Development and Validation of a Depression Risk Prediction Model for Community Elderly Individuals

Development and Validation of a Depression Risk Prediction Model for Community Elderly Individuals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Depression Risk Prediction Model for Community Elderly Individuals RUI ZHAO, Mingshu Huo, Mingyang Tan, Yan Cai, Xiuchao Geng, Mengjie Xia, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9252451/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Depression in elderly populations is a significant public health issue, particularly in community settings. This study aimed to develop and validate a risk prediction model to identify depression risk among community-dwelling elderly individuals. Objective To construct a risk prediction model of depression in the elderly in the community and to provide a screening method and theoretical basis for the early detection of depression in the elderly. Methods A total of 1479 community-dwelling elderly from Anshan and Jinzhou (June 2023–September 2025) were assessed using the General Information Questionnaire, ULS-6, LSNS-6, and PHQ-9. Logistic regression identified predictors; RStudio was used to construct static and dynamic nomograms. The model was internally validated via bootstrapping and evaluated using the C-statistic, ROC curve, and decision curve analysis (DCA). Results The model demonstrated excellent predictive performance. The detection rate of depression among the elderly in the community was 33.1%. Sex, sleep quality, living style, number of chronic diseases, social isolation, loneliness, living status, and physical exercise were predictive factors (all P < 0.05). The C statistic was 0.854 (95% CI 0.830 ~ 0.877), the optimal critical value was 0.564, the sensitivity was 76.9%, the specificity was 79.5%, and the calibration curve and Brier score showed that the prediction model was well-fitted. Conclusions The developed model effectively identifies depression risk in community elderly, potentially aiding early intervention and resource optimization. It serves as a practical screening tool for community health workers. Community Elderly Depression Risk prediction Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Depressive disorders are the most common affective disorders wherein depression is a common symptom. The main symptoms of depression is consistent low mood which is a lot less than should be expected with the individual[ 1 ]. In addition to low mood and anhedonia, other important clinical features of depression include somatic symptoms, cognitive symptoms and sleep symptoms. People may not be able to concentrate, lose interest, feel worthless or guilty, and become generally pessimistic, with some potentially becoming suicidal[ 2 ]. Late Life Depression or LLD refers to depression in elderly people (≥ 60 years). It includes both primary depression and secondary depression from other causes. It refers to primary depression commencing in older adulthood when defined narrowly[ 3 ]. The prevalence of LLD may reach as high as 40% among hospitalized patients, up to 30% among nursing home residents, and potentially 8% to 15% in community settings[ 4 ]. In comparison to depression found in other groups, LLD shows different clinical features and disease characteristics. “LLD symptoms refer to more complex neuropsychiatric and atypical presentations. In view of depression, these symptoms are primarily persistent likely to low mood and sadness”[ 5 ]. Emotionally, this expresses itself as a feeling that life is dull and meaningless, hopelessness, and loss of interest in usual other interests[ 6 ]. Some LLD patients may experience somatic anxiety symptoms such as worry, anxiety, and tension[ 7 ]. Others report physical pain, often accompanied by sleep disturbances like insomnia or hypersomnia[ 8 ]. These atypical symptoms may mask the underlying depressive symptoms. Furthermore, LLD is often considered to be associated with cognitive impairment. Cognitive impairment is frequently mistaken for the physiological aging process in the elderly and consequently overlooked[ 9 ]. Moreover, the mutual influence between LLD and cognitive impairment can significantly complicate clinical diagnosis[ 10 ]. Therefore, identifying and diagnosing LLD is relatively more challenging. In terms of disease characteristics, unlike depression in other age groups, LLD inflicts relatively greater harm on patients' health: On one hand, LLD serves as an independent risk factor for chronic conditions such as hypertension[ 11 ], cardiovascular disease[ 12 ], and diabetes in the elderly[ 13 ]. It also leads to physical, cognitive, and social functional impairments, along with heightened self-neglect[ 14 – 16 ]. On the other hand, LLD patients exhibit a higher proportion of poor response to antidepressant medications during treatment and experience higher relapse rates post-treatment[ 17 ]. Consequently, this results in reduced quality of life and increased mortality risk for these individuals. Regarding the pathogenesis of LLD, current research has yet to reach definitive conclusions. The absence of characteristic physical manifestations or specific laboratory indicators further complicates investigations into its pathological mechanisms[ 18 ]. Existing theories primarily encompass multiple hypotheses involving genetic, neurobiochemical, and psychosocial factors. It is widely recognized that the onset and progression of LLD are influenced by the combined effects of biological, psychological, and social factors. These factors do not act in isolation but interact at specific points in time, collectively driving the disease process[ 19 , 20 ]. Therefore, developing an effective predictive model for depression risk and identifying key predictors among community-dwelling older adults is crucial for the early identification and intervention of LLD. Given the presence of relevant risk factors, researchers have developed risk prediction models (RPMs) to assess the risk of developing or progressing to depression[ 21 , 22 ]. However, previous studies on RPMs have certain limitations: First, although a few models perform well in predicting depression among older adults, there remains a lack of in-depth exploration into key predictors and model efficacy for community-dwelling older populations[ 22 , 23 ]. For instance, small sample sizes or limited predictive variables may restrict the models' generalizability and reliability[ 24 , 25 ]. Second, previous studies often focused only on partial factors (such as repeated or isolated variables) or employed complex tools, resulting in operational difficulties when applied to real-world communities[ 20 ]. Moreover, the impact of important factors like comorbid chronic diseases has not been adequately assessed. For instance, some research indicates that after controlling for confounding factors[ 26 ], over half of elderly individuals with depression suffer from at least one comorbid chronic disease, yet this is frequently overlooked in predictive models. This study constructs and validates a comprehensive risk assessment model specifically designed for community-dwelling elderly populations. This model integrates multiple key predictive factors, all selected through logistic regression analysis and validated using large-sample data. This comprehensive modeling approach provides a more holistic risk assessment. Presented as a nomogram, it simplifies operation, enabling non-specialists (such as community workers) to use it easily, thereby reducing workload and improving predictive accuracy. This supports early intervention and resource allocation, providing a more reliable evidence base for community health management. Methods Study participants This research constitutes a cross-sectional study. Utilizing a convenience sampling method, we selected 1,500 elderly individuals from five communities in both Jinzhou City and Anshan City, Liaoning Province, China, between June 2023 and September 2025. The inclusion criteria were as follows: (1) individuals aged 60 or older; (2) those with clear consciousness capable of normal communication; (3) residents of the community for more than one year; and (4) voluntary participation in the study. The exclusion criteria included (1) individuals with significant physical illnesses, consciousness disorders, or mental health conditions that hindered their ability to complete the questionnaire and (2) individuals with impaired hearing and communication difficulties. According to the Events Per Variable (EPV) principle, the number of outcome events must be at least ten times the number of candidate predictor variables[ 27 ]. In a model with 13 candidate predictor variables, this necessitates a minimum of 130 outcome events. Citing the research conducted by Huang et al., the incidence of depression among community-dwelling elderly individuals was reported to be 15%[ 4 ], indicating that a sample size of 867 cases is required. Accounting for a 20% loss to follow-up, the sample size should be adjusted to at least 1084 cases. Ultimately, a total of 1479 samples were collected. Candidate variables and outcome definition Variable Classification and Measurement Tools (1) Demographic variables: This category encompasses fundamental sociodemographic indicators, including gender, age, marital status, monthly income, education level, disease type, lifestyle, sleep quality, self-rated health status, convenience of community transportation, and frequency of physical exercise. (2) Psychological variable: Loneliness was assessed in the elderly using the 6-item UCLA Loneliness Scale (ULS-6). Originally derived from the UCLA Loneliness Scale by Russell et al.[ 28 ], the ULS-6 was a simplification of the ULS-8 by Hays et al[ 29 ]. and subsequently adapted into the Chinese version by Zhou Liang et al[ 30 ]. The scale comprises 6 items rated on a 4-point scale (1= "always" to 4= "never"), yielding a total score between 6 and 24, where higher scores indicate greater loneliness. The ULS-6 demonstrates good reliability and validity among elderly individuals in Chinese community settings[ 30 ], with a Cronbach's α coefficient of 0.731 reported in this study. (3) Social network support variable: The Lubben Social Network Scale (LSNS-6) was employed to evaluate the level of social network support, rather than directly assessing the subjective experience of "sense of social alienation." Developed by Lubben et al.