Prediction of depression risk in patients with coronary heart disease based on nomogram for Chinese population: a population-based multi-center study from 2016 to 2018

Prediction of depression risk in patients with coronary heart disease based on nomogram for Chinese population: a population-based multi-center study from 2016 to 2018 | 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 Article Prediction of depression risk in patients with coronary heart disease based on nomogram for Chinese population: a population-based multi-center study from 2016 to 2018 Hongxuan Tong, Jiale Zhang, Wenyi Nie, Lijie Jiang, Lei Dong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3890258/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: This study aimed to assess the prevalence and identify risk factors associated with depression among coronary heart disease (CHD) patients at different stages in China. Methods: Conducted as a hospital-based, cross-sectional study across 48 hospitals in 23 provinces, the research spanned from October 2016 to April 2018. A total of 9044 patients were initially recruited, with 8353 deemed eligible for participation. Depression was assessed using the nine-item Patient Health Questionnaire-9 (PHQ-9) Scale. Univariate analysis identified predictors of postoperative depression, and binary logistic regression analysis was employed to ascertain risk factors associated with depressive symptoms. The predictive model was constructed using the "rms" package in R software, demonstrating robust predictive capabilities according to the ROC curve. Results: In general, both the degree and overall score based on the PHQ-9 revealed a trend: as the severity of the disease increased, so did the severity of patient depression. Univariate analysis indicated statistical differences concerning general situations and lifestyles. The binary logistic regression model highlighted the proximity of depression to risk factors such as gender, nationality, marital status, education, drinking, BMI, sleep disturbance, and disease status. Utilizing these findings, a predictive nomogram for depression was developed. The model exhibited excellent predictive ability, with an AUC of 0.768 (95% CI = 0.757–0.780). Conclusions: This study systematically investigated the prevalence of depression among coronary heart disease patients at various stages. As coronary heart disease advanced, the level of depression intensified. The nomogram developed in this study proves valuable in predicting the incidence of depression in coronary heart disease patients. Figures Figure 1 Figure 2 Figure 3 Introduction Despite a decline in mortality rates, coronary heart disease (CHD) remains a leading cause of death worldwide, primarily due to heart ischemia [ 1 ] . In 2020, CHD led to 380,000 deaths [ 2 ] . Furthermore, cardiac events and/or surgery not only significantly diminish patients' quality of life but also contribute to mental health issues, as previously reported [ 3 ] . Reports indicate that nearly 30% of patients with CHD face psychological challenges, particularly depression, highlighting a critical concern [ 4 ] . Given the prevalence of psychological symptoms such as depression or anxiety, numerous global studies have explored depression as a risk factor for incident CHD or cardiovascular morbidity and mortality in patients with established CHD [ 5 , 6 ] . The mechanisms underlying this comorbidity have not been fully elucidated. Cross-sectional studies consistently reveal a significant increase in depression incidence in CHD patients, while CHD incidence also rises considerably in patients with depression [ 7 , 8 ] . Depression is widely acknowledged to play a pivotal role in CHD pathogenesis or, at the very least, act as a predisposing factor for CHD [ 9 ] . Patients with CHD are prone to mental disorders, particularly depression, due to prolonged illness and an unsatisfactory prognosis [ 4 ] . The intricate causal relationship between the two conditions has led to the combination of psychological treatments with traditional approaches to enhance outcomes. Several studies have demonstrated that the comorbidity of CHD and depression increases the risk of cardiovascular events and mortality, resulting in higher readmission rates and medical costs [ 10 – 12 ] . However, despite advancements in clinical departments, psychological intervention and detection are infrequently implemented in cardiovascular clinical practice. In this study, we aim to construct a nomogram model for predicting depression risk in CHD patients based on a previous study that includes demographic characteristics, clinical features, psychological factors, and lifestyle. This model forms a robust foundation for early screening and intervention in depression risk among CHD patients. Therefore, we hope that this study serves as a reminder for doctors and patients to assess the risk of depression based on readily available information, such as the current disease stage and general data, facilitating early clinical prevention and intervention. Materials and methods Study Design and Participants: This was a hospital-based, cross-sectional study approved by the Ethics Committee of Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences (approval Document No. 2016EC_KY_001) on September 7, 2016. This study was registered on the WHO International Clinical Trial Registry (ClinicalTrial.gov) and the registered ID was NCT02967718. This cross-sectional study, was conducted at 48 clinical research centers across 23 provinces over the period from October 2016 to April 2018. Before commencing the study, researchers underwent standardized training in interview procedures, covering the recruitment of patients and utilization of self-reported questionnaires. Subsequently, they conducted on-site face-to-face interviews and posed questions to respondents. When necessary, supplementary information was obtained from the respondents' family members. Reference to the Declaration of Helsinki, all methods would be performed in accordance with the relevant guidelines and regulations. The survey spanned five consecutive days in each department. Inclusion criteria were as follows: (1) age 18 or older, (2) meeting criteria for healthy control population, metabolic syndromic individuals, stable coronary heart disease patients, acute coronary syndrome patients, post-PCI individuals, and heart failure patients, (3) voluntary participation with signed informed consent, and (4) completion of questionnaires independently. Exclusion criteria encompassed: (1) patients with a history of hand surgery, fever, trauma, or burn infection within the past week, active tuberculosis, or rheumatic immune disease, (2) patients with severe arrhythmias accompanied by hemodynamic changes, (3) patients with acute or subacute cerebrovascular diseases, (4) those with valvular heart disease or primary cardiomyopathy, (5) patients with acute exacerbation of chronic obstructive pulmonary disease or pulmonary heart disease or respiratory failure, (6) individuals with renal insufficiency, serum creatinine levels higher than 221 µmol/L in males and 177 µmol/L in females, (7) patients with liver dysfunction, alanine aminotransferase levels higher than 3 times the normal value or combined with cirrhosis, (8) individuals with severe primary diseases such as hematopoietic system disorders or malignant tumors, (9) organ transplant patients, (10) those with severe mental disorders, and (11) pregnant and lactating women. For participants who meet the requirements, we first sign informed consent and then proceed to follow-up studies. Patients were required to complete information collection questionnaires covering socio-demographic characteristics, the nine-item Patient Health Questionnaire-9 (PHQ-9) Scale, and lifestyle information (drinking, smoking, sleeping, etc.). Assessment Instrument: PHQ-9 The Patient Health Questionnaire-9 (PHQ-9) stands out as one of the most widely utilized self-report psychosocial assessments for evaluating participants' depressive symptoms over the preceding two weeks [ 13 ] . Positioned as an integral component of individual