Prevalence and Risk Factors of Depression in Rural Chinese Hemodialysis Patients During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study

Prevalence and Risk Factors of Depression in Rural Chinese Hemodialysis Patients During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and Risk Factors of Depression in Rural Chinese Hemodialysis Patients During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study Zhaoqi Liu, Juhua Lin, Zhiqiang Chen, Rugang Li, Junping Tang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5328560/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 Purpose This study aimed to assess the prevalence and risk factors of depression among maintenance hemodialysis (MHD) patients in rural China during the COVID-19 pandemic. Methods A cross-sectional survey was conducted in 14 hemodialysis centers in northern Guangdong Province from April to October 2021. Depression was evaluated using the Self-Rating Depression Scale. Multivariate logistic regression analysis was employed to identify associated factors. Results Of the 450 MHD patients enrolled, 160 (35.6%) met the criteria for depression, with 91.8% cases being of mild severity. After adjusting for demographic, dialysis-related, laboratory, pandemic-associated lifestyle changes, and psychological variables, discomfort during dialysis [Odds ratio (OR) 1.654, 95% Confidence Interval (CI) 1.105–2.474] and infection worry (OR 1.719, 95% CI 1.121–2.636) were significantly associated with an increased risk of depression. In contrast, college education was linked to a lower risk (OR 0.456, 95% CI 0.245–0.846). Conclusion During the COVID-19 pandemic in rural China, mild depression were common among MHD patients. Mandatory behavioral interventions did not contribute to depression, while discomfort during dialysis and infection worry emerged as risk factors, and college education was associated with a lower risk. maintenance hemodialysis COVID-19 pandemic depression rural areas Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Maintenance hemodialysis (MHD) is a primary treatment for patients with end-stage renal disease. Due to their reliance on hemodialysis equipment and medical care for survival, these patients often have low immune resistance and frequent complications [ 1 , 2 ]. In addition to suffering numerous physical symptoms, they are prone to emotional disturbances, including a high prevalence of depression [ 3 ]. This vulnerability can be exacerbated during major public health events, potentially leading to an increased incidence of mental health issues. Current evidence suggests that the COVID-19 pandemic has coincided with a widespread increase in psychiatric disorders [ 4 ], warranting the attention of the global health community. To combat the COVID-19 pandemic, various countries and governments have implemented mandatory behavioral non-pharmaceutical interventions, such as restrictions on social interactions and mask-wearing. Although these interventions limit free communication, potentially exacerbating depression, compliance with them can make individuals feel safe and help alleviate depressive symptoms. Thus, these measures are a "double-edged sword," and their effects depend on a combination of factors such as medical, social, and cultural context [ 5 ]. Considering the health vulnerability of the maintenance hemodialysis (MHD) population, it is generally believed that pandemic-associated psychological stress will have a depressive impact on MHD patients [ 6 – 13 ]. However, studies on dialysis populations have shown inconsistent results. Some research, primarily from countries with advanced healthcare systems, suggests that the pandemic has not significantly affected the mental health of dialysis patients [ 14 , 15 ]. Other findings focus on mandatory behavioral interventions and indicate that these measures do not impact mental health [ 16 ]. A large-scale epidemiological survey from South Korea found that not wearing masks indoors was most strongly associated with depression; those who did not adhere to public health measures were more likely to experience depression [ 17 ]. In rural areas, where medical resources are scarce, healthcare systems face increased pressure during major public health events [ 18 ]. Additionally, limited educational backgrounds can hinder patients' understanding of the pandemic. In rural America, 43.68% of respondents reported a negative impact on mental health during the pandemic [ 13 ]. In Guangdong Province, an economically developed region in China, a province-wide survey among the MHD population indicated that the COVID-19 pandemic had no significant overall impact on mental health. However, it did affect rural areas and low-income populations [ 19 ]. Despite these preliminary studies, further research is still lacking on the impact of the pandemic on psychological issues in rural regions. Consequently, our understanding of the unique challenges faced by rural healthcare systems and their impact on vulnerable populations remains limited. This study utilizes questionnaire data from the multi-hemodialysis center in rural area of China during the COVID-19 pandemic. It aims to investigate the depression prevalence and analyze the association between demographic factors, medical parameters, pandemic-associated lifestyle changes, and depression, thereby exploring the risk factors for depression among the rural MHD population. Methods 1.1 Patient inclusion We conducted this prospective study in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics committee of our hospital (KY-2021-038). Patients from 14 hemodialysis centers in the northern rural areas of Guangdong Province were selected for this study, conducted from April 2021 to October 2021. The inclusion criteria were as follows: (1) undergoing hemodialysis for 6 months or more; (2) aged 16 years or older; (3) fully conscious and able to complete the questionnaire independently or with assistance; (4) no serious complications in the past one month. Exclusion criteria involved patients who were unwilling to participate. 1.2 Ethical issues The study protocol was reviewed and approved by the hospital's Ethics Committee. All patients and their family members were informed about the study's details and voluntarily signed the informed consent. 