Abstract
Post-stroke depression is commonly experienced by stroke survivors and has a significant negative impact on the physical, cognitive, and social functioning of those affected. This study aims to investigate the prevalence of depressive symptoms and their associated factors in Chinese stroke patients. Research samples were selected from the China Health and Retirement Longitudinal Study 2018 survey. Depression was evaluated using the 10-item Center for Epidemiological Studies Depression Scale, with a score ≥ 10 defined as depression. Univariate and multivariable analyses were performed to examine the associations of depressive symptoms with demographics, family relationships, health status, and lifestyle. A total of 963 stroke patients were included and 57.8% of them had depressive symptoms. Depressive symptoms were significantly associated with female sex (OR 1.762, 95% CI 1.235–2.514), lower education level (non-formal education: OR 2.148, 95% CI 1.235–3.737, primary to secondary school education: OR 1.964, 95% CI 1.272–3.033), dissatisfaction with spouse (OR 1.912, 95% CI 1.075–3.401), dissatisfaction with life (OR 1.779, 95% CI 1.080–2.931), dissatisfaction with health (OR 1.592, 95% CI 1.138–2.226), pain (OR 1.392, 95% CI 1.005–1.928) and abnormal sleep (OR 1.557, 95% CI 1.126–2.152). The findings suggest the need for regular depression screening and evaluation after a stroke, and that a well-functioning support system, effective health management, and lifestyle modifications could potentially improve the mental state of stroke patients.
Similar content being viewed by others
Introduction
A stroke is a medical emergency that occurs when the blood supply to the brain is blocked by a clot or when a blood vessel in the brain ruptures. This can result in lasting brain damage, long-term disability, and even death for the individual. Globally, it was estimated that 143 million years of healthy life were lost due to stroke-related death and disability in 2019, accounting for 5.7% of the Disability-Adjusted Life Years (DALYs)1. Stroke was the leading cause of death in China2. There were 3.94 million new stroke cases and 2.19 million deaths attributed to stroke in 20193. Moreover, the burden of stroke in China has been steadily increasing over the past few years. According to a large-scale, population-based study covering 4,229, 616 Chinese adults aged 40 years and above, the prevalence of stroke increased by 13.2% from 2013 to 20194.
Post-stroke depression is a common neuropsychiatric complication experienced by stroke survivors5. It is characterized by persistent feelings of sadness, hopelessness, and loss of interest in activities that were once enjoyed6. Studies have suggested that about one-third of stroke survivors develop depression after a stroke onset5,7,8. Compared with post-stroke patients without depression, individuals with post-stroke depression have more pronounced cognitive impairment, lower quality of life, worse rehabilitation outcomes, and a higher risk of mortality7,9,10,11. Post-stroke depression has significant negative impacts on the physical, cognitive, and social functioning of stroke survivors, not only affecting the individual recovery and well-being but also adding a great burden on their families and society12,13. Early recognition and effective management of depression in stroke patients are crucial to prevent adverse outcomes and improve rehabilitation potential.
Post-stroke depression has multiple biological, psychological, and social risk factors. Age and sex were the most studied risk factors for post-stroke depression, however, conclusions about the impact of these factors on post-stroke depression remained controversial5,14. Several studies identified stroke-related characteristics such as stroke type, stage, severity, and lesion location as important predictors of post-stroke depression15,16,17. Other factors associated with post-stroke depression included impaired cognition, functional dependence, physical pain, and inability to work18,19,20. While previous research has primarily focused on the general and stroke-related factors as determinants of post-stroke depression, understanding patients' modifiable lifestyle and behaviors is equally important to reduce the risk of post-stroke depression. According to Vermeer et al., smoking and receiving stroke treatment were significantly associated with depressive symptoms in stroke survivors21. Ayerbe et al. reported an association between low activity levels and post-stroke depression20. Social support is another critical factor in combating post-stroke depression5, and family relationships are an integral part of social support, especially for Chinese elderly22. A longitudinal study by Wang et al. verified that good family functioning was an important protective factor against post-stroke depression at different phases of stroke23. However, it is important to note that evidence regarding the association between personal lifestyle, family relationships, and post-stroke depression remains limited, particularly in China. Therefore, the objective of this study is to determine the prevalence of depressive symptoms and their associations with demographics, family relationships, health status, and lifestyle in Chinese stroke patients.
