Psychological Burden and Associated Factors of the COVID-19 Pandemic on People in Quarantine and Isolation Centers in Ethiopia: A Cross-Sectional Study

Background: Globally, a lot of countries put into practice early quarantine measures as an essential COVID-19 prevention mechanism. Other than physical effects, quarantine has a major result on mental health and well-being at both the individual as well as the community level at large. Therefore, this study aimed to assess the psychological burden of COVID-19 on the people in quarantine and isolation centers and to identify associated factors for early and effective psychosocial intervention during the pandemic and beyond.Method: A cross-sectional study was done among 392 suspected cases of COVID-19 that were in quarantine and isolation centers found in Eastern Ethiopia in 2020. Participants were selected by the convenience sampling method. The common mental disorder was measured by the Self Reporting Questionnaire-20 (SRQ-20). Logistic regression was done to identify predictive factors, and a P < 0.05 was considered statistically significant.Results: The common mental disorder among suspected cases of COVID-19 in Ethiopia was found to be 13.5% (95% CI: 10.2, 17.1%). Female (AOR = 1.52, 95% CI: 1.1, 2.92), known chronic medical illness (AOR = 7.0, 95% CI: 2.2, 21.8), inadequate accessibility of personal protective equipment (AOR = 6.1, 95% CI: 2.8, 13.3), poor awareness about the pandemic (AOR = 2.90, 95% CI: 2.71, 7.54), presence of symptoms of the disease (AOR = 5.3, 95% CI: 2.57, 11.1), and substance use (AOR = 2.7, 95% CI: 1.2, 6.1) were found to be associated with a common mental disorder.Conclusion: The current study revealed that the common mental disorder was relatively high among suspected cases of COVID-19 in quarantine and isolation centers as compared with the general population. The results of the present study demonstrate that some subpopulations are more vulnerable to the pandemic's deleterious effects on mental health. Therefore, providing appropriate psychosocial intervention for the populations at risk is important to decrease the effect of common mental disorders among suspected cases of COVID-19.

Comorbidity of Mental Disorder in Patients with Chronic Dis-ease: A Comparative Study of Heroin Dependence Patients and Diabetes Mellitus Patients

Background: Chronic patients are at greater risk for a psychiatric problem than the normal population; yet, the increased rate of mental disorder among one chronic patient compared to another chronic patient is uncertain. We aimed to assess the rate of mental disorder among people with heroin dependence and diabetes mellitus in comparison with the healthy population. Methods: This cross-sectional study was carried out in Kuala Lumpur, Malaysia in 2017-2020. The study consisted of 648 participants including heroin dependence patients, diabetes mellitus patients, and healthy population. The GHQ-28 and SCL-90-R scales were used to assess mental disorder among the study populations. Results: The current study revealed the rate of mental disorder among heroin dependence patients, diabetes mellitus patients, and healthy population respectively at 52.1%, 49.5%, and 23.2% using SCL-90-R and GHQ-28. The rate of mental disorder in both heroin dependent (OR 95%= 3.59: 2.37-5.44) and diabetic groups (OR 95%=3.25: 2.14-4.92) were significantly more than the healthy population; however, the odds ratio of mental disorder was not significantly different between heroin dependent and diabetic groups. Furthermore, the results revealed an acceptable agreement between SCL-90-R and GHQ-28 to detect mental disorders (Kappa=0.60; P<0.001). Conclusion: People with diabetes mellitus and heroin dependence have significantly poorer mental health than healthy people in Malaysia have. Furthermore, the equivalent rate of mental disorder among such patients suggests that heroin dependence patients are not more distressed than diabetes mellitus patients are. However, further comparative studies are needed to prove these findings.

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.

Change in Work-Related Income Following the Uptake of Treatment for Mental Disorders Among Young Migrant and Non-migrant Women in Norway: A National Register Study

Background: Women, and migrant women in particular, are at increased risk of many common mental disorders, which may potentially impact their labor market participation and their work-related income. Previous research found that mental disorders are associated with several work-related outcomes such as loss of income, however, not much is known about how this varies with migrant background. This study investigated the change in work-related income following the uptake of outpatient mental healthcare (OPMH) treatment, a proxy for mental disorder, in young women with and without migrant background. Additionally, we looked at how the association varied by income level.Methods: Using data from four national registries, the study population consisted of women aged 23–40 years residing in Norway for at least three consecutive years between 2006 and 2013 (N = 640,527). By using a stratified linear regression with individual fixed effects, we investigated differences between majority women, descendants and eight migrant groups. Interaction analysis was conducted in order to examine differences in income loss following the uptake of OPMH treatment among women with and without migrant background.Results: Results showed that OPMH treatment was associated with a decrease in income for all groups. However, the negative effect was stronger among those with low income. Only migrant women from Western and EU Eastern Europe with a high income were not significantly affected following OPMH treatment.Conclusion: Experiencing a mental disorder during a critical age for establishment in the labor market can negatively affect not only income, but also future workforce participation, and increase dependency on social welfare services and other health outcomes, regardless of migrant background. Loss of income due to mental disorders can also affect future mental health, resulting in a vicious circle and contributing to more inequalities in the society.

