scholarly journals Predictors of Depression and Anxiety Symptoms in Brazil during COVID-19

2021 ◽  
Author(s):  
Stephen X. Zhang ◽  
Hao Huang ◽  
Jizhen Li ◽  
Antonelli-Ponti Mayra ◽  
de Paiva Farias Scheila ◽  
...  
Keyword(s):  
Regression Models ◽  
Preliminary Evidence ◽  
Anxiety Symptoms ◽  
Depression And Anxiety ◽  
Healthcare Organizations ◽  
Younger Adults ◽  
Logistic Regression Models ◽  
The Third ◽  
Anxiety Depression ◽  
Predictors Of Depression

The COVID-19 pandemic in Brazil is extremely severe, and Brazil has the third-highest number of cases in the world. The goal of the study is to identify the prevalence rates and several predictors of depression and anxiety in Brazil during the initial outbreak of COVID-19. We surveyed 482 adults in 23 Brazilian states online on 9-22 May 2020, and found 70.3% of the adults (N=339) had depressive symptoms and 67.2% (N=320) had anxiety symptoms. The results of multi-class logistic regression models revealed that females, younger adults and those with fewer children had a higher likelihood of depression and anxiety symptoms; adults who worked as employees were more likely to have anxiety symptoms than those who were self-employed or unemployed; adults who spent more time browsing COVID-19 information online were more likely to have depression and anxiety symptoms. Our results provide preliminary evidence and early warning for psychiatrists and healthcare organizations to better identify and focus on the more vulnerable sub-populations in Brazil during the ongoing COVID-19 pandemic. Keywords: COVID-19; Brazil; anxiety; depression; predictors; risk factors

scholarly journals Predictors of Depression and Anxiety Symptoms in Brazil during COVID-19

2021 ◽  
Vol 18 (13) ◽  
pp. 7026
Author(s):  
Stephen X. Zhang ◽  
Hao Huang ◽  
Jizhen Li ◽  
Mayra Antonelli-Ponti ◽  
Scheila Farias de Paiva ◽  
...  
Keyword(s):  
Regression Models ◽  
Preliminary Evidence ◽  
Anxiety Symptoms ◽  
Depression And Anxiety ◽  
Healthcare Organizations ◽  
Younger Adults ◽  
Logistic Regression Models ◽  
The Third ◽  
The World ◽  
Predictors Of Depression

The COVID-19 pandemic in Brazil is extremely severe, and Brazil has the third-highest number of cases in the world. The goal of the study is to identify the prevalence rates and several predictors of depression and anxiety in Brazil during the initial outbreak of COVID-19. We surveyed 482 adults in 23 Brazilian states online on 9–22 May 2020, and found that 70.3% of the adults (n = 339) had depressive symptoms and 67.2% (n = 320) had anxiety symptoms. The results of multi-class logistic regression models revealed that females, younger adults, and those with fewer children had a higher likelihood of depression and anxiety symptoms; adults who worked as employees were more likely to have anxiety symptoms than those who were self-employed or unemployed; adults who spent more time browsing COVID-19 information online were more likely to have depression and anxiety symptoms. Our results provide preliminary evidence and early warning for psychiatrists and healthcare organizations to better identify and focus on the more vulnerable sub-populations in Brazil during the ongoing COVID-19 pandemic.

scholarly journals Recommendations for Abnormal Behaviour Ethograms in Monkey Research

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1461
Author(s):  
Andrea Polanco ◽  
Brenda McCowan ◽  
Lee Niel ◽  
David L. Pearl ◽  
Georgia Mason
Keyword(s):  
Macaca Mulatta ◽  
Regression Models ◽  
Convergent Validity ◽  
P Value ◽  
Morphological Similarity ◽  
Abnormal Behaviour ◽  
Logistic Regression Models ◽  
The Third ◽  
Welfare Implications ◽  
Self Stimulation

