 ]. Increasing the level of perceived social support obviously alleviates depression amongst older adults [47; 48]. Improving satisfaction with marital relationships amongst rural older adults would greatly alleviate their depression as well. Consistent with the findings of numerous studies [ 39 ], rural older adults with spouses were less likely to suffer from depression than those without spouses. Spouses are a major buffer against depressive symptoms, assisting with various daily activities and providing an emotional outlet [ 49 ]. Evidence suggests that a good relationship with a spouse reduces the risk of depressive symptoms later in life than other social relationships [ 50 ]. Therefore, the present study further explored the effect of marital satisfaction on depression on this basis [ 51 ]. The results of the study showed that people without a spouse were 1.423 times more likely to be depressed than older adults who were very satisfied with their marital relationship, and those who were dissatisfied with their marital relationship were 2.978 times more likely to be depressed than those who were very satisfied with their marital relationship. Therefore, we hypothesise that in addition to having family members who provide instrumental support and take care of each other’s basic needs, it is important to maintain marital harmony in later life, enhance marital happiness [ 52 ], provide emotional support, and increase marital relationship satisfaction. Family financial support has a significant effect on depressive symptoms amongst rural older adults. The results of the study showed that parents who provided financial support to their children were less likely to experience depressive symptoms than older adults who did not provide financial support to their children. Financial support provided by rural older people for their children has a beneficial effect on their mental health. First of all, it can maintain the status of the family. With the development of the market economy, the traditional family status of rural parents is threatened. Providing some economic support to children can protect the status of parents in the family to a certain extent, and the increased status of the family can improve health. Secondly, the father's generation can exchange more care and support from their children through material wealth or economic and social resources to reduce the prevalence of depression among the rural elderly [ 53 ]. In terms of financial support from children to parents, we found that participants who received financial support from their children were more likely to develop depression. This finding is consistent with the study by Ranran Zheng et al. who found that although financial support from children can improve the quality of life of parents, it can lead to parents perceiving themselves as “useless and worthless”, which instead increases the psychological burden of the elderly and increases the tendency of depressive symptoms[ 54 ].Therefore, to some extent, we suggest that parents and children establish two-way resource exchange to form a reciprocal association system and improve the mental health of the rural elderly. Participation in physical exercise and social activities can prevent future depression [55; 56; 57]. This study showed that rural older adults who participated in physical and social activities were 33.8% less likely to suffer from depression; this result is consistent with Hyun’s study of older adults in Korea[ 58 ], in which physical activity was found to improve mental and emotional well-being and exert a positive impact on mental health [ 59 ]. A meta-analysis of prospective studies found that people who are more physically active are 17% less likely to develop depression than those who are less physically active (95% CI : 12–21%) [ 60 ]. Older adults with extensive social activities have greater access to various forms of material resources and health-related information, can achieve a sense of belonging and security through social interaction, and can enhance social connectedness, all of which contribute to the alleviation of depressive symptoms [ 61 ]. Therefore, it is recommended that communities enrich the construction of basic physical exercise facilities, organise group activities and encourage older people to participate in social activities such as physical exercise. The impact of living and working conditions on depressive symptoms in the rural elderly Indirect measurement of living and working conditions through socio-economic indicators: household expenditure and educational attainment. These findings suggest that rural older adults with higher monthly household expenditures and higher levels of education are less likely to experience depressive symptoms. This is consistent with previous findings by Lotfaliany in a community-based cohort of older adults in six countries.[ 62 ]. Higher socioeconomic status can reduce the incidence of depression by reducing unhealthy lifestyle habits and promoting the development of interpersonal relationships and social networks. This suggests that the government should provide certain financial assistance to the rural elderly to alleviate the impact on mental health due to economic stress, so as to achieve the purpose of building a healthy aging society through multiple channels. Conclusion In conclusion, the prevalence of depression among the elderly in rural China is not optimistic, women and the elderly with chronic diseases need to be focused on, and joint efforts from families, communities, and the government are needed to reduce the risk of depression. First of all, it is necessary to promote the two-way interaction between children and parents, and improve the relationship satisfaction between spouses and between parents and children. Secondly, the community should improve the configuration of infrastructure, encourage the elderly to actively participate in social activities, and improve interpersonal skills. Thirdly, in order to better reduce the social burden caused by depression, the government should not only improve the social security system, but also provide financial support and assistance to the elderly in rural areas. Limitations This study has certain limitations. Firstly, there is an inherent limitation for cross-sectional studies. In other words, it is impossible to infer a causal relationship between the coexisting risk factors and diseases. Secondly, most of the data, including depressive symptoms, were based on self-reported information that was not validated for diagnosis. Thirdly, owing to data limitations, the construction of family and public support indices was based on availability rather than connection. More rigorous scientific studies are required to address these limitations. Declarations Ethics approval and consent to participate: The studies involving human participants were reviewed and approved by the Institutional Review Board at Peking University (IRB00001052-11015). The patients/participants provided their written informed consent to participate in this study. RF registered the platform and obtained permission to use the data. Consent for publication Not applicable. Availability of data and materials The study design and sampling procedure of CHARLS can be found through the Open Research Data Platform of Peking University ( http://charls.pku.edu.cn/ ). Competing interests The authors declare that they have no competing interests" in this section. Funding This research was founded by the Young Innovative Talents Projects of Shihezi University, and the title of the project is The impact of poverty on the health of the population and the evaluation of the implementation effect of the health poverty alleviation policy in the southern region of the Xinjiang Production and Construction Corps (NO. CXBJ202005). Authors' contributions Rong Fan: Conceptualization, Investigation Data, Formal analysis, Writing original draft. Xiaoju Li: Conceptualization, Methodology. Jiaxin Dong: Writing - Review & Editing, Jielin Yang: Conceptualization, Resources, Li Zhao: Writing - Review & Editing, Xianqi Zhang: Writing - Review & Editing. All authors contributed to the article and approved the submitted version. Acknowledgements The authors thank Peking University for making the China Health and Retirement Longitudinal Study publicly available for academic use. References Wang J, Wu X, Lai W, Long E, Zhang X, Li W. Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis. BMJ open. 2017;7(8):e017173. doi: 10.1111/nyas.14506 . Zhang Y, Chen Y, Ma L. Depression and cardiovascular disease in elderly: Current understanding. J Clin Neurosci. 2018;47:1–5. doi: 10.1016/j.jocn.2017.09.022 . Nakamura T, Tsuji T, Nagamine Y, Ide K, Jeong S, Miyaguni Y. Suicide Rates. Social Capital, and Depressive Symptoms among Older Adults in Japan: An Ecological Study. Int J Environ Res Public Health.(2019) 16(24):4942. doi: 10.3390/ijerph16244942 . Liu J, Rozelle S, Xu Q, Yu N, Zhou T. Social Engagement and Elderly Health in China: Evidence from the China Health and Retirement Longitudinal Survey (CHARLS). Int J Environ Res Public Health. 2019;16(2):278. doi: 10.3390/ijerph16020278 . Liu H, Fan X, Luo H, Zhou Z, Shen C, Hu N. Comparison of Depressive Symptoms and Its Influencing Factors among the Elderly in Urban and Rural Areas: Evidence from the China Health and Retirement Longitudinal Study (CHARLS). Int J Environ Res Public Health. (2021)1 8(8):3886. doi: 10.3390/ijerph18083886 . Kim BJ, Kihl T. Suicidal ideation associated with depression and social support: a survey-based analysis of older adults in South Korea. BMC psychiatry. 2021;21(1):409. doi: 10.1186/s12888-021-03423-8 . Feng QW, Wang QN, Yang LS, Liu KY, Cao HJ, Xie SY. The association between depressive symptoms and chronic diseases among elderly people in urban and rural communities of Lu'an City. Mod Prev Med. 2018;45(20):3731–4 + 3747. Harithasan D, Mukari SZS, Ishak WS, Shahar S, Yeong WL. The impact of sensory impairment on cognitive performance, quality of life, depression, and loneliness among older adults. Int J geriatric psychiatry. 2019;35(4):358–64. doi: 10.1002/gps.5237 . Kim B, Jun H, Lee J, Kim YM. Social Support. Activities of Daily Living, and Depression among Older Japanese and Koreans Immigrants in the U.S. Soc Work Public Health. 2020;35(4):163–76. doi: 10.1080/19371918.2020.1761922 . Lin N, Wellman B, Dean A, Ensel WM. Social Support, Life Events, and Depression. Contemp Sociol. 1988;17(2):237. doi: 10.2307/2070619 . Anantapong K, Wiwattanaworaset P, Sriplung H. Association between Social Support and Frailty among Older People with Depressive Disorders. Clin Gerontol. 2020;43(4):400–10. doi: 10.1080/07317115.2020.1728002 . Srivastava S, Debnath P, Shri N, Muhammad T. The association of widowhood and living alone with depression among older adults in India. Sci Rep. 2021;11(1):21641. doi: 10.1038/s41598-021-01238-x . Walther A, Philipp M, Lozza N, Ehlert U, Emotional, Support. Depressive Symptoms, and Age-Related Alterations in Male Body Composition: Cross-Sectional Findings from the Men's Health 40 + Study. Front Psychol. 2017;8:1075. doi: 10.3389/fpsyg.2017.01075 . Choi E, Han KM, Chang J, Lee YJ, Choi KW, Han C. Social participation and depressive symptoms in community-dwelling older adults: Emotional social support as a mediator. J Psychiatr Res. 2021;5(137):589–96. doi: 10.1016/j.jpsychires.2020.10.043 . Fang L, Fang CL, Fang SH. A study of regular exercise, social support and depression in the community-dwelling older people. Int J Nurs Pract. 2022;28(4):e12954. doi: 10.1111/ijn.12954 . Wang J, Xu J, Nie Y, Pan P, Zhang X, Li Y. Effects of Social Participation and Its Diversity, Frequency, and Type on Depression in Middle-Aged and Older Persons: Evidence From China. Front Psychiatry. 2022;25(13):825460. doi: 10.3389/fpsyt.2022.825460 . Chen YY, Wong GH, Lum TY, Lou VW, Ho AH, Luo H. Neighborhood support network, perceived proximity to community facilities and depressive symptoms among low socioeconomic status Chinese elders. Aging Ment Health. 2015;20(4):423–31. doi: 10.1080/13607863.2015.1018867 . Hu H, Cao Q, Shi Z, Lin W, Jiang H, Hou Y. Social support and depressive symptom disparity between urban and rural older adults in China. J Affect Disord. 2018;237:104–11. doi: 10.1016/j.jad.2018.04.076 . Zhang Y, Harper S. The impact of son or daughter care on Chinese older adults' mental health. Soc Sci Med. 2022;306:115104. doi: 10.1016/j.socscimed.2022.115104 . Li W, Liu E, Balezentis T, Jin H, Streimikiene D. Association between socioeconomic welfare and depression among older adults: Evidence from the China health and Retirement Longitudinal Study. Soc Sci Med. 2021;275:113814. doi: 10.1016/j.socscimed.2021.113814 . Yelton B, Friedman DB, Noblet S, Lohman MC, Arent MA, Macauda MM. Social Determinants of Health and Depression among African American Adults: A Scoping Review of Current Research. Int J Environ Res Public Health. 2022;19(3):1498. doi: 10.3390/ijerph19031498 . McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351–77. doi: 10.1177/109019818801500401 . Collins AE. Health ecology and environmental management in Mozambique. Health Place. 2002;8(4):263–72. doi: 10.1016/s1353-8292(02)00005-9 . Liu Y, Zupan NJ, Shiyanbola OO, Swearingen R, Carlson JN, Jacobson NA. Factors influencing patient adherence with diabetic eye screening in rural communities: A qualitative study. PLoS One. 2018;13(11):e0206742. doi: 10.1371/journal.pone.0206742 . Lei PC, Wu YY, Li LL, Wang QZ, Ye RX, Gao J. The analysis of impact factors of depression among middle: aged and older chronic diseases patients in China based on health ecological aspect. Mod Prev Med. 2021;48(7):1253–8. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. doi: 10.1093/ije/dys203 . Yang L, Jia CX, Qin P. Reliability and validity of the Center for Epidemiologic Studies Depression Scale (CES-D) among suicide attempters and comparison residents in rural China. BMC Psychiatry. 2015;15:76. doi: 10.1186/s12888-015-0458-1 . Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10(2):77–84. Rubin R. Exploring the Relationship Between Depression and Dementia. JAMA. 2018;320(10):961–2. doi: 10.1001/jama.2018.11154 . Tengku Mohd TAM, Yunus RM, Hairi F, Hairi NN, Choo WY. Social support and depression among community dwelling older adults in Asia: a systematic review. BMJ open. 2019;9(7):e026667. doi: 10.1136/bmjopen-2018-026667 . Zhou S, Gao L, Liu F, Tian W, Jin Y, Zheng ZJ. Socioeconomic status and depressive symptoms in older people with the mediation role of social support: A population-based longitudinal study. Int J Methods Psychiatr Res. 2021;30(4):e1894. doi: 10.1002/mpr.1894 . Fang M, Mirutse G, Guo L, Ma X. Role of socioeconomic status and housing conditions in geriatric depression in rural China: A cross-sectional study. BMJ open. 2019;9(5):e024046. doi: 10.1136/bmjopen-2018-024046 . Li LW, Liu J, Zhang Z, Xu H. Late-life depression in Rural China: do village infrastructure and availability of community resources matter? Int J Geriatr Psychiatry. 2015;30(7):729–36. doi: 10.1002/gps.4217 . Subramaniam M, Abdin E, Sambasivam R, Vaingankar JA, Picco L, Pang S. Prevalence of Depression among Older Adults-Results from the Well-being of the Singapore Elderly Study. Ann Acad Med Singap. 