health management [ 14 ] , this tool is designed to identify depression through nine questions, each scored from 0 (not at all) to 3 (nearly every day), yielding a total score ranging from 0 to 27 [ 15 ] . Key breakpoints at 5, 10, 15, and 20 signify at least mild, moderate, moderately severe, and severe levels of depression [ 13 ] . The Chinese version of the PHQ-9, employed in our study, has exhibited robust reliability and validity across diverse conditions, including healthcare settings and the general population [ 16 , 17 ] . In our investigation, a total score of 5 or higher on the PHQ-9 indicates the presence of depression in respondents. To assess current suicidal or self-injurious ideation, we utilized the ninth item in the PHQ-9, which gauges thoughts related to being better off dead or engaging in self-harm (not at all 0; several days 1; more than half the days 2; nearly every day 3). A score of 1 or higher on this item indicates the presence of suicidal thoughts or self-harm concerns over the past two weeks. Statistical Analysis Statistical analysis was conducted using SPSS 20.0 and R 4.3.2 software. Descriptive analysis was employed to summarize socio-demographic and lifestyle characteristics. Binary logistic regression analysis was utilized to identify risk factors, with socio-demographic and lifestyle characteristics serving as independent variables, for depression within the entire population. The positive group, indicating the presence of depression as the main variable, was identified when the total score of PHQ-9 exceeded 4. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and statistical significance was considered when the two-sided P value was < 0.05. Result 1. Socio-Demographic and Characteristics A total of 9,044 patients were initially recruited, with 8,353 (response rate: 92.4%) completing the PHQ-9 and included in the analysis. The mean age of the total enrolled participants was 61.91 ± 12.50 years (range: 39–89 years), with 56.0% under 65 years and 44.0% aged 65 or older. Regarding gender, 52.7% were male, and 47.3% were female. The Han population comprised 94.3%, while the minority population was 5.7%. Religious beliefs were absent in 92.7% of participants, and 7.3% reported having religious beliefs. Marital status distribution was 1.0% unmarried, 92.0% married, and 7.0% divorced or widowed. Employment status revealed 19.5% unemployed, 52.6% retired, and 27.9% employed. Educational background varied, with 20.7% having primary education or below, 53.5% secondary school education, and 25.8% high education or above. In terms of technical titles, 70.2% had no title, 7.9% had a primary title, 13.5% were in the middle class, and 8.3% were in the high class. Regular exercise was reported by 47.1%, while 52.9% did not engage in regular exercise. Alcohol consumption patterns showed 76.9% never drank, 13.8% were current drinkers, and 9.3% were former drinkers. Smoking habits indicated 72.2% were never smokers, 13.9% were current smokers, and 13.9% were former smokers. The mean score on the PHQ-9 was 3.29 ± 3.64. Based on BMI standards, 2.5% were underweight, 64.8% were of normal weight, and 32.7% were overweight. Regarding sleep quality, 74.7% reported normal sleep, while 25.3% reported poor sleep. The study included six distinct groups: healthy individuals, those with metabolic syndrome (MS), stable coronary heart disease population, acute coronary syndrome patients, post-PCI individuals, and heart failure patients. Their mean ages were 50.77 ± 10.87, 59.26 ± 11.79, 65.17 ± 10.174, 64.14 ± 10.68, 63.62 ± 10.67, and 71.84 ± 10.86, respectively. Comparisons between individuals with and without depression across different factors were conducted; detailed results are presented in Table 1 . Table 1 Comparison of different factors between study population with and without depression. Variable Without Depression (n = 6037) With Depression (n = 2316) t/Z/x2 p number percentage number percentage Disease group Health 1479 24.50% 155 6.69% 747.343 0.000 MS 1113 18.44% 261 11.27% SCHD 2130 35.28% 1005 43.39% ACS 439 7.27% 224 9.67% P-PCI 477 7.90% 153 6.61% HF 399 6.61% 518 22.37% Age 235.015 0.000 <65 3688 61.09% 984 42.49% ≥ 65 2349 38.91% 1332 57.51% Gender 39.288 0.000 Male 3316 54.93% 1095 47.28% Female 2721 45.07% 1221 52.72% Nation 79.997 0.000 Han 5781 95.76% 2101 90.72% non-Han 256 4.24% 215 9.28% Religion 43.921 0.000 No 5664 93.82% 2075 89.59% Yes 373 6.18% 241 10.41% Marital status 56.526 0.000 Never married 56 0.93% 26 1.12% Divorced/widowed 347 5.75% 241 10.41% married 5634 93.32% 2049 88.47% Employ status 206.97 0.000 Umemployed 1122 18.59% 498 21.50% Retired 2954 48.93% 1430 61.74% Employed 1961 32.48% 388 16.75% Education 116.906 0.000 primaryeducationorbelow 1115 18.47% 617 26.64% secondaryschooleducation 3187 52.79% 1261 54.45% high education or above 1735 28.74% 438 18.91% Technical titles 5.329 0.149 none 4212 70.38% 1596 69.85% primary 462 7.72% 186 8.14% intermediate 797 13.32% 334 14.62% high 514 8.59% 169 7.40% Exercise 0.237 0.627 Yes 2851 47.23% 1080 46.63% No 3186 52.77% 1236 53.37% Drinking 40.162 0.000 never 4567 75.65% 1848 79.79% once, has quit drinking 548 9.08% 237 10.23% current drinking 922 15.27% 231 9.97% Smoking 17.586 0.000 never 4295 71.14% 1713 73.96% once, has quit smoking 828 13.72% 335 14.46% current smoking 914 15.14% 268 11.57% BMI 8.625 0.013 BMI<18.5 133 2.20% 74 3.20% 18.5 ≥ BMI ≥ 24.9 3883 64.32% 1510 65.20% BMI ≥ 25 2021 33.48% 732 31.61% Sleeping 1033.541 0.000 good 5081 84.16% 1158 50.00% not good 956 15.84% 1158 50.00% 2. Comparison of PHQ-9 scores and Corresponding Depression Rates in Each Group Within the entire population, the prevalence of depressive symptoms was 27.7% (2316/8353; 95% CI: 26.8–28.7%). Specifically, the prevalence of mild (PHQ-9 score: 5–9), moderate (PHQ-9 score: 10–14), and severe (PHQ-9 score: 15–27) depressive symptoms was 21.0% (1758/8353; 95% CI: 20.2–21.9%), 5.1% (424/8353; 95% CI: 4.6–5.6%), and 1.6% (134/8353; 95% CI: 1.4–1.9%), respectively. The prevalence of moderate to severe depressive symptoms (PHQ-9 score: 10–27) was 6.7% (558/8353, 95% CI: 6.2–7.2%). The scores and proportions of people with depressive symptoms among each group are depicted in Fig. 1 . Regarding detailed scores, pairwise comparisons among the six groups revealed statistical differences in each pair, except for the comparison between SCHD and ACS, as well as the comparison between P-PCI and MS. 3. Logistic Binary Regression Analysis of Risk Factors for Depression We employed a binary logistic regression model to identify relevant factors associated with depressive and anxious symptoms in the entire population. The odds ratios (ORs) and 95% confidence intervals (CIs) are reported in Table 2 . Significant risk factors for depressive symptoms include Gender (Male or Female) (OR = 1.40; 95% CI: 1.22–1.61), Nation (Han or Minority) (OR = 1.62; 95% CI: 1.24–2.11), Marital status: Never married (OR = 1.70; 95% CI: 1.00-2.89), education: primary education or below (OR = 1.40; 95% CI: 1.15–1.72), education: secondary school education (OR = 1.26; 95% CI: 1.08–1.47), drinking: once, has quit drinking (OR = 1.30; 95% CI: 1.02–1.66), BMI<18.5 (OR = 1.78; 95% CI: 1.25–2.53), 18.5 ≥ BMI ≥ 24.9 (OR = 1.23; 95% CI: 1.09–1.39), Sleep disturbance (OR = 4.49; 95% CI: 4.00-5.04), Status: MS (OR = 2.28; 95% CI: 1.79–2.92), Status: CAD (OR = 3.54; 95% CI: 2.87–4.37), Status: ACS (OR = 4.24; 95% CI: 3.28–5.48), Status: P-PCI (OR = 3.14; 95% CI: 2.39–4.14), Status: HF (OR = 8.79; 95% CI: 6.86–11.27). Additionally, we presented the characteristics and comparison of subjects with and without depressive symptoms in different groups (see Supplement Table 1 ). Table 2 The binary logistic regression analysis to determine the strongest predictors of depressive in all population depression Characteristics OR(95%CI) P Value age (<65 or ≥ 65) 1.11(0.97–1.26) 0.12 Gender (Male or Female) 1.40(1.22–1.61) P<0.001 Nation (Han or Minority) 1.62(1.24–2.11) P<0.001 Religion (No or Yes) 0.89(0.69–1.13) 0.33 Marital status: married Ref. Marital status: Never married 1.70(1.00-2.89) P<0.05 Marital