Results
Demographic characteristics and depressive symptoms of stroke patients
Among the 963 stroke patients, the mean CESD-10 score was 11.3 points, with 557 individuals (57.8%) exhibiting depressive symptoms. Most patients were aged 60 years or older (74.2%), had primary to secondary education (65.9%), were married (77.5%), lived in rural areas (67.7%), and about half were male (54.5%).
The univariate analysis showed that sex and education level were significantly associated with depressive symptoms in stroke patients. Female participants were more likely to report depressive symptoms than male participants (68.5% vs. 49.0%, P < 0.05). Patients with non-formal education, and with primary to secondary school education had a higher proportion of depressive symptoms than those with high school and above education (69.4% vs. 58.6% vs. 37.1%, P < 0.05) (Table 1).
Family relationships and depressive symptoms of stroke patients
Most of the patients had 1–2 children (67.4%) and were cared for by family members (69.9%). The vast majority of patients were satisfied with their children (93.6%), 76.7% were satisfied with their spouse, and 84.3% were satisfied with their life.
The univariate analysis showed that family care, relationships with children and spouses, and life satisfaction were significantly associated with depressive symptoms in stroke patients. Patients without family caregivers had a higher proportion of depressive symptoms than those with (63.4% vs. 55.4%, P < 0.05). Patients who were dissatisfied with their children were more likely to have depressive symptoms than those who were satisfied (81.5% vs. 56.2%, P < 0.05). The proportion of depressive symptoms was higher in patients who were dissatisfied with their spouse than that of those who were satisfied with their spouse (80.8% vs. 53.7%, P < 0.05), and who did not have a spouse (80.8% vs. 64.0%, P < 0.05). Compared with patients who were satisfied with their life, patients who were dissatisfied were more likely to have depressive symptoms (80.8% vs. 53.6%, P < 0.05) (Table 2).
Health status and depressive symptoms of stroke patients
Regarding health status, half of patients (52.9%) rated their health as being poor while only 8.6% rated their health as being good. Half of patients (50.5%) were satisfied with their health and half (49.5%) were not. The majority of patients (79.1%) had two or more co-morbid conditions. A total of 68.3%, 26.3%, 38.9% and 17.2% had hypertension, diabetes mellitus, heart disease, and lung disease, respectively. Most of the patients (69.8%) suffered from physical pain, and 39.1% were able to work normally.
The univariate analysis showed that self-rated health, health satisfaction, co-morbid conditions, heart disease, lung disease, physical pain, and work ability were significantly associated with depressive symptoms in stroke patients. Specifically, the proportion of depressive symptoms was higher in patients with poor self-rated health than that of those with fair (66.2% vs. 50.9%, P < 0.05) and good self-rated health (66.2% vs. 37.3%, P < 0.05). Patients who were dissatisfied with their health were more likely to have depressive symptoms than patients who were satisfied (69.6% vs. 46.3%, P < 0.05). Patients with two or more co-morbid conditions had a higher proportion of depressive symptoms than those with only one (61.4% vs. 44.4%, P < 0.05) and without (61.4% vs. 44.0%, P < 0.05). Patients who had heart disease (64.8% vs. 53.4%, P < 0.05), lung disease (66.9% vs. 56.0%, P < 0.05), and physical pain (64.3% vs. 43.0%, P < 0.05) were more likely to experience depressive symptoms. Patients who were unable to work long hours (65.8% vs. 47.7%, P < 0.05) and work at all (62.6% vs. 47.7%, P < 0.05) had a higher proportion of depressive symptoms compared to those who were able to work normally (Table 3).
Lifestyle and depressive symptoms of stroke patients
Most stroke patients did not drink alcohol (70.8%), did not smoke (74.7%), and had physical activities (86.9%), while only half (54.2%) participated in social activities. Most of the patients (70.2%) had normal sleep, and 69.3% were receiving stroke treatment.
The univariate analysis showed that patients with abnormal sleep had a higher proportion of depressive symptoms than those with normal sleep (69.3% vs. 53.0%, P < 0.05). Patients who were under stroke treatment were more likely to have depressive symptoms than those who were not (60.3% vs. 52.4%, P < 0.05) (Table 4).
