Prevalence, correlates, and network analysis of depression and its association with quality of life in survivors with myocardial infarction during the COVID-19 pandemic.

BACKGROUND: Depression is common among myocardial infarction (MI) survivors and is strongly associated with poor quality of life (QOL). The aim of this study was to examine the prevalence, correlates and the network structure of depression, and its association with QOL in MI survivors during the COVID-19 pandemic. METHODS: This cross-sectional study evaluated depression and QOL in MI survivors with the Chinese version of the nine-item Patient Health Questionnaire (PHQ-9) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Univariable analyses, multivariable analyses, and network analyses were performed. RESULTS: The prevalence of depression (PHQ-9 total score ≥ 5) among 565 MI survivors during the COVID-19 pandemic was 38.1 % (95 % CI: 34.1-42.1 %), which was significantly associated with poor QOL. Patients with depression were less likely to consult a doctor regularly after discharge, and more likely to experience more severe anxiety symptoms and fatigue. Item PHQ4 "Fatigue" was the most central symptom in the network, followed by PHQ6 "Guilt" and PHQ2 "Sad mood". The flow network showed that PHQ4 "Fatigue" had the highest negative association with QOL. CONCLUSION: Depression was prevalent among MI survivors during the COVID-19 pandemic and was significantly associated with poor QOL. Those who failed to consult a doctor regularly after discharge or reported severe anxiety symptoms and fatigue should be screened for depression. Effective interventions for MI survivors targeting central symptoms, especially fatigue, are needed to reduce the negative impact of depression and improve QOL.

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

COVID-19 vaccine acceptance and perceived stigma in patients with depression: a network perspective.

The association between coronavirus disease (COVID-19) vaccine acceptance and perceived stigma of having a mental illness is not clear. This study examined the association between COVID-19 vaccine acceptance and perceived stigma among patients with recurrent depressive disorder (depression hereafter) using network analysis. Participants were 1149 depressed patients (842 men, 307 women) who completed survey measures of perceived stigma and COVID-19 vaccine attitudes. T-tests, chi-square tests, and Kruskal-Wallis tests were used to compare differences in demographic and clinical characteristics between depressed patients who indented to accepted vaccines and those who were hesitant. Hierarchical multiple regression analyses assessed the unique association between COVID-19 vaccine acceptance and perceived stigma, independent of depression severity. Network analysis examined item-level relations between COVID-19 vaccine acceptance and perceived stigma after controlling for depressive symptoms. Altogether, 617 depressed patients (53.7%, 95 confidence intervals (CI) %: 50.82-56.58%) reported they would accept future COVID-19 vaccination. Hierarchical multiple regression analyses indicated higher perceived stigma scores predicted lower levels of COVID-19 vaccination acceptance (ß = -0.125, P < 0.001), even after controlling for depression severity. In the network model of COVID-19 vaccination acceptance and perceived stigma nodes, "Feel others avoid me because of my illness", "Feel useless", and "Feel less competent than I did before" were the most influential symptoms. Furthermore, "COVID-19 vaccination acceptance" had the strongest connections with illness stigma items reflecting social rejection or social isolation concerns ("Employers/co-workers have discriminated", "Treated with less respect than usual", "Sense of being unequal in my relationships with others"). Given that a substantial proportion of depressed patients reported hesitancy with accepting COVID-19 vaccines and experiences of mental illness stigma related to social rejection and social isolation, providers working with this group should provide interventions to reduce stigma concerns toward addressing reluctance in receiving COVID-19 vaccines.

The backbone symptoms of depression: a network analysis after the initial wave of the COVID-19 pandemic in Macao.