1.3 Data collection The Self-Rating Depression Scale (SDS), a widely used 20-item self-reported questionnaire, was utilized to assess depression. Upon obtaining informed consent, SDS questionnaires were distributed. Nurses, who were uniformly trained, explained the survey content and the instructions for completing the questionnaire, ensuring patients could complete it independently or with assistance. The SDS scores range from 25 to 100, with a score of 50 or above indicating clinical depression: 50–59 signifies mild depression, 60–69 moderate, and 70 or above severe [ 20 , 21 ]. Other collected clinical data included: (1) demographic information (age, sex, education level, marital status, and monthly family income); (2) nutritional status (serum albumin levels) and anemia indicators; (3) dialysis details (years on dialysis, frequency of sessions, single-session fluid removal volume and duration, symptoms experienced during dialysis); and (4) pandemic-related information (daily masking duration, discussions with other patients about the pandemic, understanding of pandemic updates). 1.4 Statistical methods Statistical analyses were performed using SPSS software version 26.0. An independent sample t -test was employed to compare continuous variables between two groups, while the chi-square test was used for categorical data. The Bonferroni correction was used to adjust α value for multiple group comparisons. Multivariate logistic regression analysis was conducted to identify depression-associated risk factors. Results 2.1 Clinical characteristics A total of 450 hemodialysis patients completed the survey, of which 238 (52.9%) were male, with an average age of 54.9 ± 13.9 years. The overall SDS score was 45.1 ± 8.8, with emotional and psychological symptoms scoring 7.3 ± 2.3, physical and behavioral symptoms 17.7 ± 3.6, and social and cognitive symptoms 20.1 ± 4.9. Among the 450 patients, 160 were identified as having depression, including 147 (91.8%) with mild, 11 (6.9%) with moderate, and 2 (1.3%) with severe depression. The clinical characteristics of patients with or without depression are summarized in Table 1 . Compared to non-depressed individuals, those with depression were less educated and had lower incomes, and more frequently self-reported worry about COVID-19 infection. No significant differences were found in laboratory indicators or in lifestyle during the pandemic (Table 1 ). Table 1 Clinical characteristics of patients without and with depression. Non-Depressed N = 290 Depressed N = 160 t / χ 2 P value Demographic indicators Age 54.5 ± 14.2 55.6 ± 13.4 −0.849 0.396 Male [n (%)] 160 (55.2%) 78 (48.8%) 1.707 0.191 College education 60 (21.3%) 15 (9.6%) 9.624 0.002 Marriage 1.772 0.621 Unmarried 37 (12.8%) 14 (8.8%) Married 225 (77.6%) 130 (81.3%) Divorced 12 (4.1%) 6 (3.8%) Widowed 16 (5.5%) 10 (6.3%) Monthly income ≥ 5, 000 yuan 127 (43.8%) 44 (27.5%) 11.618 < 0.001 Dialysis parameters HD vintage 0.556 0.757 < 1 year 67 (23.1%) 42 (26.3%) 1–3 year 55 (19.0%) 29 (18.1%) ≥ 3 year 168 (57.9%) 89 (55.6%) HD frequency 1.654 0.437 ≤ Twice a week 56 (19.3%) 32 (20.0%) Five times every 2 weeks 219 (75.5%) 115 (71.9%) Three times a week 15 (5.2%) 13 (8.1%) Discomfort during dialysis 121 (41.7%) 89 (55.6%) 8.005 0.005 Laboratory parameters Hemoglobin 3.679 0.159 < 90 g/L 66 (22.8%) 45 (28.1%) 90–129 g/L 212 (73.1%) 104 (65.0%) ≥ 130 g/L 12 (4.1%) 11 (6.9%) Serum albumin 4.744 0.192 < 30 g/L 15 (5.2%) 14 (8.8%) 30–34 g/L 68 (23.4%) 43 (26.9%) 35–39 g/L 160 (55.2%) 73 (45.6%) ≥ 40 g/L 47 (16.2%) 30 (18.8%) Lifestyle change Daily masking time 6.004 0.050 < 2h 100 (34.5%) 41 (25.6%) 2–4h 61 (21.0%) 48 (30.0%) ≥ 4h 129 (44.5%) 71 (44.4%) Mask discomfort 96 (33.1%) 67 (41.9%) 3.434 0.064 Daily concern for pandemic 212 (73.1%) 126 (78.8%) 1.759 0.185 Reduce outdoor activities 183 (63.1%) 106 (66.3%) 0.444 0.505 Psycho Well-being Infection worry 166 (57.2%) 113 (70.6%) 7.839 0.005 2.2 Depressive severity analysis Given the predominance of mild depression among patients, we utilized the SDS scores to assess its severity across clinically distinct subgroups. Demographic analyses revealed no significant differences in SDS scores based on sex or marital status (Fig. 1 A and 1 B), but higher educational levels and monthly incomes correlated with lower SDS scores (Fig. 1 C and 1 D), indicating an education-income link with depression . SDS scores were unaffected by laboratory results and dialysis parameters, with no variations observed for serum albumin, hemoglobin levels (Fig. 2 A, 2 B), or dialysis specifics (volume, frequency, session duration, years on dialysis or discomfort during dialysis; Fig. 3 A- 3 E). Patients experiencing mask discomfort had higher SDS scores than those without (Fig. 4 A), while mask duration did not impact scores significantly (Fig. 4 B), indicating subjective emotional responses play a role in mask-related distress. Other lifestyle changes due to the pandemic, such as daily pandemic concerns and reduced outdoor activities, did not influence SDS scores (Fig. 4 C and 4 D). However, anxiety about COVID-19 infection was associated with higher SDS scores (Fig. 4 E), emphasizing personal psychological factors' impact on depression over routine activity modifications. Taken above at all, individuals with low income, limited education, mask discomfort, and heightened COVID-19 anxiety are at increased risk for depression, highlighting the need for integrated psychological and physical health support, especially during crises. 2.3 Depression-associated risk factors To investigate the independent relationships of above characteristics with depression during the pandemic, we conducted a Wald stepwise multivariate Logistic regression analysis on the variables listed in Table 1 . Given a strong association observed between education and income in preliminary analysis (data not shown), only education was included in the modeling as a potential variable. After adjusting for multiple factors, three variables remained significant in the model: college education was associated with a lower risk of depression compared to those without a college education, while discomfort during dialysis and infection worry were linked to an increased risk of depression (Table 2 ). Table 2 Independent risk factors for depression Variables Odds Ratio 95% Confidential Interval P value College education 0.456 0.245–0.846 0.005 Discomfort during dialysis 1.654 1.105–2.474 0.014 Infection concern 1.719 1.121–2.636 0.013 Discussion This study provided the epidemiological characteristics of depression in 450 MHD patients during the COVID-19 pandemic in rural areas of northern Guangdong Province, China, and found that about 1/3 of the patients met the criteria for depression with SDS scores of