2016;45(4):123–33. van't Veer-Tazelaar PJ, van Marwijk HW, Jansen AP, Rijmen F, Kostense PJ, van Oppen P. Depression in old age (75+), the PIKO study. J Affect Disord. 2008;106(3):295–9. doi: 10.1016/j.jad.2007.07.004 . El-Gilany AH, Elkhawaga GO, Sarraf BB. Depression and its associated factors among elderly: A community-based study in Egypt. Archives of gerontology and geriatrics. (2018) 77:103–107. doi: 10.1016/j.archger.2018.04.011 . Pilania M, Bairwa M, Khurana H, Kumar N. Prevalence and Predictors of Depression in Community-Dwelling Elderly in Rural Haryana, India. Indian J Community Med. 2017;42(1):13–8. doi: 10.4103/0970-0218.199792 . Fatma A, Hikmet Y. The Relationships Between Life Satisfaction, Gender, Social Security, and Depressive Symptoms Among Elderly in Turkey. Educational Gerontol. 2015;41(4):305–14. doi: 10.1080/03601277.2013.852923 . Picco L, Subramaniam M, Abdin E, Vaingankar JA, Chong SA. Gender differences in major depressive disorder: findings from the Singapore Mental Health Study. Singap Med J. 2017;58(11):649–55. doi: 10.11622/smedj.2016144 . Vu HTT, Lin V, Pham T, Pham TL, Nguyen AT, Nguyen HT. Determining Risk for Depression among Older People Residing in Vietnamese Rural Settings. Int J Environ Res public health. 2019;16(15):2654. doi: 10.3390/ijerph16152654 . Glei DA, Goldman N, Liu IW, Weinstein M. Sex differences in trajectories of depressive symptoms among older Taiwanese: the contribution of selected stressors and social factors. Aging & mental health. 2013;17(6):773–83. doi: 10.1080/13607863.2013.781119 . Bi YH, Pei JJ, Hao C, Yao W, Wang HX. The relationship between chronic diseases and depression in middle-aged and older adults: A 4-year follow-up study from the China Health and Retirement Longitudinal Study. J Affect Disord. 2021;289:160–6. doi: 10.1016/j.jad.2021.04.032 . Read JR, Sharpe L, Modini M, Dear BF. Multimorbidity and depression: A systematic review and meta-analysis. J Affect Disord. 2017;221:36–46. doi: 10.1016/j.jad.2017.06.009 . Murayama Y, Ohba H, Yasunaga M, Nonaka K, Takeuchi R, Nishi M. The effect of intergenerational programs on the mental health of elderly adults. Aging Ment Health. 2015;19(4):306–14. doi: 10.1080/13607863.2014.933309 . Kong LN, Hu P, Yao Y, Zhao QH. Social support as a mediator between depression and quality of life in Chinese community-dwelling older adults with chronic disease. Geriatr Nurs. 2019;40(3):252–6. doi: 10.1016/j.gerinurse.2018.10.014 . Zhang C, Xue Y, Zhao H, Zheng X, Zhu R, Du Y. Prevalence and related influencing factors of depressive symptoms among empty-nest elderly in Shanxi, China. J Affect Disord. 2019;245:750–6. doi: 10.1016/j.jad.2018.11.045 . Jacobson NC, Lord KA, Newman MG. Perceived emotional social support in bereaved spouses mediates the relationship between anxiety and depression. J Affect Disord. 2017;211:83–91. doi: 10.1016/j.jad.2017.01.011 . Walther A, Philipp M, Lozza N, Ehlert U. Emotional Support, Depressive Symptoms, and Age-Related Alterations in Male Body Composition: Cross-Sectional Findings from the Men's Health 40 + Study. Front Psychol. 2017;8:1075. doi: 10.3389/fpsyg.2017.01075 . Tiedt AD, Saito Y, Crimmins EM. Depressive, Symptoms. Transitions to Widowhood, and Informal Support From Adult Children Among Older Women and Men in Japan. Res Aging. 2016;38(6):619–42. doi: 10.1177/0164027515595442 . Stafford M, Antonucci TC, Zaninotto P. Joint Trajectories of Spousal Social Support and Depressive Symptoms in Older Age. J Aging Health. 2019;31(5):760–82. doi: 10.1177/0898264317747077 . Leonard MT, Chatkoff DK, Maier KJ. Couples' Relationship Satisfaction and Its Association with Depression and Spouse Responses Within the Context of Chronic Pain Adjustment. Pain Manag Nurs. 2017;19(4):400–7. doi: 10.1016/j.pmn.2017.10.008 . Hung M, Voss MW, Bounsanga J, Graff T, Birmingham WC. Assessing spousal support and health in an aging population: support and strain amidst changing social dynamics. Soc Work Health Care. 2019;58(4):345–67. doi: 10.1080/00981389.2019.1569577 . Yin XX, Zhou R. Socio-economic Status, intergenerational support behavior and health poverty in old age: based on the empirical analysis of 2113 urban elderly living alone in five cities. Popul Dev. 2021;27(5):46–57. Zheng R, Yu M, Huang L, Wang F, Gao B, Fu D. Effect of intergenerational exchange patterns and intergenerational relationship quality on depressive symptoms in the elderly: An empirical study on CHARLS data. Front Public Health. 2022;10:1009781. doi: 10.3389/fpubh.2022.1009781 . Pearce M, Garcia L, Abbas A, Strain T, Schuch FB, Golubic R. Association Between Physical Activity and Risk of Depression: A Systematic Review and Meta-analysis. JAMA psychiatry. 2022;79(6):550–9. doi: 10.1001/jamapsychiatry.2022.0609 . Kim SY, Park JH, Lee MY, Oh KS, Shin DW, Shin YC. Physical activity and the prevention of depression: A cohort study. General hospital psychiatry. (2019) 60: 90–97. doi: 10.1016/j.genhosppsych.2019.07.010 . Stevens M, Lieschke J, Cruwys T, Cárdenas D, Platow MJ, Reynolds KJ. Better together: How group-based physical activity protects against depression. Soc Sci Med. 2021;286:114337. doi: 10.1016/j.socscimed.2021.114337 . Roh HW, Hong CH, Lee Y, Oh BH, Lee KS, Chang KJ. Participation in Physical, Social, and Religious Activity and Risk of Depression in the Elderly: A Community-Based Three-Year Longitudinal Study in Korea. PLoS One. 2015;10(7):e0132838. doi: 10.1371/journal.pone.0132838 . Farioli Vecchioli S, Sacchetti S, Nicolis di Robilant V, Cutuli D. The role of physical exercise and omega-3 fatty acids on depressive illness in the elderly. Curr Neuropharmacol. 2018;16(3):308–26. doi: 10.2174/1570159X15666170912113852 . Schuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES. Physical Activity and Incident Depression: A Meta-Analysis of Prospective Cohort Studies. Am J Psychiatry. 2018;175(7):631–48. doi: 10.1176/appi.ajp.2018.17111194 . Croezen S, Avendano M, Burdorf A, van Lenthe FJ. Social participation and depression in old age: a fixed-effects analysis in 10 European countries. Am J Epidemiol. 2015;182(2):168–76. doi: 10.1093/aje/kwv015 . Lotfaliany M, Hoare E, Jacka FN, Kowal P, Berk M, Mohebbi M. Variation in the prevalence of depression and patterns of association, sociodemographic and lifestyle factors in community-dwelling older adults in six low- and middle-income countries. J Affect Disord. 2019;251:218–26. doi: 10.1016/j.jad.2019.01.054 . Tables Table 1 Demographic characteristics of the sample(N=5660) variable Mean ± SD/n (%) variable n (%) Age(years) 67.90±6.00 ＜6 1949(34.43) 60~69 3775 (66.70) ≥6 3711(65.57) 70~79 1603 (28.32) Number of NCDs ≥80 282 (4.98) 0 1236 (21.84) Gender 1~2 2777 (49.06) Male 2856 (50.46) ≥3 1647 (29.10) Female 2804 (49.54) Health satisfaction Education Completely satisfied 186 (3.29) Illiterate 3267 (57.72) Very satisfied 1412 (24.95) Literate 1296 (22.90) Somewhat satisfied 2533 (44.75) Elementary school 804 (14.20) Not very satisfied 1095 (19.35) High school and above 293 (5.18) Not at all satisfied 434 (7.67) Smoking ADL score Former smoking 2532 (44.73) ≤6 3547 (62.67) Never smoking 3128 (55.27) ＞6 2113 (37.33) Drinking in the past year Monthly household expenditure (Yuan) No 3805 (67.23) ＜500 1296 (22.90) Yes 1855 (32.77) 500~2000 2831 (50.02) Sleep hours ≥2000 1533 (27.08) Table 2 Prevalence of depression by participants’ demographic characteristics variable No depressives n (%) Depressive symptoms n (%) c 2 test p -value Total 3329(58.82) 2331(41.18) Age(years) 4.911 0.086 60~69 2253(59.68) 1522(40.32) 70~79 906(56.52) 697(43.48) ≥80 170(60.28) 112(39.72) Gender 157.330 ＜0.001 Male 1912(66.95) 944(33.05) Female 1417(50.53) 1387(49.47) Education 108.672 ＜0.001 Illiterate 1748(53.50) 1519(46.50) Literate 805(61.11) 491(37.89) Elementary school 564(70.15) 2409(29.85) High school and above 212(72.35) 81(27.65) Smoking ＜0.001 Former smoking 1626(64.22) 906(35.78) Never smoking 1703(54.44) 1425(45.56) Drinking in the past year 67.657 ＜0.001 No 2095(55.06) 1710(44.95) Yes 1234(66.52) 621(33.48) Sleep hours 0.023 0.879 ＜6 1149(58.95) 800(41.05) ≥6 2180(58.74) 1531(41.26) Number of NCDs 215.414 ＜0.001 0 903(73.06) 333(26.94) 1~2 1667(60.03) 1110(39.97) ≥3 759(46.08) 888(53.92) Health satisfaction 694.854 ＜0.001 Completely satisfied 139(74.73) 47(25.27) Very satisfied 1075(76.13) 337(23.87) Somewhat satisfied 1615(63.76) 918(36.24) Not very satisfied 416(37.99) 679(62.01) Not at all satisfied 84(19.35) 350(80.65) ADL score 403.575 ＜0.001 ≤ 6 2446(68.96) 1101(31.04) ＞6 883(41.79) 1230(58.21)) Monthly household expenditure (Yuan) 32.601 ＜0.001 ＜500 681(52.55) 615(47.45) 500~2000 1682(59.41) 1149(40.59) ≥2000 966(58.82) 2331(41.18) Health insurance 3.147 0.076 No 81(51.92) 75(48.08) Yes 3248(59.01) 2256(40.99) Pension 0.170 0.680 No 426(58.12) 307(41.88) Yes 2903(58.92) 2024(41.08) Table 3 Association between social support and depressive symptoms variable No depressives n (%) Depressive symptoms n (%) c 2 test p -value Family support Visiting parents Monthly 19.210 ＜0.001 No 470(52.22) 430(47.78) Yes 2859(60.06) 1901(39.94) Contracting parents Monthly 15.814 ＜0.001 No 191(49.23) 197(50.77) Yes 3138(59.52) 2134(40.48) Take Care of Grand Children 5.441 0.020 No 2566(59.67) 1734(40.33) Yes 763(56.10) 597(43.90) Money support from children 18.677 ＜0.001 No 2035(61.18) 1291(38.82) Yes 1294(55.44) 1040(44.56) Money support for children 12.871 ＜0.001 No 2841(57.90) 2066(42.10) Yes 488(64.81) 265(35.19) Marriage satisfaction 345.547 ＜0.001 Very satisfied 1599(69.22) 711(30.78) Somewhat satisfied 1273(58.13) 917(41.87) Not satisfied 120(25.64) 348(74.36) No spouse 337(48.70) 355(51.30) Children satisfaction 188.767 ＜0.001 Completely satisfied 268(69.79) 116(30.21) Very satisfied 1846(63.85) 1045(36.15) Somewhat satisfied 1131(54.06) 961(45.94) Not very satisfied 51(27.42) 135(72.58) Not at all satisfied 12(18.75) 52(81.25) No child 21(48.84) 22(51.16) Social activities Went to community club 17.549 ＜0.001 No 2746(57.63) 2019(42.37) Yes 583(65.14) 312(34.86) Went to sport, social 12.452 ＜0.001 No 3166(58.34) 2261(41.66) Yes 163(69.96) 70(30.04) Used the internet 27.020 ＜0.001 No 3188(58.20) 2290(41.80) Yes 141(77.47) 41(22.53) Other social activities 4.869 0.027 No 3286(58.67) 2315(41.33) Yes 43(72.88) 16(27.12) Community support Exercising facilities 16.620 ＜0.001 No 1488(55.98) 1170(44.02) Yes 1841(61.33) 1161(38.67) Dancing team or other exercise organizations 23.554 ＜0.001 No 2385(56.93) 1804(43.07) Yes 944(64.17) 527(35.83) Organizations for helping the elderly and the handicapped 28.206 ＜0.001 No 2489(58.82) 1883(43.07) Yes 840(65.22) 448(34.78) Elderly association 24.719 ＜0.001 No 2490(57.04) 1875(42.96) Yes 839(64.79) 456(35.21) Table4 Results of the logistic regression analysis for correlates of depression Variables Model 1 Model 2 Model 3 Model 4 Age (60~69) 70~79 0.993(0.870~1.134) 0.991(0.868~1.131) 0.923(0.803~1.060) 0.908(0.789~1.044) ≥80 0.848(0.646~1.112) 0.845(0.644~1.109) 0.752(0.566~0.998) ** 0.710(0.534~0.943) ** Gender (Male) Female 1.640(1.457~1.846) *** 1.620(1.366~1.921) *** 1.519(1.272~1.815) *** 1.406(1.170~1.689) *** Number of NCDs (0) 1~2 1.376(1.175~1.611) *** 1.374(1.173~1.609) *** 1.395(1.187~1.640) *** 1.382(1.175~1.625) *** ≥3 1.698(1.424~2.025) *** 1.693(1.420~2.020) *** 1.739(1.450~2.084) *** 1.736(1.447~2.082) *** ADL score (≤6) ＞6 2.228(1.968~2.522) *** 2.223(1.963~2.517) *** 2.051(1.801~2.335) *** 1.968(1.726~2.244) *** Health satisfaction (Completely satisfied) Very satisfied 0.895(0.623~1.288) 0.899(0.625~1.293) 0.926(0.627~1.367) 0.925(0.626~1.368) Somewhat satisfied 1.660(1.168~2.359) ** 1.666(1.172~2.367) ** 1.498( 1.025~2.188) ** 1.533(1.048~2.244) ** Not very satisfied 3.758(2.607~5.418) *** 3.757(2.606~5.416) *** 3.178(2.145~4.708) *** 3.307(2.228~4.909) *** Not at all satisfied 7.993(5.245~12.181) *** 7.987(5.240~12.175) *** 6.159(3.945~9.617) *** 6.333(4.050~9.902) *** Smoking (Former smoking) Never smoking 0.987(0.836~1.164) 0.997(0.842~1.181) 1.008(0.850~1.195) Drinking in the past year (No) Yes 0.942(0.820~1.082) 0.944(0.819~1.088) 0.947(0.822~1.092) Visiting parents Monthly (No) Yes 0.771(0.650~0.914) ** 0.769(0.649~0.913) ** Contracting parents Monthly (No) Yes 0.923(0.723~1.178) 0.951(0.744~1.215) Take Care of Grand Children (No) Yes 1.054(0.910~1.220) 1.067(0.921~1.236) Money support from children (No) Yes 1.180(1.029~1.353) ** 1.177(1.026~1.351) ** Money support for children (No) Yes 0.794(0.659~956) ** 0.792(0.657~0.954) ** Marriage satisfaction (Very satisfied) Somewhat satisfied 1.196(1.029~1.390) ** 1.202(1.033~1.398) ** Not satisfied 2.998(2.322~3.873) *** 2.978(2.304~3.849) *** No spouse 1.480(1.197~1.829) *** 1.423(1.150~1.761) ** Children satisfaction (Completely satisfied) Very satisfied 1.253(0.965~1.629) * 1.241(0.955~1.614) Somewhat satisfied 1.460(1.112~1.917) ** 1.456(1.108~1.914) ** Not very satisfied 2.964(1.898~4.630) *** 2.890(1.846~4.526) *** Not at all satisfied 3.774(1.796~7.930) *** 3.640(1.736~7.635) ** No child 1.756(0.856~3.606) 1.566(0.760~3.226) Went to community club (No) Yes 0.892(0.753~1.055) 0.918(0.775~1.088) Went to sport, social (No) Yes 0.655(0.475~0.902) ** 0.662(0.480~0.913) ** Used the internet (No) Yes 0.602(0.408~0.886) ** 0.682(0.459~1.012) * Other social activities (No) Yes 0.630(0.334~1.187) 0.661(0.350~1.249) Exercising facilities (No) Yes 0.961(0.840~1.100) 0.969(0.846~1.110) Dancing team or other exercise organizations (No) Yes 0.896(0.767~1.047) 0.9350(0.799~1.094) Organizations for helping the elderly and the handicapped (No) Yes 0.856(0.724~1.012) * 0.855(0.723~1.012) * Elderly association (No) Yes 0.935(0.792~1.104) 0.943(0.798~1.114) Education (Illiterate) Literate 0.859(0.735~1.003) * Elementary school 0.718(0.590~0.873) ** High school and above 0.737(0.544~0.999) ** Monthly household expenditure (＜500) (Yuan) 500~2000 0.797(0.685~0.928) ** ≥2000 0.757(0.635~0.902) ** Negelkerke R 2 0.227 0.227 0.272 0.277 Percentage Correct 69.7% 69.9% 71.2% 71.7% * p < 0.1; ** p < 0.05; *** p < 0.001 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-2388890","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":162398153,"identity":"6c7358b6-97e3-42cf-bbc1-1e5e07299c71","order_by":0,"name":"Rong Fan","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Fan","suffix":""},{"id":162398154,"identity":"baa2b377-a066-48d4-9ebe-430a3f81b53a","order_by":1,"name":"Xiaoju Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACPmYGhgMMDGwM/MyM7T8+MEgQ1sIG0yLZ3nxAcgZRWmAMgzPHEqR5iHEYGzuP4YEfFXyJDTdyDIxt/ljk8TcwP3uA32FsCQd7zrAlNs7IMUjObZMoljjAZm6AXwvzgQO8bWyJzRI5BodzGyQSGw7wsOH1ERszY8PBv0AtbRI5hs0WfyQS5xPWwnzgMMiWHp5jycwMbBKJGwhrYUs4LHOGzXgGe/Mxxt42icSNh9nM8Grh5z9j/PFNxTHZ/YcZ2xh+/KlLnHe8+RkRscNwDInNTIR6IKghTtkoGAWjYBSMTAAAH59FFxpr0gUAAAAASUVORK5CYII=","orcid":"","institution":"Shihezi University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaoju","middleName":"","lastName":"Li","suffix":""},{"id":162398155,"identity":"06f5567e-bedf-4dfa-9dbd-3c186d730797","order_by":2,"name":"Jiaxin Dong","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Dong","suffix":""},{"id":162398156,"identity":"10c670e7-08f7-46c0-b57c-bf321bba54e0","order_by":3,"name":"Jielin Yang","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jielin","middleName":"","lastName":"Yang","suffix":""},{"id":162398157,"identity":"6f65b56c-31ff-4d16-9d85-c59bb3ed214a","order_by":4,"name":"Li Zhao","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhao","suffix":""},{"id":162398158,"identity":"c0a1da2b-631b-44cc-a5c5-034d999de1a2","order_by":5,"name":"Xianqi zhang","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xianqi","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2022-12-17 16:14:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2388890/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2388890/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31023747,"identity":"90d65c81-d2be-4f2a-a37f-7851cc780e20","added_by":"auto","created_at":"2023-01-03 14:26:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40145,"visible":true,"origin":"","legend":"\u003cp\u003eFramework diagram of health ecological model\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2388890/v1/540d4ad182a503dad862ca28.jpeg"},{"id":35518156,"identity":"4c6db2e2-2d40-4f31-b509-b66c69069ef1","added_by":"auto","created_at":"2023-04-10 11:59:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":574903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2388890/v1/32bdcd40-0310-4a29-8534-a6b18c86f30f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influencing factors of depressive symptoms in the elderly in rural China: Based on a health ecological model","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation ageing is a global demographic trend that has been exacerbated in recent decades by increasing life expectancy and declining fertility rates. China is no exception to this trend, according to the 2021 data of China\u0026rsquo;s National Bureau of Statistics, with18.70% of its population being over 60 years of age. Previous reports also predicted that China could become a deeply ageing society by 2021. The increasing degree of ageing and the expanding size of the older population have brought great impact to society, resulting in the increased prominence of various problems related to older adults. Therefore, improving the quality of life of this population and achieving healthy ageing have become urgent issues in today\u0026rsquo;s society.