status:Divorced/widowed 1.10(0.90–1.35) 0.34 Employ status: Employed Ref. Employ status: Umemployed 1.09(0.89–1.33) 0.42 Employ status: Retired 1.11(0.94–1.32) 0.21 education: high education or above Ref. education: primary education or below 1.40(1.15–1.72) 0.001<P<0.01 education: secondary school education 1.26(1.08–1.47) 0.001<P<0.01 drinking: current drinking Ref. drinking: never 1.14(0.94–1.39) 0.18 drinking: once, has quit drinking 1.30(1.02–1.66) 0.01<P<0.05 Smoking: current smoking Ref. Smoking: never 0.99(0.82–1.21) 0.94 Smoking: once, has quit smoking 0.95(0.76–1.18) 0.63 BMI ≥ 25 Ref. BMI<18.5(1) 1.78(1.25–2.53) 0.001<P<0.01 18.5 ≥ BMI ≥ 24.9 1.23(1.09–1.39) P<0.001 Sleep disturbance 4.49(4.00-5.04) P<0.001 Staus:Health Ref. Staus:MS 2.28(1.79–2.92) P<0.001 Staus:SCHD 3.54(2.87–4.37) P<0.001 Staus:ACS 4.24(3.28–5.48) P<0.001 Staus:P-PCI 3.14(2.39–4.14) P<0.001 Staus:HF 8.79(6.86–11.27) P<0.001 4. Establishment of a Nomogram for Predicting Depression Risk Utilizing logistic regression analysis, we developed a nomogram model to predict the risk of depression in coronary heart disease (Fig. 2 ). Each factor was assigned a score ranging from 0 to 100, and the cumulative scores were computed to derive the total model score. The total score was then used to predict the risk probability of early depression in coronary heart disease. The risk of depression increases with the total score. The population categories were assigned numbers: 1 for health, 2 for metabolic syndrome, 3 for stable coronary heart disease, 4 for acute coronary syndrome, 5 for post-PCI surgery, and 6 for heart failure patients. Gender was coded as 1 for male and 2 for female. Ethnicity was coded as 1 for Han ethnicity and 2 for non-Han ethnicity. Marital status was coded as 1 for unmarried, 2 for divorced and widowed, and 3 for married. Educational status was coded as 1 for primary education or below, 2 for secondary school education, and 3 for high education or above. Drinking status was coded as 1 for none, 2 for abstained, and 3 for still drinking. BMI was coded as 1 for BMI < 18.5, 2 for BMI ≥ 18.5 and < 24.9, and 3 for BMI ≥ 25. Sleep quality was coded as 0 for good and 1 for bad. Based on the ROC curve, the AUC value for our model in this study was 0.768 (95% CI: 0.757–0.786), indicating a certain level of accuracy and providing a valuable reference for assessing the risk of depression in patients with coronary heart disease (Fig. 3 ). Discussion According to clinical epidemiological studies, psychological disorders are prevalent comorbidities in patients with coronary heart disease (CHD), significantly surpassing the rates observed in the general population, albeit with variations in screening tools and sampled populations [ 18 ] . Previous investigations into psychological disorders among CHD patients have reported depression incidence ranging from 34.6–51%, with acute coronary syndrome showing rates of 31–45%, markedly higher than the World Health Organization's estimated 4.3% in the general population [ 19 ] . While numerous studies have explored the psychological aspects of coronary heart disease, a systematic evaluation of the psychological status across different stages of CHD remains lacking. Hence, our current study aimed to fill this gap by assessing the extent of depression at various stages of coronary heart disease. In general, both the degree and overall scores on the PHQ-9 indicated a trend: the more severe the disease, the more pronounced the depression and anxiety in patients, with heart failure (HF) showing significantly higher rates than other groups. Interestingly, post-percutaneous coronary intervention (PCI), patients experienced a relief in depression, contrary to prior studies suggesting increased depression scores following cardiac coronary artery bypass graft surgery [ 20 ] . This divergence may stem from increased patient confidence in follow-up prognosis. Furthermore, it contrasts with previous findings suggesting persistent depression despite treatment [ 18 ] . Notably, in the healthy population, 9.7% exhibited depressive symptoms, a percentage higher than a study involving a rural Chinese population sample (5.9%), as determined by PHQ-9 ≥ 5 [ 21 ] . Subsequently, we conducted comparisons between individuals with and without depression across various factors. Statistical differences were observed for most general and lifestyle factors, excluding technical titles and exercise. Building on these findings, the binary logistic regression model explored risk factors contributing to depressive symptoms. Overall, depression correlated closely with gender, nation, marital status, education, drinking, BMI, sleep disturbance, and disease status. Paradoxically, while one study suggested no gender effect on depression [ 22 ] , another attributed gender's significance to the sex role hypothesis [ 23 ] . In our study, gender was closely associated with depression, with women being more prone. Marital status also significantly related to depression [ 24 ] , aligning with previous research demonstrating higher depression risk with lower education levels [ 21 , 25 , 26 ] . Similar to prior studies, alcohol consumption and abnormal BMI were associated with depression [ 27 – 31 ] . Sleep status showed a strong association with depression, and vice versa [ 32 , 33 ] . Recognizing the reciprocal relationship, the integrated approach addressing both sleep problems and depression emerges as a vital strategy in preventing heart disease [ 34 – 36 ] . In this study, the nomogram developed serves as a useful tool for predicting the risk of depression in CHD patients. This prediction model exhibits strong predictive capabilities, excellent calibration, and valuable clinical utility. It combines multiple predictors and presents them in a visual format, allowing medical professionals to easily assess the likelihood of depression based on the sum of relevant risk factors. At the same time, patients can obtain additional resources and information through the nomogram prediction model easily, improve their understanding of their own mental health status, and actively participate in the treatment and management process. Based on the above scoring results, we hope to quickly identify potential depression in patients, so as to prevent and intervene in advance, prevent the promoting effect of depression itself on the disease, and also make depression intervention in coronary heart disease patients more important. Conclusion Depression is one of common symptom in coronary heart disease, and they can affect each other accentuating further disease. This study would like to help clinicians and researchers understand the degree of depression in patients with coronary heart disease quickly and accurately, especial in the different stages of coronary heart disease. At the same time, we find that depression is close to the risk factors of Gender, Nation, Marital status, education, drinking, BMI, Sleep disturbance, and Status of disease. Base on above, this study develops predictive nomogram models referred to these risk factors, in order to give an early judgment, prevention and intervention for coronary heart disease accurately. Abbreviations CHD: Coronary heart disease SCHD: Stable of Coronary heart disease MS: Metabolic syndrome ACS: Acute coronary syndrome P-PCI: Post-percutaneous coronary intervention HF: heart failure PHQ-9: Patient Health Questionnare-9 MDD: major depressive disorder ICT: The information collection form ORs: Odds ratios CIs: 95% confidence intervals Declarations Acknowledgments We thank all those who provided excellent technical support and assistance during the study. Authors’ Contributions JQH and HXT designed the study. HXT drafted the manuscript and draw the figures. JQH revised the manuscript for important intellectual content. HXT and LD finished data statistics and analysis. LJJ checked the data statistics and analysis. JLZ sorted out and eliminated the data. All the authors have read and approved the final version of the manuscript. Funding The design of the study and the collection, analysis, and interpretation of data were supported by the National Nonprofit Institute Research Grant for Institute of Basic Theory for Chinese Medicine, CACMS, No. YZ-202142 and No. YZ-202240, and Guangzhou Foshan Science and Technology Innovation Project (No. 2020001005585). Availability of data and materials The data used to support the findings of this study are available from the corresponding author upon request. Ethics approval and consent to participate removed for peer review Consent for publication Not applicable. Conflicts of Interest The authors declare that they have no competing interests. References RICHARDS S H, ANDERSON L, JENKINSON C E, et al. 