Background: Coronavirus disease in 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus-2 started in Wuhan and caused a global outbreak, including Indonesia. Indonesia is ranked fourth as the most populous country globally, with disparities in health, socioeconomic, and education. In this review, the authors intend to report the COVID-19 pandemic situation in Indonesia. Methods: We collect the relevant information from various resources such as government official data and press releases news outlets, as well as COVID-related studies based in Indonesia. Results: Indonesia does not have a uniform and one-way disaster preparedness system; this problem can be seen at the beginning of the pandemic where policy changes often occur and confuse the public. This situation has an impact on the high rate of transmission and death due to COVID-19. In addition, the pandemic is forcing people to implement social restrictions that affect people's income. Various efforts have been made by the government to deal with COVID-19, but the results are still not satisfactory. Conclusion: The pandemic of COVID-19 affects all Indonesians, physically, mentally, and economically. Fighting the pandemic is not one man's job; the Indonesian government is required to collaborate with the citizens to conquer the disaster of COVID-19 pandemic.

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Objective: A certain portion of patients with depression is under-diagnosed and has attracted the attention in the field of natural language processing (NLP). In this study, we intended to explore the feasibility of transferring unstructured textual records into a screening tool to early detect depression. Methods: We recruited 22,355 medical records in Mandarin traditional Chinese from the psychiatry emergency department of a military psychiatry center from 2004 to 2019. We preprocessed all the context of present illness histories as corpus and the presence of clinical diagnoses of depression as an outcome. A state-of-the-art NLP model was developed based on a pretrained bidirectional encoder representation from transformers (BERT) model along with several convolutional neural network (CNN) and trained by the training set (80% of original data) of total samples (BERT_general) and of civilian samples (BERT_civilian) and of military samples (BERT_military) independently. The receiver operating characteristic (ROC) and area under curve (AUC) of three trained models were compared for predicting depression for the test dataset (20% of original data) of general and specific samples. Results: The experimental results demonstrated excellent performance of BERT_general for general samples (AUC = 0.93, sensitivity = 0.817, specificity = 0.920 for optimal cut-off point) and civilian sample (AUC = 0.91, sensitivity = 0.851, specificity = 0.851 for optimal cut-off point). BERT_general showed a significant underperformance of for military samples (AUC = 0.79, sensitivity = 0.712, specificity = 0.732, p < 0.05 for optimal cut-off point). That of BERT_military was slight higher (AUC = 0.82, sensitivity = 0.708, specificity = 0.786 for optimal cut-off point) for military samples. Conclusion: This study showed the feasibility of applying deep learning technique as a depression-detection assistant tool in Mandarin Chinese medical records. However, the subjects' specific situation, e.g., military status, is warranted for further investigation.

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Objective: In this study, we intended to study the association between depressive symptoms and the status of sarcopenia or obesity in patients with mild-to-moderate Alzheimer's disease (AD). Methods: We enrolled 176 outpatients aged 65 to 89 years with mild-to-moderate AD. The study participants were divided into four groups according to the presence or absence of sarcopenia and obesity. We analyzed differences among the four groups and used multiple logistic regression to examine associations with depressive symptoms. Results: Most of the patients were obese without sarcopenia (n [%] = 94 [53.5%]). The patients with sarcopenia either with or without obesity were significantly more male, compared to those without sarcopenia (p < 0.001). The obese patients either with or without sarcopenia tended to be significantly older (p < 0.01), and to have significantly higher prevalence of depressive symptoms (p < 0.05), and significantly more receiving antidepressant therapy (p < 0.05), compared to those in the nonobesity groups. After adjusting for covariates, we further found that the obese patients either with or without sarcopenia were significantly positively associated with depressive symptoms compared to the nonsarcopenia/nonobesity group (odds ratio [OR] [95% confidence interval (CI)] = 6.88 [1.11–42.71], p < 0.05; OR [95% CI] = 5.95 [1.82–19.43], p < 0.01), respectively, and those patients with sarcopenia without obesity did not have any significant depressive symptoms. Conclusion: Obesity could be a potential confounder for the association between sarcopenia and depressive symptoms in patients with AD. Future studies suggest that depression interventions using reducing adiposity or increasing muscle mass need to be considered.