\u003c/p\u003e \u003cp\u003eDepression is one of the most common mental health disorders amongst older people and is expected to jump to the number one disease burden by 2030 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 350\u0026nbsp;million people suffer from depression worldwide, and over 95\u0026nbsp;million people suffer from depression in China. Compared with the general population, people with depression have a 20-fold higher risk of suicide and a 60% increased risk of cardiovascular disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Depression has been identified as a risk factor for death, poor health, and disability in older adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a disparity in socioeconomic development between urban and rural China. For example, urban residents born between 1940 and 1989 are 4.9 times more likely to attend elementary school than rural residents. In 2013, government investment in health care in rural areas was only one-third of that in urban areas. Moreover, 54.8% of older adults in urban areas in China participate in social activities, and this percentage is nearly 10% higher than those in rural areas [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, urbanization is affecting the health of the population by changing the social environment, and the imbalance in economic development between rural and urban areas has led to a much higher prevalence of depression among older people in rural areas than in urban areas, some studies have shown that the prevalence of depression among older people in rural areas is 1.88 times higher than in urban areas [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As such, it is important to focus on the mental health of rural older adults.\u003c/p\u003e \u003cp\u003eDepression in older adults may be influenced by a variety of factors that can be categorised as biological, genetic, and abnormal receptor expression, sociodemographic factors and so on. In detail, general demographic characteristics include domicile, age, gender, marital status, and education level [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Health status includes the type and number of non-communicable diseases (NCDs), functional disability, and pain [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Health behaviours include physical activity, smoking, alcohol consumption, and sleep schedules [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, a growing body of research has found that social support networks have a direct impact on the mental health of older adults [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNan proposed social support as perceived or actual instrumental or expressive support provided by communities, social networks, and trusted partners, a concept that provides information about the types and sources of social support that has been well applied in social support research in China [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is substantial evidence explaining that high-quality social support can improve the mental health of older adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Some studies have found that older people who live alone and are widowed are more likely to experience depressive symptoms and that an older spouse can provide not only mutual help in daily care but also more emotional support [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], moreover, emotional support, as an important part of social support, can effectively reduce the level of depression in older adults [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As for social activities, research studies conducted in Korea and China have shown that participation in two or three social activities, donations, volunteer work, and physical exercise can effectively prevent and alleviate the development of depression in older adults [14; 15; 16]. In terms of community support, Yan-Yan Chen suggested that increasing the accessibility of community infrastructure could help improve common depressive symptoms in older adults [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Hu suggested that the government provision of free or discounted community services for older adults and the design of community-based preventive mental health service centres would promote recreation, social interaction, and social contact for older adults to alleviate depression [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough numerous studies have concluded that differences in social support have been identified as important determinants of depressive symptoms, they have mainly focused on specific populations in a particular region or examined the association between single dimensions, such as socioeconomic well-being, childcare, and depression [19; 20]. Little is known about the relationship between different dimensions of social support and depression in older adults in rural China.\u003c/p\u003e\n\u003ch3\u003eTheory And Hypotheses\u003c/h3\u003e\n\u003cp\u003eAt present, there are many theories about the influencing factors of depression, such as social determinants of health, social ecology theory proposed by Bronfenbrenne, Grossman's theory of health production, Anderson theory, health ecological model and so on [21; 22]. Among them, the health ecological model proposed by Collins in 2002, which incorporates ecological theory, argues that the factors affecting the health of a population are multidimensional, it can be divided into five main dimensions: personal characteristics, health behaviours, interpersonal networks, living and working conditions and policy environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Based on this theoretical model, Yao Liu et al. explored the factors affecting the adherence to eye screening in patients with diabetes in rural communities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Lei and colleagues took middle-aged and elderly patients with chronic diseases as research objects, to explore the influencing factors of depression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Different from previous studies, based on this theoretical model and using a nationally representative dataset from the 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS), this study aims to understand the prevalence of depression among older adults in rural China, conduct a comprehensive and systematic analysis of the factors influencing depression in this population, with a focus on the associations between different dimensions of social support and depressive symptoms. To provide empirical evidence for improving the mental health of the elderly in rural China, and to provide reference for other countries facing similar situations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the above, our main hypothesis is that high-quality social support can reduce depression amongst rural older adults. By communicating with children, increasing couple satisfaction, and actively participating in social activities, rural older adults are likely to relieve their depression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eData were derived from CHARLS organised by the Academy of Social Sciences of Peking University. A baseline survey of these data was conducted in 2011, and a follow-up was conducted every 2\u0026thinsp;~\u0026thinsp;3 years. CHARLS, employing a multistage stratification with a probability proportional to size sampling method, collected high-quality data from a nationally representative sample of Chinese residents aged 45 years and above from 28 provinces, municipal cities, and autonomous regions. This study used the latest follow-up data from 2018. The study design and sampling procedure of CHARLS can be found through the Open Research Data Platform of Peking University ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The inclusion criteria covered 7,538 rural Chinese aged 60 years and above; those who had deleted key variables and reported incomplete social support and depression related programs during the survey, emotional or mental problems, and memory-related diseases (Alzheimer\u0026rsquo;s disease, brain atrophy, and Parkinson\u0026rsquo;s disease) were excluded, resulting in the removal of 1,878 cases. Censored data were supplemented with the 2013 and 2015 and wave data. Ultimately, 5,660 participants (2,856 men and 2,804 women) were enrolled in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eDependent Variable\u003c/h2\u003e \u003cp\u003eDepressive symptoms were measured using the short form of the Centre for Epidemiological Studies Depression Scale (CES-D), which is a useful mental health measure for Chinese older people [27; 28]. It contains 10 items: (1) troubled by trivial matters, (2) troubled by inattention, (3) feeling frustrated, (4) laborious to do anything, (5) full of hope, (6) feeling fear, (7) poor sleep, (8) feeling happy, (9) feeling lonely, and (10) unable to continue living. Depressive symptoms over the past week were measured from 0 (little or no time [\u0026lt;\u0026thinsp;1 d]) to 3 (most or all the time [5\u0026ndash;7 days]). Before summing up the item scores, the scores for questions 5 and 8 were converted. Thus, the total score of the scale ranged from 0 to 30, with a score of 10 or more indicating reasonable levels of sensitivity and specificity amongst Chinese older adults [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus, respondents with scores\u0026thinsp;\u0026ge;\u0026thinsp;10 or those who were taking antidepressant medication were considered to have depressive symptoms [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIndependent variable\u003c/h2\u003e \u003cp\u003eThis study selected appropriate variables for each layer under the guidance of a model of health ecological.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePersonal characteristics\u003c/strong\u003e \u003cp\u003eThis variable included age (60\u0026ndash;69, 70\u0026ndash;79, and \u0026ge;\u0026thinsp;80 years), gender (male or female), health satisfaction (completely satisfied, very satisfied, somewhat satisfied, not very satisfied, not at all satisfied), and number of NCDs (0, 1\u0026ndash;2, or \u0026ge;\u0026thinsp;3). Activities of daily living (ADL) were measured using a six-item scale in CHARLS that included shopping, doing household work, making phone calls, taking medications, preparing hot meals, and managing money. The scores were 0 for no difficulty and 1 for having difficulty in each activity. A total score greater than 6 was classified as having difficulty in daily activities, and a score less than or equal to 6 was classified as not having such difficulty.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHealth behaviour\u003c/strong\u003e \u003cp\u003eThis variable included smoking status (yes or no), alcohol consumption (yes or no), and sleep duration (\u0026lt;\u0026thinsp;6 hours or \u0026ge;\u0026thinsp;6 hours).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInterpersonal networks\u003c/b\u003e: This study uses different dimensions of social support: family support, participation in social activities and community support to measure access to interpersonal networks [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e(1) Family support\u003c/strong\u003e \u003cp\u003eThis includes whether the children visit their parents monthly (yes or no), whether the children contact their parents by phone/message/on wechat /by mail/ by email monthly (yes or no), whether the participants take care of grandchildren, whether the participants receive financial support from children (yes or no), whether the participants offer financial support for children (yes or no), their satisfaction with their marriage (very satisfied, somewhat satisfied, not satisfied, no spouse), and their satisfaction with their children (very satisfied, somewhat satisfied, not satisfied, no child).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e(2) Social activities\u003c/strong\u003e \u003cp\u003eThis includes whether the participants visit a community club, engage in sports, social, use the internet, and join other social activities (yes or no).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(3) Community support\u003c/b\u003e: This includes community infrastructure and resources, which was measured by the following items: whether the village/community has outside exercising facilities (yes or no), whether the village/community has a dancing team or other exercise groups (yes or no), whether the village/community has organisations to help the older adults and those with disabilities (yes or no), and whether the village/community has an association for older adults (yes or no).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLiving and working conditions\u003c/strong\u003e \u003cp\u003eThe combined concept of socio-economic status can be used to measure living and working conditions, and existing studies typically use education levels. income and expenditure to measure socio-economic status [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Education level was classified as illiterate, primary school and below, or junior school and above. Monthly household expenditures were \u0026lt;\u0026thinsp;500, 500\u0026ndash;2,000, and \u0026ge;\u0026thinsp;2,000.