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Korean survey data reveals an association of chronic laryngitis with tinnitus in men [J]. PloS one, 2018, 13(1): e0191148. ZHAI K, GAO X, WANG G. The Role of Sleep Quality in the Psychological Well-Being of Final Year UndergraduateStudents in China [J]. International journal of environmental research and public health, 2018, 15(12). REDLINE S, FOODY J. Sleep disturbances: time to join the top 10 potentially modifiable cardiovascular risk factors? [J]. Circulation, 2011, 124(19): 2049-51. FORD D E, KAMEROW D B. Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention? [J]. Jama, 1989, 262(11): 1479-84. Additional Declarations No competing interests reported. Supplementary Files supplementTable1.Characteristicsandcomparisonofsubjectswithandwithoutdepressivesymptomsindifferentgroup.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3890258","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270412983,"identity":"eebd1d4d-fd17-4f0e-b7b7-97e18a42819b","order_by":0,"name":"Hongxuan Tong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACCRiDmfnAgQ8/iNDBA9fCzpZ4cGYPSVr4eYwPc7ARocVeuvnYw69th+XNmXk+HGbgYZDnFztAwBaZY+nGMmcOG+5s5t1wuMCCwXDm7ARCDssxk5aoOMy44TBQywwehgSD20RpMThsv+Ewz4PDPGxEapH8UHE4EaiFgUgtN9LSpBnOpCdvOMxmAAxkCcJ+YZ+RfEzyZ5u17Ybzhx9/+PDDRp5fmoAWEGDmQbAlcCtDBozEJJNRMApGwSgYwQAAbbRCq/PMkwQAAAAASUVORK5CYII=","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Hongxuan","middleName":"","lastName":"Tong","suffix":""},{"id":270412984,"identity":"8cb20e5b-d2e2-44d3-9f8e-37d43101f98f","order_by":1,"name":"Jiale Zhang","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiale","middleName":"","lastName":"Zhang","suffix":""},{"id":270412985,"identity":"61c7dda0-3802-47d9-9977-01fd024cdde9","order_by":2,"name":"Wenyi Nie","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Nie","suffix":""},{"id":270412986,"identity":"faeec916-64ac-4302-8bfd-482fd2f4b40e","order_by":3,"name":"Lijie Jiang","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lijie","middleName":"","lastName":"Jiang","suffix":""},{"id":270412987,"identity":"772456c4-6d53-45f8-85e8-799ceef60478","order_by":4,"name":"Lei Dong","email":"","orcid":"","institution":"China traditional Chinese medicine science and technology development center","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Dong","suffix":""},{"id":270412988,"identity":"5d6f97bf-21c1-4976-9743-3f641e042ad4","order_by":5,"name":"Jingqing Hu","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jingqing","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-01-23 07:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3890258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3890258/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50569157,"identity":"51f0a942-1e63-413a-8ab8-7037dc396e86","added_by":"auto","created_at":"2024-02-02 15:28:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":337201,"visible":true,"origin":"","legend":"\u003cp\u003eThe PHQ-9 score and proportion of depression degrees in different groups\u003c/p\u003e","description":"","filename":"Figure1.ThePHQ9scoreandproportionofdepressiondegreesindifferentgroups.png","url":"https://assets-eu.researchsquare.com/files/rs-3890258/v1/2a99722c976b0fddbaca0c9e.png"},{"id":50569156,"identity":"29d196fa-116e-4dca-85b9-1b0b6eef88e3","added_by":"auto","created_at":"2024-02-02 15:28:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":308232,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the probability of depression for coronary heart disease\u003c/p\u003e","description":"","filename":"Figure2.Nomogramforpredictingtheprobabilityofdepressionforcoronaryheartdisease.png","url":"https://assets-eu.researchsquare.com/files/rs-3890258/v1/52b892f747bb3bb838e57e96.png"},{"id":50569155,"identity":"bc89d5ac-daf5-41d5-ac93-e08e55d8ed38","added_by":"auto","created_at":"2024-02-02 15:28:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":577486,"visible":true,"origin":"","legend":"\u003cp\u003eThe roc curve for the nomogram model in prediction of depression.\u003c/p\u003e","description":"","filename":"Figure3.Theroccurveforthenomogrammodelinpredictionofdepression.png","url":"https://assets-eu.researchsquare.com/files/rs-3890258/v1/7a6cc19ef807dab6951fe8d8.png"},{"id":67609246,"identity":"4c8cd4d7-1d54-4e2d-8cf0-88afd2a348a9","added_by":"auto","created_at":"2024-10-28 05:24:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2042956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3890258/v1/9e9b4f61-1b91-4d73-b90f-2d394d3985cf.pdf"},{"id":50569158,"identity":"161e174d-084d-41ff-aac8-7b281bff0b49","added_by":"auto","created_at":"2024-02-02 15:28:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32444,"visible":true,"origin":"","legend":"","description":"","filename":"supplementTable1.Characteristicsandcomparisonofsubjectswithandwithoutdepressivesymptomsindifferentgroup.docx","url":"https://assets-eu.researchsquare.com/files/rs-3890258/v1/1699e66e3aecb161444072d5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of depression risk in patients with coronary heart disease based on nomogram for Chinese population: a population-based multi-center study from 2016 to 2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite a decline in mortality rates, coronary heart disease (CHD) remains a leading cause of death worldwide, primarily due to heart ischemia\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In 2020, CHD led to 380,000 deaths \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Furthermore, cardiac events and/or surgery not only significantly diminish patients' quality of life but also contribute to mental health issues, as previously reported \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Reports indicate that nearly 30% of patients with CHD face psychological challenges, particularly depression, highlighting a critical concern\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the prevalence of psychological symptoms such as depression or anxiety, numerous global studies have explored depression as a risk factor for incident CHD or cardiovascular morbidity and mortality in patients with established CHD\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The mechanisms underlying this comorbidity have not been fully elucidated. Cross-sectional studies consistently reveal a significant increase in depression incidence in CHD patients, while CHD incidence also rises considerably in patients with depression\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Depression is widely acknowledged to play a pivotal role in CHD pathogenesis or, at the very least, act as a predisposing factor for CHD\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Patients with CHD are prone to mental disorders, particularly depression, due to prolonged illness and an unsatisfactory prognosis\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The intricate causal relationship between the two conditions has led to the combination of psychological treatments with traditional approaches to enhance outcomes.