Background Vitamin A deficiency (VAD) is a prominent and widespread public health problem in developing countries, including Bangladesh. About 2% of all deaths among under-five children are attributable to VAD. Evidence-based information is required to understand the influential factors to increase vitamin A supplementation (VAS) coverage and reduce VAD. We investigated the potential factors affecting VAS coverage and its significant predictors among Bangladeshi children aged 6 to 59 months using the VAS clustered data extracted from the latest Bangladesh Demographic and Health Survey 2014. Methods Data were analysed using mixed logistic regression (MLR) modelling approach in the generalised linear mixed model framework. The MLR model performs better than logistic regression for analysing the clustered data because of its minimum Akaike information criterion value. The likelihood ratio test showed that the variance component was significant. Therefore, the clustering effect among children was inevitable to use. Results VAS coverage among under-five children was 63.6%, which is not optimal and below the WHO’s recommendation and the country’s target of 90%. Children aged 25 to 36 months (AOR = 2.07, 95% CI: 1.711 to 2.513), who had higher educated mothers (AOR = 1.37, p = 0.033, 95% CI: 1.026–1.820) and fathers (AOR = 1.32, p = 0.027, 95% CI: 1.032–1.683), whose mothers had media exposure (AOR = 1.22, p = 0.006, 95% CI: 1.059–1.408) and NGO membership (AOR = 1.24, p = 0.002, 95% CI: 1.089–1.422) were more likely to consume VAS. Conclusion The relevant authorities should create proactive awareness programs for highly vulnerable local communities, specifically targeted to educate the children’s mothers about the necessity and benefits of childhood nutrition.
Background Stress-related mental health problems are one of the most common causes of the burden in university students worldwide. Many studies have been conducted to predict the prevalence of stress among university students, however most of these analyses were predominantly performed using the basic logistic regression (LR) model. As an alternative, we used the advanced machine learning (ML) approaches for detecting significant risk factors and to predict the prevalence of stress among Bangladeshi university students. Methods This prevalence study surveyed 355 students from twenty-eight different Bangladeshi universities using questions concerning anthropometric measurements, academic, lifestyles, and health-related information, which referred to the perceived stress status of the respondents (yes or no). Boruta algorithm was used in determining the significant prognostic factors of the prevalence of stress. Prediction models were built using decision tree (DT), random forest (RF), support vector machine (SVM), and LR, and their performances were evaluated using parameters of confusion matrix, receiver operating characteristics (ROC) curves, and k-fold cross-validation techniques. Results One-third of university students reported stress within the last 12 months. Students’ pulse rate, systolic and diastolic blood pressures, sleep status, smoking status, and academic background were selected as the important features for predicting the prevalence of stress. Evaluated performance revealed that the highest performance observed from RF (accuracy = 0.8972, precision = 0.9241, sensitivity = 0.9250, specificity = 0.8148, area under the ROC curve (AUC) = 0.8715, k-fold accuracy = 0.8983) and the lowest from LR (accuracy = 0.7476, precision = 0.8354, sensitivity = 0.8250, specificity = 0.5185, AUC = 0.7822, k-fold accuracy = 07713) and SVM with polynomial kernel of degree 2 (accuracy = 0.7570, precision = 0.7975, sensitivity = 0.8630, specificity = 0.5294, AUC = 0.7717, k-fold accuracy = 0.7855). Overall, the RF model performs better and authentically predicted stress compared with other ML techniques, including individual and interaction effects of predictors. Conclusion The machine learning framework can be detected the significant prognostic factors and predicted this psychological problem more accurately, thereby helping the policy-makers, stakeholders, and families to understand and prevent this serious crisis by improving policy-making strategies, mental health promotion, and establishing effective university counseling services.
Background WHO estimated 20% of adolescents (10–19 years) have mental health problems. We examined the prevalence and associated risk predictors of overweight/obesity and perceived stress using eating behaviors and physical activity among school-and-college-going urban adolescents in Bangladesh. Methods A cross-sectional study with a multistage sampling technique was employed to select 4609 adolescent students, aged 13–19 years, from all eight Bangladesh divisions during January–June 2019. Data were collected using a self-administered questionnaire containing Turconi Physical Activity Questionnaire (PAQ), Adolescent Stress Questionnaire (ASQ), Dutch Eating Behavior Questionnaire (DEBQ), and Anthropometric measurements. Logistic regression and different association measures assessed relationships among adolescent characteristics. Results The major 61.5% of adolescents were in moderate-to-extremely-severe levels of stress, 28.2% were overweight/obese, only 2.7% had a very active lifestyle, and 30.5% had a sedentary lifestyle. Perceived stress was positively and significantly correlated with eating behaviors and body mass index, whereas physical activity was significantly associated with the prevalence of overweight/obesity and high stress. The prevalence of overweight/obesity (53.8%) and high stress (52.5%) was higher in males. Adolescents’ obesity was 2.212 times more likely who had a sedentary lifestyle (95% CI 1.377–3.552), 1.13 times more likely for those who had experienced stress due to school/leisure conflict (95% CI 1.051–1.222), and 1.634 times more likely for those who were tempted by restrained eating behavior (95% CI 1.495–1.786). Conclusion Stress on secondary school-and-college-going students needs to be recognized, and strategies need to be developed to improve adolescents’ mental health.
In business, dynamic models often provide valuable insights into the complex interactions between variables over time. But recent research contends that the lagged dependent variable specification is too problematic for use in most situations. More specifically, if residuals autocorrelation is present in a dynamic equation where lagged values of the dependent variable appear as regressors, Ordinary least squares (OLS) estimates are biased and generally inconsistent. For this reason it is important to have available tests against autocorrelation, particularly when it is a dynamic model. The Breusch-Godfrey (BG) test is the most appropriate test in the presence of stochastic regressors such as lagged values of the dependent variable for higher order autocorrelation, which is asymptotically equivalent to the Durbin-Watson h test for first order autocorrelation. But Durbin h test is not applicable for second or higher order autocorrelation. Moreover these existing tests are not suitable for one-sided higher order autoregressive schemes. Whenever the sign of the parameters are known of an econometric model, usual two-sided tests are no longer valid. In this situation, we propose a distance-based one-sided Lagrange Multiplier (DLM) test, a likelihood based test, to test one-sided alternative. Monte Carlo simulations are conducted to compare power properties of the proposed DLM test with the BG test. It is found that the DLM test shows substantially improved power than two-sided counterparts for most of the cases considered.
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