Laboratory monkey ethograms currently include subcategories of abnormal behaviours that are based on superficial morphological similarity. Yet, such ethograms may be misclassifying behaviour, with potential welfare implications as different abnormal behaviours are likely to have distinct risk factors and treatments. We therefore investigated the convergent validity of four hypothesized subcategories of abnormal behaviours (‘motor’, e.g., pacing; ‘self-stimulation’, e.g., self-sucking; ‘postural’, e.g., hanging; and ‘self-abuse’, e.g., self-biting). This hypothesis predicts positive relationships between the behaviours within each subcategory. Rhesus macaque (Macaca mulatta) data on 19 abnormal behaviours were obtained from indoor-housed animals (n = 1183). Logistic regression models, controlling for sex, age, and the number of observations, revealed that only 1/6 ‘motor’ behaviours positively predicted pacing, while 2/3 ‘self-abuse’ behaviours positively predicted self-biting (one-tailed p-value < 0.05). Furthermore, ‘self-stimulation’ behaviours did not predict self-sucking, and none of the ‘postural’ behaviours predicted hanging. Thus, none of the subcategories fully met convergent validity. Subsequently, we created four new valid subcategories formed of comorbid behaviours. The first consisted of self-biting, self-hitting, self-injurious behaviour, floating limb, leg-lifting, and self-clasping. The second comprised twirling, bouncing, rocking, swinging, and hanging. The third comprised pacing and head-twisting, while the final subcategory consisted of flipping and eye-poking. Self-sucking, hair-plucking, threat-biting, and withdrawn remained as individual behaviours. We encourage laboratories to replicate the validation of these subcategories first, and for scientists working with other species to validate their ethograms before using them in welfare assessments.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Mengyang Tang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.Methods: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Depression and Anxiety among University Students: a Comparison Between COVID-19 Pandemic Panic Period and Post-Panic Period in Bangladesh

2021 ◽  
Author(s):  
Gowranga Kumar Paul ◽  
Meshbahur Rahman ◽  
Shayla Naznin ◽  
Mashfiqul Haq Chowdhury ◽  
Md Jamal Uddin
Keyword(s):  
University Students ◽  
Anxiety Symptoms ◽  
Health Concern ◽  
Depression Symptoms ◽  
Depression And Anxiety ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Time Period ◽  
The Difference ◽  
The University

Abstract Background: The current COVID-19 pandemic is the biggest public health concern. It harmed everyone, both physically and mentally. Because of panic situations in COVID-19 pandemic, students all over the world, including those in Bangladesh, are suffering from depression and anxiety. Considering this, we aimed to assess psycho-emotional changes of the university students through investigating their level of depression and anxiety effects during panic and post-panic period of COVID-19 pandemic in Bangladesh.Methods: Cross-sectional online surveys were conducted among university students in Bangladesh from April to July 2020 (panic period, n=170) and then from August to November 2020 (post-panic period, n=170). The PHQ-9 and GAD-7 questionnaires were used to assess respondents' depression and anxiety levels, respectively. We used continuous scores to assess the severity of depression and anxiety symptoms. We also computed binary depression and anxiety scores. Multivariable logistic regression models were used to analyze the data. Results: The proportion of depression symptoms was 49.4% during the panic period and 52.4% after the panic period. Anxiety symptoms were experienced by 38.2% of students during the panic period, and this percentage was nearly identical in the post-panic interval. Depression levels increased in the post-panic period and urban students have significantly (P< 0.05) higher levels of depression and anxiety than their counterparts. Female students also exhibited significantly more anxious symptoms (p=0.002) than male. Depression symptoms significantly vary by family types, students place of residence whereas students age, gender, education, family head's occupation, time period and family economic condition found no significant association with the depression.Conclusions: Students during the post-panic period have a higher prevalence of depression and anxiety symptoms than during the panic period. Although the difference was small, it was still concerning for university students in Bangladesh because it interfered with their academic life.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. Methods Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Risk of Interruption of Doctoral Studies and Mental Health in PhD Students