\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated that the comorbidity of CHD and depression increases the risk of cardiovascular events and mortality, resulting in higher readmission rates and medical costs\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. However, despite advancements in clinical departments, psychological intervention and detection are infrequently implemented in cardiovascular clinical practice. In this study, we aim to construct a nomogram model for predicting depression risk in CHD patients based on a previous study that includes demographic characteristics, clinical features, psychological factors, and lifestyle. This model forms a robust foundation for early screening and intervention in depression risk among CHD patients. Therefore, we hope that this study serves as a reminder for doctors and patients to assess the risk of depression based on readily available information, such as the current disease stage and general data, facilitating early clinical prevention and intervention.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design and Participants:\u003c/h2\u003e\n \u003cp\u003eThis was a hospital-based, cross-sectional study approved by the Ethics Committee of Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences (approval Document No. 2016EC_KY_001) on September 7, 2016. This study was registered on the WHO International Clinical Trial Registry (ClinicalTrial.gov) and the registered ID was NCT02967718. This cross-sectional study, was conducted at 48 clinical research centers across 23 provinces over the period from October 2016 to April 2018. Before commencing the study, researchers underwent standardized training in interview procedures, covering the recruitment of patients and utilization of self-reported questionnaires. Subsequently, they conducted on-site face-to-face interviews and posed questions to respondents. When necessary, supplementary information was obtained from the respondents\u0026apos; family members. Reference to the Declaration of Helsinki, all methods would be performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n \u003cp\u003eThe survey spanned five consecutive days in each department. Inclusion criteria were as follows: (1) age 18 or older, (2) meeting criteria for healthy control population, metabolic syndromic individuals, stable coronary heart disease patients, acute coronary syndrome patients, post-PCI individuals, and heart failure patients, (3) voluntary participation with signed informed consent, and (4) completion of questionnaires independently.\u003c/p\u003e\n \u003cp\u003eExclusion criteria encompassed: (1) patients with a history of hand surgery, fever, trauma, or burn infection within the past week, active tuberculosis, or rheumatic immune disease, (2) patients with severe arrhythmias accompanied by hemodynamic changes, (3) patients with acute or subacute cerebrovascular diseases, (4) those with valvular heart disease or primary cardiomyopathy, (5) patients with acute exacerbation of chronic obstructive pulmonary disease or pulmonary heart disease or respiratory failure, (6) individuals with renal insufficiency, serum creatinine levels higher than 221 \u0026micro;mol/L in males and 177 \u0026micro;mol/L in females, (7) patients with liver dysfunction, alanine aminotransferase levels higher than 3 times the normal value or combined with cirrhosis, (8) individuals with severe primary diseases such as hematopoietic system disorders or malignant tumors, (9) organ transplant patients, (10) those with severe mental disorders, and (11) pregnant and lactating women.\u003c/p\u003e\n \u003cp\u003eFor participants who meet the requirements, we first sign informed consent and then proceed to follow-up studies. Patients were required to complete information collection questionnaires covering socio-demographic characteristics, the nine-item Patient Health Questionnaire-9 (PHQ-9) Scale, and lifestyle information (drinking, smoking, sleeping, etc.).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment Instrument: PHQ-9\u003c/h2\u003e\n \u003cp\u003eThe Patient Health Questionnaire-9 (PHQ-9) stands out as one of the most widely utilized self-report psychosocial assessments for evaluating participants\u0026apos; depressive symptoms over the preceding two weeks\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Positioned as an integral component of individual health management\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, this tool is designed to identify depression through nine questions, each scored from 0 (not at all) to 3 (nearly every day), yielding a total score ranging from 0 to 27\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Key breakpoints at 5, 10, 15, and 20 signify at least mild, moderate, moderately severe, and severe levels of depression \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The Chinese version of the PHQ-9, employed in our study, has exhibited robust reliability and validity across diverse conditions, including healthcare settings and the general population\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eIn our investigation, a total score of 5 or higher on the PHQ-9 indicates the presence of depression in respondents. To assess current suicidal or self-injurious ideation, we utilized the ninth item in the PHQ-9, which gauges thoughts related to being better off dead or engaging in self-harm (not at all 0; several days 1; more than half the days 2; nearly every day 3). A score of 1 or higher on this item indicates the presence of suicidal thoughts or self-harm concerns over the past two weeks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analysis was conducted using SPSS 20.0 and R 4.3.2 software. Descriptive analysis was employed to summarize socio-demographic and lifestyle characteristics. Binary logistic regression analysis was utilized to identify risk factors, with socio-demographic and lifestyle characteristics serving as independent variables, for depression within the entire population. The positive group, indicating the presence of depression as the main variable, was identified when the total score of PHQ-9 exceeded 4. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and statistical significance was considered when the two-sided P value was \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003e1. Socio-Demographic and Characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 9,044 patients were initially recruited, with 8,353 (response rate: 92.4%) completing the PHQ-9 and included in the analysis. The mean age of the total enrolled participants was 61.91\u0026thinsp;\u0026plusmn;\u0026thinsp;12.50 years (range: 39\u0026ndash;89 years), with 56.0% under 65 years and 44.0% aged 65 or older. Regarding gender, 52.7% were male, and 47.3% were female. The Han population comprised 94.3%, while the minority population was 5.7%. Religious beliefs were absent in 92.7% of participants, and 7.3% reported having religious beliefs. Marital status distribution was 1.0% unmarried, 92.0% married, and 7.0% divorced or widowed.\u003c/p\u003e\n\u003cp\u003eEmployment status revealed 19.5% unemployed, 52.6% retired, and 27.9% employed. Educational background varied, with 20.7% having primary education or below, 53.5% secondary school education, and 25.8% high education or above. In terms of technical titles, 70.2% had no title, 7.9% had a primary title, 13.5% were in the middle class, and 8.3% were in the high class. Regular exercise was reported by 47.1%, while 52.9% did not engage in regular exercise. Alcohol consumption patterns showed 76.9% never drank, 13.8% were current drinkers, and 9.3% were former drinkers. Smoking habits indicated 72.2% were never smokers, 13.9% were current smokers, and 13.9% were former smokers. The mean score on the PHQ-9 was 3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64.\u003c/p\u003e\n\u003cp\u003eBased on BMI standards, 2.5% were underweight, 64.8% were of normal weight, and 32.7% were overweight. Regarding sleep quality, 74.7% reported normal sleep, while 25.3% reported poor sleep. The study included six distinct groups: healthy individuals, those with metabolic syndrome (MS), stable coronary heart disease population, acute coronary syndrome patients, post-PCI individuals, and heart failure patients. Their mean ages were 50.77\u0026thinsp;\u0026plusmn;\u0026thinsp;10.87, 59.26\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79, 65.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.174, 64.14\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68, 63.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.67, and 71.