Severe and common mental disorders and risk of hospital admissions for Ambulatory Care Sensitive Conditions (ACSCs): prospective cohort study using UK Biobank

Background People with mental disorders have an excess chronic disease burden. One mechanism to potentially reduce the public health and economic costs of mental disorders is to reduce preventable hospital admissions. Ambulatory care sensitive conditions (ACSCs) are a defined set of chronic and acute illnesses not considered to require hospital treatment if patients receive adequate primary healthcare. We examined the relationship between both severe and common mental disorders and risk of emergency hospital admissions for ACSCs and factors associated with increased risk. Methods Baseline data from England (N=445,814) were taken from UK Biobank, which recruited participants aged 37-73 years during 2006 to 2010, and were linked to hospital admission records up to 31st December 2019. Participants were grouped into those who had a history of either schizophrenia, bipolar disorder, depression or anxiety, or no record of mental disorder. Cox proportional hazard models (for the first admission) and Prentice, Williams and Peterson Total Time models (PWP-TT, which account for all admissions) were used to assess the risk (using hazard ratios (HR)) of hospitalisation for ACSCs among those with mental disorders compared to those without, adjusting for factors in different domains, including sociodemographic (e.g. age, sex, ethnicity), socioeconomic (e.g. deprivation, education level), health and biomarkers (e.g. multimorbidity, inflammatory markers), health-related behaviours (e.g. smoking, alcohol consumption), social isolation (e.g. social participation, social contact) and psychological (e.g. depressive symptoms, loneliness). Results People with schizophrenia had the highest risk of hospital admission for ACSCs compared to those with no mental disorder (HR=4.40, 95% CI: 4.04 - 4.80). People with bipolar disorder (HR=2.48, 95% CI: 2.28 - 2.69) and depression or anxiety (HR=1.76, 95% CI: 1.73 - 1.80) also had higher risk. Associations were more conservative when accounting for all admissions. Although adjusting for a range of factors attenuated the observed associations, they still persisted, with socioeconomic and health-related variables contributing most. Conclusions People with severe mental disorders had highest risk of preventable hospital admissions, with the risk also elevated amongst those with depression and anxiety. Ensuring people with mental disorders receive adequate ambulatory care is essential to reduce the large health inequalities experienced by these groups.

Indigenous Knowledge On Medicinal Plants Practiced At Myagdi District, Nepal

Indigenous knowledge on medicinal plants and practices is outdistancing and vulnerable to loss if not properly documented. A survey on medicinal plants and their practices was carried in the Myagdi district. Around 40 tribal people from four different villages were interviewed using a semi-structured questionnaire. Documentation of the indigenous knowledge was done in written form and the pictures of available medicinal plants were taken. The study showed that 93.51% of the respondent has used medicinal plants and 87.20% found them effective in curing several diseases and injuries. They are commonly used to cure diseases like bone fractures, abdominal pain, fever, common cold, dysentery, eye opacity, scabies, worm infection, reproductive problems, mental disorder, and cardiovascular problems. As reported, with access to modern pharmaceuticals, the use of medicinal plants has been less practiced these days. The knowledge on medicinal plants and practices are restricted to older-aged groups of the community (76.32%) reflecting that valuable indigenous knowledge is on the wane. This study portrays the commonly used medicinal plants along with their preparation techniques practiced in the study area.

Starving to death and the anorexic frame of mind

Because some forms of self-starvation such as hunger striking are exempt from attributions of pathology, and due to incomplete understanding of its etiology, anorexia nervosa (AN) is and must presently be defined by psychological criteria as well as behavioral and bodily measures. Although opaque, typical motivational frames of mind in AN lack the apparent cognitive and volitional dysfunction usually indicating disorder. In contrast to other conditions that exhibit more evident dysfunction, this distinguishes AN from the perspective of medical epistemology: the opacity of AN motivation jeopardizing the epistemic warrant for assigning it to the category of a mental disorder (and so influencing decisions over diagnosis and recovery). This seems to invite non-medical approaches to its prevention and care.