Background: The coronavirus disease 2019 (COVID-19) pandemic disrupted the working lives of Macau residents, possibly leading to mental health issues such as depression. The pandemic served as the context for this investigation of the network structure of depressive symptoms in a community sample. This study aimed to identify the backbone symptoms of depression and to propose an intervention target. Methods: This study recruited a convenience sample of 975 Macao residents between 20th August and 9th November 2020. In an electronic survey, depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9). Symptom relationships and centrality indices were identified using directed and undirected network estimation methods. The undirected network was constructed using the extended Bayesian information criterion (EBIC) model, and the directed network was constructed using the Triangulated Maximally Filtered Graph (TMFG) method. The stability of the centrality indices was evaluated by a case-dropping bootstrap procedure. Wilcoxon signed rank tests of the centrality indices were used to assess whether the network structure was invariant between age and gender groups. Results: Loss of energy, psychomotor problems, and guilt feelings were the symptoms with the highest centrality indices, indicating that these three symptoms were backbone symptoms of depression. The directed graph showed that loss of energy had the highest number of outward projections to other symptoms. The network structure remained stable after randomly dropping 50% of the study sample, and the network structure was invariant by age and gender groups. Conclusion: Loss of energy, psychomotor problems and guilt feelings constituted the three backbone symptoms during the pandemic. Based on centrality and relative influence, loss of energy could be targeted by increasing opportunities for physical activity.

Optimizing the dynamic zero-COVID policy in China.

There has been no consensus about the best public health strategy for managing COVID-19 due to differences in sociocultural, political and economic contexts between countries. The central government of China has emphasized the importance of maintaining the dynamic zero-COVID policy in combating resurgences of new variants. To optimize the dynamic zero-COVID policy for future COVID-19 outbreaks in China, this article outlines a comprehensive strategy that should be considered.

COVID-19 prevention and control strategies: learning from the Macau model.

Background: Macau is a densely populated international tourist city. Compared to most tensely populated countries/territories, the prevalence and mortality of COVID-19 in Macau are lower. The experiences in Macau could be helpful for other areas to combat the COVID-19 pandemic. This article introduced the endeavours and achievements of Macau in combatting the COVID-19 pandemic. Method: Both qualitative and quantitative analysis methods were used to explore the work, measures, and achievements of Macau in dealing with the COVID-19 pandemic. Results: The results revealed that Macau has provided undifferentiated mask purchase reservation services, COVID-19 vaccination services to all residents and non-residents in Macau along with delivering multilingual services, in Chinese, English and Portuguese, to different groups of the population. To facilitate the travels of people, business and trades between Macau and mainland China, the Macau government launched the Macau Health Code System, which uses the health status declaration, residence history declaration, contact history declaration of the declarant to match various relevant backend databases within the health authority and provide a risk-related colour code operations. The Macau Health Code System connects to the Chinese mainland's own propriety health code system seamlessly, whilst effectively protecting the privacy of the residents. Macau has also developed the COVID-19 Vaccination Appointment system, the Nucleic Acid Test Appointment system, the Port and Entry/Exit Quarantine system, the medical and other supporting systems. Conclusion: The efforts in Macau have achieved remarkable results in COVID-19 prevention and control, effectively safeguarding the lives and health of the people and manifesting the core principle of "serving the public". The measures used are sustainable and can serve as an important reference for other countries/regions.

Suicidality in clinically stable bipolar disorder and schizophrenia patients during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has a disproportionate impact on vulnerable subpopulations, including those with severe mental illness (SMI). This study examined the one-year prevalence of suicidal ideation (SI), suicide plans (SP), and suicide attempts (SA) in bipolar disorder (BD) and schizophrenia (SCZ) patients during the pandemic. Prevalence rates were compared between the two disorders and associated factors were examined. A survey was conducted in six tertiary psychiatric hospitals and psychiatric units. People with a diagnosis of BD or SCZ were invited to participate. SI, SP, and SA (suicidality for short) were assessed and associated factors were examined using binary logistical regression. The 1-year prevalence of SI, SP and SA in BD patients were 58.3%, (95% CI: 54.1-62.6%), 38.4% (95% CI: 34.3-42.6%) and 38.6% (95% CI: 34.5-42.8%), respectively, which were higher than the corresponding figures in SCZ patients (SI: 33.2%, 95% CI: 28.6-37.8%; SP: 16.8%, 95% CI: 13.2-20.5%; SA: 19.4%, 95% CI: 15.5-23.3%). Patients with younger age, experience of cyberbullying, a history of SA among family or friends, a higher fatigue and physical pain score, inpatient status, and severe depressive symptoms were more likely to have suicidality. The COVID-19 pandemic was associated with increased risk of suicidality, particularly in BD patients. It is of importance to regularly screen suicidality in BD and SCZ patients during the pandemic even if they are clinically stable.

Network analysis of depression, anxiety, insomnia and quality of life among Macau residents during the COVID-19 pandemic.