Multi-variable analysis of depressive symptoms of stroke patients
The regression analysis further confirmed the significant associations between sex, educational level, relationships with spouse, life satisfaction, health satisfaction, physical pain, sleep duration, and depressive symptoms. Females were more likely to have depressive symptoms than males (OR 1.762, 95% CI 1.235–2.514). The probability of having depressive symptoms in patients with non-formal education and primary school to secondary school education was found to be 2.148 times (95% CI 1.235–3.737) and 1.964 times (95% CI 1.272–3.033) higher than that of patients with high school education and above, respectively. Patients who were not satisfied with their spouse (OR 1.912, 95% CI 1.075–3.401) and life (OR 1.779, 95% CI 1.080–2.931) had a higher likelihood of experiencing depressive symptoms than those who were. Health dissatisfaction (OR 1.592, 95% CI 1.138–2.226) and physical pain (OR 1.392, 95% CI 1.005–1.928) were also significantly associated with depressive symptoms. Patients with abnormal sleep tended to have depressive symptoms compared with those with normal sleep (OR 1.557, 95% CI 1.126–2.152) (Table 5).
Further stratified analyses by gender showed that, in males, patients with primary to secondary school education tended to report depressive symptoms compared with those with high school education and above (OR 2.252, 95% CI 1.359–3.730), and patients who were not satisfied with their spouse (OR 2.559, 95% CI 1.116–5.869) as well as health (OR 1.722, 95% CI 1.115–2.661) had a higher likelihood of experiencing depressive symptoms than those who were (Supplementary Table 1). In females, patients aged 60 years or older were less likely to report depressive symptoms than those aged 45–59 years old (OR 0.505, 95% CI 0.285–0.893), and patients without family caregivers (OR 1.720, 95% CI 1.035–2.861) and with abnormal sleep (OR 1.882, 95% CI 1.137–3.116) were more likely to report depressive symptoms (Supplementary Table 2).
Discussion
Nearly 60% of stroke patients in the study reported experiencing depressive symptoms. In the univariate analysis, significant associations were found between demographic characteristics, family relationships, health status, lifestyle, and depressive symptoms. The results of multivariable analysis further showed that female sex, lower education level, dissatisfaction with the spouse, dissatisfaction with life, dissatisfaction with health, physical pain, and abnormal sleep were significantly associated with depressive symptoms in Chinese stroke patients.
This study showed that the prevalence of depressive symptoms among stroke patients was 57.8%, which was higher than that previously reported in China24,25,26,27 and other countries28,29. In two hospital-based surveys in China24,25, the proportions of stroke patients with depression were 41.1% and 31.4%, respectively. This discrepancy is partly due to the use of different diagnostic instruments. In most studies24,25,27, depression was clinically diagnosed according to International Classification of Diseases (ICD) codes, whereas our study used the CESD-10, a self-rated scale, to assess depressive symptoms, which might have identified more sub-threshold or mild cases. When different studies used the same diagnostic instrument, the reported prevalence rates were closer. Sit et al. investigated the prevalence of post-stroke depression using the CESD-20 items in Hong Kong, China, and reported a prevalence of 69% and 48% at 48 h and 6 months after stroke, respectively30. On another note, ignoring the classification of different stroke types and stroke stages may also result in differences in depression prevalence. However, lower prevalence rates in the United States (23.29%)28, Finland, and Sweden (12.8%)29 might be due to greater awareness of mental health and more preventive and treatment measures for depression in these countries.
Our analysis showed that female sex was significantly associated with depressive symptoms. This is consistent with previous studies27,28,31,32,33, including two large prospective studies from the United Kingdom and Denmark32,33. However, there is still ongoing debate in the literature regarding whether females are more susceptible to post-stroke depression than males. In a systematic analysis by Ryck et al., of 21 studies reviewed, only 7 studies identified female sex as a risk factor for post-stroke depression34. According to Wang et al., the association between sex and depression in stroke patients might be influenced by the stage of depression being evaluated. In their study, they observed that female sex was an independent risk factor for post-stroke depression in the acute stroke phase instead of the chronic phase27. The results of our univariate and multi-variable analyses showed that stroke patients with lower education levels had a higher probability of having depressive symptoms. Likewise, a cohort study indicated that education level (high school or above) was a protective factor for post-stroke depression at 3 months after stroke event27.