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86, respectively. Comparisons between individuals with and without depression across different factors were conducted; detailed results are presented in Table \u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparison of different factors between study population with and without depression.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWithout Depression (n\u0026thinsp;=\u0026thinsp;6037)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWith Depression (n\u0026thinsp;=\u0026thinsp;2316)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et/Z/x2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e747.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-PCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e235.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e39.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e79.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e43.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e56.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmploy status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e206.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUmemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e116.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprimaryeducationorbelow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esecondaryschooleducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh education or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnical\u0026nbsp;titles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e5.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eintermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e40.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eonce, has quit drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecurrent drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e17.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eonce, has quit smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e8.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026ge;\u0026thinsp;BMI\u0026thinsp;\u0026ge;\u0026thinsp;24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleeping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e1033.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enot good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e2. Comparison of PHQ-9 scores and Corresponding Depression Rates in Each Group\u003c/h2\u003e\n\u003cp\u003eWithin the entire population, the prevalence of depressive symptoms was 27.7% (2316/8353; 95% CI: 26.8\u0026ndash;28.7%). Specifically, the prevalence of mild (PHQ-9 score: 5\u0026ndash;9), moderate (PHQ-9 score: 10\u0026ndash;14), and severe (PHQ-9 score: 15\u0026ndash;27) depressive symptoms was 21.0% (1758/8353; 95% CI: 20.2\u0026ndash;21.9%), 5.1% (424/8353; 95% CI: 4.6\u0026ndash;5.6%), and 1.6% (134/8353; 95% CI: 1.4\u0026ndash;1.9%), respectively. The prevalence of moderate to severe depressive symptoms (PHQ-9 score: 10\u0026ndash;27) was 6.7% (558/8353, 95% CI: 6.2\u0026ndash;7.2%). The scores and proportions of people with depressive symptoms among each group are depicted in Fig. \u003cspan\u003e1\u003c/span\u003e. Regarding detailed scores, pairwise comparisons among the six groups revealed statistical differences in each pair, except for the comparison between SCHD and ACS, as well as the comparison between P-PCI and MS.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp;\u003cstrong\u003eLogistic Binary Regression Analysis of Risk Factors for Depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed a binary logistic regression model to identify relevant factors associated with depressive and anxious symptoms in the entire population. The odds ratios (ORs) and 95% confidence intervals (CIs) are reported in Table \u003cspan\u003e2\u003c/span\u003e. Significant risk factors for depressive symptoms include Gender (Male or Female) (OR\u0026thinsp;=\u0026thinsp;1.40; 95% CI: 1.22\u0026ndash;1.61), Nation (Han or Minority) (OR\u0026thinsp;=\u0026thinsp;1.62; 95% CI: 1.24\u0026ndash;2.11), Marital status: Never married (OR\u0026thinsp;=\u0026thinsp;1.70; 95% CI: 1.00-2.89), education: primary education or below (OR\u0026thinsp;=\u0026thinsp;1.40; 95% CI: 1.15\u0026ndash;1.72), education: secondary school education (OR\u0026thinsp;=\u0026thinsp;1.26; 95% CI: 1.08\u0026ndash;1.47), drinking: once, has quit drinking (OR\u0026thinsp;=\u0026thinsp;1.30; 95% CI: 1.02\u0026ndash;1.66), BMI\u0026lt;18.5 (OR\u0026thinsp;=\u0026thinsp;1.78; 95% CI: 1.25\u0026ndash;2.53), 18.5\u0026thinsp;\u0026ge;\u0026thinsp;BMI\u0026thinsp;\u0026ge;\u0026thinsp;24.9 (OR\u0026thinsp;=\u0026thinsp;1.23; 95% CI: 1.09\u0026ndash;1.39), Sleep disturbance (OR\u0026thinsp;=\u0026thinsp;4.49; 95% CI: 4.00-5.04), Status: MS (OR\u0026thinsp;=\u0026thinsp;2.28; 95% CI: 1.79\u0026ndash;2.92), Status: CAD (OR\u0026thinsp;=\u0026thinsp;3.54; 95% CI: 2.87\u0026ndash;4.37), Status: ACS (OR\u0026thinsp;=\u0026thinsp;4.24; 95% CI: 3.28\u0026ndash;5.48), Status: P-PCI (OR\u0026thinsp;=\u0026thinsp;3.14; 95% CI: 2.39\u0026ndash;4.14), Status: HF (OR\u0026thinsp;=\u0026thinsp;8.79; 95% CI: 6.86\u0026ndash;11.27). Additionally, we presented the characteristics and comparison of subjects with and without depressive symptoms in different groups (see Supplement Table \u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe binary logistic regression analysis to determine the strongest predictors of depressive in all population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003edepression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage (\u0026lt;65 or \u0026ge;\u0026thinsp;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11(0.97\u0026ndash;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Male or Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(1.22\u0026ndash;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNation (Han or Minority)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62(1.24\u0026ndash;2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReligion (No or Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89(0.69\u0026ndash;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status: married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status: Never married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70(1.00-2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status:Divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10(0.90\u0026ndash;1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmploy status: Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmploy status: Umemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09(0.89\u0026ndash;1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmploy status: Retired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11(0.94\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeducation: high education or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeducation: primary education or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(1.15\u0026ndash;1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u0026lt;P\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeducation: secondary school education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26(1.08\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u0026lt;P\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edrinking: current drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edrinking: never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14(0.94\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edrinking: once, has quit drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30(1.02\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026lt;P\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking: current smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking: never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99(0.82\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking: once, has quit smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95(0.76\u0026ndash;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026lt;18.5(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78(1.25\u0026ndash;2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u0026lt;P\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026ge;\u0026thinsp;BMI\u0026thinsp;\u0026ge;\u0026thinsp;24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23(1.09\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49(4.00-5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaus:Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaus:MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28(1.79\u0026ndash;2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaus:SCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.54(2.87\u0026ndash;4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaus:ACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24(3.28\u0026ndash;5.