BACKGROUND: Although the Coronavirus Disease 2019 (COVID-19) has greatly impacted individuals' mental health and quality of life, network analysis studies of associations between symptoms of common syndromes during the pandemic are lacking, particularly among Macau residents. This study investigated the network structure of insomnia, anxiety, and depression and explored their associations with quality of life in this population. METHOD: This online survey was conducted in Macau between August 18 and November 9, 2020. Insomnia, anxiety, depressive symptoms, and quality of life were assessed with the Insomnia Severity Index, Generalized Anxiety Disorder Scale, Patient Health Questionnaire, and World Health Organization Quality of Life-brief version, respectively. Analyses were performed to identify central symptoms and bridge symptoms of this network and their links to quality of life. RESULTS: 975 participants enrolled in this survey. The prevalence of depressive, anxiety and insomnia symptoms were 38.5% (95% confidence interval (CI): 35.5%-41.5%), 28.8% (95%CI: 26.0%-31.7%), and 27.6% (95% CI: 24.8%-30.4%), respectively. "Sleep maintenance" had the highest expected influence centrality, followed by "Trouble relaxing", "Interference with daytime functioning", "Irritability", and "Fatigue". Five bridge symptoms were identified: "Sleep problems", "Restlessness", "Irritability", "Severity of sleep onset", and "Motor activity". The insomnia symptom, "Sleep dissatisfaction", had the strongest direct relation to quality of life. CONCLUSION: Insomnia symptoms played a critical role in the distress symptom network regarding node and bridge centrality as well as associations with quality of life among Macau residents. Close attention to these symptoms may be critical to reducing risk and preventing exacerbations in common forms of distress in this population.

Network analysis of depressive and anxiety symptoms in adolescents during the later stage of the COVID-19 pandemic.

Network analysis is an effective approach for examining complex relationships between psychiatric symptoms. This study was designed to examine item-level relationships between depressive and anxiety symptoms using network analysis in an adolescent sample and identified the most central symptoms within the depressive-anxiety symptoms network model. Depressive and anxiety symptoms were assessed using the Patient Health Questionire-9 (PHQ-9) and Generalized Anxiety Disorder Screener (GAD-7), respectively. The structure of depressive and anxiety symptoms was characterized using "Strength" and "Bridge Strength" as centrality indices in the symptom network. Network stability was tested using a case-dropping bootstrap procedure. Finally, a Network Comparison Test (NCT) was conducted to examine whether network characteristics differed on the basis of gender, school grade and residence. Network analysis revealed that nodes PHQ2 ("Sad mood"), GAD6 ("Irritability"), GAD3 ("Worry too much"), and PHQ6 ("Guilty") were central symptoms in the network model of adolescents. Additionally, bridge symptoms linking anxiety and depressive symptoms in this sample were nodes PHQ6 ("Guilty"), PHQ2 ("Sad mood"), and PHQ9 ("Suicide ideation"). Gender, school grade and residence did not significantly affect the network structure. Central symptoms (e.g., Sad mood, Irritability, Worry too much, and Guilty) and key bridge symptoms (e.g., Guilty, Sad mood, and Suicide ideation) in the depressive and anxiety symptoms network may be useful as potential targets for intervention among adolescents who are at risk for or suffer from depressive and anxiety symptoms.

Prevalence of suicidality in clinically stable patients with major depressive disorder during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic is associated with an increased risk of mental health problems including suicide in many subpopulations, but its influence on stable patients with major depressive disorder (MDD) has been studied fleetingly. This study examined the one-year prevalence of suicidality including suicidal ideation (SI), suicide plans (SP), and suicide attempts (SA) as well as their correlates in clinically stable MDD patients during the COVID-19 pandemic. METHODS: A cross-sectional, observational study was conducted between October 1, 2020, and October 15, 2021, in six tertiary psychiatric hospitals. Socio-demographic information, clinical data and one-year prevalence of suicidality were recorded. RESULTS: Altogether, 1718 participants who met the eligibility criteria were included. The overall one-year prevalence of suicidality during the COVID-19 pandemic was 68.04% (95% confidence intervals (CI) =65.84-70.25%), with one-year SI prevalence of 66.4% (95%CI = 64.18-68.65%), SP prevalence of 36.26% (95%CI = 33.99-38.54%), and SA prevalence of 39.35% (95%CI = 37.04-41.66%). Binary logistic regression analyses revealed male gender, married marital status, college education level and above and age were negatively associated with risk of suicidality. Urban residence, unemployed work status, experiences of cyberbullying, a history of suicide among family members or friends, and more severe fatigue, physical pain, and residual depressive symptoms were positively associated with risk of suicidality. CONCLUSIONS: Suicidality is common among clinically stable MDD patients during the COVID-19 pandemic. Regular suicide screening and preventive measures should be provided to clinically stable MDD patients during the pandemic.