more than 50. After univariate and multivariate analysis, we found that discomfort during dialysis, and infection worry were risk factors associated with depression. Higher education was associated with a lower risk of depression. This study adopts an SDS score > 50 as the criterion for depression and reveals that the incidence of depression among MHD patients in rural China during the pandemic era is 35.6%, which surpasses the pre-pandemic rate reported by Abdel-Kader K et al. They utilized the Patient Health Questionnaire-9 (PHQ-9) for depression assessment, identifying 25% of MHD patients as depressed based on a PHQ-9 score > 9 threshold [ 3 ]. Given the weak correlation between PHQ-9 and SDS scores in the general population, with a mere 0.29 correlation coefficient [ 22 ], no direct conversion between these measures is feasible. Consequently, studies employing distinct criteria for depression assessment are not directly comparable. A similar variance due to different assessment methodologies was observed by Ibrahim M et al., who employed the Beck Depression Inventory during a pandemic and found 66.2% of their MHD sample exhibiting depressive symptoms, with 61.4% meeting diagnostic criteria for depression [ 10 ]. Another key factor contributing to the discrepancy in reported depression rates is the rural setting of our investigation, which contrasts with the non-rural areas studied by Hao W et al. Using identical depression assessment criteria, they reported a depression detection rate of 32.1% among 321 hemodialysis patients, slightly lower than our findings [ 8 ]. This difference may be attributed to demographic variations between the two studies, such as a higher proportion of our subjects with monthly income lower than 5000 yuan (62% compared to 53%) and dialysis vintage more than a year (75.8% versus 67.0%). Our study focused on the association between pandemic-associated lifestyle changes and depression. We observed a trend toward an increased depression rate among patients who wore masks for longer periods; however, this increase was not statistically significant when compared to those without depression. Additionally, mask discomfort was not associated with depression. These results suggest that mask-wearing itself does not elevate the risk of depression. After adjusting for multiple variables, the mask-related index remained unassociated with depression. Similarly, other lifestyle changes, such as daily concerns about the pandemic and reduced outdoor activities, were also not linked to depression. These findings support the notion that mandatory behavioral interventions do not contribute to depression in the MHD population in rural areas of China. Due to the limited educational levels in rural area, it is challenging to precisely quantify the restrictions on social activities during the survey period. As a result, the study design incorporates only a binary measure of whether outdoor activities were reduced. Additional limitation includes incomplete clinical databases in parts of centers, which limit full depression-related laboratory data and comorbidies are not included in the analytical model. This cross-sectional survey was conducted from March to October 2021, a period that corresponds to the national vaccination campaign stage following the shift from the first large-scale outbreak to sporadic outbreaks in mainland China. Therefore, the interpretation of the study's findings should take into account the sociological context of this specific timeframe. The multicenter and prospective design is a notable strength of this study, particularly given the difficulties in acquiring data from rural areas during the pandemic. Importantly, the three risk factors for depression that were identified in our research are easily recognizable, thereby highlighting the feasibility of reproducing these results within clinical contexts. Our findings offer persuasive evidence to guide intervention strategies for this demographic in future public health crises. In conclusion, during the COVID-19 pandemic in rural China, mild depression were common among MHD patients. Mandatory behavioral interventions did not contribute to depression, while discomfort during dialysis and infection worry emerged as risk factors, and college education was associated with a lower risk. Declarations Acknowledgement: We thank the following hospitals that participated in this research: Yuebei People's Hospital, Shaoguan City First People's Hospital Shaozhou People's Hospital 419 Hospital of Ministry of Nuclear Industry Shaoguan City Kai De Hospital Shaoguan City Qujiang District People's Hospital Shaoguan City Women's Hospital Nanxiong City People's Hospital Lechang City People's Hospital Shixing County People's Hospital Xinfeng County People's Hospital Ruyuan County People's Hospital Yingde City Chinese Medicine Hospital Lechang City Chinese Medicine Hospital Author contributions M He, ZQ Liu,JH Lin,ZQ Chen,RG Li,JP Tang,Q Liu,and L Nin designed the study.M He, ZQ Chen, RG Li, ,JP Tang,Q Liu,ZQ Liu,JH Lin,and L Nin collected, analyzed, and interpreted the clinical data. M He, ZQ Chen, RG Li, ,JP Tang,Q Liu,ZQ Liu,JH Lin,and L Nin wrote and revised the manuscript. All authors confirmed the integrity of the data and analysis. All authors read and approved the final manuscript. Funding No funding. Data availability All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author. Ethics declarations Conflict of interest This manuscript is submitted without conflict of interest and the manuscript is published with the consent of all authors. Ethical approval The present study has been approved by the ethics committee of Yuebei People's Hospital (KY-2021-038). Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Author information Authors and Affiliations Department of Nephrology, Yuebei People's Hospital, Guangdong Medical University, Shaoguan City 512026, Guangdong, China Min He & Zhaoqi Liu & Juhua Lin Department of Nephrology, Yuebei People's Hospital,, Shaoguan City 512026, Guangdong, China Zhiqiang Chen& Rugang Li & Junping Tang & Quan Liu & Ling Nin Corresponding authors Correspondence to Min He. 