Our study revealed that family support and relationships play crucial roles in overcoming depression after a stroke. In the univariate analysis, we found that stroke patients without family caregivers had a higher proportion of being depressed than those with family caregivers. Moreover, we observed that stroke patients who were dissatisfied with their children in the univariate analysis and dissatisfied with their spouse and life in both univariate and multi-variable analyses were more likely to experience depressive symptoms. These findings agree with a population-based study that identified family support as a major protective factor against depression among Chinese elderly35. Stroke patients need the support of their family members during the recovery process, especially if they have disabilities and dysfunctions. A well-established family support system can provide life and emotional support to stroke patients and help them deal with psychological stress.
Self-rated health is a subjective measure of an individual’s overall physical, psychological, social, and functional status36. In stroke patients, self-rated health can reflect the individual’s adjustment and recovery conditions after a stroke event37. Our study reported a significant link between poor self-rated health and depressive symptoms in the univariate analysis. Likewise, Araújo et al. identified emotional functioning as a correlate of an individual’s self-rated health after stroke in the chronic phase38. In addition, we also observed a significant association between health dissatisfaction and depressive symptoms in stroke patients. Multi-morbidity is increasingly common and has a bi-directional relationship with depression39. Univariate analysis results indicated that depressive symptoms in stroke patients were significantly associated with co-morbid conditions (two or more) and the type of chronic conditions, specifically heart disease and lung disease. Similarly, Jørgensen et al. found that a high level of somatic comorbidity was a significant risk factor for depression in patients with and without stroke. Additionally, their study identified diabetes as an important risk factor for depression33. Consistent with previous literature40, our study confirmed the association between physical pain and depressive symptoms in Chinese stroke patients. Consistent with previous studies20, stroke survivors with inability to work were more likely to be depressed. One possible reason for this is that stroke patients who were unable to work may be more concerned about their economic stability and were prone to depression41.
Our univariate and multi-variable analyses revealed a significant association between sleep duration and depressive symptoms. Similar relations between insomnia, sleep disorder, and depression were found in previous studies24,28. Unexpectedly, our univariate analysis results showed that stroke patients who were under stroke treatment were more likely to experience depressive symptoms than those who were not, although this association became insignificant in multivariable analysis. This might be because individuals who have more concerns about their disability and functional status are more likely to seek stroke treatment21.
The gender-stratified analyses conducted in our study unveiled significant discrepancies in the factors influencing depressive symptoms among males and females. Notably, among males, education level, satisfaction with one's spouse, and satisfaction with one's health status emerged as prominent factors, while among females, age, family caregivers, and sleep patterns appeared to be key contributors. However, it is worth noting that a recent study assessing gender differences in the influence of the five most established risk factors, including functional dependency, a history of pre-stroke depression, social support, stroke severity, and cognitive impairment, found no gender-specific effects42. This suggests a wider relevance of these risk factors across genders. Nevertheless, the impact of gender in the context of stroke and depression warrants further investigation and highlights the need for more gender-specific studies in this area.
This study has several implications for health policy and practice. First, our study reported a high prevalence of depressive symptoms (57.8%), suggesting the mental health status of stroke patients is an urgent attention and resolution in China. Second, female and less educated stroke patients were found to be more likely to experience depressive symptoms, highlighting the need for routine screening and assessment of depression among these high-risk groups. Third, our findings underscore the significance of a well-functioning family support system for stroke patients’ mental health and well-being, as demonstrated by the significant correlation between family relationships and depressive symptoms. Fourth, given the correlation between health status and depressive symptoms, our study underscores the importance of pain management and health management in clinical practice in stroke patients. Finally, our study found that lifestyle, particularly sleep duration, was significantly associated with depressive symptoms, suggesting that adjusting sleep habits might improve depression status in stroke patients.
One of the strengths of this study is that it estimated the prevalence and associated factors of depressive symptoms among stroke patients in China using a nationwide dataset. Another strength of this study is that, besides social demographic characteristics and health status, modifiable factors like family relationships and lifestyle were investigated in this study for their potential association with depressive symptoms, which could help in planning interventions. However, our study has several limitations that need to be considered. First, we recognized that our study evaluated depressive symptoms by using CESD-10, rather than diagnosing clinical depression with ICD codes. The use of this method may limit our ability to directly compare our results with previous research that examined diagnosed clinical depression. Second, several prospective studies with large sample sizes showed that stroke severity and pre-stroke depression were strong predictors of depression in stroke patients32,33. However, because the assessment of stroke in the CHARLS was mainly based on self-report, and not on detailed diagnostic criteria or specific assessment methods, the CHARLS dataset did not include clinical information about the stroke, such as type (ischemic strokes or hemorrhagic strokes), stage, severity, lesion location, or pre-stroke depression, which were therefore not analyzed. We acknowledge the importance of this issue and stress the need for caution in interpreting our findings given the limitations of the available data. Third, as this study only involved middle-aged and elderly stroke patients in China, the generalizability of our findings may be limited.
