Introduction: Bipolar affective disorder (BPAD) is a severe mental illness causing significant problems in the lives of individuals with the disorder and those who care for them as well are. Most of the time people with mental illness are taken care of by mental health professionals, while little priority is given to their caregivers. There are numerous studies in India attempting to understand the impact of illness on caregivers and the problems faced by the caregivers of persons with BPAD. Methods: The study aimed to assess the family caregivers' stress and burden among caregivers of persons with BPAD at a tertiary care center in Dharwad, India. Descriptive research design and simple random sampling was used for recruiting 50 samples. Apart from sociodemographic schedule, burden assessment scale and perceived stress scale (PSS) were used for data collection. Results: The mean age of caregivers was 44.76 years. The average duration of illness of the patients was 9.80 years and their mean annual income was 35,500 rupees reported in the study. The mean score of impact of wellbeing subscale was higher (11.34) when compared with other sub scales of the burden assessment scale, and the overall score of burden assessment scale (31.82) indicated high levels of burden. PSS mean was 21.44, indicating high stress levels. Conclusion: The study concludes that burden and perceived stress were elevated among the caregivers of people with BPAD during the COVID-19 pandemic.

Introduction: Suicidal ideation is a public health concern. The percentage of prevalence of suicidal ideation found among attendees of primary and religious care centers in Thailand is unclear. The aim of this study was to evaluate suicidal ideation and associated factors among clients of primary care and religious care centers in Thailand. Methods: In a cross-sectional interview survey, 1214 consecutively sampled adult clients from three religious and three primary care centers participated in the study in 2018–2019. They completed information on suicidal ideation, social and demographic factors, chronic conditions, and mental and substance use disorders. Logistic regression was used to assess predictors of suicidal ideation. Results: One in ten clients (10.0%) reported suicidal ideation. The prevalence of suicidal ideation was higher in clients attending religious compared to primary care, but the difference was not significant. In adjusted logistic regression analysis, depressive disorder (adjusted odds ratio [AOR]: 3.49, 3.49, 95% confidence interval [CI]: 1.86–6.56), anxiety disorder (AOR: 6.03, 95% CI: 2.82–12.95), somatization disorder (AOR: 2.03, 95% CI: 1.17–3.53), cancer (AOR: 2.56, 95% CI: 1.02–6.50), and sore joints (AOR: 2.24: 95% CI: 1.16–4.31) were positive associated with suicidal ideation, while secondary education (AOR: 0.39, 95% CI: 0.20–0.77), employed (AOR: 0.52, 95% CI: 0.32–0.85), and high social support (AOR: 0.28, 95% CI: 0.13–0.56) were negatively associated with suicidal ideation. Conclusion: The prevalence of suicidal ideation was higher in clients attending religious care compared to primary care, but the difference was not significant. Factors associated with suicidal ideation include sociodemographic factors, mental disorders, and chronic conditions.

Introduction: India approved the “Covishield” vaccine for emergency use and began the first vaccination drive from January 16, 2021. As the new coronavirus disease-2019 (COVID-19) vaccine was developed within a short period, public acceptance for this new vaccine remains uncertain. Vaccine hesitancy remains an important challenge in the immunization against COVID-19. The aim of the current study was to assess different hesitancies, attitude, and beliefs about COVID-19 vaccine among health-care workers. Methods: A cross-sectional study was carried out among health-care professionals from 5^th to January 15, just before the commencement of the first vaccination drive. All the participants were requested to fill out semi-structured pro forma containing following sections: (1) demographic details and (2) attitude and belief questionnaire. Following this, a lecture of around 45–60 min was conducted by trained professionals comprising of an education module. After the educational session, participants' willingness to take the vaccine was reassessed. Results: Totally, 598 participants participated in the study. Of these, 47% participants were not willing to take the vaccine. Participants with age <35 years, experience <5 years, and working in rural areas have statistically significant unwillingness to take vaccine, on Chi-square test. Getting sick from vaccine, contracting the disease after vaccination, fear of adverse effect, uncertain efficacy, and death due to vaccine are concerns related to vaccination hesitancy. After the educational session, 82% of the participants were willing to take the vaccine. Conclusion: Inadequate knowledge about vaccine contributed significantly to denial for vaccination. Educational program was effective in addressing the concerns of participants about vaccination, improving vaccine usage and hence control of the COVID-19 pandemic.