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He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACefaGxAcJBjY8/OzNB4jTYthz4LHBh4o0OcmeYwlEWnMj8ZnkjDOHjQ1u5BgQp4NxRnKCNG/b4cQNZ858vPGGwU5Ot4GAFnaeZwnGvG3piTOP9262nMOQbGx2gJAt7TkJybxt1ol9Z85uk+ZhOJC4jZAWhgP5Hw7ztjEnNtzIeUaklhMJiY0zzjgbC9zIYSNOCzCQkxmggWxsOceACL8AozL9BzQqH954U2EnR1ALCpDgITJqkLWQqmMUjIJRMApGBAAAZcxLyBfYZdoAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-10-25 00:08:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5328560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5328560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67782122,"identity":"4f461d83-88aa-4ecf-a22c-467cda5c958c","added_by":"auto","created_at":"2024-10-29 16:06:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83701,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic analysis of SDS scores in different subgroups\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5328560/v1/64f89c44f23909f81f2303eb.png"},{"id":67782154,"identity":"25399aa8-ba51-456b-9851-9ad0bb883250","added_by":"auto","created_at":"2024-10-29 16:06:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47546,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic analysis of SDS scores in different laboratory results\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5328560/v1/a41ec7fd1e81030d679b5df4.png"},{"id":67783455,"identity":"1ac3d5ce-ecf5-4d8b-8b68-bb3419c53661","added_by":"auto","created_at":"2024-10-29 16:22:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84044,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic analysis of SDS scores in different dialysis specifics\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5328560/v1/008184ccc269fd41fc96842b.png"},{"id":67783158,"identity":"15cfbc24-3f9b-4db6-8f92-916f4febfb72","added_by":"auto","created_at":"2024-10-29 16:14:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68380,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic analysis of SDS scores in different lifestyle changes\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5328560/v1/0f5f31dac855d2f2ffeecbe5.png"},{"id":67861387,"identity":"064afaec-155c-4043-aa5b-d8834645d1ae","added_by":"auto","created_at":"2024-10-30 12:51:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":803471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5328560/v1/13a0a408-89c1-4ea9-b06e-9ade96eaab48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Risk Factors of Depression in Rural Chinese Hemodialysis Patients During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaintenance hemodialysis (MHD) is a primary treatment for patients with end-stage renal disease. Due to their reliance on hemodialysis equipment and medical care for survival, these patients often have low immune resistance and frequent complications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to suffering numerous physical symptoms, they are prone to emotional disturbances, including a high prevalence of depression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This vulnerability can be exacerbated during major public health events, potentially leading to an increased incidence of mental health issues.\u003c/p\u003e \u003cp\u003eCurrent evidence suggests that the COVID-19 pandemic has coincided with a widespread increase in psychiatric disorders [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], warranting the attention of the global health community. To combat the COVID-19 pandemic, various countries and governments have implemented mandatory behavioral non-pharmaceutical interventions, such as restrictions on social interactions and mask-wearing. Although these interventions limit free communication, potentially exacerbating depression, compliance with them can make individuals feel safe and help alleviate depressive symptoms. Thus, these measures are a \"double-edged sword,\" and their effects depend on a combination of factors such as medical, social, and cultural context [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the health vulnerability of the maintenance hemodialysis (MHD) population, it is generally believed that pandemic-associated psychological stress will have a depressive impact on MHD patients [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, studies on dialysis populations have shown inconsistent results. Some research, primarily from countries with advanced healthcare systems, suggests that the pandemic has not significantly affected the mental health of dialysis patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Other findings focus on mandatory behavioral interventions and indicate that these measures do not impact mental health [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A large-scale epidemiological survey from South Korea found that not wearing masks indoors was most strongly associated with depression; those who did not adhere to public health measures were more likely to experience depression [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn rural areas, where medical resources are scarce, healthcare systems face increased pressure during major public health events [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, limited educational backgrounds can hinder patients' understanding of the pandemic. In rural America, 43.68% of respondents reported a negative impact on mental health during the pandemic [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In Guangdong Province, an economically developed region in China, a province-wide survey among the MHD population indicated that the COVID-19 pandemic had no significant overall impact on mental health. However, it did affect rural areas and low-income populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite these preliminary studies, further research is still lacking on the impact of the pandemic on psychological issues in rural regions. Consequently, our understanding of the unique challenges faced by rural healthcare systems and their impact on vulnerable populations remains limited.\u003c/p\u003e \u003cp\u003eThis study utilizes questionnaire data from the multi-hemodialysis center in rural area of China during the COVID-19 pandemic. It aims to investigate the depression prevalence and analyze the association between demographic factors, medical parameters, pandemic-associated lifestyle changes, and depression, thereby exploring the risk factors for depression among the rural MHD population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1 Patient inclusion\u003c/h2\u003e\n \u003cp\u003eWe conducted this prospective study in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics committee of our hospital (KY-2021-038). Patients from 14 hemodialysis centers in the northern rural areas of Guangdong Province were selected for this study, conducted from April 2021 to October 2021. The inclusion criteria were as follows: (1) undergoing hemodialysis for 6 months or more; (2) aged 16 years or older; (3) fully conscious and able to complete the questionnaire independently or with assistance; (4) no serious complications in the past one month. Exclusion criteria involved patients who were unwilling to participate.