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy environment\u003c/strong\u003e \u003cp\u003ePublic support was proxied by social security and welfare for older people, referring to whether the participants have pensions (yes or no) and health insurance (yes or no).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eThe data were expressed as a percentage of the classification value. Firstly, a chi-square test was used to explore the differences in depressive symptoms among rural older people with different characteristics. Secondly, based on the results of the univariate analysis, model 1,2,3,4 was constructed using logistic regression based on the health ecological model to analyse the relationship between personal characteristics, health behaviour, interpersonal networks, living and working environment and depressive symptoms. The level of statistical significance was \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided). All analyses were conducted using SPSS 26.0 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe Characteristic of the Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sociodemographic characteristics of the 5,660 older adult respondents were presented in table 1. The average age was 67.90 years (range = 60\u0026ndash;108, SD = 6.00), with those aged between 60 and 69 years totalling 3,775 (66.70%).\u0026nbsp;The number of male respondents was 2,856 (50.46%), most of the participants were illiterate at 3,267 (57.72%),\u0026nbsp;former smoking was 2,532(44.73%), or drinking in the past was1,855 (32.77%),\u0026nbsp;3,711(65.57%) participants slept\u0026nbsp;\u0026ge;6 hours at night,\u0026nbsp;many of them had one or two NCDs (49.06%),\u0026nbsp;self-rated health satisfaction was 2,533(44.75%) for somewhat satisfied, a total of 2,133 (37.33%) subjects scored more than 6 in ADL. Overall, 50.02% of the respondents have a monthly household expenditure of 500-2000 yuan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Sociodemographic Characteristics and Depressive Symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean CESD-10 score was 9.21 (SD = 6.722) and ranged from 0 to 30. Specifically, 2,331 participants (41.18%) demonstrated depressive symptoms. This included 398 (7.0%) with a score of 0 and 12 (0.2%) with a score of 30. Statistically significant differences\u0026nbsp;were observed in the incidence of depressive symptoms amongst the subgroups. For example, the rate was higher in females (49.47%) than in males (33.05%) (\u003cem\u003eP\u003c/em\u003e＜0.001), in participants with a lower level of education than in those with a high level of education (\u003cem\u003eP\u003c/em\u003e＜0.001), in those with three or more NCDs (53.92%) than in those with no chronic disease (26.94%) (\u003cem\u003eP\u003c/em\u003e＜0.001), in those with more difficulties in daily living activities (58.21%) than in those without (31.04%) (\u003cem\u003eP\u003c/em\u003e＜0.001), , and in participants who had\u0026nbsp;\u0026lt;500 monthly household expenditures (47.45%) than in those with \u0026ge;2,000\u0026nbsp;monthly household expenditures (41.18%) (\u003cem\u003eP\u003c/em\u003e＜0.001).\u0026nbsp;However,\u0026nbsp;the age(\u003cem\u003eP\u003c/em\u003e=0.086), sleep hours(\u003cem\u003eP\u003c/em\u003e=0.879), health insurance (\u003cem\u003eP\u003c/em\u003e=0.076) and pension(\u003cem\u003eP\u003c/em\u003e=0.680) were not significantly different between the two groups of participants.\u0026nbsp;The results are presented in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Social Support and Depressive Symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 illustrates the relationship between participants\u0026rsquo; social support and depressive symptoms. There were statistically significant differences in the prevalence of depressive symptoms between the different social support dimensions: family support, social activities, and community support subgroups\u0026nbsp;(\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;For example, the prevalence of depressive symptoms amongst parents whose children visited or contracted monthly both were significantly lower than amongst those who did not receive such visit or contract (39.94% vs. 47.78%; 40.48% vs.50.77%, all\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001). Participants who were very satisfied with their marital status had a significantly lower risk of developing depressive symptoms than those who did not satisfied (30.78% vs. 74.36%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Participants who participated in some social activities were less likely to suffer from depression than those who did not\u0026nbsp;participate(\u003cem\u003eP\u003c/em\u003e＜0.05). Older people living with community support are less likely to experience depressive symptoms than those without some community support (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression analysis on influencing factors of the key variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariables that were statistically significant in univariate analysis and had an impact on depressive symptoms in previous studies were included in the regression analysis. Based on the health ecological model, model 1,2,3,4 was constructed and after adjusting all the variables in model 4, the results showed that:\u0026nbsp;participants\u0026nbsp;were at higher risk of\u0026nbsp;depressive symptoms\u0026nbsp;among females (\u003cem\u003eOR\u003c/em\u003e=1.406, 95% \u003cem\u003eCI\u003c/em\u003e: 1.170\u0026ndash;\u0026nbsp;1.689）,\u0026nbsp;with NCDS \u0026ge;3 (\u003cem\u003eOR\u003c/em\u003e=1.736, 95%\u003cem\u003eCI\u003c/em\u003e :1.447\u0026ndash;2.082), and with limited ability to living daily activities (\u003cem\u003eOR\u003c/em\u003e=\u0026nbsp;1.968, 95%\u003cem\u003eCI\u003c/em\u003e: 1.726\u0026ndash;2.244).\u0026nbsp;The elderly giving financial support to their children was significantly associated with a decrease in the prevalence of\u0026nbsp;depressive symptoms (\u003cem\u003eOR\u003c/em\u003e=0.792, 95%\u003cem\u003eCI\u003c/em\u003e:\u0026nbsp;0.657\u0026ndash;0.954). Participants who were not satisfied (\u003cem\u003eOR\u003c/em\u003e=2.978, 95%\u003cem\u003eCI\u003c/em\u003e: 2.304\u0026minus;3.849) or did not have a spouse (\u003cem\u003eOR\u003c/em\u003e=1.423, 95%\u003cem\u003eCI\u003c/em\u003e: 1.150\u0026minus;1.761) were significantly more likely to have depressive symptoms than those who were very satisfied with their marital status.\u0026nbsp;Compared to those who were not participated in sport or social activities, subjects who were participated had a significant lower to have depressive symptoms (\u003cem\u003eOR\u003c/em\u003e=0.662, 95%\u003cem\u003eCI\u003c/em\u003e: 0.480\u0026minus;0.913). The degree of fit of the regression model improved gradually with the addition of each factor at each level. Model 4 accounted for 27.7% of the total variation, as shown in Table 4.\u003c/p\u003e"},{"header":"Disscussion","content":"\u003cp\u003eThe aim of this study was to investigate the prevalence of depressive symptoms in rural older people in China, to clarify the factors influencing depressive symptoms in this population based on health ecological theory, and to focus on the association between different social support dimensions and depressive symptoms. The findings here may provide a theoretical basis for intervention strategies and measures to improve depressive symptoms in rural older people.\u003c/p\u003e \u003cp\u003eThe prevalence of depression in the rural older adults was 41.18%, which is consistent with that in previous population-based studies that reported a 38.47\u0026ndash;46.15% prevalence of depression in rural older adults, including the works by Fang (CHARLS, 2015) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and Li (CHARLS, 2011) in the context of China [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. From a cross-sectional comparison perspective, the prevalence of depression amongst rural older adults in the study was higher than that in the studies conducted by Abdin [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] on older adults in Singapore (3.7%), Hong Kong, China (17.8%), and urban China (23.5%). This may be related to the fact that developed areas have better infrastructure, advanced medical technology, and better social support and welfare than rural areas.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of personal characteristics on depressive symptoms in the rural elderly\u003c/h2\u003e \u003cp\u003eThe results of this study showed that rural older adults aged over 80 years were less likely than rural older adults aged 60\u0026ndash;70 years to have depression (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.710, 95% \u003cem\u003eCI\u003c/em\u003e: 0.534\u0026ndash;0.943). By contrast, a study by Tazelaar showed that age is positively associated with depression symptoms and that symptoms increase with age [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Meanwhile, the study by El-Gilany revealed that people over 85 years of age are less likely to have depression than those aged 60\u0026ndash;75 years (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2, 95% \u003cem\u003eCI\u003c/em\u003e: 0.1\u0026ndash;0.9) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The present study also showed that depression is significantly higher in women than in men (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.406, 95% \u003cem\u003eCI\u003c/em\u003e:1.170\u0026ndash;1.689). This result was comparable to various other studies. For India, Pilania noted a significant correlation between depression in rural older adults and depression in women (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.7, 95% \u003cem\u003eCI\u003c/em\u003e: 1.4\u0026ndash;5.0) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A study by Altun for Turkish older adults showed that older female adults are 2.53 times more likely to suffer from depression than older male adults (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.53, 95% \u003cem\u003eCI\u003c/em\u003e: 1.17\u0026ndash;5.50) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, El-Gilany (2018) concluded that the prevalence of depression is significantly higher in women than in men (51.3% and 38.6%, respectively) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These gender differences are likely a result of a myriad of factors, including biological, social, demographic, and psychological effects [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Older women have a longer life expectancy, are more vulnerable to health problems and negative life events, and are prone to feelings of loneliness and helplessness [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, differences in the perceived level and availability of social support tend to contribute to differences in depression levels between men and women [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study found that (1) participants with difficulties in daily activities and rural older adults with two or more NCDs were more likely to have depressive symptoms and that (2) older adults who rated their health as \u0026lsquo;not at all satisfied\u0026rsquo; were much more likely to have depressive symptoms than those who rated their health as \u0026ldquo;completely satisfied\u0026rdquo; (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.333; 95% \u003cem\u003eCI\u003c/em\u003e: 4.050\u0026ndash;9.902). Similar results were reported by Yu-Han Bi in a 2021 study that found a dose-response relationship between the incidence of depression and the number of chronic diseases [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, this finding is consistent with a meta-analysis by Read based on data from 40 studies, which revealed that people with multiple disorders have twice the risk of developing depression [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Impaired physical health reduces the quality of life of older adults and limits their social reach. At the same time, high medical costs increase their psychological burden and likelihood of developing depressive symptoms. Therefore, for the elderly with physical diseases, the government should improve the medical security system, encourage patients to actively seek medical treatment, organize volunteers to provide them with instrumental support, and improve their daily activities to alleviate their negative emotions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of interpersonal networks on depressive symptoms in the rural elderly\u003c/h2\u003e \u003cp\u003eEmotional support and nice parent\u0026ndash;child relationships provided by visiting parents help alleviate depression in older adults [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Previous studies have used the number of children and grandchildren to measure children's support for their parents [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Both objective and subjective perspectives were used in this investigation: whether children visit their parents monthly and parents' ratings of their satisfaction with their children. The results of this study showed that parents who received monthly child visits were 0.769 times less likely to have depressive symptoms than those who did not receive such visits (95% \u003cem\u003eCI\u003c/em\u003e: 0.649\u0026ndash;0.913). Depression was significantly lower amongst older adults who had good relationships with their children than amongst those who had bad relationships with their children. This may be due to the fact that older adults in rural areas are limited by socioeconomic conditions and their own circumstances; they have a single way to obtain emotional support and are less involved in entertainment and interactive activities. Resulting in few sources of social support in daily life, other than visits from children [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Hence, it is important to appropriately increase the frequency of children\u0026rsquo;s meetings with older adults and encourage more communication channels and opportunities [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Increasing the level of perceived social support obviously alleviates depression amongst older adults [47; 48].\u003c/p\u003e \u003cp\u003eImproving satisfaction with marital relationships amongst rural older adults would greatly alleviate their depression as well. Consistent with the findings of numerous studies [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], rural older adults with spouses were less likely to suffer from depression than those without spouses. Spouses are a major buffer against depressive symptoms, assisting with various daily activities and providing an emotional outlet [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Evidence suggests that a good relationship with a spouse reduces the risk of depressive symptoms later in life than other social relationships [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Therefore, the present study further explored the effect of marital satisfaction on depression on this basis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The results of the study showed that people without a spouse were 1.423 times more likely to be depressed than older adults who were very satisfied with their marital relationship, and those who were dissatisfied with their marital relationship were 2.978 times more likely to be depressed than those who were very satisfied with their marital relationship. Therefore, we hypothesise that in addition to having family members who provide instrumental support and take care of each other\u0026rsquo;s basic needs, it is important to maintain marital harmony in later life, enhance marital happiness [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], provide emotional support, and increase marital relationship satisfaction.