Anxiety and depressive symptoms in college students during the late stage of the COVID-19 outbreak: a network approach.

Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms "Nervousness" and "Uncontrollable worry". "Fatigue" has the highest node strength in the anxiety and depression network, followed by "Excessive worry", "Trouble relaxing", and "Uncontrollable worry". "Motor" showed the highest bridge strength, followed by "Feeling afraid" and "Restlessness". The whole network was robust in both stability and accuracy tests. Central symptoms "Fatigue", "Excessive worry", "Trouble relaxing" and "Uncontrollable worry", and critical bridge symptoms "Motor", "Feeling afraid" and "Restlessness" were highlighted in this study. Targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.

Changes in Nursing Students' Career Choices Following the COVID-19 Pandemic in China.

Background: Health professionals including nurses have experienced heavy workload and great physical and mental health challenges during the coronavirus disease 19 (COVID-19) pandemic, which may affect nursing students' career choices. This study examined the changes in nursing students' career choices after the onset of the COVID-19 pandemic in China. Methods: This study was conducted in five University nursing schools in China between September 14, 2020 and October 7, 2020. Career choices before and after the COVID-19 pandemic were collected and analyzed. Results: In total, 1,070 nursing students participated in the study. The reported choice of nursing as future career increased from 50.9% [95% confidence interval (CI): 47.9-53.9%] before the COVID-19 pandemic to 62.7% (95%CI: 59.8-65.6%) after the onset of COVID-19 pandemic. Students who chose nursing as their future career following the COVID-19 outbreak had less severe depression and anxiety compared to those who did not choose nursing, but the associations of depression and anxiety with career choice disappeared in multivariable analyses. Binary logistic regression analysis revealed that male gender [odds ratio (OR) = 0.68, 95% CI: 0.50-0.91], rural residence (OR = 1.53, 95%CI: 1.17-2.00), fourth year students (OR = 0.50, 95%CI: 0.35-0.72), negative experiences during the COVID-19 pandemic (OR = 0.66, 95%CI: 0.47-0.92), and good health (OR = 4.6, 95%CI: 1.78-11.87) were significantly associated with the choice of nursing as future career after the onset of the COVID-19 pandemic. Conclusions: The COVID-19 pandemic appeared to have a positive influence on the career choice of nursing among Chinese nursing students.

Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic is associated with increased risk of insomnia symptoms (insomnia hereafter) in health-care professionals. Network analysis is a novel approach in linking mechanisms at the symptom level. The aim of this study was to characterize the insomnia network structure in mental health professionals during the COVID-19 pandemic. PATIENTS AND METHODS: A total of 10,516 mental health professionals were recruited from psychiatric hospitals or psychiatric units of general hospitals nationwide between March 15 and March 20, 2020. Insomnia was assessed with the insomnia severity index (ISI). Centrality index (ie, strength) was used to identify symptoms central to the network. The stability of network was examined using a case-dropping bootstrap procedure. The network structures between different genders were also compared. RESULTS: The overall network model showed that the item ISI7 (interference with daytime functioning) was the most central symptom in mental health professionals with the highest strength. The network was robust in stability and accuracy tests. The item ISI4 (sleep dissatisfaction) was connected to the two main clusters of insomnia symptoms (ie, the cluster of nocturnal and daytime symptoms). No significant gender network difference was found. CONCLUSION: Interference with daytime functioning was the most central symptom, suggesting that it may be an important treatment outcome measure for insomnia. Appropriate treatments, such as stimulus control techniques, cognitive behavioral therapy and relaxation training, could be developed. Moreover, addressing sleep satisfaction in treatment could simultaneously ameliorate daytime and nocturnal symptoms.