\u003c/p\u003e\u003cbr\u003e1.2 Ethical issues\n\u003c/div\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the hospital\u0026apos;s Ethics Committee. All patients and their family members were informed about the study\u0026apos;s details and voluntarily signed the informed consent.\u003c/p\u003e\n\u003cp\u003e1.3 Data collection\u003c/p\u003e\n\u003cp\u003eThe Self-Rating Depression Scale (SDS), a widely used 20-item self-reported questionnaire, was utilized to assess depression. Upon obtaining informed consent, SDS questionnaires were distributed. Nurses, who were uniformly trained, explained the survey content and the instructions for completing the questionnaire, ensuring patients could complete it independently or with assistance. The SDS scores range from 25 to 100, with a score of 50 or above indicating clinical depression: 50\u0026ndash;59 signifies mild depression, 60\u0026ndash;69 moderate, and 70 or above severe [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eOther collected clinical data included: (1) demographic information (age, sex, education level, marital status, and monthly family income); (2) nutritional status (serum albumin levels) and anemia indicators; (3) dialysis details (years on dialysis, frequency of sessions, single-session fluid removal volume and duration, symptoms experienced during dialysis); and (4) pandemic-related information (daily masking duration, discussions with other patients about the pandemic, understanding of pandemic updates).\u003c/p\u003e\n\u003ch3\u003e1.4 Statistical methods\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS software version 26.0. An independent sample \u003cem\u003et\u003c/em\u003e-test was employed to compare continuous variables between two groups, while the chi-square test was used for categorical data. The Bonferroni correction was used to adjust \u0026alpha; value for multiple group comparisons. Multivariate logistic regression analysis was conducted to identify depression-associated risk factors.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Clinical characteristics\u003c/h2\u003e \u003cp\u003eA total of 450 hemodialysis patients completed the survey, of which 238 (52.9%) were male, with an average age of 54.9 ± 13.9 years. The overall SDS score was 45.1 ± 8.8, with emotional and psychological symptoms scoring 7.3 ± 2.3, physical and behavioral symptoms 17.7 ± 3.6, and social and cognitive symptoms 20.1 ± 4.9.\u003c/p\u003e \u003cp\u003eAmong the 450 patients, 160 were identified as having depression, including 147 (91.8%) with mild, 11 (6.9%) with moderate, and 2 (1.3%) with severe depression. The clinical characteristics of patients with or without depression are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared to non-depressed individuals, those with depression were less educated and had lower incomes, and more frequently self-reported worry about COVID-19 infection. No significant differences were found in laboratory indicators or in lifestyle during the pandemic (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients without and with depression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Depressed\u003c/p\u003e \u003cp\u003eN = 290\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepressed\u003c/p\u003e \u003cp\u003eN = 160\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e/\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic indicators\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.5 ± 14.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.6 ± 13.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e−0.849\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale [n (%)]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (55.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (48.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.707\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (21.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (9.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.624\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.772\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (12.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (77.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (81.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (5.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (6.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income ≥ 5, 000 yuan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (43.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (27.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.618\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis parameters\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD vintage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 1 year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (23.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (26.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–3 year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (19.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (18.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 3 year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (57.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (55.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD frequency\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.654\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≤ Twice a week\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (19.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (20.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFive times every 2 weeks\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (75.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (71.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree times a week\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (5.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (8.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort during dialysis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (41.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (55.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory parameters\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.679\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 90 g/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (22.