\u003c/p\u003e \u003cp\u003eFamily financial support has a significant effect on depressive symptoms amongst rural older adults. The results of the study showed that parents who provided financial support to their children were less likely to experience depressive symptoms than older adults who did not provide financial support to their children. Financial support provided by rural older people for their children has a beneficial effect on their mental health. First of all, it can maintain the status of the family. With the development of the market economy, the traditional family status of rural parents is threatened. Providing some economic support to children can protect the status of parents in the family to a certain extent, and the increased status of the family can improve health. Secondly, the father's generation can exchange more care and support from their children through material wealth or economic and social resources to reduce the prevalence of depression among the rural elderly [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In terms of financial support from children to parents, we found that participants who received financial support from their children were more likely to develop depression. This finding is consistent with the study by Ranran Zheng et al. who found that although financial support from children can improve the quality of life of parents, it can lead to parents perceiving themselves as \u0026ldquo;useless and worthless\u0026rdquo;, which instead increases the psychological burden of the elderly and increases the tendency of depressive symptoms[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].Therefore, to some extent, we suggest that parents and children establish two-way resource exchange to form a reciprocal association system and improve the mental health of the rural elderly.\u003c/p\u003e \u003cp\u003eParticipation in physical exercise and social activities can prevent future\u003c/p\u003e \u003cp\u003edepression [55; 56; 57]. This study showed that rural older adults who participated in physical and social activities were 33.8% less likely to suffer from depression; this result is consistent with Hyun\u0026rsquo;s study of older adults in Korea[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], in which physical activity was found to improve mental and emotional well-being and exert a positive impact on mental health [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. A meta-analysis of prospective studies found that people who are more physically active are 17% less likely to develop depression than those who are less physically active (95% \u003cem\u003eCI\u003c/em\u003e: 12\u0026ndash;21%) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Older adults with extensive social activities have greater access to various forms of material resources and health-related information, can achieve a sense of belonging and security through social interaction, and can enhance social connectedness, all of which contribute to the alleviation of depressive symptoms [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Therefore, it is recommended that communities enrich the construction of basic physical exercise facilities, organise group activities and encourage older people to participate in social activities such as physical exercise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of living and working conditions on depressive symptoms in the rural elderly\u003c/h2\u003e \u003cp\u003eIndirect measurement of living and working conditions through socio-economic indicators: household expenditure and educational attainment. These findings suggest that rural older adults with higher monthly household expenditures and higher levels of education are less likely to experience depressive symptoms. This is consistent with previous findings by Lotfaliany in a community-based cohort of older adults in six countries.[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Higher socioeconomic status can reduce the incidence of depression by reducing unhealthy lifestyle habits and promoting the development of interpersonal relationships and social networks. This suggests that the government should provide certain financial assistance to the rural elderly to alleviate the impact on mental health due to economic stress, so as to achieve the purpose of building a healthy aging society through multiple channels.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the prevalence of depression among the elderly in rural China is not optimistic, women and the elderly with chronic diseases need to be focused on, and joint efforts from families, communities, and the government are needed to reduce the risk of depression. First of all, it is necessary to promote the two-way interaction between children and parents, and improve the relationship satisfaction between spouses and between parents and children. Secondly, the community should improve the configuration of infrastructure, encourage the elderly to actively participate in social activities, and improve interpersonal skills. Thirdly, in order to better reduce the social burden caused by depression, the government should not only improve the social security system, but also provide financial support and assistance to the elderly in rural areas.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has certain limitations. Firstly, there is an inherent limitation for cross-sectional studies. In other words, it is impossible to infer a causal relationship between the coexisting risk factors and diseases. Secondly, most of the data, including depressive symptoms, were based on self-reported information that was not validated for diagnosis. Thirdly, owing to data limitations, the construction of family and public support indices was based on availability rather than connection. More rigorous scientific studies are required to address these limitations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Institutional Review Board at Peking University (IRB00001052-11015). The patients/participants provided their written informed consent to participate in this study. RF registered the platform and obtained permission to use the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design and sampling procedure of CHARLS can be found through the Open Research Data Platform of Peking University ( http://charls.pku.edu.cn/ ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u0026quot; in this section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was founded by the Young Innovative Talents Projects of Shihezi University, and the title of the project is The impact of poverty on the health of the population and the evaluation of the implementation effect of the health poverty alleviation policy in the southern region of the Xinjiang Production and Construction Corps (NO. CXBJ202005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRong Fan: Conceptualization, Investigation Data, Formal analysis, Writing original draft. Xiaoju Li: Conceptualization, Methodology. Jiaxin Dong: Writing - Review \u0026amp; Editing, Jielin Yang: Conceptualization, Resources, Li Zhao: Writing - Review \u0026amp; Editing, Xianqi Zhang: Writing - Review \u0026amp; Editing. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Peking University for making the China Health and Retirement Longitudinal Study publicly available for academic use.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang J, Wu X, Lai W, Long E, Zhang X, Li W. Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis. BMJ open. 2017;7(8):e017173. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/nyas.14506\u003c/span\u003e\u003cspan address=\"10.1111/nyas.14506\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Chen Y, Ma L. Depression and cardiovascular disease in elderly: Current understanding. J Clin Neurosci. 2018;47:1\u0026ndash;5. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jocn.2017.09.022\u003c/span\u003e\u003cspan address=\"10.1016/j.jocn.2017.09.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura T, Tsuji T, Nagamine Y, Ide K, Jeong S, Miyaguni Y. Suicide Rates. Social Capital, and Depressive Symptoms among Older Adults in Japan: An Ecological Study. Int J Environ Res Public Health.(2019) 16(24):4942. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph16244942\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16244942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Rozelle S, Xu Q, Yu N, Zhou T. Social Engagement and Elderly Health in China: Evidence from the China Health and Retirement Longitudinal Survey (CHARLS). Int J Environ Res Public Health. 2019;16(2):278. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph16020278\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16020278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Fan X, Luo H, Zhou Z, Shen C, Hu N. Comparison of Depressive Symptoms and Its Influencing Factors among the Elderly in Urban and Rural Areas: Evidence from the China Health and Retirement Longitudinal Study (CHARLS). Int J Environ Res Public Health. (2021)1 8(8):3886. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph18083886\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18083886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim BJ, Kihl T. Suicidal ideation associated with depression and social support: a survey-based analysis of older adults in South Korea. BMC psychiatry. 2021;21(1):409. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12888-021-03423-8\u003c/span\u003e\u003cspan address=\"10.1186/s12888-021-03423-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng QW, Wang QN, Yang LS, Liu KY, Cao HJ, Xie SY. The association between depressive symptoms and chronic diseases among elderly people in urban and rural communities of Lu'an City. Mod Prev Med. 2018;45(20):3731\u0026ndash;4 + 3747.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarithasan D, Mukari SZS, Ishak WS, Shahar S, Yeong WL. The impact of sensory impairment on cognitive performance, quality of life, depression, and loneliness among older adults. Int J geriatric psychiatry. 2019;35(4):358\u0026ndash;64. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gps.5237\u003c/span\u003e\u003cspan address=\"10.1002/gps.5237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim B, Jun H, Lee J, Kim YM. Social Support. Activities of Daily Living, and Depression among Older Japanese and Koreans Immigrants in the U.S. Soc Work Public Health. 2020;35(4):163\u0026ndash;76. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/19371918.2020.1761922\u003c/span\u003e\u003cspan address=\"10.1080/19371918.2020.1761922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin N, Wellman B, Dean A, Ensel WM. Social Support, Life Events, and Depression. Contemp Sociol. 1988;17(2):237. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/2070619\u003c/span\u003e\u003cspan address=\"10.2307/2070619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnantapong K, Wiwattanaworaset P, Sriplung H. Association between Social Support and Frailty among Older People with Depressive Disorders. Clin Gerontol. 2020;43(4):400\u0026ndash;10. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07317115.2020.1728002\u003c/span\u003e\u003cspan address=\"10.1080/07317115.2020.1728002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrivastava S, Debnath P, Shri N, Muhammad T. The association of widowhood and living alone with depression among older adults in India. Sci Rep. 2021;11(1):21641. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-01238-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-01238-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalther A, Philipp M, Lozza N, Ehlert U, Emotional, Support. Depressive Symptoms, and Age-Related Alterations in Male Body Composition: Cross-Sectional Findings from the Men's Health 40 + Study. Front Psychol. 2017;8:1075. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2017.01075\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2017.01075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi E, Han KM, Chang J, Lee YJ, Choi KW, Han C. Social participation and depressive symptoms in community-dwelling older adults: Emotional social support as a mediator. J Psychiatr Res. 2021;5(137):589\u0026ndash;96. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpsychires.2020.10.043\u003c/span\u003e\u003cspan address=\"10.1016/j.jpsychires.2020.10.043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang L, Fang CL, Fang SH. A study of regular exercise, social support and depression in the community-dwelling older people. Int J Nurs Pract. 2022;28(4):e12954. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ijn.12954\u003c/span\u003e\u003cspan address=\"10.1111/ijn.12954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Xu J, Nie Y, Pan P, Zhang X, Li Y. Effects of Social Participation and Its Diversity, Frequency, and Type on Depression in Middle-Aged and Older Persons: Evidence From China. Front Psychiatry. 2022;25(13):825460. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyt.2022.825460\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2022.825460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen YY, Wong GH, Lum TY, Lou VW, Ho AH, Luo H. Neighborhood support network, perceived proximity to community facilities and depressive symptoms among low socioeconomic status Chinese elders. Aging Ment Health. 2015;20(4):423\u0026ndash;31. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13607863.2015.1018867\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2015.1018867\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu H, Cao Q, Shi Z, Lin W, Jiang H, Hou Y. Social support and depressive symptom disparity between urban and rural older adults in China. J Affect Disord. 2018;237:104\u0026ndash;11. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2018.04.076\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2018.04.076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Harper S. The impact of son or daughter care on Chinese older adults' mental health. Soc Sci Med. 2022;306:115104. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2022.115104\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2022.115104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Liu E, Balezentis T, Jin H, Streimikiene D. Association between socioeconomic welfare and depression among older adults: Evidence from the China health and Retirement Longitudinal Study. Soc Sci Med. 2021;275:113814. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2021.113814\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2021.113814\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYelton B, Friedman DB, Noblet S, Lohman MC, Arent MA, Macauda MM. Social Determinants of Health and Depression among African American Adults: A Scoping Review of Current Research. Int J Environ Res Public Health. 