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1695
Author(s):  
Sara M. González-Betancor ◽  
Pablo Dorta-González
Keyword(s):  
Mental Health ◽  
Regression Models ◽  
Doctoral Program ◽  
Well Being ◽  
Poor Mental Health ◽  
Doctoral Studies ◽  
Logistic Regression Models ◽  
Highly Educated ◽  
Explanatory Factors ◽  
Anxiety Depression

PhD students report a higher prevalence of mental illness symptoms than highly educated individuals in the general population. This situation presents a serious problem for universities. Thus, the knowledge about this phenomenon is of great importance in decision-making. In this paper we use the Nature PhD survey 2019 and estimate several binomial logistic regression models to analyze the risk of interrupting doctoral studies. This risk is measured through the desire of change in either the supervisor or the area of expertise, or the wish of not pursue a PhD. Among the explanatory factors, we focus on the influence of anxiety/depression, discrimination, and bullying. As control variables we use demographic characteristics and others related with the doctoral program. Insufficient contact time with supervisors, and exceeding time spent studying crossing the 50-h week barrier, are risk factors of PhD studies interruption, but the most decisive risk factor is poor mental health. Universities should therefore foster an environment of well-being, which allows the development of autonomy and resilience of their PhD students or, when necessary, which fosters the development of conflict resolution skills.

scholarly journals Medication Management of Anxiety and Depression by Primary Care Pediatrics Providers: A Retrospective Electronic Health Record Study

2021 ◽  
Author(s):  
Talia Roshini Lester ◽  
Yair Bannett ◽  
Rebecca M. Gardner ◽  
Heidi M. Feldman ◽  
Lynne C. Huffman
Keyword(s):  
Primary Care ◽  
Logistic Regression ◽  
Electronic Health Record ◽  
Regression Models ◽  
Medication Management ◽  
Anxiety And Depression ◽  
Health Record ◽  
Logistic Regression Models ◽  
Medication Prescription ◽  
Anxiety Depression

Objectives: To describe medication management of children diagnosed with anxiety and depression by primary care providers. Study Design/Methods: We performed a retrospective cross-sectional analysis of electronic health record (EHR) structured data. All visits for pediatric patients seen at least twice during a four-year period within a network of primary care clinics in Northern California were included. Descriptive statistics summarized patient variables and most commonly prescribed medications. For each subcohort (anxiety, depression, and both (anxiety+depression)), logistic regression models examined the variables associated with medication prescription. Results: Of all patients (N=93,025), 2.8% (n=2635) had a diagnosis of anxiety only, 1.5% (n=1433) depression only, and 0.79% (n=737) both anxiety and depression (anxiety+depression); 18% of children with anxiety and/or depression had comorbid ADHD. A total of 14.0% with anxiety (n=370), 20.3% with depression (n=291), and 47.5% with anxiety+depression (n=350) received a psychoactive non-stimulant medication. For anxiety only and depression only, sertraline, citalopram, and fluoxetine were most commonly prescribed. For anxiety+depression, citalopram, sertraline, and escitalopram were most commonly prescribed. The top prescribed medications also included benzodiazepines. Logistic regression models showed that older age and having developmental or mental health comorbidities were independently associated with increased likelihood of medication prescription for children with anxiety, depression, and anxiety+depression. Insurance type and sex were not associated with medication prescription. Conclusions: PCPs prescribe medications more frequently for patients with anxiety+depression than for patients with either diagnosis alone. Medication choices generally align with current recommendations. Future research should focus on the use of benzodiazepines due to safety concerns in children.

scholarly journals The mental health impact of non-pharmacological interventions aimed at impeding viral transmission during the COVID-19 pandemic in a general adult population and the factors associated with adherence to these mitigation strategies