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (28.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90–129 g/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (73.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (65.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 130 g/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.744\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 30 g/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (5.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30–34 g/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (23.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (26.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35–39 g/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (55.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (45.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 40 g/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (16.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (18.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifestyle change\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily masking time\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 2h\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (34.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (25.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2–4h\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (21.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (30.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 4h\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (44.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (44.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMask discomfort\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (33.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (41.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.434\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily concern for pandemic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (73.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (78.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.759\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduce outdoor activities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (63.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (66.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsycho Well-being\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection worry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (57.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (70.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.839\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Depressive severity analysis\u003c/h3\u003e\n\u003cp\u003eGiven the predominance of mild depression among patients, we utilized the SDS scores to assess its severity across clinically distinct subgroups. Demographic analyses revealed no significant differences in SDS scores based on sex or marital status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), but higher educational levels and monthly incomes correlated with lower SDS scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), indicating an education-income link with depression .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSDS scores were unaffected by laboratory results and dialysis parameters, with no variations observed for serum albumin, hemoglobin levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), or dialysis specifics (volume, frequency, session duration, years on dialysis or discomfort during dialysis; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePatients experiencing mask discomfort had higher SDS scores than those without (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), while mask duration did not impact scores significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), indicating subjective emotional responses play a role in mask-related distress. Other lifestyle changes due to the pandemic, such as daily pandemic concerns and reduced outdoor activities, did not influence SDS scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). However, anxiety about COVID-19 infection was associated with higher SDS scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), emphasizing personal psychological factors' impact on depression over routine activity modifications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaken above at all, individuals with low income, limited education, mask discomfort, and heightened COVID-19 anxiety are at increased risk for depression, highlighting the need for integrated psychological and physical health support, especially during crises.\u003c/p\u003e\n\u003ch3\u003e2.3 Depression-associated risk factors\u003c/h3\u003e\n\u003cp\u003eTo investigate the independent relationships of above characteristics with depression during the pandemic, we conducted a Wald stepwise multivariate Logistic regression analysis on the variables listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Given a strong association observed between education and income in preliminary analysis (data not shown), only education was included in the modeling as a potential variable. After adjusting for multiple factors, three variables remained significant in the model: college education was associated with a lower risk of depression compared to those without a college education, while discomfort during dialysis and infection worry were linked to an increased risk of depression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent risk factors for depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidential Interval\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.245–0.846\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort during dialysis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.654\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.105–2.474\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection concern\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.121–2.636\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis study provided the epidemiological characteristics of depression in 450 MHD patients during the COVID-19 pandemic in rural areas of northern Guangdong Province, China, and found that about 1/3 of the patients met the criteria for depression with SDS scores of more than 50. After univariate and multivariate analysis, we found that discomfort during dialysis, and infection worry were risk factors associated with depression. Higher education was associated with a lower risk of depression.