[ 31 ], this scale encompasses two dimensions: family and friends, each comprising three items. It captures the strength of social connections by quantifying objective indicators, such as the frequency of social interactions and the availability of support. Each item is scored from 0 to 5 points, resulting in a total score that ranges from 0 to 30 points. A higher score indicates a greater level of social network support, while a score of ≤ 12 points signifies a risk of social isolation. A study indicates that the LSNS-6 is suitable for assessing the social network status of older adults, in this study, the Cronbach's α coefficient for this scale was 0.754[ 32 , 33 ]. Outcome Variable: Depressive Symptoms The Patient Health Questionnaire Depression Scale (PHQ-9) was employed to assess depressive symptoms in the elderly population within the community. This scale[ 34 ] comprises nine items, each rated on a scale from 0 to 3. A score of 0 signifies the absence of depression, 1 indicates symptoms occurring on a few days, 2 reflects symptoms present for more than half the days, and 3 denotes symptoms experienced almost every day. Over a two-week period, the total possible score is 27 points, with higher scores indicating greater levels of depression. In China, this questionnaire is extensively utilized for screening individuals with depression. Furthermore, research has demonstrated its appropriateness for identifying depression among elderly community members[ 35 ]. In this study, the Cronbach's α coefficient for this instrument was determined to be 0.728. Data Collection Prior to the investigation, it is essential to obtain the support and consent of the community head. During the investigation, researchers conducted on-site assessments with the consent of the elderly participants, adhering to uniform and standardized instructions. Following the investigation, the questionnaires were collected immediately to identify any inaccuracies or omissions. Corrections were made, and the questionnaires were subsequently reconfirmed. Upon completion of data collection, two individuals entered the data and conducted verification. In total, 1,500 questionnaires were distributed for this study, yielding 1,479 valid responses, which corresponds to an effective recovery rate of 98.6%. The valid responses were categorized into a modeling group (1,035 cases) and a validation group (444 cases) in a 7:3 ratio. Data Statistics In this study, SPSS 26.0 was used for statistical analysis, and the measurement data were measured by means of mean±standard deviation ( \(\:\stackrel{-}{\chi\:}\pm\:s\) ) and T-test. The rate of counting data (%) was described and \(\:{}^{2}\) test was adopted. Statistically significant variables in univariate regression analysis were incorporated into multiple logistic regression analyses to obtain independent predictors of depression in elderly people in the community. RStudio (car, rms, Proc, dcurves, Replot, DynNom) were used to analyze the data, dividing the training set and the validation set according to 7:3. Statistically significant variables in multiple logistic regressions were included in the production of RStudio columns, and static and dynamic columns were constructed, respectively.The static graph can show the depression risk index of the elderly in the community, and the dynamic graph can accurately draw the depression risk index according to different situations of high-risk groups. The working characteristic curve (receiver operating characteristic, ROC) of the subject is drawn, the difference of the area under the curve (area under curve, AUC) is sensed directly, the calibration curve is calibrated to evaluate the calibration degree, and the practicability of the curve evaluation model (decision curve analysis, DCA) is analyzed by decision curve. The Hosmer-Lemeshow test was used to evaluate the degree of fit of the model, which was repeatedly sampled 1000 times for internal verification through the bootstrap method. The consistency index (C-index, > 0.7) was used to evaluate the sensitivity of the risk prediction model, and the Brier score (< 0.25) was used to evaluate the composite index. P < 0.05 was considered statistically significant. Results Demographic Characteristics of Community-Dwelling Older Adults and Univariate Analysis of Depression A total of 1479 elderly patients were included in this study, including 775 males and 704 females. 493 were unmarried/divorced/widowed, and 355 were married. According to the depression assessment tool, the elderly in the community were divided into the depressed group (n = 489, 33.06%) and a non-depressed group (n = 990, 66.94%). Thirteen variables, such as general information, loneliness, and social isolation of the elderly, were included in univariate analysis, among which the differences in age, marriage, and education level were not statistically significant ( P > 0.05), and the other variables were statistically significant, and the specific variables were shown in Table 1 . Table 1 Variables with statistically significant differences in the incidence of depression among community elderly [case (percentage, %)] Variable Non-depressive (n = 990) Depressive (n = 489) χ 2 P Sex 8.432 0.004 Male 545(55.1%) 230(47.0%) Female 445(44.9%) 259(53.0%) Age(years) 60ཞ65 506(51.1%) 262(53.6%) 0.812 0.666 65ཞ70 411(41.5%) 192(39.3%) > 70 73(7.4%) 35(7.2%) Marital status single 78(7.9%) 45(9.2%) 2.192 0.534 divorced 157(15.9%) 67(13.7%) Married 654(66.1%) 332(67.9%) Widowed 101(10.2%) 45(9%) Monthly income (RMB) < 3000 107(10.8%) 149(30.5%) 95.457 5000 268(27.1%) 76(15.5%) Education level Primary school and below 184(18.6%) 109(18.6%) 6.304 0.098 junior high school 340(34.3%) 182(37.2%) high school 245(24.7%) 107(21.9%) College degree or above 221(22.3%) 91(18.6%) Type of disease (species) 0–1 315(31.8%) 87(17.8%) 118.172 < 0.001 2 501(50.6%) 189(38.7%) ≥ 3 174(17.6%) 213(43.6%) Sleep quality 101.426 < 0.001 Poor 414(41.8%) 309(63.2%) Moderate 277(28.0%) 141(28.8%) Good 299(30.2%) 39(8%) Self-rated health 63.783 < 0.001 Poor 515(52.0%) 262(53.6%) Moderate 196(19.8%) 167(34.2%) Good 279(28.2%) 60(12.3%) Mode of residence 36.251 < 0.001 live with children 751(75.9%) 297(60.7%) Not live with children/ Living alone 239(24.1%) 192(39.3%) Working condition 14.144 0.003 retire 407(41.1%) 180(36.8%) Work 235(23.7%) 93(19.0%) Long-term illness/disability 184(18.6%) 128(26.2%) other 164(16.6%) 88(18.0%) Physical exercise 94.497 < 0.001 < 2 times a week 289(29.2%) 240(49.1%) 3–5 times a week 379(38.3%) 194(39.7%) almost everyday 322(32.53%) 55(11.2%) Loneliness 8.639 0.003 none 635(64.1%) 275(56.2%) yes 355(35.9%) 214(43.8%) Social isolation 122.848 < 0.001 none 828(83.6%) 279(57.1%) yes 162(16.4%) 210(42.9%) Multivariate logistic regression analysis of depression in the elderly in the community The statistically significant variables of univariate analysis were logistic regression analysis, and the independent variable was assigned as shown in Table 2 , and the dependent variable was taken as the dependent variable (yes = 1, no = 0), whether the elderly in the community had depressive symptoms. The results showed that gender, disease type, sleep quality, living style, social isolation, physical activity, living conditions, and loneliness were independent risk factors for depression in the elderly in the community (all P < 0.05), as shown in Table 3 . Table 2 Variable assignment table for binary classification logistic regression analysis of the influencing factors of depression in the elderly in the community Variable Assignment description Sex Male = 0, Female = 1 Monthly income (RMB) 5000(Z 1 = 1, Z 2 = 1) Type of disease (species) 0ཞ1 type(Z 1 = 0, Z 2 = 0), 2 types(Z1 = 0, Z 2 = 1), 3 types or more (Z 1 = 1, Z 2 = 1) Mode of residence live with children = 0, Not living with children/ Living alone = 1 Working condition retire(Z 1 = 0, Z 2 = 0, Z 3 = 0),Work༈Z 1 = 0,Z 2 =0,Z 3 = 1༉, Long-term illness/disability༈Z 1 = 0,Z 2 =1,Z 3 = 0༉, other༈Z 1 = 1,Z 2 =0,Z 3 = 0༉ Sleep quality Poor(Z 1 = 0, Z 2 = 0), moderate(Z 1 = 0, Z 2 = 1), Good(Z 1 = 1, Z 2 = 1) Physical exercise < 2 times a week(Z 1 = 0, Z 2 = 0), 3–5 times a week(Z 1 = 0, Z 2 = 1), almost everyday(Z 1 = 1, Z 2 = 1) Social isolation none = 0, yes = 1 loneliness none = 0, yes = 1 Table 3 Logistic regression analysis of factors influencing depression in the elderly in the community (n = 1479) Variable B SE Wald χ 2 P OR 95% CI Constant terms -1.299 0.378 11.847 0.001 0.273 - Sex -0.494 0.141 12.220 0.000 0.610 0.462ཞ0.805 Monthly income (RMB) 64.256 0.000 3000–5000 1.781 0.228 60.933 0.000 5.935 3.795ཞ9.281 5000 0.615 0.181 11.568 0.100 1.849 1.297ཞ2.635 Mode of residence -0.343 0.148 5.342 0.021 0.71 0.530ཞ0.949 Type of disease (species) 62.121 0.000 2–3 -1.444 0.193 56.187 0.000 0.236 0.162ཞ0.344 > 3 -0.959 0.162 35.100 0.000 0.383 0.279ཞ0.527 Sleep quality 45.483 0.000 moderate 1.425 0.211 45.419 0.000 4.158 2.747ཞ6.293 good 1.186 0.227 27.192 0.000 3.275 2.097ཞ5.115 Physical exercise 52.783 0.000 3–5 times a week 1.458 0.201 52.781 0.000 4.298 2.900ཞ6.369 almost everyday 1.036 0.200 26.766 0.000 2.818 1.903ཞ4.172 Self-rated health 31.152 0.000 moderate 0.657 0.190 12.001 0.001 1.929 1.330ཞ2.798 good 1.160 0.208 31.004 0.001 3.191 2.121ཞ4.801 Working condition 5.905 0.116 work 0.064 0.199 0.103 0.748 1.066 0.722ཞ1.574 Long-term illness/disability -0.373 0.223 2.804 0.094 0.688 0.445ཞ1.066 other 0.024 0.218 0.012 0.913 1.024 0.668ཞ1.569 loneliness -0.927 0.140 44.072 0.000 0.396 0.301ཞ0.520 Social isolation -1.462 0.155 89.338 0.000 0.232 0.171ཞ0.314 Construction and evaluation of depression risk prediction model for elderly people in the community and evaluation of test efficiency Based on the independent risk factors obtained by logistic regression analysis, a risk prediction model for depression in elderly people in the community was constructed. The column diagram is shown in Figs. 1 and 2 , and Fig. 1 is a static column diagram, which can roughly judge the depression risk index range of patients. Figure 2 is a dynamic nomogram, and the depression risk index can be directly derived for each patient. For example, the red dot on each variable represents the patient's situation, and finally, the patient's risk index is 0.839. The Hosmer-Lemeshow test was used to verify the fitting effect ( P = 0.865). The ROC curve was used to verify the prediction effect of the model, as shown in Fig. 3 . The area under the ROC curve of the training set was 85.4%, 95% 95%CI (0.830 ~ 0.877), and the area under the ROC curve of the verification set was 83.8%, 95% 95%CI (0.796 ~ 0.880). The best cut-off value of the whole data was the maximum value of the Jordan index of 0.564; the sensitivity was 76.9%, and the specificity was 79.5%. The model was well differentiated and calibrated in predicting depression risk by bootstrap resampling 1000 times, with a C statistic of 0.854 (95% CI 0.830 ~ 0.877) and a Brier score of 0.140, and the calibration curves of both the training set and the validation set show that the predicted values by the model are basically consistent with the actual occurrence values, as shown in Fig. 4 . Within the prediction range of this model, the net benefit rate of the DCA curve is higher than that of the two extreme curves (all negative or all positive), indicating that this model can benefit the elderly in the community and has a certain application value, see Fig. 5 for details. Discussion The depression risk prediction model for the elderly in the community is scientific and easy to implement Under the influence of various factors in society, the incidence of depression among the elderly in