2020 ◽  
Author(s):  
Omid V. Ebrahimi ◽  
Asle Hoffart ◽  
Sverre Urnes Johnson
Keyword(s):  
Psychological Symptoms ◽  
Adult Population ◽  
Information Access ◽  
Health Impact ◽  
Anxiety Symptoms ◽  
Mitigation Strategies ◽  
Depression And Anxiety ◽  
Younger Adults ◽  
Symptoms Of Depression ◽  
General Adult Population

This epidemiological investigation assesses the prevalence of depression and anxiety symptoms during the COVID-19 pandemic. A total of 10,061 adults participated in the study. Symptoms of depression and anxiety were two to three times higher compared to pre-pandemic samples. Those who predominantly socially distanced themselves revealed substantially higher symptoms than their counterparts. Females, ethnic and sexual orientation minorities, younger adults, unemployed individuals, and those with a psychiatric diagnosis reported higher prevalence of psychological symptoms. Worry about prolonged duration of physical distancing protocols and frustration of autonomy was associated with elevation in symptoms of depression and anxiety. Increased competence to deal with the crisis was associated with less adverse symptoms. Physical exercise, experiencing nature, and distraction with activities were associated with less depressive symptoms, but not anxiety. The extent of information access about the pandemic was associated with reduced anxiety symptoms. Furthermore, adherence to mitigation protocols was investigated. Younger adults and males reported lowest adherence. Altruistic attitudes, in addition to mandatory as opposed to voluntary adherence was associated with higher adherence. Worrying about significant others’ health was associated with higher, while worry about duration of pandemic protocols was associated with lower adherence rates.

scholarly journals Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults

2020 ◽  
Author(s):  
André O Werneck ◽  
Danilo R Silva ◽  
Deborah C Malta ◽  
Paulo R B Souza-Júnior ◽  
Luiz O Azevedo ◽  
...  
Keyword(s):  
Mental Health ◽  
Academic Achievement ◽  
Regression Models ◽  
Health Indicators ◽  
Movement Behavior ◽  
Tv Viewing ◽  
Working At Home ◽  
Younger Adults ◽  
Logistic Regression Models ◽  
Daily Time

Abstract Our aim was to analyze the prevalence of unhealthy movement behavior clusters before and during the COVID-19 pandemic, as well as to investigate whether changes in the number of unhealthy behaviors during the COVID-19 pandemic quarantine were associated with mental health indicators. Data of 38,353 Brazilian adults from a nationwide behavior research were used. For movement behaviors, participants reported the frequency and duration of physical activity and daily time on TV viewing and computer/tablet use before and during the pandemic period. Participants also reported the frequency of loneliness, sadness (feeling sad, crestfallen, or depressed), and anxiety feelings (feeling worried, anxious, or nervous) during the pandemic period. Sex, age group, highest academic achievement, working status during quarantine, country region, and time adhering to the quarantine were used as correlates. We used descriptive statistics and logistic regression models for the data analysis. The prevalence of all movement behavior clusters increased during the COVID-19 pandemic. The cluster of all three unhealthy movement behaviors increased from 4.6% (95% confidence interval [CI]: 3.9–5.4) to 26.2% (95% CI: 24.8–27.7). Younger adults, people with higher academic achievement, not working or working at home, and those with higher time in quarantine presented higher clustering. People that increased one and two or three unhealthy movement behaviors were, respectively, more likely to present loneliness (odds ratio [OR] = 1.41 [95% CI: 1.21–1.65] and OR = 1.71 [95% CI: 1.42–2.07]), sadness (OR = 1.25 [95% CI: 1.06–1.48] and OR = 1.73 [95% CI: 1.42–2.10]), and anxiety (OR = 1.34 [95% CI: 1.13–1.57] and OR = 1.78 [95% CI: 1.46–2.17]) during the COVID-19 quarantine. Clustering of unhealthy movement behaviors substantially increased and was associated with poorer mental health during the COVID-19 pandemic.

Sign in / Sign up

Export Citation Format

Share Document