\u003c/p\u003e\u003cp\u003eThis study adopts an SDS score \u0026gt; 50 as the criterion for depression and reveals that the incidence of depression among MHD patients in rural China during the pandemic era is 35.6%, which surpasses the pre-pandemic rate reported by Abdel-Kader K et al. They utilized the Patient Health Questionnaire-9 (PHQ-9) for depression assessment, identifying 25% of MHD patients as depressed based on a PHQ-9 score \u0026gt; 9 threshold [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Given the weak correlation between PHQ-9 and SDS scores in the general population, with a mere 0.29 correlation coefficient [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], no direct conversion between these measures is feasible. Consequently, studies employing distinct criteria for depression assessment are not directly comparable. A similar variance due to different assessment methodologies was observed by Ibrahim M et al., who employed the Beck Depression Inventory during a pandemic and found 66.2% of their MHD sample exhibiting depressive symptoms, with 61.4% meeting diagnostic criteria for depression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Another key factor contributing to the discrepancy in reported depression rates is the rural setting of our investigation, which contrasts with the non-rural areas studied by Hao W et al. Using identical depression assessment criteria, they reported a depression detection rate of 32.1% among 321 hemodialysis patients, slightly lower than our findings [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This difference may be attributed to demographic variations between the two studies, such as a higher proportion of our subjects with monthly income lower than 5000 yuan (62% compared to 53%) and dialysis vintage more than a year (75.8% versus 67.0%).\u003c/p\u003e\u003cp\u003eOur study focused on the association between pandemic-associated lifestyle changes and depression. We observed a trend toward an increased depression rate among patients who wore masks for longer periods; however, this increase was not statistically significant when compared to those without depression. Additionally, mask discomfort was not associated with depression. These results suggest that mask-wearing itself does not elevate the risk of depression. After adjusting for multiple variables, the mask-related index remained unassociated with depression. Similarly, other lifestyle changes, such as daily concerns about the pandemic and reduced outdoor activities, were also not linked to depression. These findings support the notion that mandatory behavioral interventions do not contribute to depression in the MHD population in rural areas of China.\u003c/p\u003e\u003cp\u003eDue to the limited educational levels in rural area, it is challenging to precisely quantify the restrictions on social activities during the survey period. As a result, the study design incorporates only a binary measure of whether outdoor activities were reduced. Additional limitation includes incomplete clinical databases in parts of centers, which limit full depression-related laboratory data and comorbidies are not included in the analytical model.\u003c/p\u003e\u003cp\u003eThis cross-sectional survey was conducted from March to October 2021, a period that corresponds to the national vaccination campaign stage following the shift from the first large-scale outbreak to sporadic outbreaks in mainland China. Therefore, the interpretation of the study's findings should take into account the sociological context of this specific timeframe.\u003c/p\u003e\u003cp\u003eThe multicenter and prospective design is a notable strength of this study, particularly given the difficulties in acquiring data from rural areas during the pandemic. Importantly, the three risk factors for depression that were identified in our research are easily recognizable, thereby highlighting the feasibility of reproducing these results within clinical contexts. Our findings offer persuasive evidence to guide intervention strategies for this demographic in future public health crises.\u003c/p\u003e\u003cp\u003eIn conclusion, during the COVID-19 pandemic in rural China, mild depression were common among MHD patients. Mandatory behavioral interventions did not contribute to depression, while discomfort during dialysis and infection worry emerged as risk factors, and college education was associated with a lower risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eWe thank the following hospitals that participated in this research: \u003c/p\u003e\n\u003cp\u003eYuebei People\u0026apos;s Hospital, Shaoguan City First People\u0026apos;s Hospital \u003c/p\u003e\n\u003cp\u003eShaozhou People\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003e419 Hospital of Ministry of Nuclear Industry\u003c/p\u003e\n\u003cp\u003eShaoguan City Kai De Hospital\u003c/p\u003e\n\u003cp\u003eShaoguan City Qujiang District People\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003eShaoguan City Women\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003eNanxiong City People\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003eLechang City People\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003eShixing County People\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003eXinfeng County People\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003eRuyuan County People\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003eYingde City Chinese Medicine Hospital\u003c/p\u003e\n\u003cp\u003eLechang City Chinese Medicine Hospital\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM He, ZQ Liu,JH Lin,ZQ Chen,RG Li,JP Tang,Q Liu,and L Nin designed the study.M He, ZQ Chen, RG Li, ,JP Tang,Q Liu,ZQ Liu,JH Lin,and L Nin collected, analyzed, and interpreted the clinical data. M He, ZQ Chen, RG Li, ,JP Tang,Q Liu,ZQ Liu,JH Lin,and L Nin wrote and revised the manuscript. All authors confirmed the integrity of the data and analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript is submitted without conflict of interest and the manuscript is published with the consent of all authors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study has been approved by the ethics committee of Yuebei People\u0026apos;s Hospital (KY-2021-038).