2022;19(3):1498. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph19031498\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19031498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351\u0026ndash;77. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/109019818801500401\u003c/span\u003e\u003cspan address=\"10.1177/109019818801500401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins AE. Health ecology and environmental management in Mozambique. Health Place. 2002;8(4):263\u0026ndash;72. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1353-8292(02)00005-9\u003c/span\u003e\u003cspan address=\"10.1016/s1353-8292(02)00005-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Zupan NJ, Shiyanbola OO, Swearingen R, Carlson JN, Jacobson NA. Factors influencing patient adherence with diabetic eye screening in rural communities: A qualitative study. PLoS One. 2018;13(11):e0206742. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0206742\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0206742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei PC, Wu YY, Li LL, Wang QZ, Ye RX, Gao J. The analysis of impact factors of depression among middle: aged and older chronic diseases patients in China based on health ecological aspect. Mod Prev Med. 2021;48(7):1253\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61\u0026ndash;8. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dys203\u003c/span\u003e\u003cspan address=\"10.1093/ije/dys203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Jia CX, Qin P. Reliability and validity of the Center for Epidemiologic Studies Depression Scale (CES-D) among suicide attempters and comparison residents in rural China. BMC Psychiatry. 2015;15:76. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12888-015-0458-1\u003c/span\u003e\u003cspan address=\"10.1186/s12888-015-0458-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10(2):77\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubin R. Exploring the Relationship Between Depression and Dementia. JAMA. 2018;320(10):961\u0026ndash;2. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2018.11154\u003c/span\u003e\u003cspan address=\"10.1001/jama.2018.11154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTengku Mohd TAM, Yunus RM, Hairi F, Hairi NN, Choo WY. Social support and depression among community dwelling older adults in Asia: a systematic review. BMJ open. 2019;9(7):e026667. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2018-026667\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2018-026667\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou S, Gao L, Liu F, Tian W, Jin Y, Zheng ZJ. Socioeconomic status and depressive symptoms in older people with the mediation role of social support: A population-based longitudinal study. Int J Methods Psychiatr Res. 2021;30(4):e1894. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mpr.1894\u003c/span\u003e\u003cspan address=\"10.1002/mpr.1894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang M, Mirutse G, Guo L, Ma X. Role of socioeconomic status and housing conditions in geriatric depression in rural China: A cross-sectional study. BMJ open. 2019;9(5):e024046. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2018-024046\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2018-024046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi LW, Liu J, Zhang Z, Xu H. Late-life depression in Rural China: do village infrastructure and availability of community resources matter? Int J Geriatr Psychiatry. 2015;30(7):729\u0026ndash;36. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gps.4217\u003c/span\u003e\u003cspan address=\"10.1002/gps.4217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramaniam M, Abdin E, Sambasivam R, Vaingankar JA, Picco L, Pang S. Prevalence of Depression among Older Adults-Results from the Well-being of the Singapore Elderly Study. Ann Acad Med Singap. 2016;45(4):123\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan't Veer-Tazelaar PJ, van Marwijk HW, Jansen AP, Rijmen F, Kostense PJ, van Oppen P. Depression in old age (75+), the PIKO study. J Affect Disord. 2008;106(3):295\u0026ndash;9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2007.07.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2007.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Gilany AH, Elkhawaga GO, Sarraf BB. Depression and its associated factors among elderly: A community-based study in Egypt. Archives of gerontology and geriatrics. (2018) 77:103\u0026ndash;107. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.archger.2018.04.011\u003c/span\u003e\u003cspan address=\"10.1016/j.archger.2018.04.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePilania M, Bairwa M, Khurana H, Kumar N. Prevalence and Predictors of Depression in Community-Dwelling Elderly in Rural Haryana, India. Indian J Community Med. 2017;42(1):13\u0026ndash;8. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/0970-0218.199792\u003c/span\u003e\u003cspan address=\"10.4103/0970-0218.199792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFatma A, Hikmet Y. The Relationships Between Life Satisfaction, Gender, Social Security, and Depressive Symptoms Among Elderly in Turkey. Educational Gerontol. 2015;41(4):305\u0026ndash;14. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/03601277.2013.852923\u003c/span\u003e\u003cspan address=\"10.1080/03601277.2013.852923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicco L, Subramaniam M, Abdin E, Vaingankar JA, Chong SA. Gender differences in major depressive disorder: findings from the Singapore Mental Health Study. Singap Med J. 2017;58(11):649\u0026ndash;55. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11622/smedj.2016144\u003c/span\u003e\u003cspan address=\"10.11622/smedj.2016144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVu HTT, Lin V, Pham T, Pham TL, Nguyen AT, Nguyen HT. Determining Risk for Depression among Older People Residing in Vietnamese Rural Settings. Int J Environ Res public health. 2019;16(15):2654. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph16152654\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16152654\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlei DA, Goldman N, Liu IW, Weinstein M. Sex differences in trajectories of depressive symptoms among older Taiwanese: the contribution of selected stressors and social factors. Aging \u0026amp; mental health. 2013;17(6):773\u0026ndash;83. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13607863.2013.781119\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2013.781119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi YH, Pei JJ, Hao C, Yao W, Wang HX. The relationship between chronic diseases and depression in middle-aged and older adults: A 4-year follow-up study from the China Health and Retirement Longitudinal Study. J Affect Disord. 2021;289:160\u0026ndash;6. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2021.04.032\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2021.04.032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRead JR, Sharpe L, Modini M, Dear BF. Multimorbidity and depression: A systematic review and meta-analysis. J Affect Disord. 2017;221:36\u0026ndash;46. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2017.06.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2017.06.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurayama Y, Ohba H, Yasunaga M, Nonaka K, Takeuchi R, Nishi M. The effect of intergenerational programs on the mental health of elderly adults. Aging Ment Health. 2015;19(4):306\u0026ndash;14. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13607863.2014.933309\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2014.933309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong LN, Hu P, Yao Y, Zhao QH. Social support as a mediator between depression and quality of life in Chinese community-dwelling older adults with chronic disease. Geriatr Nurs. 2019;40(3):252\u0026ndash;6. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gerinurse.2018.10.014\u003c/span\u003e\u003cspan address=\"10.1016/j.gerinurse.2018.10.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Xue Y, Zhao H, Zheng X, Zhu R, Du Y. Prevalence and related influencing factors of depressive symptoms among empty-nest elderly in Shanxi, China. J Affect Disord. 2019;245:750\u0026ndash;6. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2018.11.045\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2018.11.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobson NC, Lord KA, Newman MG. Perceived emotional social support in bereaved spouses mediates the relationship between anxiety and depression. J Affect Disord. 2017;211:83\u0026ndash;91. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2017.01.011\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2017.01.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalther A, Philipp M, Lozza N, Ehlert U. Emotional Support, Depressive Symptoms, and Age-Related Alterations in Male Body Composition: Cross-Sectional Findings from the Men's Health 40 + Study. Front Psychol. 2017;8:1075. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2017.01075\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2017.01075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiedt AD, Saito Y, Crimmins EM. Depressive, Symptoms. Transitions to Widowhood, and Informal Support From Adult Children Among Older Women and Men in Japan. Res Aging. 2016;38(6):619\u0026ndash;42. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0164027515595442\u003c/span\u003e\u003cspan address=\"10.1177/0164027515595442\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStafford M, Antonucci TC, Zaninotto P. Joint Trajectories of Spousal Social Support and Depressive Symptoms in Older Age. J Aging Health. 2019;31(5):760\u0026ndash;82. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0898264317747077\u003c/span\u003e\u003cspan address=\"10.1177/0898264317747077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeonard MT, Chatkoff DK, Maier KJ. Couples' Relationship Satisfaction and Its Association with Depression and Spouse Responses Within the Context of Chronic Pain Adjustment. Pain Manag Nurs. 2017;19(4):400\u0026ndash;7. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pmn.2017.10.008\u003c/span\u003e\u003cspan address=\"10.1016/j.pmn.2017.10.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHung M, Voss MW, Bounsanga J, Graff T, Birmingham WC. Assessing spousal support and health in an aging population: support and strain amidst changing social dynamics. Soc Work Health Care. 2019;58(4):345\u0026ndash;67. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00981389.2019.1569577\u003c/span\u003e\u003cspan address=\"10.1080/00981389.2019.1569577\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin XX, Zhou R. Socio-economic Status, intergenerational support behavior and health poverty in old age: based on the empirical analysis of 2113 urban elderly living alone in five cities. Popul Dev. 2021;27(5):46\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng R, Yu M, Huang L, Wang F, Gao B, Fu D. Effect of intergenerational exchange patterns and intergenerational relationship quality on depressive symptoms in the elderly: An empirical study on CHARLS data. Front Public Health. 2022;10:1009781. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2022.1009781\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.1009781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePearce M, Garcia L, Abbas A, Strain T, Schuch FB, Golubic R. Association Between Physical Activity and Risk of Depression: A Systematic Review and Meta-analysis. JAMA psychiatry. 2022;79(6):550\u0026ndash;9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamapsychiatry.2022.0609\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2022.0609\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SY, Park JH, Lee MY, Oh KS, Shin DW, Shin YC. Physical activity and the prevention of depression: A cohort study. General hospital psychiatry. (2019) 60: 90\u0026ndash;97. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.genhosppsych.2019.07.010\u003c/span\u003e\u003cspan address=\"10.1016/j.genhosppsych.2019.07.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens M, Lieschke J, Cruwys T, C\u0026aacute;rdenas D, Platow MJ, Reynolds KJ. Better together: How group-based physical activity protects against depression. Soc Sci Med. 2021;286:114337. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2021.114337\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2021.114337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoh HW, Hong CH, Lee Y, Oh BH, Lee KS, Chang KJ. Participation in Physical, Social, and Religious Activity and Risk of Depression in the Elderly: A Community-Based Three-Year Longitudinal Study in Korea. PLoS One. 2015;10(7):e0132838. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0132838\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0132838\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarioli Vecchioli S, Sacchetti S, Nicolis di Robilant V, Cutuli D. The role of physical exercise and omega-3 fatty acids on depressive illness in the elderly. Curr Neuropharmacol. 2018;16(3):308\u0026ndash;26. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1570159X15666170912113852\u003c/span\u003e\u003cspan address=\"10.2174/1570159X15666170912113852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES. Physical Activity and Incident Depression: A Meta-Analysis of Prospective Cohort Studies. Am J Psychiatry. 2018;175(7):631\u0026ndash;48. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1176/appi.ajp.2018.17111194\u003c/span\u003e\u003cspan address=\"10.1176/appi.ajp.2018.17111194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCroezen S, Avendano M, Burdorf A, van Lenthe FJ. Social participation and depression in old age: a fixed-effects analysis in 10 European countries. Am J Epidemiol. 2015;182(2):168\u0026ndash;76. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwv015\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwv015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLotfaliany M, Hoare E, Jacka FN, Kowal P, Berk M, Mohebbi M. Variation in the prevalence of depression and patterns of association, sociodemographic and lifestyle factors in community-dwelling older adults in six low- and middle-income countries. J Affect Disord. 2019;251:218\u0026ndash;26. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2019.01.054\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2019.01.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Demographic characteristics of the sample(N=5660)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD/n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e67.90\u0026plusmn;6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e＜6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e1949(34.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003e60~69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e3775 (66.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u0026ge;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e3711(65.