Rural and urban differences in the prevalence and determinants of Type-2 diabetes in Bangladesh.

OBJECTIVE: To estimate the prevalence of Type 2 Diabetes (T2D) in urban and rural settings and identify the specific risk factors for each location. METHOD: We conducted this study using data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS), sourced from the DHS website. The survey employed a stratified two-stage sampling method, which included 7,658 women and 7,048 men aged 18 and older who had their blood glucose levels measured. We utilized chi-square tests and ordinal logistic regression to analyze the association between various selected variables in both urban and rural settings and their relationship with diabetes and prediabetes. RESULTS: The prevalence of T2D was 10.8% in urban areas and 7.4% in rural areas, while pre-diabetes affected 31.4% and 27% of the populations in these respective settings. The study found significant factors influencing diabetes in both urban and rural regions, particularly in the 55-64 age group (Urban: AOR = 1.88, 95% CI [1.46, 2.42]; Rural: AOR = 1.87, 95% CI [1.54, 2.27]). Highly educated individuals had lower odds of T2D, while wealthier and overweight participants had higher odds in both areas. In rural regions, T2D risk was higher among caffeinated drink consumers and those not engaged in occupation-related physical activity, while these factors did not show significant influence in urban areas. Furthermore, urban participants displayed a significant association between T2D and hypertension. CONCLUSION: Our study outlines a comprehensive strategy to combat the increasing prevalence of T2D in both urban and rural areas. It includes promoting healthier diets to control BMI level, encouraging regular physical activity, early detection through health check-ups, tailored awareness campaigns, improving healthcare access in rural regions, stress management in urban areas, community involvement, healthcare professional training, policy advocacy like sugary drink taxation, research, and monitoring interventions. These measures collectively address the T2D challenge while accommodating the distinct features of urban and rural settings.

Prediction of suicidal ideation with associated risk factors among university students in the southern part of Bangladesh: Machine learning approach.

BACKGROUND: The prevalence of suicidal ideation has become an urgent issue, particularly among adolescents. The primary objective of this research is to determine the prevalence of suicidal ideation among students in the southern region of Bangladesh and to predict this phenomenon using machine learning (ML) models. METHODS: The data collection process involved using a simple random sampling technique to gather information from university students located in the southern region of Bangladesh during the period spreading from April 2022 to June 2022. Upon accounting for missing values and non-response rates, the ultimate sample size was determined to be 584, with 51.5 % of participants identifying as male and 48.5 % female. RESULTS: A significant proportion of students, precisely 19.9 %, reported experiencing suicidal ideation. Most participants were female (77 %) and unmarried (78 %). Within the machine learning (ML) framework, KNN exhibited the highest accuracy score of 91.45 %. In addition, the Random Forest (RF), and Categorical Boosting (CatBoost) algorithms exhibited comparable levels of accuracy, achieving scores of 90.60 and 90.59 respectively. LIMITATIONS: Using a cross-sectional design in research limits the ability to establish causal relationships. CONCLUSION: Mental health practitioners can employ the KNN model alongside patients' medical histories to detect those who may be at a higher risk of attempting suicide. This approach enables healthcare professionals to take appropriate measures, such as counselling, encouraging regular sleep patterns, and addressing depression and anxiety, to prevent suicide attempts.

Prevalence and determinants of hypertension in South-Asian Urban Communities: findings from Demographic and Health Surveys (DHS) data of South Asian countries.

Hypertension increases risk of stroke and other cardiovascular diseases, however, its prevalence and determinants in South Asian urban communities using country representative community-based datasets is lacking. This study evaluated prevalence of hypertension and it's determinants among urban residents of three South Asian countries. Urban population data from demographic and health surveys in Bangladesh, India, and Nepal were extracted. Hypertension prevalence was defined as systolic/diastolic blood pressure ≥ 140/ 90 mmHg. Age, education, wealth, physical activity, alcohol, BMI were considered as risk factors associated with the increased risk of hypertension. We performed binary logistic regression and calculated adjusted Odds Ratios (AOR) with 95% confidence interval (CI) to assess factors related to hypertension. Hypertension prevalence was 37.4% in India, 25.1% in Bangladesh and 18.4% in Nepal. Prevalence increased with age in all settings. Females had reduced odds of hypertension in Bangladesh (AOR 0.75; CI: 0.69, 0.81) and Nepal (AOR 0.62; CI: 0.54, 0.71), but higher risk in India (AOR 2.54; CI: 2.45, 2.63). Low education, caffeine consumption, obesity was associated with higher prevalence of hypertension in all three countries. Smokers had increased odds of hypertension in India (AOR 1.11; CI: 1.06, 1.15) and Nepal (AOR 1.23; 1.02, 1.47). Overall, hypertension prevalence is high in all three countries. Modifiable socioeconomic and lifestyle factors (education, wealth index, smoking status, caffeine consumption and BMI) associated with hypertension. Comprehensive hypertension pacific and sensitive interventions (including behavioral modification treatments and timely screening and access to health care) are urgently needed to prevent and control hypertension among urban populations in South Asia.

A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women.

Domestic violence against women is a prevalent in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. This study aims to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019-2020. We employed seven machine learning algorithms to achieve this goal, including ANN, KNN, RF, DT, XGBoost, LightGBM, and CatBoost. Our analysis revealed that the LightGBM and RF models achieved the highest accuracy in predicting women's vulnerability to domestic violence in Liberia, with 81% and 82% accuracy rates, respectively. One of the key features identified across multiple algorithms was the number of people who had experienced emotional violence. These findings offer important insights into the underlying characteristics and risk factors associated with domestic violence against women in Liberia. By utilizing machine learning techniques, we can better predict and understand this complex issue, ultimately contributing to the development of more effective prevention and intervention strategies.

Knowledge, attitudes, and factors determining the willingness for COVID-19 vaccination among students in Bangladesh: An online-based cross-sectional study.

Aim: The most effective way to avoid COVID-19 is through immunization against the virus that causes the disease. The primary objective of this study was to assess the extent of knowledge, attitudes, acceptability, and factors influencing COVID-19 vaccination acceptance among higher secondary and university students in Bangladesh. Subject and methods: A structured questionnaire-based online survey was conducted among 451 students residing in Khulna and Gopalganj cities from February to August, 2022. The willingness to accept the COVID-19 vaccine was compared with a few covariates using the chi-square test, and we then used binary logistic regression to identify the determinants that led Bangladeshi students to receive the COVID-19 vaccine. Results: During the study period, almost 70% of the students obtained immunizations, with 56% of men and 44% of women reporting having done so. The age range of 26-30 years had the highest percentage of students who had received vaccinations, and 83.9% of students agreed that the COVID-19 vaccine is crucial for students. The results of the binary logistic regression clearly show that gender, degree of education, and respondents' willingness, encouragement, and beliefs have a significant impact on students' eagerness to receive COVID-19 vaccination. Conclusion: The rising trend in vaccination status among Bangladeshi students is highlighted by this study. Additionally, our results eloquently demonstrate that vaccination status varies by gender, education level, willingness, encouragement, and respondent's viewpoint. The outcomes of this study are essential for health policy makers and other interested parties to successfully organize their immunization program for young adults and children at various levels.