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRights and permissions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u0026apos;s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u0026apos;s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Nephrology, Yuebei People\u0026apos;s Hospital, Guangdong Medical University, Shaoguan City 512026, Guangdong, China\u003c/p\u003e\n\u003cp\u003eMin He \u0026amp; Zhaoqi Liu \u0026amp; Juhua Lin\u003c/p\u003e\n\u003cp\u003eDepartment of Nephrology, Yuebei People\u0026apos;s Hospital,, Shaoguan City 512026, Guangdong, China\u003c/p\u003e\n\u003cp\u003eZhiqiang Chen\u0026amp; Rugang Li \u0026amp; Junping Tang \u0026amp; Quan Liu \u0026amp; Ling Nin\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Min He.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePecoits-Filho R, Okpechi IG, Donner JA, Harris D, Aljubori HM, Bello AK, Bellorin-Font E, Caskey FJ, Collins A, Cueto-Manzano AM, Feehally J, Goh BL, Jager KJ, Nangaku M, Rahman M, Sahay M, Saleh A, Sola L, Turan Kazancioglu R, Walker RC, Walker R, Yao Q, Yu X, Zhao MH, Johnson DW (2020) Capturing and monitoring global differences in untreated and treated end-stage kidney disease, kidney replacement therapy modality, and outcomes. 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Ther Apher Dial 27(6):1070-1078. https://doi.org/10.1111/1744-9987.14031\u003c/li\u003e\n\u003cli\u003eIbrahim M, Saeed E, Hamarsheh I, Al Zabadi H, Ahmead M (2023) Depression and death anxiety among patients undergoing hemodialysis during the COVID-19 pandemic in Palestine: a cross sectional study. Front Psychiatry 14:1247801. https://doi.org/10.3389/fpsyt.2023.1247801\u003c/li\u003e\n\u003cli\u003eAskaryzadeh Mahani M, Ghasemi M, Arab M, Baniasadi Z, Omidi A, Irani PS (2023) The correlation between caregiver burden with depression and quality of life among informal caregivers of hemodialysis and thalassemia patients during the COVID-19 pandemic: a cross-sectional study. BMC Nurs 22(1):183. https://doi.org/10.1186/s12912-023-01351-4\u003c/li\u003e\n\u003cli\u003eLee J, Steel J, Roumelioti ME, Erickson S, Myaskovsky L, Yabes JG, Rollman BL, Weisbord S, Unruh M, Jhamb M (2020) Psychosocial Impact of COVID-19 Pandemic on Patients with End-Stage Kidney Disease on Hemodialysis. Kidney360 1(12):1390-1397. https://doi.org/10.34067/KID.0004662020\u003c/li\u003e\n\u003cli\u003eMueller JT, McConnell K, Burow PB, Pofahl K, Merdjanoff AA, Farrell J (2021) Impacts of the COVID-19 pandemic on rural America. Proc Natl Acad Sci U S A 118(1):2019378118 [pii]. https://doi.org/10.1073/pnas.2019378118\u003c/li\u003e\n\u003cli\u003eBonenkamp AA, Druiventak TA, van Eck van der Sluijs A, van Ittersum FJ, van Jaarsveld BC, Abrahams AC, DOMESTICO study group, (2021) The Impact of COVID-19 on the mental health of dialysis patients. J Nephrol 34(2):337-344. https://doi.org/10.1007/s40620-021-01005-1\u003c/li\u003e\n\u003cli\u003eNadort E, Rijkers N, Schouten RW, Hoogeveen EK, Bos W, Vleming LJ, Westerman M, Schouten M, Dekker M, Smets Y, Shaw PC, Farhat K, Dekker FW, van Oppen P, Siegert C, Broekman B (2022) Depression, anxiety and quality of life of hemodialysis patients before and during the COVID-19 pandemic. 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J Rural Stud 96:305-315. https://doi.org/10.1016/j.jrurstud.2022.10.022\u003c/li\u003e\n\u003cli\u003eChen Y, Wu Y, Hu P, Fu X, Liu S, Song L, Dong W, Yu X, Liang X (2022) Psychological impact and implementation of preventative measures in hemodialysis centers during the COVID-19 pandemic: a provincial questionnaire survey in China. Int Urol Nephrol 54(3):601-608. https://doi.org/10.1007/s11255-021-02875-x\u003c/li\u003e\n\u003cli\u003eDunstan DA, Scott N (2019) Clarification of the cut-off score for Zung\u0026apos;s self-rating depression scale. BMC Psychiatry 19(1):177. https://doi.org/10.1186/s12888-019-2161-0\u003c/li\u003e\n\u003cli\u003eZUNG WW (1965) A SELF-RATING DEPRESSION SCALE. Arch Gen Psychiatry 12:63-70. https://doi.org/10.1001/archpsyc.1965.01720310065008\u003c/li\u003e\n\u003cli\u003eWang W, Bian Q, Zhao Y, Li X, Wang W, Du J, Zhang G, Zhou Q, Zhao M (2014) Reliability and validity of the Chinese version of the Patient Health Questionnaire (PHQ-9) in the general population. Gen Hosp Psychiatry 36(5):539-544. https://doi.org/10.1016/j.genhosppsych.2014.05.021\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":"maintenance hemodialysis, COVID-19 pandemic, depression, rural areas","lastPublishedDoi":"10.21203/rs.3.rs-5328560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5328560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the prevalence and risk factors of depression among maintenance hemodialysis (MHD) patients in rural China during the COVID-19 pandemic.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted in 14 hemodialysis centers in northern Guangdong Province from April to October 2021. Depression was evaluated using the Self-Rating Depression Scale. Multivariate logistic regression analysis was employed to identify associated factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 450 MHD patients enrolled, 160 (35.6%) met the criteria for depression, with 91.8% cases being of mild severity. After adjusting for demographic, dialysis-related, laboratory, pandemic-associated lifestyle changes, and psychological variables, discomfort during dialysis [Odds ratio (OR) 1.654, 95% Confidence Interval (CI) 1.105\u0026ndash;2.474] and infection worry (OR 1.719, 95% CI 1.121\u0026ndash;2.636) were significantly associated with an increased risk of depression. In contrast, college education was linked to a lower risk (OR 0.456, 95% CI 0.245\u0026ndash;0.846).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDuring the COVID-19 pandemic in rural China, mild depression were common among MHD patients. Mandatory behavioral interventions did not contribute to depression, while discomfort during dialysis and infection worry emerged as risk factors, and college education was associated with a lower risk.\u003c/p\u003e","manuscriptTitle":"Prevalence and Risk Factors of Depression in Rural Chinese Hemodialysis Patients During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-29 16:06:15","doi":"10.21203/rs.3.rs-5328560/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":"08cc5f5d-c9d8-47fd-97bb-96336671eb85","owner":[],"postedDate":"October 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-30T12:43:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-29 16:06:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5328560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5328560","identity":"rs-5328560","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}