Depressive symptoms were highly prevalent in Chinese stroke patients and were associated with various factors related to demographics, family relationships, health status, and lifestyle. Our findings suggest that routine screening and assessment of depression after a stroke is necessary, and that a well-functioning support system, effective health management, and lifestyle modifications may improve depression in stroke patients.
Methods
Study design and data source
This is a national cross-sectional survey that focuses on identifying factors associated with depressive symptoms of stroke patients. This study selected samples from the China Health and Retirement Longitudinal Study (CHARLS) 2018 dataset, which includes data from 28 provinces, 150 counties and districts, and 450 urban and rural communities. CHARLS is the first nationally representative health survey of the Chinese elderly population aged 45 years and above, covering information on social, economic, and health status. CHARLS employed a multi-stage stratified Probability Proportional to Size (PPS) method for participant selection. The national baseline survey of CHARLS was conducted in 2011, and follow-up surveys have been conducted every two years since then. As of the fourth wave of data updates in 2018, the number of respondents to CHARLS was 19,816. More details regarding the survey's objectives and methods have been previously published43.
In this study, we included a total of 963 patients aged 45 years and older who had self-reported having had a stroke as eligible samples. In addition, it is important to note that our evaluation did not distinguish between ischemic and hemorrhagic strokes due to the reliance on self-report for stroke assessment and the lack of detailed diagnostic criteria in the dataset.
Depression measurement
Depression was assessed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10) in CHARLS. The CESD-10 measures the frequency of certain feelings or behaviors in the past week. It consists of 10 questions with four options for each question: rarely or none of the time (< 1 day), some or a little of the time (1–2 days), occasionally or a moderate amount of the time (3–4 days), and most or all of the time (5–7 days). Positive feelings or behaviors are scored as 3, 2, 1, and 0 points respectively, while negative ones are scored as 0, 1, 2, and 3 points. The total score ranges between 0 and 30 points. A higher score indicates more severe depressive symptoms. Following previous studies44, we used a cut-off point of 10 points. Respondents with a score of 10 or more were classified as having depressive symptoms and coded as 1, while those with a lower score were considered non-depressed and coded as 0.
Research variables
Research variables were categorized into four groups: demographics, family relationships, health status, and lifestyle. Demographic variables included age, sex, education level, marital status, and place of residence. Family relationships contained the number of children, family care, relationships with children, relationships with the spouse, and life satisfaction. Health status comprised self-rated health, health satisfaction, chronic comorbidity, physical pain, and work ability. Lifestyle consisted of drinking, smoking, physical activity, social activity, sleep duration (sleep between 5 and 9 hours was considered normal), and stroke treatment. Physical activity was measured primarily based on whether the participant engaged in vigorous, moderate, or mild activities for at least 10 min each week45,46. Social activities included interacting with friends, playing board games, helping family, friends, or neighbors who do not live with the participant, going to a club, participating in a community organization, doing voluntary or charity work, caring for sick or disabled adult who does not live with the participant, attending an educational or training course, investing in stocks, using the Internet, and other social activities. Participants who engaged in any of these social activities in the past month were classified into one group and those who did not were classified into another group47,48.
Statistical analysis
We processed the data using SPSS 22.0 (SPSS, Inc., Chicago, USA). Descriptive statistics were conducted to understand the prevalence of depressive symptoms and variable distribution. Then, the Chi-square test was adopted for univariate analysis to explore the associations of depressive symptoms with demographics, family relationships, health status, and lifestyle. Variables with P-value < 0.2 in univariate analysis were included in binary logistic regression for multi-variable analysis to further examine these associations. The results of the multi-variable analysis were presented as adjusted ORs and 95% CI. The significant level was set at 5%.
Ethical aspects
The CHARLS survey was approved by the Biomedical Ethics Review Committee of Peking University, China (approval number: IRB00001052-11015), and informed consent was obtained from all participants before conducting interviews. All research procedures were performed by the Declaration of Helsinki and other relevant guidelines and institutional regulations applied to studies involving human participants.