the community is increasing year by year. A total of 1479 subjects in this study, including 489 elderly people with depression, accounting for 33.1%, are similar to the 36.8% results of Yucui Pu et al[ 36 ]. The results of Aihong Liu et al's study of depressive symptoms in the elderly in Wuhan of China were 14.04%[ 37 ], which may be caused by the regional and cultural differences between the north and the south. The huge burden of depression and the rapid development of population aging make it of great significance to construct a model for predicting depression risk in the elderly in the community. In this study, eight indicators, including gender, monthly income, chronic disease, sleep quality, loneliness, social isolation, physical exercise and living status, were screened by logistic regression to establish a visual risk prediction model. The eight factors included in the model can be obtained by filling in and asking a simple questionnaire, without any economic costs, and the compliance of the elderly is high. Community members can screen out the risk levels of the elderly based on the static nomogram risk, and according to the optimal cutoff value of 0.564, the high-risk and low-risk groups can be distinguished early. Then the dynamic prediction was made for the elderly at high risk, and the total score of 8 predictors was calculated to formulate the targeted nursing measures. It can identify the depressive symptoms of the elderly early, avoid the waste of medical resources, reduce the suicide rate and disability rate of the elderly, and improve the quality of life of the elderly. Analysis of influencing factors related to depression risk prediction in the elderly This study shows that the incidence of depression in elderly women is higher than that in elderly men. Similar to the Ferri study by Christine[ 38 ], elderly women may be more likely than men to experience poverty, decline in physical function and have health problems, which may be related to the fact that women are more likely to become caregivers in life and spend more energy[ 39 ]. As an independent factor affecting depression, it has a greater impact on chronic diseases in the elderly. Relevant studies have shown that elderly people with multiple chronic diseases have varying degrees of increased mental state and are highly correlated with negative health outcomes[ 40 , 41 ].This study showed that depressive symptoms occurred 1.57 times more in older adults living alone than in other older adults. Research has demonstrated this[ 42 ], which may be due to older people living alone having poorer health and being more susceptible to influence may have alcohol-related problems and engaging in risky behaviors, which can lead to psychological problems. In this study, the incidence of depression in the elderly with high loneliness is 1.25 times that of the normal elderly, which is consistent with the research result of C.J. Brush that there is an inevitable correlation between depression and loneliness[ 43 ]. When individuals report an increase in loneliness, the brain structures execute commands that cause a spike in negative emotions that produce depressive symptoms. Social isolation is an independent factor in the risk of depression in a predictive model of community older adults[ 44 ]. As they age, they are socially disadvantaged and more likely to feel isolated, which can hurt mental health. Older adults have a much higher risk of depression from multiple diseases than healthy older adults. Patients with a wide range of comorbidities will have sensory retardation, and even have difficulties in understanding, expressing and recognizing their emotions, resulting in the accumulation of negative emotions and thoughts, leading to depressive symptoms. Research shows that sleep quality is negatively correlated with the occurrence of depression in the elderly, and the better the sleep quality, the lower the likelihood of depression, which is consistent with the results of this study[ 45 ]. Potential causes may be hypothalamic-pituitary-adrenal axis hyperactivity, active biochemical pathways, and inflammatory cytokines in older adults with persistent sleep disorders. Community workers should pay attention to the social communication, anemia and loneliness of elderly women in the community while actively preventing depression[ 46 ]. The general situation of the elderly in the community is kept in the archives, and more attention is paid to the elderly who live alone, have poor sleep, and suffer from various chronic diseases. Community health service centers can often hold some lectures and activities related to depression prevention and treatment, and regularly conduct screening to reduce and delay the occurrence of depression symptoms in the elderly in the community[ 47 , 48 ]. The prediction effect of the depression risk prediction model of the elderly in the community is better Based on Logistic regression analysis, this study screened out 8 influencing factors, and built a nomogram model for predicting the risk of early depressive episodes in elderly people in the community. From the point of view of the calibration degree of the model, the P-values of the risk prediction model and the H-L test in the external validation of the model were both greater than 0.05, indicating that there was no significant difference between the predicted risk of depression and the actual risk of depression, and the model prediction had a good consistency[ 23 ]. It can be seen that the model has good predictive performance through strict internal and external verification, the construction methodology is rigorous, and the model has a certain scientific nature. Secondly, the transformation of the prediction model into a risk- scoring system improves the community operability of the model. At the same time, community health workers can roughly judge whether the elderly have depression risk according to the static state, and then use the dynamic nomogram to obtain the accurate depression risk score of the elderly with depression risk. The higher the score, the higher the depression risk. The sensitivity and specificity of this model are 76.9% and 79.5%, which can effectively reduce misjudgment and avoid waste of medical resources while maintaining effective sensitivity. Conclusions Based on factors such as gender, disease type, sleep quality, lifestyle, loneliness, social isolation, physical exercise, and living status, this nomogram is simple and easy to construct, and can save the workload of community workers and accurately predict the depression risk index of elderly people in the community. In this study, the nomogram constructed based on gender, disease type, sleep status, living style, loneliness, social isolation, and mood disorder was simple and easy, which could better predict the risk of depression in the elderly in the community. Compared with previous studies, this study enriches the risk prediction tools for depression in the elderly in the community to a certain extent. However, the overall sample size of this study is not large, and it is recommended to expand the sample size in the future. And you can go to the community health service center to cooperate to incorporate the laboratory test results into the risk factors, obtain more representative results, and provide a more effective basis for the prevention and treatment of depression in the elderly in the community. Strength and Limitations The depression risk prediction model constructed in this study demonstrated excellent discrimination. The area under the curve of the training set reached 0.854, and that of the validation set was 0.838, indicating that the model has good predictive capabilities. The model incorporates multiple predictive factors such as demographics, physical health, and social psychology, including gender, monthly income, disease type, sleep quality, physical exercise, social isolation, and loneliness, providing a comprehensive perspective for risk assessment. The study not only constructed a static nomogram but also developed a dynamic nomogram, enabling nursing staff and non-professionals to conduct convenient and intuitive risk scoring based on the specific circumstances of the elderly, calculate the specific probability of depression risk, and facilitate early screening.However, this study is a cross-sectional survey, and all data were collected at the same time point. The study only underwent internal validation and lacks validation in an independent external population. The validity and generalizability of the model need to be further confirmed, and we will further address this deficiency. Declarations Acknowledgements Thank all the co-authors as well as the elderly participants who took part in this research. Author contributions Rui Zhao: Conceptualization, Methodology, Investigation, Formal analysis, Writing - original draft; Mingshu Huo: Data curation, Investigation; Mingyang Tan: Visualization, Investigation; Yan Cai: Resources, Supervision; Xiuchao Geng: Software, Validation; Mengjie Xia: Visualization; Investigation; Yingzhi Chen:Visualization, Investigation; Xiaohong Liu: Conceptualization, Funding acquisition, Resources, Supervision, Writing - review & editing. Funding This paper is supported by:General research projects of the Zhejiang provincial department of education(Grant No.Y202455543); The special project of "Provincial and Municipal Cooperation" of Zhejiang Philosophy and Social Science Planning Project in 2024 (Grant No. 24SSHZ203YB);Taizhou Science and Technology Plan Project (Grant No. 25ywa41); Zhejiang Provincial Philosophy and Social Sciences Planning Program (Grant No. 26NDJC024YBM). Availability of data and materials Data may be shared during this study upon reasonable request. Readers may contact Rui Zhao ( [email protected] ) to submit raw data. Ethics approval and consent to participate The studies involving human subjects was approved by Jinzhou Medical University (ethical approval number : JZMULL2023080). At the beginning of the study, all participants were informed about the purpose of the study and their right to participate voluntarily. Written informed consent was obtained from all participants for this study. All methods were performed in accordance with relevant guidelines and regulations.Clinical trial number: not applicable. Consent for publication Not applicable. Competing interests All authors declared that they had no competing interests. References Tan J, Ma C, Zhu C, Wang Y, Zou X, Li H, et al. Prediction models for depression risk among older adults: systematic review and critical appraisal. 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J Epidemiol Community Health. 2011;65 Suppl 1:A243.1-A243. https://doi.org/10.1136/jech.2011.142976i.19. Van Smeden M, De Groot JAH, Moons KGM, Collins GS, Altman DG, Eijkemans MJC, et al. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Med Res Methodol. 2016;16:163. https://doi.org/10.1186/s12874-016-0267-3. Russell D, Peplau LA, Ferguson ML. Developing a Measure of Loneliness. Journal of Personality Assessment. 