Introduction: World Health Organization declared coronavirus disease-2019 (COVID-19) as global pandemic on March 20, 2020. Highly contagious nature of this new virus and high propensity for human-to-human transmission led to various challenges, one of them is stigma and discrimination. This led to ill-treatment, devaluation of affected individuals, termination of employment, abandonment, and physical violence. The current study attempts to assess the magnitude of social stigma and discrimination among COVID-19 survivors. Methods: Longitudinal study was conducted among COVID-19 survivors from June 2020 to February 2021 at tertiary care center, Jamnagar, Gujarat, India. Participants were contacted through telephone, interview was conducted at 1 and 6 months, using pro forma containing demographic details, “Stigma questionnaire” and “Discrimination questionnaire.” Results: A total of 420 participants of age 18–60 years participated in the study. Participants experienced personalized stigma had concerns regarding disclosure of illness and public attitudes. Discrimination at their respective workplace was also observed. Stigma was statistically significant related to gender and geographical area (F = 3.879, P < 0.05, R² = 0.45), while discrimination statistically significant related to geographical area (F = 2.407, P < 0.05, R² = 0.028). The stigma and discrimination was still prevalent after 6 months; however, there was a reduction in overall stigmatization which was statistically significant (P < 0.05). Conclusion: The large cohort of participants faced significant social stigma and discrimination in terms of personalized stigma, negative self-image, and workplace discrimination. Increasing awareness, knowledge about illness, and availability of treatment facilities can contribute in lowering the stigma.

Introduction: The purpose of this research was to predict mental illness among university students using various machine learning (ML) algorithms. Methods: A structured questionnaire-based online survey was conducted on 2121 university students (private and public) living in Bangladesh. After obtaining informed consent, the participants completed a web-based survey examining sociodemographic variables and behavioral tests (including the Patient Health Questionnaire (PHQ-9) scale and the Generalized Anxiety Disorder Assessment-7 scale). This study applied six well-known ML algorithms, namely logistic regression, random forest (RF), support vector machine (SVM), linear discriminate analysis, K-nearest neighbors, Naïve Bayes, and which were used to predict mental illness among university students from Dhaka city in Bangladesh. Results: Of the 2121 eligible respondents, 45% were male and 55% were female, and approximately 76.9% were 21–25 years old. The prevalence of severe depression and severe anxiety was higher for women than for men. Based on various performance parameters, the results of the accuracy assessment showed that RF outperformed other models for the prediction of depression (89% accuracy), while SVM provided the best result than other models for the prediction of anxiety (91.49% accuracy). Conclusion: Based on these findings, we recommend that the RF algorithm and the SVM algorithm were more moderate than any other ML algorithm used in this study to predict the mental health status of university students in Bangladesh (depression and anxiety, respectively). Finally, this study proposes to apply RF and SVM classification when the prediction of mental illness status is the core interest.

Introduction: Violence against women is becoming more prevalent over the world, particularly in India. Assessing the causes of violence in community will aid in planning supports for victims. This study aimed to compare the performance of various regression models for count data and focused on choosing appropriate count regression model to identify factors related with the number of domestic violence experienced by young married women. Methods: Data for this study were retrieved from “The Youth in India: Situation and Needs Study.” The current study took the data of 1495 married women in Tamil Nadu. Factors associated with physical violence considered for the study were place of residence, age of husband and wife, education of husband and wife, dowry, miscarriage, abortion, and marriage type. Ordinary least square, Poisson regression, and negative binomial regression models were fitted for the data, and the best fitted model was identified using Akaike information criterion (AIC) and Bayesian information criterion (BIC). Results: Proportion of married women who have perpetrated any forms of physical violence was 30.8%. Among the fitted models, negative binomial regression model (AIC = 3020.621, BIC = 3079.030) was found to be the best model to predict violence. Significant factors identified were type of residence, marriage type, education of wife and spouse, miscarriage, and abortion. Conclusion: To tackle this public health issue, multisectoral approaches such as boosting literacy, raising awareness about legal assistance, and monitoring victims of violence at primary health facilities should be implemented. Comprehensive model testing is highly suggested for determining the best acceptable analytic model when dependent variable being studied comprises count data.