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Author(s): Mohammed A. Mamun (corresponding author) [1,2,3]; Firoj Al-Mamun [1,2,3]; Md Emran Hasan [3,4]; Nitai Roy [5]; Moneerah Mohammad ALmerab [6]; Mohammad Muhit [2]; Mst. Sabrina Moonajilin [1] 1. Introduction [...], Background: Diabetes management poses significant challenges worldwide, with individuals often facing increased risks of mental health issues, including suicidal behaviors. While traditional statistical methods have been used to assess risk factors, this study introduces machine learning techniques to predict suicidal behaviors among diabetic patients. Therefore, this study aims to investigate the prevalence and risk factors of suicidal behaviors among individuals with diabetes in Bangladesh using both traditional statistical methods and machine learning models. Methods: A cross-sectional study was carried out with 390 diabetic patients, assessing demographic characteristics, diabetes-related variables, mental health indicators, and suicidal behaviors. Statistical analyses including chi-square tests and logistic regression, and various machine learning models, were employed to examine associations and predict risk factors. Results: This study found that 13.8% of the individuals with diabetes reported lifetime suicidal behavior and 9.0% reported past-year suicidal behavior. Significant risk factors associated with higher rates of suicidal behaviors included younger age, marital status, lower monthly income, higher educational attainment, being insulin users, not having a family history of diabetes, and suffering from anxiety. Among the machine learning models tested, the CatBoost model outperformed others, obtaining low log loss values of 0.45 and 0.32 for lifetime and past-year suicidal behaviors, respectively. CatBoost demonstrated excellent accuracy rates of 83.97% and 88.46%, significantly higher than those of traditional logistic regression models. The most significant predictors identified by machine learning were treatment type, followed by occupation, anxiety, age group, and having chronic diseases. Conclusion: The findings suggest the urgent need for comprehensive interventions and support systems for individuals managing diabetes, particularly those at higher risk of suicidal behaviors. Machine learning offers improved predictive power and the ability to identify complex patterns in risk factors. Collaborative efforts between healthcare providers, mental health professionals, and public health initiatives are essential for prioritizing mental well-being in diabetes management.

Background and Aims: Infection with the hepatitis B virus is a serious public health problem that is growing all over the world. Therefore, in this context, there is no exception to public participation in disease burden reduction. Consequently, for the first time in Bangladesh, the current study aims to assess the level of vaccination status, knowledge, attitude, and practice of hepatitis B infection among general people. Methods: A cross‐sectional study was carried out between December 15, 2021, and January 17, 2022, including sociodemographic information as well as questions about vaccination status and knowledge, attitude, and practice related to hepatitis B. Data were analyzed using descriptive (frequency) and inferential statistics (Mann–Whitney U, Kruskal–Wallis H, χ2, binary logistic regression, and spearman's rho correlation coefficient). Results: Results indicated that about one‐third (37.9%) of the 807 participants had received hepatitis B vaccine, with an overall mean score of 11.506 ± 5.403 for knowledge, 5.435 ± 1.038 and 4.252 ± 1.776 for attitude and practice, respectively. Risk factors related to vaccination were age, religion, educational qualification, occupation, residence area, marital status, comorbidity, and family member suffering from hepatitis B. Higher level of knowledge was significantly found among the young people aged between 10 and 29; had higher secondary or tertiary education (median = 13); were employed (median = 13.5, interquartile range [IQR] = 8); living in divisional city (median = 13, IQR = 7); were single (media = 13, IQR = 7); and whose family members were suffering from hepatitis B. Besides, poor practice was observed among those aged between 50 and higher (p = 0.004), had no formal education [p < 0.001), a retired or housewife (p < 0.001), divorced or widowed (p < 0.001), absence of comorbidity (p = 0.02), and whose family members were not infected with hepatitis B (p < 0.001). Conclusions: The results exposed that vaccination rates and preventative behavior are unsatisfactory, which will hinder efforts to eradicate hepatitis B worldwide by the year 2030. [ABSTRACT FROM AUTHOR]

This study aimed to determine the prevalence of and factors associated with suicidal ideation among university students in Bangladesh. We collected cross-sectional data from 407 students aged between 18 and 27 years at Jahangirnagar University (Dhaka, Bangladesh), using a convenience sampling technique. Of the total sample, 13.8% reported having suicidal ideation during the past 12 months. In multiple logistic regression analysis, being female, being a fifth-year student, lower socioeconomic status, exposure to traumatic events, family suicide history, and depression were associated with suicidal ideation. The findings emphasize the need for appropriate support services for university students with a focus on mental health wellbeing and suicide prevention. [ABSTRACT FROM AUTHOR]