1978;42:290–4. https://doi.org/10.1207/s15327752jpa4203_11. Hays R, DiMatteo MR. A Short-Form Measure of Loneliness. J of Personality Assessment. 1987;51:69–81. https://doi.org/10.1207/s15327752jpa5101_6. Zhou L, Li Z, Hu M, Xiao S. Reliability and validity of ULS-8 loneliness scale in elderly samples in a rural community. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2012;37:1124–8. https://doi.org/10.3969/j.issn.1672-7347.2012.11.008. Lubben J, Blozik E, Gillmann G, Iliffe S, Von Renteln Kruse W, Beck JC, et al. Performance of an Abbreviated Version of the Lubben Social Network Scale Among Three European Community-Dwelling Older Adult Populations. The Gerontologist. 2006;46:503–13. https://doi.org/10.1093/geront/46.4.503. Wu Z, Yan Y, Cai H, Qi S, Xu M, Wang T, et al. Unveiling interrelationships through structural equation modeling: family function, social networks, and social phobia in peritoneal dialysis patients. Int Urol Nephrol. 2025;57:2291–300. https://doi.org/10.1007/s11255-025-04396-3. Buckley TD, Becker TD, Burnette D. Validation of the abbreviated Lubben Social Network Scale (LSNS‐6) and its association with self‐rated health amongst older adults in Puerto Rico. Health Social Care Comm. 2022;30. https://doi.org/10.1111/hsc.13977. Liu Z, Yu Y, Hu M, Liu H, Zhou L, Xiao S. PHQ-9 and PHQ-2 for Screening Depression in Chinese Rural Elderly. PLoS ONE. 2016;11:e0151042. https://doi.org/10.1371/journal.pone.0151042. Xiaoyan Sun, Yixue Li, Canqing Yu, Liming Li. Reliability and validity of depression scales of Chinese version: a systematic review. 2017. DOI: 10.3760/cma.j.issn.0254-6450.2017.01.021. Cui L, Ding D, Chen J, Wang M, He F, Yu S. Factors affecting the evolution of Chinese elderly depression: a cross-sectional study. BMC Geriatr. 2022;22:109. https://doi.org/10.1186/s12877-021-02675-z. Ai, Y., Hu, H., Wang, L., et al. Correlation between cognitive function and depression level among community-dwelling elderly in Wuhan. Chinese Journal of General Practice, 2019, 18(16): 1927-1931. https://doi.org/10.3870/j.issn.1001-4152.2019.16.095. DeGrande H, Enrique Espinoza L. The price of care: alcohol misuse as a moderator of financial hardship and mental health outcomes of U.S. women caregivers. Arch Womens Ment Health. 2026;29:25. https://doi.org/10.1007/s00737-025-01672-0. Qiu L, Li J. The social differentiation of depression among Chinese adults: dynamic perspectives and intersectional views. J Chin Sociol. 2025;12:4. https://doi.org/10.1186/s40711-025-00229-z. Köse A, Arayici ME, Simsek H. The association of cardiometabolic multimorbidity with depression and length of hospitalization: a population-based cross-sectional study among adults in Turkey. BMC Public Health. 2025;25:2009. https://doi.org/10.1186/s12889-025-23306-x. Ye B, Xie R, Mishra SR, Dai X, Chen H, Chen X, et al. Bidirectional association between physical multimorbidity and subclinical depression in Chinese older adults: Findings from a prospective cohort study. Journal of Affective Disorders. 2022;296:169–74. https://doi.org/10.1016/j.jad.2021.09.067. Lim LL, Kua E-H. Living Alone, Loneliness, and Psychological Well-Being of Older Persons in Singapore. Current Gerontology and Geriatrics Research. 2011;2011:1–9. https://doi.org/10.1155/2011/673181. Kagan M, Zychlinski E, Greenblatt‐Kimron L. The mediating roles of optimism, loneliness, and psychological distress in the association between a sense of community and meaning in life among older adults. American J of Comm Psychol. 2024;73:419–30. https://doi.org/10.1002/ajcp.12717. Del Casale A, Mancino S, Arena JF, Spitoni GF, Campanini E, Adriani B, et al. Neural Functioning in Late-Life Depression: An Activation Likelihood Estimation Meta-Analysis. Geriatrics. 2024;9:87. https://doi.org/10.3390/geriatrics9040087. Zhu J, Xu L, Sun L, Qin D. Negative life events, sleep quality, and depression among older adults in Shandong Province, China: A conditional process analysis based on economic income. Geriatrics Gerontology Int. 2024;24:751–7. https://doi.org/10.1111/ggi.14914. Michalak SS, Sterna W. Coexistence and clinical implications of anemia and depression in the elderly population. Psychiatr Pol. 2023;57:517–28. https://doi.org/10.12740/PP/147079. Chen Y, Zuo X. Associations between home- and community-based services (HCBSs) and depressive symptoms in older adults: a nationally representative cross-sectional survey in China. BMC Health Serv Res. 2025;25:1115. https://doi.org/10.1186/s12913-025-12993-2. Schoevers RA, Smit F, Deeg DJH, Cuijpers P, Dekker J, Van Tilburg W, et al. Prevention of Late-Life Depression in Primary Care: Do We Know Where to Begin? AJP. 2006;163:1611–21. https://doi.org/10.1176/ajp.2006.163.9.1611. 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-9252451","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635803481,"identity":"453e70d2-71b8-4353-bfe2-e3494893135a","order_by":0,"name":"RUI ZHAO","email":"","orcid":"","institution":"Taizhou University","correspondingAuthor":false,"prefix":"","firstName":"RUI","middleName":"","lastName":"ZHAO","suffix":""},{"id":635803485,"identity":"e9f5ad6c-c7d5-4116-91ac-0fb8b46805f9","order_by":1,"name":"Mingshu Huo","email":"","orcid":"","institution":"Dalian Jinzhou First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mingshu","middleName":"","lastName":"Huo","suffix":""},{"id":635803493,"identity":"74c61dcf-3020-4e5a-a646-6062d5492677","order_by":2,"name":"Mingyang Tan","email":"","orcid":"","institution":"Yantai Nanshan University","correspondingAuthor":false,"prefix":"","firstName":"Mingyang","middleName":"","lastName":"Tan","suffix":""},{"id":635803495,"identity":"c4357cc0-413b-4e3f-b794-ca895069d70b","order_by":3,"name":"Yan Cai","email":"","orcid":"","institution":"Jinzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Cai","suffix":""},{"id":635803498,"identity":"6f09c2d7-b673-4850-b1bc-712071773575","order_by":4,"name":"Xiuchao Geng","email":"","orcid":"","institution":"Taizhou 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11:45:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154763,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStatic nomogram of depression risk prediction for elderly people in community\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9252451/v1/5afc2a4bc82fce5a54576ffa.png"},{"id":108806564,"identity":"d780df40-ecad-4cad-9dff-bbbd811e3100","added_by":"auto","created_at":"2026-05-08 15:28:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamic nomogram of depression risk prediction for elderly people in community\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9252451/v1/479dbac48413e5fd97ce4ee2.png"},{"id":108695376,"identity":"0aa6ba63-a581-44fb-9053-a405fe1e5bc4","added_by":"auto","created_at":"2026-05-07 11:45:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eArea under ROC curve of the depression risk prediction model for elderly people in the community (left: training set right: validation set)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9252451/v1/31afd9ff55fefb8bb199b4fc.png"},{"id":108806684,"identity":"0da58e2a-49f1-4472-a35e-f4114f55a62d","added_by":"auto","created_at":"2026-05-08 15:29:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClassification calibration curve of depression risk prediction model for elderly people in community (left: training set right: verification set)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9252451/v1/f320daa7f8c0170c90ae165c.png"},{"id":108695379,"identity":"a1c7c600-8b8e-49cf-be12-b784211ae4a7","added_by":"auto","created_at":"2026-05-07 11:45:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCA curve of the depression risk prediction model for the elderly in the community (left: training set right: verification set)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: None means that all samples are negative, no intervention, and the net benefit is 0; All indicates that all samples are positive and the net benefit of the intervention is a backslash with a negative slope; Nomogram model means a scoring model using a nomogram.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9252451/v1/53f0b6f67dca9553611317d5.png"},{"id":108810081,"identity":"3e0101ff-8b19-4077-a212-9a417c6dac76","added_by":"auto","created_at":"2026-05-08 15:57:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1065462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9252451/v1/d725b2d5-eab8-440e-a3e7-bd139d567361.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Depression Risk Prediction Model for Community Elderly Individuals","fulltext":[{"header":"Background","content":"\u003cp\u003eDepressive disorders are the most common affective disorders wherein depression is a common symptom. The main symptoms of depression is consistent low mood which is a lot less than should be expected with the individual[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition to low mood and anhedonia, other important clinical features of depression include somatic symptoms, cognitive symptoms and sleep symptoms. People may not be able to concentrate, lose interest, feel worthless or guilty, and become generally pessimistic, with some potentially becoming suicidal[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Late Life Depression or LLD refers to depression in elderly people (\u0026ge;\u0026thinsp;60 years). It includes both primary depression and secondary depression from other causes. It refers to primary depression commencing in older adulthood when defined narrowly[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The prevalence of LLD may reach as high as 40% among hospitalized patients, up to 30% among nursing home residents, and potentially 8% to 15% in community settings[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In comparison to depression found in other groups, LLD shows different clinical features and disease characteristics. \u0026ldquo;LLD symptoms refer to more complex neuropsychiatric and atypical presentations. In view of depression, these symptoms are primarily persistent likely to low mood and sadness\u0026rdquo;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Emotionally, this expresses itself as a feeling that life is dull and meaningless, hopelessness, and loss of interest in usual other interests[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Some LLD patients may experience somatic anxiety symptoms such as worry, anxiety, and tension[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Others report physical pain, often accompanied by sleep disturbances like insomnia or hypersomnia[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These atypical symptoms may mask the underlying depressive symptoms. Furthermore, LLD is often considered to be associated with cognitive impairment. Cognitive impairment is frequently mistaken for the physiological aging process in the elderly and consequently overlooked[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, the mutual influence between LLD and cognitive impairment can significantly complicate clinical diagnosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, identifying and diagnosing LLD is relatively more challenging. In terms of disease characteristics, unlike depression in other age groups, LLD inflicts relatively greater harm on patients' health: On one hand, LLD serves as an independent risk factor for chronic conditions such as hypertension[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], cardiovascular disease[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and diabetes in the elderly[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It also leads to physical, cognitive, and social functional impairments, along with heightened self-neglect[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. On the other hand, LLD patients exhibit a higher proportion of poor response to antidepressant medications during treatment and experience higher relapse rates post-treatment[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, this results in reduced quality of life and increased mortality risk for these individuals.