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaus:P-PCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14(2.39\u0026ndash;4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaus:HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.79(6.86\u0026ndash;11.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e4. Establishment of a Nomogram for Predicting Depression Risk\u003c/h2\u003e\n\u003cp\u003eUtilizing logistic regression analysis, we developed a nomogram model to predict the risk of depression in coronary heart disease (Fig. \u003cspan\u003e2\u003c/span\u003e). Each factor was assigned a score ranging from 0 to 100, and the cumulative scores were computed to derive the total model score. The total score was then used to predict the risk probability of early depression in coronary heart disease. The risk of depression increases with the total score. The population categories were assigned numbers: 1 for health, 2 for metabolic syndrome, 3 for stable coronary heart disease, 4 for acute coronary syndrome, 5 for post-PCI surgery, and 6 for heart failure patients. Gender was coded as 1 for male and 2 for female. Ethnicity was coded as 1 for Han ethnicity and 2 for non-Han ethnicity. Marital status was coded as 1 for unmarried, 2 for divorced and widowed, and 3 for married. Educational status was coded as 1 for primary education or below, 2 for secondary school education, and 3 for high education or above. Drinking status was coded as 1 for none, 2 for abstained, and 3 for still drinking. BMI was coded as 1 for BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5, 2 for BMI\u0026thinsp;\u0026ge;\u0026thinsp;18.5 and \u0026lt;\u0026thinsp;24.9, and 3 for BMI\u0026thinsp;\u0026ge;\u0026thinsp;25. Sleep quality was coded as 0 for good and 1 for bad. Based on the ROC curve, the AUC value for our model in this study was 0.768 (95% CI: 0.757\u0026ndash;0.786), indicating a certain level of accuracy and providing a valuable reference for assessing the risk of depression in patients with coronary heart disease (Fig. \u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccording to clinical epidemiological studies, psychological disorders are prevalent comorbidities in patients with coronary heart disease (CHD), significantly surpassing the rates observed in the general population, albeit with variations in screening tools and sampled populations\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Previous investigations into psychological disorders among CHD patients have reported depression incidence ranging from 34.6\u0026ndash;51%, with acute coronary syndrome showing rates of 31\u0026ndash;45%, markedly higher than the World Health Organization's estimated 4.3% in the general population\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. While numerous studies have explored the psychological aspects of coronary heart disease, a systematic evaluation of the psychological status across different stages of CHD remains lacking. Hence, our current study aimed to fill this gap by assessing the extent of depression at various stages of coronary heart disease.\u003c/p\u003e \u003cp\u003eIn general, both the degree and overall scores on the PHQ-9 indicated a trend: the more severe the disease, the more pronounced the depression and anxiety in patients, with heart failure (HF) showing significantly higher rates than other groups. Interestingly, post-percutaneous coronary intervention (PCI), patients experienced a relief in depression, contrary to prior studies suggesting increased depression scores following cardiac coronary artery bypass graft surgery\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This divergence may stem from increased patient confidence in follow-up prognosis. Furthermore, it contrasts with previous findings suggesting persistent depression despite treatment\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Notably, in the healthy population, 9.7% exhibited depressive symptoms, a percentage higher than a study involving a rural Chinese population sample (5.9%), as determined by PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;5\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubsequently, we conducted comparisons between individuals with and without depression across various factors. Statistical differences were observed for most general and lifestyle factors, excluding technical titles and exercise. Building on these findings, the binary logistic regression model explored risk factors contributing to depressive symptoms. Overall, depression correlated closely with gender, nation, marital status, education, drinking, BMI, sleep disturbance, and disease status. Paradoxically, while one study suggested no gender effect on depression\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, another attributed gender's significance to the sex role hypothesis\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In our study, gender was closely associated with depression, with women being more prone. Marital status also significantly related to depression\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, aligning with previous research demonstrating higher depression risk with lower education levels\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Similar to prior studies, alcohol consumption and abnormal BMI were associated with depression\u003csup\u003e[\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Sleep status showed a strong association with depression, and vice versa\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Recognizing the reciprocal relationship, the integrated approach addressing both sleep problems and depression emerges as a vital strategy in preventing heart disease \u003csup\u003e[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, the nomogram developed serves as a useful tool for predicting the risk of depression in CHD patients. This prediction model exhibits strong predictive capabilities, excellent calibration, and valuable clinical utility. It combines multiple predictors and presents them in a visual format, allowing medical professionals to easily assess the likelihood of depression based on the sum of relevant risk factors. At the same time, patients can obtain additional resources and information through the nomogram prediction model easily, improve their understanding of their own mental health status, and actively participate in the treatment and management process. Based on the above scoring results, we hope to quickly identify potential depression in patients, so as to prevent and intervene in advance, prevent the promoting effect of depression itself on the disease, and also make depression intervention in coronary heart disease patients more important.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDepression is one of common symptom in coronary heart disease, and they can affect each other accentuating further disease. This study would like to help clinicians and researchers understand the degree of depression in patients with coronary heart disease quickly and accurately, especial in the different stages of coronary heart disease. At the same time, we find that depression is close to the risk factors of Gender, Nation, Marital status, education, drinking, BMI, Sleep disturbance, and Status of disease. Base on above, this study develops predictive nomogram models referred to these risk factors, in order to give an early judgment, prevention and intervention for coronary heart disease accurately.