Data availability
All of the data generated and/or analyzed in this study are available in the [CHARLS] repository [https://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.html], which is open and free of charge to researchers worldwide. However, to protect the privacy of respondents, users are required to register an account with their real name and institution before accessing the data.
References
GBD. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 20, 795–820. https://doi.org/10.1016/s1474-4422(21)00252-0 (2021).
GBD. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1204–1222. https://doi.org/10.1016/s0140-6736(20)30925-9 (2020).
Ma, Q. et al. Temporal trend and attributable risk factors of stroke burden in China, 1990–2019: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 6, e897–e906. https://doi.org/10.1016/s2468-2667(21)00228-0 (2021).
Tu, W. J. et al. Prevalence of stroke in China, 2013–2019: A population-based study. Lancet Reg. Health West Pac. 28, 100550. https://doi.org/10.1016/j.lanwpc.2022.100550 (2022).
Das, J. & Rajanikant, G. K. Post stroke depression: The sequelae of cerebral stroke. Neurosci. Biobehav. Rev. 90, 104–114. https://doi.org/10.1016/j.neubiorev.2018.04.005 (2018).
Hackett, M. L., Anderson, C. S. & House, A. O. Management of depression after stroke: A systematic review of pharmacological therapies. Stroke 36, 1098–1103. https://doi.org/10.1161/01.STR.0000162391.27991.9d (2005).
Ayerbe, L., Ayis, S., Wolfe, C. D. & Rudd, A. G. Natural history, predictors and outcomes of depression after stroke: Systematic review and meta-analysis. Br. J. Psychiatry J. Mental Sci. 202, 14–21. https://doi.org/10.1192/bjp.bp.111.107664 (2013).
Hackett, M. L. & Pickles, K. Part I: Frequency of depression after stroke: An updated systematic review and meta-analysis of observational studies. Int. J. Stroke 9, 1017–1025. https://doi.org/10.1111/ijs.12357 (2014).
Gillen, R., Tennen, H., McKee, T. E., Gernert-Dott, P. & Affleck, G. Depressive symptoms and history of depression predict rehabilitation efficiency in stroke patients. Arch Phys. Med. Rehabil. 82, 1645–1649. https://doi.org/10.1053/apmr.2001.26249 (2001).
Serrano, S., Domingo, J., Rodríguez-Garcia, E., Castro, M. D. & del Ser, T. Frequency of cognitive impairment without dementia in patients with stroke: A two-year follow-up study. Stroke 38, 105–110. https://doi.org/10.1161/01.STR.0000251804.13102.c0 (2007).
Cai, W., Mueller, C., Li, Y. J., Shen, W. D. & Stewart, R. Post stroke depression and risk of stroke recurrence and mortality: A systematic review and meta-analysis. Ageing Res. Rev. 50, 102–109. https://doi.org/10.1016/j.arr.2019.01.013 (2019).
Berg, A., Lönnqvist, J., Palomäki, H. & Kaste, M. Assessment of depression after stroke: A comparison of different screening instruments. Stroke 40, 523–529. https://doi.org/10.1161/strokeaha.108.527705 (2009).
Robinson, R. G. & Jorge, R. E. Post-stroke depression: A Review. Am. J. Psychiatry 173, 221–231. https://doi.org/10.1176/appi.ajp.2015.15030363 (2016).
Guo, J., Wang, J., Sun, W. & Liu, X. The advances of post-stroke depression: 2021 update. J. Neurol. 269, 1236–1249. https://doi.org/10.1007/s00415-021-10597-4 (2022).
Ghaffari, A., Akbarfahimi, M. & Rostami, H. R. Discriminative factors for post-stroke depression. Asian J. Psychiatr. 48, 101863. https://doi.org/10.1016/j.ajp.2019.101863 (2020).
Chau, J. P. C. et al. Factors associated with post-stroke depression in Chinese Stroke Survivors. J. Stroke Cerebrovasc. Dis. 30, 106076. https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.106076 (2021).
Hackett, M. L. & Anderson, C. S. Predictors of depression after stroke: A systematic review of observational studies. Stroke 36, 2296–2301. https://doi.org/10.1161/01.STR.0000183622.75135.a4 (2005).
Lin, F. H., Yih, D. N., Shih, F. M. & Chu, C. M. Effect of social support and health education on depression scale scores of chronic stroke patients. Medicine (Baltimore) 98, e17667. https://doi.org/10.1097/md.0000000000017667 (2019).