\u003c/p\u003e \u003cp\u003eRegarding the pathogenesis of LLD, current research has yet to reach definitive conclusions. The absence of characteristic physical manifestations or specific laboratory indicators further complicates investigations into its pathological mechanisms[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Existing theories primarily encompass multiple hypotheses involving genetic, neurobiochemical, and psychosocial factors. It is widely recognized that the onset and progression of LLD are influenced by the combined effects of biological, psychological, and social factors. These factors do not act in isolation but interact at specific points in time, collectively driving the disease process[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, developing an effective predictive model for depression risk and identifying key predictors among community-dwelling older adults is crucial for the early identification and intervention of LLD. Given the presence of relevant risk factors, researchers have developed risk prediction models (RPMs) to assess the risk of developing or progressing to depression[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, previous studies on RPMs have certain limitations: First, although a few models perform well in predicting depression among older adults, there remains a lack of in-depth exploration into key predictors and model efficacy for community-dwelling older populations[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For instance, small sample sizes or limited predictive variables may restrict the models' generalizability and reliability[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Second, previous studies often focused only on partial factors (such as repeated or isolated variables) or employed complex tools, resulting in operational difficulties when applied to real-world communities[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, the impact of important factors like comorbid chronic diseases has not been adequately assessed. For instance, some research indicates that after controlling for confounding factors[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], over half of elderly individuals with depression suffer from at least one comorbid chronic disease, yet this is frequently overlooked in predictive models.\u003c/p\u003e \u003cp\u003eThis study constructs and validates a comprehensive risk assessment model specifically designed for community-dwelling elderly populations. This model integrates multiple key predictive factors, all selected through logistic regression analysis and validated using large-sample data. This comprehensive modeling approach provides a more holistic risk assessment. Presented as a nomogram, it simplifies operation, enabling non-specialists (such as community workers) to use it easily, thereby reducing workload and improving predictive accuracy. This supports early intervention and resource allocation, providing a more reliable evidence base for community health management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eThis research constitutes a cross-sectional study. Utilizing a convenience sampling method, we selected 1,500 elderly individuals from five communities in both Jinzhou City and Anshan City, Liaoning Province, China, between June 2023 and September 2025. The inclusion criteria were as follows: (1) individuals aged 60 or older; (2) those with clear consciousness capable of normal communication; (3) residents of the community for more than one year; and (4) voluntary participation in the study. The exclusion criteria included (1) individuals with significant physical illnesses, consciousness disorders, or mental health conditions that hindered their ability to complete the questionnaire and (2) individuals with impaired hearing and communication difficulties.\u003c/p\u003e \u003cp\u003eAccording to the Events Per Variable (EPV) principle, the number of outcome events must be at least ten times the number of candidate predictor variables[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In a model with 13 candidate predictor variables, this necessitates a minimum of 130 outcome events. Citing the research conducted by Huang et al., the incidence of depression among community-dwelling elderly individuals was reported to be 15%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], indicating that a sample size of 867 cases is required. Accounting for a 20% loss to follow-up, the sample size should be adjusted to at least 1084 cases. Ultimately, a total of 1479 samples were collected.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCandidate variables and outcome definition\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariable Classification and Measurement Tools\u003c/h2\u003e \u003cp\u003e(1) Demographic variables: This category encompasses fundamental sociodemographic indicators, including gender, age, marital status, monthly income, education level, disease type, lifestyle, sleep quality, self-rated health status, convenience of community transportation, and frequency of physical exercise.\u003c/p\u003e \u003cp\u003e(2) Psychological variable: Loneliness was assessed in the elderly using the 6-item UCLA Loneliness Scale (ULS-6). Originally derived from the UCLA Loneliness Scale by Russell et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the ULS-6 was a simplification of the ULS-8 by Hays et al[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. and subsequently adapted into the Chinese version by Zhou Liang et al[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The scale comprises 6 items rated on a 4-point scale (1= \"always\" to 4= \"never\"), yielding a total score between 6 and 24, where higher scores indicate greater loneliness. The ULS-6 demonstrates good reliability and validity among elderly individuals in Chinese community settings[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], with a Cronbach's α coefficient of 0.731 reported in this study.\u003c/p\u003e \u003cp\u003e(3) Social network support variable: The Lubben Social Network Scale (LSNS-6) was employed to evaluate the level of social network support, rather than directly assessing the subjective experience of \"sense of social alienation.\" Developed by Lubben et al.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], this scale encompasses two dimensions: family and friends, each comprising three items. It captures the strength of social connections by quantifying objective indicators, such as the frequency of social interactions and the availability of support. Each item is scored from 0 to 5 points, resulting in a total score that ranges from 0 to 30 points. A higher score indicates a greater level of social network support, while a score of \u0026le;\u0026thinsp;12 points signifies a risk of social isolation. A study indicates that the LSNS-6 is suitable for assessing the social network status of older adults, in this study, the Cronbach's α coefficient for this scale was 0.754[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome Variable: Depressive Symptoms\u003c/h3\u003e\n\u003cp\u003eThe Patient Health Questionnaire Depression Scale (PHQ-9) was employed to assess depressive symptoms in the elderly population within the community. This scale[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] comprises nine items, each rated on a scale from 0 to 3. A score of 0 signifies the absence of depression, 1 indicates symptoms occurring on a few days, 2 reflects symptoms present for more than half the days, and 3 denotes symptoms experienced almost every day. Over a two-week period, the total possible score is 27 points, with higher scores indicating greater levels of depression. In China, this questionnaire is extensively utilized for screening individuals with depression. Furthermore, research has demonstrated its appropriateness for identifying depression among elderly community members[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, the Cronbach's α coefficient for this instrument was determined to be 0.728.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003ePrior to the investigation, it is essential to obtain the support and consent of the community head. During the investigation, researchers conducted on-site assessments with the consent of the elderly participants, adhering to uniform and standardized instructions. Following the investigation, the questionnaires were collected immediately to identify any inaccuracies or omissions. Corrections were made, and the questionnaires were subsequently reconfirmed. Upon completion of data collection, two individuals entered the data and conducted verification. In total, 1,500 questionnaires were distributed for this study, yielding 1,479 valid responses, which corresponds to an effective recovery rate of 98.6%. The valid responses were categorized into a modeling group (1,035 cases) and a validation group (444 cases) in a 7:3 ratio.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Statistics\u003c/h2\u003e \u003cp\u003eIn this study, SPSS 26.0 was used for statistical analysis, and the measurement data were measured by means of mean\u0026plusmn;standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\chi\\:}\\pm\\:s\\)\u003c/span\u003e\u003c/span\u003e) and T-test. The rate of counting data (%) was described and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{}^{2}\\)\u003c/span\u003e\u003c/span\u003etest was adopted. Statistically significant variables in univariate regression analysis were incorporated into multiple logistic regression analyses to obtain independent predictors of depression in elderly people in the community. RStudio (car, rms, Proc, dcurves, Replot, DynNom) were used to analyze the data, dividing the training set and the validation set according to 7:3. Statistically significant variables in multiple logistic regressions were included in the production of RStudio columns, and static and dynamic columns were constructed, respectively.The static graph can show the depression risk index of the elderly in the community, and the dynamic graph can accurately draw the depression risk index according to different situations of high-risk groups. The working characteristic curve (receiver operating characteristic, ROC) of the subject is drawn, the difference of the area under the curve (area under curve, AUC) is sensed directly, the calibration curve is calibrated to evaluate the calibration degree, and the practicability of the curve evaluation model (decision curve analysis, DCA) is analyzed by decision curve. The Hosmer-Lemeshow test was used to evaluate the degree of fit of the model, which was repeatedly sampled 1000 times for internal verification through the bootstrap method. The consistency index (C-index, \u0026gt;\u0026thinsp;0.7) was used to evaluate the sensitivity of the risk prediction model, and the Brier score (\u0026lt;\u0026thinsp;0.25) was used to evaluate the composite index. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic Characteristics of Community-Dwelling Older Adults and Univariate Analysis of Depression\u003c/h2\u003e\n \u003cp\u003eA total of 1479 elderly patients were included in this study, including 775 males and 704 females. 493 were unmarried/divorced/widowed, and 355 were married. According to the depression assessment tool, the elderly in the community were divided into the depressed group (n\u0026thinsp;=\u0026thinsp;489, 33.06%) and a non-depressed group (n\u0026thinsp;=\u0026thinsp;990, 66.94%). Thirteen variables, such as general information, loneliness, and social isolation of the elderly, were included in univariate analysis, among which the differences in age, marriage, and education level were not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the other variables were statistically significant, and the specific variables were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariables with statistically significant differences in the incidence of depression among community elderly [case (percentage, %)]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNon-depressive\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;990)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDepressive\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;489)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e545(55.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e230(47.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e445(44.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e259(53.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e60ཞ65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e506(51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e262(53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e65ཞ70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e411(41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e192(39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e73(7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35(7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e78(7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e45(9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e157(15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e67(13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e654(66.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e332(67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e101(10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e45(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMonthly income (RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e107(10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e149(30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e95.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3000ཞ5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e615(62.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e264(54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e268(27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e76(15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrimary school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e184(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e109(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ejunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e340(34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e182(37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e245(24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e107(21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCollege degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e221(22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e91(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eType of disease (species)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e315(31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e87(17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e118.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e501(50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e189(38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e174(17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e213(43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e101.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e414(41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e309(63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e277(28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e141(28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e299(30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e39(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e63.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e515(52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e262(53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e196(19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e167(34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e279(28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e60(12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMode of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e36.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elive with children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e751(75.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e297(60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNot live with children/ Living alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e239(24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e192(39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWorking condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e14.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eretire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e407(41.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e180(36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e235(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e93(19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLong-term illness/disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e184(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e128(26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e164(16.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e88(18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhysical exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e289(29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e240(49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u0026ndash;5 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e379(38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e194(39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ealmost everyday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e322(32.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e55(11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e635(64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e275(56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e355(35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e214(43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSocial isolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e122.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e828(83.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e279(57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e162(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e210(42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariate logistic regression analysis of depression in the elderly in the community\u003c/h2\u003e\n \u003cp\u003eThe statistically significant variables of univariate analysis were logistic regression analysis, and the independent variable was assigned as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the dependent variable was taken as the dependent variable (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0), whether the elderly in the community had depressive symptoms. The results showed that gender, disease type, sleep quality, living style, social isolation, physical activity, living conditions, and loneliness were independent risk factors for depression in the elderly in the community (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariable assignment table for binary classification logistic regression analysis of the influencing factors of depression in the elderly in the community\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAssignment description\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;0, Female\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMonthly income (RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;3000(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0), 3000ཞ5000(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1), \u0026gt;\u0026thinsp;5000(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eType of disease (species)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0ཞ1 type(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0), 2 types(Z1\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1), 3 types or more\u003c/p\u003e\n \u003cp\u003e(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMode of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elive with children\u0026thinsp;=\u0026thinsp;0, Not living with children/ Living alone\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWorking condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eretire(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0),Work༈Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0,Z\u003csub\u003e2\u003c/sub\u003e=0,Z\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1༉, Long-term illness/disability༈Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0,Z\u003csub\u003e2\u003c/sub\u003e=1,Z\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0༉, other༈Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1,Z\u003csub\u003e2\u003c/sub\u003e=0,Z\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0༉\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePoor(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0), moderate(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1), Good(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhysical exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2 times a week(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0), 3\u0026ndash;5 times a week(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1), almost everyday(Z\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1, Z\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSocial isolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003enone\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eloneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003enone\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression analysis of factors influencing depression in the elderly in the community (n\u0026thinsp;=\u0026thinsp;1479)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eWald\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eConstant terms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e11.