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003e70~79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e1603 (28.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eNumber of NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e282 (4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e1236 (21.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e1~2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e2777 (49.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e2856 (50.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e1647 (29.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e2804 (49.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eHealth satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eEducation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eCompletely satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e186 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e3267 (57.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e1412 (24.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eLiterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e1296 (22.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eSomewhat satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e2533 (44.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e804 (14.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eNot very satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e1095 (19.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e293 (5.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eNot at all satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e434 (7.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eADL score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eFormer smoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e2532 (44.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u0026le;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e3547 (62.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eNever smoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e3128 (55.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;＞6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e2113 (37.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eDrinking in the past year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003eMonthly household expenditure (Yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e3805 (67.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;＜500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e1296 (22.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e1855 (32.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u0026nbsp; 500~2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e2831 (50.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\"\u003e\n \u003cp\u003eSleep hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.53405017921147%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.56989247311828%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.043010752688172%\"\u003e\n \u003cp\u003e1533 (27.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePrevalence of depression by participants\u0026rsquo; demographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003eNo depressives\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003eDepressive symptoms\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003etest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e3329(58.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e2331(41.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e4.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e60~69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e2253(59.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1522(40.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e70~79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e906(56.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e697(43.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e170(60.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e112(39.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e157.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1912(66.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e944(33.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1417(50.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1387(49.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e108.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1748(53.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1519(46.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eLiterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e805(61.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e491(37.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e564(70.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e2409(29.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e212(72.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e81(27.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026nbsp;Former smoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1626(64.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e906(35.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eNever smoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1703(54.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1425(45.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eDrinking in the past year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e67.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e2095(55.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1710(44.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1234(66.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e621(33.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eSleep hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e＜6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1149(58.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e800(41.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026ge;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e2180(58.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1531(41.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eNumber of NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e215.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e903(73.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e333(26.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e1~2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1667(60.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1110(39.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e759(46.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e888(53.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eHealth satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e694.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eCompletely satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e139(74.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e47(25.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1075(76.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e337(23.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eSomewhat satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1615(63.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e918(36.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026nbsp;Not very satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e416(37.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e679(62.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026nbsp;Not at all satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e84(19.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e350(80.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eADL score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e403.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026le; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e2446(68.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1101(31.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.678119349005424%\"\u003e\n \u003cp\u003e＞6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e883(41.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1230(58.21))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eMonthly household expenditure (Yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e32.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026nbsp;＜500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e681(52.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e615(47.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026nbsp;500~2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e1682(59.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e1149(40.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e966(58.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e2331(41.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e3.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e81(51.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e75(48.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e3248(59.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e2256(40.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003ePension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e426(58.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e307(41.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.678119349005424%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\"\u003e\n \u003cp\u003e2903(58.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.869801084990957%\"\u003e\n \u003cp\u003e2024(41.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" width=\"82.53968253968254%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssociation between social support and depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo depressives\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive symptoms\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ec\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003etest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003eFamily support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eVisiting parents Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e19.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e470(52.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e430(47.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2859(60.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1901(39.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eContracting parents Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e15.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e191(49.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e197(50.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e3138(59.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e2134(40.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eTake Care of Grand Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e5.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2566(59.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1734(40.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e763(56.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e597(43.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eMoney support from children\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e18.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2035(61.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1291(38.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e1294(55.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1040(44.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eMoney support for children\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e12.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2841(57.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e2066(42.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e488(64.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e265(35.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eMarriage satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e345.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e1599(69.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e711(30.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eSomewhat satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e1273(58.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e917(41.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eNot satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e120(25.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e348(74.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.811287477954146%\"\u003e\n \u003cp\u003eNo spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e337(48.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e355(51.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eChildren satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e188.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eCompletely satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e268(69.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e116(30.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e1846(63.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1045(36.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eSomewhat satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e1131(54.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e961(45.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNot very satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e51(27.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e135(72.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNot at all satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e12(18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e52(81.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e21(48.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e22(51.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003eSocial activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eWent to community club\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e17.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2746(57.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e2019(42.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e583(65.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e312(34.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eWent to sport, social\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e12.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e3166(58.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e2261(41.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e163(69.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e70(30.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eUsed the internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e27.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e3188(58.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e2290(41.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e141(77.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e41(22.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eOther social activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e4.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e3286(58.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e2315(41.