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Background: The relation between mental illness and criminal offenses is controversial. In Taiwan, offenders with mental illness may be sentenced to custodial protection. Nevertheless, the recidivism rates remain high in such populations. Factors associated with recidivism among those offenders warrant further exploration. Methods: We retrospectively identified five persons with mental illness who had repeated forensic psychiatric evaluation for at least three times through reviewing forensic psychiatry records over the past decade at a psychiatric center. The related sociodemographic characteristics and psychiatric profiles in the 22 offenses were also collected and correlated with verdicts. Results: Those five offenders had family and personal histories of mental illnesses. They were relatively young at the first offense and unemployed, unmarried during the period of committing the offenses. Some offenders had a history of substance use, self-harm attempts, other criminal behaviors, and poor adherence to treatments before the offense. All the concluded characteristics of forensic psychiatric evaluations were consistent with the court judgments. Conclusion: Social dysfunction, poor adherence to treatments, history of substance use, and previous criminal records were prominent among those five repeated offenders.

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Background: Sleep quality is an important predictor for prognosis of bipolar disorder (BD). Factors associated with sleep quality in BD such as childhood trauma experience merit investigation. Methods: We used the Pittsburgh Sleep Quality Index (PSQI), Childhood Trauma Questionnaire (CTQ), and Measurement of Support Functions (MSF) to access patients with BD-I (n = 31), and those with BD-II (n = 34). Results: We found that 71.4% of patients with BD-I and 90.9% of those with BD-II had poor sleep quality. Significantly higher CTQ physical abuse score and poor life quality were found among patients with BD-II (b = −0.008, Wald χ² (1) = 5.024, p < 0.05). This effect remained robust (b = −0.012, Wald χ² (1) = 8.150, p < 0.01) after controlling the use of drug (sedative, benzodiazepine, antipsychotic, and antidepressant). Moreover, the experience of childhood trauma was associated with poor sleep quality among patients with BD-II. A buffer effect of social support between physical abuse and daytime dysfunction, as measured by PSQI, was found in patients with BD-II, but not in those with BD-I. Conclusion: Social adversity and support were associated with sleep quality in patients with BD-II. This finding implied a stress-buffering model in patients with BD-II. But the underlying biological mechanism remains unclear.

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Objective: Persons with mental illness (PWMI) have reduced chances of getting married to individuals without mental illness, yet, so much controversy surrounds the propriety or otherwise of a psychiatric patient marrying another psychiatric patient (marital homogamy). The factors that are associated with patients' disposition towards homogamy have received little attention from researchers, creating a gap in literature. In this study, I intended to examine the proportion of psychiatric patients that are negatively disposed towards marrying another psychiatric patient and to find the factors that are associated with their disposition. Methods: A descriptive cross-sectional design was used, and participants included 208 consecutive attendees at the psychiatric outpatient clinic of a tertiary hospital in Benin City, Nigeria. Data were collected from the participants using a sociodemographic data collection sheet and three standard instruments - the Bogardus Social Distance Scale, the World Psychiatric Association Stigma Questionnaire, and the Brief Psychiatric Rating Scale. Results: About two-thirds of the patients (67.3%) were not willing to marry a psychiatric patient, citing concerns about heredity of mental illness; potential financial difficulty and lack of social/spousal support; and perceived dangerousness of psychiatric patients as the reasons. About two-thirds of them desired moderate to high social distance from another psychiatric patient. Three factors - the desire for low social distance (p < 0.001), belief that marriage is a necessity in life (p < 0.001), and belief that people with mental illness who recovered are eligible to marry (p < 0.001) - significantly differentiated between patients who were willing to marry a psychiatric patient and those not willing. Conclusion: Many patients in this study expressed a negative disposition toward marrying a psychiatric patient, yet previous researchers reported that they have reduced chances of marrying persons without mental illness. Appropriate intervention to address the factors that are related to their negative disposition may change their disposition and increase their chances of getting married, either to a nonmentally ill individual or a PWMI if they must not remain single all their life.

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