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.462ཞ0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMonthly income (RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e64.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3000\u0026ndash;5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e60.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.795ཞ9.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e11.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.297ཞ2.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMode of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.530ཞ0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eType of disease (species)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e62.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e56.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.162ཞ0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e35.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.279ཞ0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e45.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e45.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e4.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.747ཞ6.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003egood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e27.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e3.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.097ཞ5.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhysical exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e52.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u0026ndash;5 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e52.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e4.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.900ཞ6.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ealmost everyday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e26.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.903ཞ4.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e31.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.330ཞ2.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003egood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e31.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e3.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.121ཞ4.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWorking condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.722ཞ1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLong-term illness/disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.445ཞ1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.668ཞ1.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eloneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e44.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.301ཞ0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSocial isolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-1.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e89.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.171ཞ0.314\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\u003cstrong\u003eConstruction and evaluation of depression risk prediction model for elderly people in the community and evaluation of test efficiency\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBased on the independent risk factors obtained by logistic regression analysis, a risk prediction model for depression in elderly people in the community was constructed. The column diagram is shown in Figs. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is a static column diagram, which can roughly judge the depression risk index range of patients. Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is a dynamic nomogram, and the depression risk index can be directly derived for each patient. For example, the red dot on each variable represents the patient\u0026apos;s situation, and finally, the patient\u0026apos;s risk index is 0.839.\u003c/p\u003e\n \u003cp\u003eThe Hosmer-Lemeshow test was used to verify the fitting effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.865). The ROC curve was used to verify the prediction effect of the model, as shown in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The area under the ROC curve of the training set was 85.4%, 95% 95%CI (0.830\u0026thinsp;~\u0026thinsp;0.877), and the area under the ROC curve of the verification set was 83.8%, 95% 95%CI (0.796\u0026thinsp;~\u0026thinsp;0.880). The best cut-off value of the whole data was the maximum value of the Jordan index of 0.564; the sensitivity was 76.9%, and the specificity was 79.5%. The model was well differentiated and calibrated in predicting depression risk by bootstrap resampling 1000 times, with a C statistic of 0.854 (95% CI 0.830\u0026thinsp;~\u0026thinsp;0.877) and a Brier score of 0.140, and the calibration curves of both the training set and the validation set show that the predicted values by the model are basically consistent with the actual occurrence values, as shown in Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Within the prediction range of this model, the net benefit rate of the DCA curve is higher than that of the two extreme curves (all negative or all positive), indicating that this model can benefit the elderly in the community and has a certain application value, see Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for details.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eThe depression risk prediction model for the elderly in the community is scientific and easy to implement\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUnder the influence of various factors in society, the incidence of depression among the elderly in the community is increasing year by year. A total of 1479 subjects in this study, including 489 elderly people with depression, accounting for 33.1%, are similar to the 36.8% results of Yucui Pu et al[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The results of Aihong Liu et al's study of depressive symptoms in the elderly in Wuhan of China were 14.04%[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which may be caused by the regional and cultural differences between the north and the south. The huge burden of depression and the rapid development of population aging make it of great significance to construct a model for predicting depression risk in the elderly in the community.\u003c/p\u003e \u003cp\u003eIn this study, eight indicators, including gender, monthly income, chronic disease, sleep quality, loneliness, social isolation, physical exercise and living status, were screened by logistic regression to establish a visual risk prediction model. The eight factors included in the model can be obtained by filling in and asking a simple questionnaire, without any economic costs, and the compliance of the elderly is high. Community members can screen out the risk levels of the elderly based on the static nomogram risk, and according to the optimal cutoff value of 0.564, the high-risk and low-risk groups can be distinguished early. Then the dynamic prediction was made for the elderly at high risk, and the total score of 8 predictors was calculated to formulate the targeted nursing measures. It can identify the depressive symptoms of the elderly early, avoid the waste of medical resources, reduce the suicide rate and disability rate of the elderly, and improve the quality of life of the elderly.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of influencing factors related to depression risk prediction in the elderly\u003c/h2\u003e \u003cp\u003eThis study shows that the incidence of depression in elderly women is higher than that in elderly men. Similar to the Ferri study by Christine[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], elderly women may be more likely than men to experience poverty, decline in physical function and have health problems, which may be related to the fact that women are more likely to become caregivers in life and spend more energy[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As an independent factor affecting depression, it has a greater impact on chronic diseases in the elderly. Relevant studies have shown that elderly people with multiple chronic diseases have varying degrees of increased mental state and are highly correlated with negative health outcomes[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].This study showed that depressive symptoms occurred 1.57 times more in older adults living alone than in other older adults. Research has demonstrated this[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which may be due to older people living alone having poorer health and being more susceptible to influence may have alcohol-related problems and engaging in risky behaviors, which can lead to psychological problems. In this study, the incidence of depression in the elderly with high loneliness is 1.25 times that of the normal elderly, which is consistent with the research result of C.J. Brush that there is an inevitable correlation between depression and loneliness[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen individuals report an increase in loneliness, the brain structures execute commands that cause a spike in negative emotions that produce depressive symptoms. Social isolation is an independent factor in the risk of depression in a predictive model of community older adults[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. As they age, they are socially disadvantaged and more likely to feel isolated, which can hurt mental health. Older adults have a much higher risk of depression from multiple diseases than healthy older adults. Patients with a wide range of comorbidities will have sensory retardation, and even have difficulties in understanding, expressing and recognizing their emotions, resulting in the accumulation of negative emotions and thoughts, leading to depressive symptoms. Research shows that sleep quality is negatively correlated with the occurrence of depression in the