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHD: Coronary heart disease\u003c/p\u003e\n\u003cp\u003eSCHD: Stable of Coronary heart disease\u003c/p\u003e\n\u003cp\u003eMS: Metabolic syndrome\u003c/p\u003e\n\u003cp\u003eACS: Acute coronary syndrome\u003c/p\u003e\n\u003cp\u003eP-PCI: Post-percutaneous coronary intervention\u003c/p\u003e\n\u003cp\u003eHF: heart failure\u003c/p\u003e\n\u003cp\u003ePHQ-9: Patient Health Questionnare-9\u003c/p\u003e\n\u003cp\u003eMDD: major depressive disorder\u003c/p\u003e\n\u003cp\u003eICT: The information collection form\u003c/p\u003e\n\u003cp\u003eORs: Odds ratios\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCIs: 95% confidence intervals\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all those who provided excellent technical support and assistance during the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eJQH and HXT designed the study. HXT drafted the manuscript and draw the figures. JQH revised the manuscript for important intellectual content. HXT and LD finished data statistics and analysis. LJJ checked the data statistics and analysis. JLZ sorted out and eliminated the data. All the authors have read and approved the final version of the manuscript.\u003cbr\u003e \u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe design of the study and the collection, analysis, and interpretation of data were supported by the National Nonprofit Institute Research Grant for Institute of Basic Theory for Chinese Medicine, CACMS, No. YZ-202142 and No. YZ-202240, and Guangzhou Foshan Science and Technology Innovation Project (No. 2020001005585).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eremoved for peer review\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflicts of Interest\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRICHARDS S H, ANDERSON L, JENKINSON C E, et al. 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PloS one, 2014, 9(6): e99692.\u003c/li\u003e\n\u003cli\u003eMUMANG A A, SYAMSUDDIN S, MARIA I L, et al. Gender Differences in Depression in the General Population of Indonesia: Confounding Effects [J]. Depression research and treatment, 2021, 2021: 3162445.\u003c/li\u003e\n\u003cli\u003eROBINSON K M, MONSIVAIS J J. Depression, Depressive Somatic or Nonsomatic Symptoms, and Function in a Primarily Hispanic Chronic Pain Population [J]. ISRN Pain, 2013, 2013: 401732.\u003c/li\u003e\n\u003cli\u003eEDMEALEM A, OLIS C S. Factors Associated with Anxiety and Depression among Diabetes, Hypertension, and Heart Failure Patients at Dessie Referral Hospital, Northeast Ethiopia [J]. Behavioural neurology, 2020, 2020: 3609873.\u003c/li\u003e\n\u003cli\u003eARSLANTAS D, \u0026Uuml;NSAL A, OZBABALıK D. Prevalence of depression and associated risk factors among the elderly in Middle Anatolia, Turkey [J]. Geriatrics \u0026amp; gerontology international, 2014, 14(1): 100-8.\u003c/li\u003e\n\u003cli\u003eARACHCHI N S M, GANEGAMA R, HUSNA A W F, et al. Suicidal ideation and intentional self-harm in pregnancy as a neglected agenda in maternal health; an experience from rural Sri Lanka [J]. Reproductive health, 2019, 16(1): 166.\u003c/li\u003e\n\u003cli\u003eGEA A, BEUNZA J J, ESTRUCH R, et al. Alcohol intake, wine consumption and the development of depression: the PREDIMED study [J]. BMC Med, 2013, 11: 192.\u003c/li\u003e\n\u003cli\u003eHASIN D S, GOODWIN R D, STINSON F S, et al. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions [J]. Archives of general psychiatry, 2005, 62(10): 1097-106.\u003c/li\u003e\n\u003cli\u003eHASIN D S, STINSON F S, OGBURN E, et al. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions [J]. Archives of general psychiatry, 2007, 64(7): 830-42.\u003c/li\u003e\n\u003cli\u003eRAUD B, GAY C, GUIGUET-AUCLAIR C, et al. Level of obesity is directly associated with the clinical and functional consequences of knee osteoarthritis [J]. Scientific reports, 2020, 10(1): 3601.\u003c/li\u003e\n\u003cli\u003eVAFA M, AZIZI-SOLEIMAN F, KAZEMI S M, et al. Comparing the effectiveness of vitamin D plus iron vs vitamin D on depression scores in anemic females: Randomized triple-masked trial [J]. Medical journal of the Islamic Republic of Iran, 2019, 33: 64.\u003c/li\u003e\n\u003cli\u003eHOSHIKAWA M, UCHIDA S, HIRANO Y. A Subjective Assessment of the Prevalence and Factors Associated with Poor Sleep Quality Amongst Elite Japanese Athletes [J]. Sports medicine - open, 2018, 4(1): 10.\u003c/li\u003e\n\u003cli\u003eBAN M J, KIM W S, PARK K N, et al. Korean survey data reveals an association of chronic laryngitis with tinnitus in men [J]. PloS one, 2018, 13(1): e0191148.\u003c/li\u003e\n\u003cli\u003eZHAI K, GAO X, WANG G. The Role of Sleep Quality in the Psychological Well-Being of Final Year UndergraduateStudents in China [J]. International journal of environmental research and public health, 2018, 15(12).\u003c/li\u003e\n\u003cli\u003eREDLINE S, FOODY J. Sleep disturbances: time to join the top 10 potentially modifiable cardiovascular risk factors? [J]. Circulation, 2011, 124(19): 2049-51.\u003c/li\u003e\n\u003cli\u003eFORD D E, KAMEROW D B. Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention? [J]. Jama, 1989, 262(11): 1479-84.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3890258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3890258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to assess the prevalence and identify risk factors associated with depression among coronary heart disease (CHD) patients at different stages in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConducted as a hospital-based, cross-sectional study across 48 hospitals in 23 provinces, the research spanned from October 2016 to April 2018. A total of 9044 patients were initially recruited, with 8353 deemed eligible for participation. Depression was assessed using the nine-item Patient Health Questionnaire-9 (PHQ-9) Scale. Univariate analysis identified predictors of postoperative depression, and binary logistic regression analysis was employed to ascertain risk factors associated with depressive symptoms. The predictive model was constructed using the \"rms\" package in R software, demonstrating robust predictive capabilities according to the ROC curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn general, both the degree and overall score based on the PHQ-9 revealed a trend: as the severity of the disease increased, so did the severity of patient depression. Univariate analysis indicated statistical differences concerning general situations and lifestyles. The binary logistic regression model highlighted the proximity of depression to risk factors such as gender, nationality, marital status, education, drinking, BMI, sleep disturbance, and disease status. Utilizing these findings, a predictive nomogram for depression was developed. The model exhibited excellent predictive ability, with an AUC of 0.768 (95% CI = 0.757–0.780).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study systematically investigated the prevalence of depression among coronary heart disease patients at various stages. As coronary heart disease advanced, the level of depression intensified. The nomogram developed in this study proves valuable in predicting the incidence of depression in coronary heart disease patients.\u003c/p\u003e","manuscriptTitle":"Prediction of depression risk in patients with coronary heart disease based on nomogram for Chinese population: a population-based multi-center study from 2016 to 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 15:28:32","doi":"10.21203/rs.3.rs-3890258/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"975b3761-4bc2-481c-afef-73ae00aac6c7","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T05:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 15:28:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3890258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3890258","identity":"rs-3890258","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}