Zhao, W. Y. et al. The association between functional status and physical pain with depressive symptoms after a stroke event: A cross-sectional analysis of the China Health and Retirement Longitudinal Study 2018. Front. Psychiatry 13, 927856. https://doi.org/10.3389/fpsyt.2022.927856 (2022).
Ayerbe, L., Ayis, S., Rudd, A. G., Heuschmann, P. U. & Wolfe, C. D. Natural history, predictors, and associations of depression 5 years after stroke: The South London Stroke Register. Stroke 42, 1907–1911. https://doi.org/10.1161/strokeaha.110.605808 (2011).
Vermeer, J. et al. Correlates of depressive symptoms in individuals attending outpatient stroke clinics. Disabil. Rehabil. 39, 43–49. https://doi.org/10.3109/09638288.2016.1140837 (2017).
Chi, I. & Chou, K. L. Social support and depression among elderly Chinese people in Hong Kong. Int. J. Aging Hum. Dev. 52, 231–252. https://doi.org/10.2190/v5k8-cnmg-g2up-37qv (2001).
Wang, X. et al. Family functioning is associated with post-stroke depression in first-ever stroke survivors: A longitudinal study. Neuropsychiatr. Dis. Treat. 18, 3045–3054. https://doi.org/10.2147/ndt.S393331 (2022).
Wang, L. et al. Association of post stroke depression with social factors, insomnia, and neurological status in Chinese elderly population. Neurol. Sci. 37, 1305–1310. https://doi.org/10.1007/s10072-016-2590-1 (2016).
Sun, N. et al. A survey on 465 patients with post-stroke depression in China. Arch. Psychiatr. Nurs. 28, 368–371. https://doi.org/10.1016/j.apnu.2014.08.007 (2014).
Zhang, W. N., Pan, Y. H., Wang, X. Y. & Zhao, Y. A prospective study of the incidence and correlated factors of post-stroke depression in China. PLoS One 8, e78981. https://doi.org/10.1371/journal.pone.0078981 (2013).
Wang, Z. et al. Post-stroke depression: Different characteristics based on follow-up stage and gender-a cohort perspective study from Mainland China. Neurol. Res. 39, 996–1005. https://doi.org/10.1080/01616412.2017.1364514 (2017).
Lyu, Y., Li, W. & Tang, T. Prevalence trends and influencing factors of post-stroke depression: A study based on the national health and nutrition examination survey. Med. Sci. Monit. 28, e933367. https://doi.org/10.12659/msm.933367 (2022).
Hörnsten, C., Lövheim, H., Nordström, P. & Gustafson, Y. The prevalence of stroke and depression and factors associated with depression in elderly people with and without stroke. BMC Geriatr. 16, 174. https://doi.org/10.1186/s12877-016-0347-6 (2016).
Sit, J. W., Wong, T. K., Clinton, M. & Li, L. S. Associated factors of post-stroke depression among Hong Kong Chinese: A longitudinal study. Psychol. Health Med. 12, 117–125. https://doi.org/10.1080/14622200500358978 (2007).
Shi, Y., Yang, D., Zeng, Y. & Wu, W. Risk factors for post-stroke depression: A meta-analysis. Front. Aging Neurosci. 9, 218. https://doi.org/10.3389/fnagi.2017.00218 (2017).
Liu, L. et al. Trajectories of depressive symptoms 10 years after stroke and associated risk factors: A prospective cohort study. Lancet 402(Suppl 1), S64. https://doi.org/10.1016/s0140-6736(23)02111-6 (2023).
Jørgensen, T. S. et al. Incidence of depression after stroke, and associated risk factors and mortality outcomes, in a large cohort of Danish Patients. JAMA Psychiatry 73, 1032–1040. https://doi.org/10.1001/jamapsychiatry.2016.1932 (2016).
De Ryck, A. et al. Risk factors for poststroke depression: Identification of inconsistencies based on a systematic review. J. Geriatr. Psychiatry Neurol. 27, 147–158. https://doi.org/10.1177/0891988714527514 (2014).
Yu, J., Li, J., Cuijpers, P., Wu, S. & Wu, Z. Prevalence and correlates of depressive symptoms in Chinese older adults: A population-based study. Int. J. Geriatr. Psychiatry 27, 305–312. https://doi.org/10.1002/gps.2721 (2012).