elderly, and the better the sleep quality, the lower the likelihood of depression, which is consistent with the results of this study[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Potential causes may be hypothalamic-pituitary-adrenal axis hyperactivity, active biochemical pathways, and inflammatory cytokines in older adults with persistent sleep disorders. Community workers should pay attention to the social communication, anemia and loneliness of elderly women in the community while actively preventing depression[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The general situation of the elderly in the community is kept in the archives, and more attention is paid to the elderly who live alone, have poor sleep, and suffer from various chronic diseases. Community health service centers can often hold some lectures and activities related to depression prevention and treatment, and regularly conduct screening to reduce and delay the occurrence of depression symptoms in the elderly in the community[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe prediction effect of the depression risk prediction model of the elderly in the community is better\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on Logistic regression analysis, this study screened out 8 influencing factors, and built a nomogram model for predicting the risk of early depressive episodes in elderly people in the community. From the point of view of the calibration degree of the model, the P-values of the risk prediction model and the H-L test in the external validation of the model were both greater than 0.05, indicating that there was no significant difference between the predicted risk of depression and the actual risk of depression, and the model prediction had a good consistency[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It can be seen that the model has good predictive performance through strict internal and external verification, the construction methodology is rigorous, and the model has a certain scientific nature. Secondly, the transformation of the prediction model into a risk- scoring system improves the community operability of the model. At the same time, community health workers can roughly judge whether the elderly have depression risk according to the static state, and then use the dynamic nomogram to obtain the accurate depression risk score of the elderly with depression risk. The higher the score, the higher the depression risk. The sensitivity and specificity of this model are 76.9% and 79.5%, which can effectively reduce misjudgment and avoid waste of medical resources while maintaining effective sensitivity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on factors such as gender, disease type, sleep quality, lifestyle, loneliness, social isolation, physical exercise, and living status, this nomogram is simple and easy to construct, and can save the workload of community workers and accurately predict the depression risk index of elderly people in the community. In this study, the nomogram constructed based on gender, disease type, sleep status, living style, loneliness, social isolation, and mood disorder was simple and easy, which could better predict the risk of depression in the elderly in the community. Compared with previous studies, this study enriches the risk prediction tools for depression in the elderly in the community to a certain extent. However, the overall sample size of this study is not large, and it is recommended to expand the sample size in the future. And you can go to the community health service center to cooperate to incorporate the laboratory test results into the risk factors, obtain more representative results, and provide a more effective basis for the prevention and treatment of depression in the elderly in the community.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrength and Limitations\u003c/h2\u003e \u003cp\u003eThe depression risk prediction model constructed in this study demonstrated excellent discrimination. The area under the curve of the training set reached 0.854, and that of the validation set was 0.838, indicating that the model has good predictive capabilities. The model incorporates multiple predictive factors such as demographics, physical health, and social psychology, including gender, monthly income, disease type, sleep quality, physical exercise, social isolation, and loneliness, providing a comprehensive perspective for risk assessment. The study not only constructed a static nomogram but also developed a dynamic nomogram, enabling nursing staff and non-professionals to conduct convenient and intuitive risk scoring based on the specific circumstances of the elderly, calculate the specific probability of depression risk, and facilitate early screening.However, this study is a cross-sectional survey, and all data were collected at the same time point. The study only underwent internal validation and lacks validation in an independent external population. The validity and generalizability of the model need to be further confirmed, and we will further address this deficiency.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank all the co-authors as well as the elderly participants who took part in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRui Zhao: Conceptualization, Methodology, Investigation, Formal analysis, Writing - original draft; Mingshu Huo: Data curation, Investigation; Mingyang Tan: Visualization, Investigation; Yan Cai: Resources, Supervision; Xiuchao Geng: Software, Validation; Mengjie Xia: Visualization; Investigation; Yingzhi Chen:Visualization, Investigation; Xiaohong Liu: Conceptualization, Funding acquisition, Resources, Supervision, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper is supported by:General research projects of the Zhejiang provincial department of education(Grant No.Y202455543); The special project of \u0026quot;Provincial and Municipal Cooperation\u0026quot; of Zhejiang Philosophy and Social Science Planning Project in 2024 (Grant No. 24SSHZ203YB);Taizhou Science and Technology Plan Project (Grant No. 25ywa41); Zhejiang Provincial Philosophy and Social Sciences Planning Program (Grant No. 26NDJC024YBM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData may be shared during this study upon reasonable request. Readers may contact Rui Zhao ([email protected]) to submit raw data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human subjects was approved by Jinzhou Medical University (ethical approval number : JZMULL2023080). At the beginning of the study, all participants were informed about the purpose of the study and their right to participate voluntarily. Written informed consent was obtained from all participants for this study. All methods were performed in accordance with relevant guidelines and regulations.Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declared that they had no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTan J, Ma C, Zhu C, Wang Y, Zou X, Li H, et al. Prediction models for depression risk among older adults: systematic review and critical appraisal. Ageing Research Reviews. 2023;83:101803. https://doi.org/10.1016/j.arr.2022.101803.\u003c/li\u003e\n\u003cli\u003eInal Azizoğlu S. Postpartum depression: A mini-review. 2022. https://doi.org/10.5281/ZENODO.7400409.\u003c/li\u003e\n\u003cli\u003eYuan Y, Peng C, Burr JA, Lapane KL. 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BMC Health Serv Res. 2025;25:1115. https://doi.org/10.1186/s12913-025-12993-2.\u003c/li\u003e\n\u003cli\u003eSchoevers RA, Smit F, Deeg DJH, Cuijpers P, Dekker J, Van Tilburg W, et al. Prevention of Late-Life Depression in Primary Care: Do We Know Where to Begin? AJP. 2006;163:1611\u0026ndash;21. https://doi.org/10.1176/ajp.2006.163.9.1611.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Community, Elderly, Depression, Risk prediction, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-9252451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9252451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepression in elderly populations is a significant public health issue, particularly in community settings. This study aimed to develop and validate a risk prediction model to identify depression risk among community-dwelling elderly individuals.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo construct a risk prediction model of depression in the elderly in the community and to provide a screening method and theoretical basis for the early detection of depression in the elderly.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 1479 community-dwelling elderly from Anshan and Jinzhou (June 2023\u0026ndash;September 2025) were assessed using the General Information Questionnaire, ULS-6, LSNS-6, and PHQ-9. Logistic regression identified predictors; RStudio was used to construct static and dynamic nomograms. The model was internally validated via bootstrapping and evaluated using the C-statistic, ROC curve, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe model demonstrated excellent predictive performance. The detection rate of depression among the elderly in the community was 33.1%. Sex, sleep quality, living style, number of chronic diseases, social isolation, loneliness, living status, and physical exercise were predictive factors (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The C statistic was 0.854 (95% CI 0.830\u0026thinsp;~\u0026thinsp;0.877), the optimal critical value was 0.564, the sensitivity was 76.9%, the specificity was 79.5%, and the calibration curve and Brier score showed that the prediction model was well-fitted.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe developed model effectively identifies depression risk in community elderly, potentially aiding early intervention and resource optimization. It serves as a practical screening tool for community health workers.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Depression Risk Prediction Model for Community Elderly Individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 11:45:22","doi":"10.21203/rs.3.rs-9252451/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"171290431831074567518485190757644432996","date":"2026-04-29T02:14:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T11:25:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T09:42:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T02:30:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T02:30:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-03-28T11:41:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89e93f38-59be-4a03-8795-a627b588eb4c","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"171290431831074567518485190757644432996","date":"2026-04-29T02:14:15+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T11:45:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 11:45:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9252451","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9252451","identity":"rs-9252451","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}