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e43(72.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e16(27.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003eCommunity support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eExercising facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e16.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e1488(55.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1170(44.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e1841(61.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1161(38.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eDancing team or other exercise organizations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e23.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2385(56.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1804(43.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e944(64.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e527(35.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eOrganizations for helping the elderly and the handicapped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e28.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2489(58.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1883(43.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e840(65.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e448(34.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eElderly association\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e24.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e2490(57.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e1875(42.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.63668430335097%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\"\u003e\n \u003cp\u003e839(64.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.753086419753085%\"\u003e\n \u003cp\u003e456(35.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.756613756613756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Results of the logistic regression analysis for correlates of depression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eAge (60~69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e70~79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e0.993(0.870~1.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.991(0.868~1.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.923(0.803~1.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.908(0.789~1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e0.848(0.646~1.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.845(0.644~1.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.752(0.566~0.998) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.710(0.534~0.943)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eGender (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e1.640(1.457~1.846) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.620(1.366~1.921) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.519(1.272~1.815) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.406(1.170~1.689) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNumber of NCDs (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1~2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e1.376(1.175~1.611)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.374(1.173~1.609) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.395(1.187~1.640)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.382(1.175~1.625) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e1.698(1.424~2.025)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.693(1.420~2.020) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.739(1.450~2.084)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.736(1.447~2.082)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eADL score (\u0026le;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e＞6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e2.228(1.968~2.522)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e2.223(1.963~2.517) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e2.051(1.801~2.335)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.968(1.726~2.244) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eHealth satisfaction (Completely satisfied)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e0.895(0.623~1.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.899(0.625~1.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.926(0.627~1.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.925(0.626~1.368)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eSomewhat satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e1.660(1.168~2.359)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.666(1.172~2.367)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.498( \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1.025~2.188)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.533(1.048~2.244) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNot very satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e3.758(2.607~5.418)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e3.757(2.606~5.416)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e3.178(2.145~4.708)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e3.307(2.228~4.909)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNot at all satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.930232558139537%\"\u003e\n \u003cp\u003e7.993(5.245~12.181)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e7.987(5.240~12.175)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e6.159(3.945~9.617)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e6.333(4.050~9.902)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eSmoking (Former smoking)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNever smoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.987(0.836~1.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.997(0.842~1.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.008(0.850~1.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eDrinking in the past year (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.942(0.820~1.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.944(0.819~1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.947(0.822~1.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eVisiting parents Monthly (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.771(0.650~0.914)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.769(0.649~0.913) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eContracting parents Monthly (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.923(0.723~1.178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.951(0.744~1.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eTake Care of Grand Children (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.054(0.910~1.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.067(0.921~1.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eMoney support from children (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.180(1.029~1.353)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.177(1.026~1.351) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eMoney support for children (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.794(0.659~956) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.792(0.657~0.954) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eMarriage satisfaction (Very satisfied)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eSomewhat satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.196(1.029~1.390)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.202(1.033~1.398) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNot satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e2.998(2.322~3.873)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e2.978(2.304~3.849) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNo spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.480(1.197~1.829)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.423(1.150~1.761)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eChildren satisfaction (Completely satisfied)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.253(0.965~1.629)\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.241(0.955~1.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eSomewhat satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.460(1.112~1.917)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.456(1.108~1.914) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNot very satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e2.964(1.898~4.630)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e2.890(1.846~4.526) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNot at all satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e3.774(1.796~7.930)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e3.640(1.736~7.635) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNo child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.756(0.856~3.606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e1.566(0.760~3.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eWent to community club (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.892(0.753~1.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.918(0.775~1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eWent to sport, social (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.655(0.475~0.902) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.662(0.480~0.913) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eUsed the internet (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.602(0.408~0.886) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.682(0.459~1.012) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eOther social activities (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.630(0.334~1.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.661(0.350~1.249)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eExercising facilities (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.961(0.840~1.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.969(0.846~1.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eDancing team or other exercise organizations (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.896(0.767~1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.9350(0.799~1.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eOrganizations for helping the elderly and the handicapped (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.856(0.724~1.012)\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.855(0.723~1.012)\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eElderly association (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.935(0.792~1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.943(0.798~1.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eEducation (Illiterate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eLiterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.859(0.735~1.003)\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.718(0.590~0.873)\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.737(0.544~0.999) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eMonthly household expenditure\u0026nbsp;(＜500) (Yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e500~2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.797(0.685~0.928) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026ge;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.757(0.635~0.902) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNegelkerke\u0026nbsp;R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003ePercentage Correct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.930232558139537%\"\u003e\n \u003cp\u003e69.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e69.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e71.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.767441860465116%\"\u003e\n \u003cp\u003e71.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"social support, depressive symptoms, elderly, rural, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-2388890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2388890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepressive symptoms has become an increasingly important public health issue, contributing to disability and disease burden around the world. Studies have found that social support is strongly associated with depression in rural older people, so it is important to explore the factors influencing depression in rural older people in a comprehensive manner and to analyze the association between social support and depression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOn the basis of a theoretical model of health ecological, data were obtained from The China Health and Retirement Longitudinal Study in the 2018, with a sample consisting of 5,660 rural individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. Then, Chi-square test and logistic regression analyses were used for statistical description and inference.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eResults indicate that the prevalence of depressive symptoms amongst rural older adults in China is 41.18%. The logistic regression analysis reveals that being female (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.406, 95% \u003cem\u003eCI\u003c/em\u003e: 1.170\u0026ndash;1.689), having\u0026thinsp;\u0026ge;\u0026thinsp;3 non-communicable diseases (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.736, 95% \u003cem\u003eCI\u003c/em\u003e: 1.447\u0026ndash;2.082), being not satisfied with spouse (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.978, 95% \u003cem\u003eCI\u003c/em\u003e: 2.304\u0026ndash;3.849), and being not at all satisfied with children (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.640, 95% \u003cem\u003eCI\u003c/em\u003e: 1.736\u0026ndash;7.635) are significantly correlated with depression.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe prevalence of depression amongst rural Chinese older adults is obviously high. Women and the elderly with chronic diseases need to be focused on. Hence, this study suggests that promoting interactivity amongst family members, increasing their relationship satisfaction, and encouraging active participation in social activities are necessary to further reduce the risk of depression amongst rural Chinese older adults. The government should not only improve the social security system, but also provide financial support and assistance to the elderly in rural China.\u003c/p\u003e","manuscriptTitle":"Influencing factors of depressive symptoms in the elderly in rural China: Based on a health ecological model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-03 14:26:07","doi":"10.21203/rs.3.rs-2388890/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e150909-df25-4f36-ac5e-198f447ffc17","owner":[],"postedDate":"January 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-04-10T11:59:35+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-03 14:26:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2388890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2388890","identity":"rs-2388890","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}