Tomioka, K., Kurumatani, N. & Hosoi, H. Self-rated health predicts decline in instrumental activities of daily living among high-functioning community-dwelling older people. Age Ageing 46, 265–270. https://doi.org/10.1093/ageing/afw164 (2017).
Yap, K. H., Warren, N., Reidpath, D. D. & Allotey, P. Interpretations of self-rated health in stroke survivors from a semi-rural community in South East Asia. Int. J. Qual. Stud. Health Well-Being 14, 1613875. https://doi.org/10.1080/17482631.2019.1613875 (2019).
de Fretas Araújo, É. et al. Self-rated health determinants in post-stroke individuals. J. Rehabil. Med. 52, jrm00080. https://doi.org/10.2340/16501977-2712 (2020).
Read, J. R., Sharpe, L., Modini, M. & Dear, B. F. Multimorbidity and depression: A systematic review and meta-analysis. J. Affect. Disord. 221, 36–46. https://doi.org/10.1016/j.jad.2017.06.009 (2017).
Bair, M. J., Robinson, R. L., Katon, W. & Kroenke, K. Depression and pain comorbidity: A literature review. Arch Intern. Med. 163, 2433–2445. https://doi.org/10.1001/archinte.163.20.2433 (2003).
Daniel, K., Wolfe, C. D., Busch, M. A. & McKevitt, C. What are the social consequences of stroke for working-aged adults? A systematic review. Stroke 40, e431-440. https://doi.org/10.1161/strokeaha.108.534487 (2009).
Volz, M., Ladwig, S. & Werheid, K. Gender differences in post-stroke depression: A longitudinal analysis of prevalence, persistence and predictive value of known risk factors. Neuropsychol. Rehabil. 31, 1–17. https://doi.org/10.1080/09602011.2019.1648301 (2021).
Zhao, Y., Hu, Y., Smith, J. P., Strauss, J. & Yang, G. Cohort profile: The China Health and Retirement Longitudinal Study (CHARLS). Int. J. Epidemiol. 43, 61–68. https://doi.org/10.1093/ije/dys203 (2014).
Boey, K. W. Cross-validation of a short form of the CES-D in Chinese elderly. Int. J. Geriatr. Psychiatry 14, 608–617. https://doi.org/10.1002/(sici)1099-1166(199908)14:8%3c608::aid-gps991%3e3.0.co;2-z (1999).
Du, X., Liao, J., Ye, Q. & Wu, H. Multidimensional internet use, social participation, and depression among middle-aged and elderly Chinese individuals: Nationwide cross-sectional study. J. Med. Internet Res. 25, e44514. https://doi.org/10.2196/44514 (2023).
Xu, S. et al. Health and wellbeing among the empty nest and non-empty nest elderly in China-Results from a national cross-sectional study. PLoS One 18, e0291231. https://doi.org/10.1371/journal.pone.0291231 (2023).
Chen, C. et al. The influence of social participation and depressive symptoms on cognition among middle-aged and older adults. Heliyon 10, e24110. https://doi.org/10.1016/j.heliyon.2024.e24110 (2024).
Zhou, P. et al. Association between chronic diseases and depression in the middle-aged and older adult Chinese population-a seven-year follow-up study based on CHARLS. Front. Public Health 11, 1176669. https://doi.org/10.3389/fpubh.2023.1176669 (2023).
Acknowledgements
The authors would like to thank the China Health and Retirement Longitudinal Study (CHARLS) and the field team and every respondent in the survey for their contributions.
Funding
This work was supported by [National Social Science Found of China] Grant number [20&ZD122], [University of Macau research funding] Grant numbers [MYRG2022-00257-ICMS] and [QRCM-IRG2022-001].
Author information
Authors and Affiliations
Contributions
GZ, YB, and WX conceived and designed the study, YL and JH conducted the data acquisition, WX, YL, and LH analyzed and interpreted the data, WX, YL, JH and LH drafted the manuscript, YB and GZ critically revised the article. All authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
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/.
About this article
Cite this article
Xiao, W., Liu, Y., Huang, J. et al. Analysis of factors associated with depressive symptoms in stroke patients based on a national cross-sectional study. Sci Rep 14, 9268 (2024). https://doi.org/10.1038/s41598-024-59837-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-024-59837-3
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
