Individual-level preventive measures during the first wave of COVID-19 pandemic among Bangladeshi residents

COVID-19 causes mild to severe respiratory illness in humans. Government and non-government authorities along with telecommunication, print and electronic media undertook extensive advertising campaign regarding protective measures against COVID-19 to raise the public awareness. Therefore, this web-based cross-sectional study was conducted to evaluate peoples' responses towards COVID-19 during the first wave of COVID-19 in Bangladesh. We performed univariate and multivariate analyses to estimate the association between demographic characteristics, awareness, and individual preventive measures. The overall awareness level of the majority of the respondents (89%, n=920) was good, but the overall score for individual-level preventive measures during lockdown was poor to moderate. The relation between a good level of awareness and a higher level of educational status was found statistically significant (aOR 5.87, 95% CI: 1.58-21.86). Service holders were two times more likely to follow COVID-19 prevention practices than students (aOR 2.08, 95% CI: 1.24-3.51). Despite having adequate knowledge on awareness, many respondents were reluctant to follow preventive measures during the lockdown. The outcomes of this study highlight the requirement for stringent execution of preventative measures by law enforcement agencies to stop the transmission of the COVID-19 virus

Machine Learning Algorithm-Based Contraceptive Practice among Ever-Married Women in Bangladesh: A Hierarchical Machine Learning Classification Approach

Contraception enables women to exercise their human right to choose the number and spacing of their children. The present study identified the best model selection procedure and predicted contraceptive practice among women aged 15–49 years in the context of Bangladesh. The required information was collected through a well-known nationally representative secondary dataset, the Bangladesh Demographic and Health Survey (BDHS), 2014. To identify the best model, we applied a hierarchical logistic regression classifier in the machine learning process. Seven well-known ML algorithms, such as logistic regression (LR), random forest (RF), naïve Bayes (NB), least absolute shrinkage and selection operation (LASSO), classification trees (CT), AdaBoost, and neural network (NN) were applied to predict contraceptive practice. The validity computation findings showed that the highest accuracy of 79.34% was achieved by the NN method. According to the values obtained from the ROC, NN (AUC = 86.90%) is considered the best method for this study. Moreover, NN (Cohen’s kappa statistic = 0.5626) shows the most extreme discriminative ability. From our research, we suggest using the artificial neural network technique to predict contraceptive use among Bangladeshi women. Our results can help researchers when trying to predict contraceptive practice

Inequalities in adequate maternal healthcare opportunities: evidence from Bangladesh Demographic and Health Survey 2017–2018

Objectives This study investigated the inequalities in access to maternal healthcare services in Bangladesh.Design and setting This study used cross-sectional data from the nationally representative Bangladesh Demographic and Health Survey conducted in 2017–2018. The survey encompassed diverse regions and households across Bangladesh. The study used the Human Opportunity Index (HOI) and Shapley’s decomposition technique to measure the inequality in access to maternal healthcare opportunities.Participants This study included 20 127 women aged 15–49 years. Among them, 5012 women had live births in the preceding 3 years of the survey, forming the study sample.Primary and secondary outcome measures This study has no secondary outcome variable. The primary dependent variable is ‘adequate maternal healthcare’, a dichotomous variable.Results Household wealth status contributed the highest to inequality in accessing adequate maternal healthcare services (41.4%) such as receiving at least four antenatal care (ANC) visits (39.7%), access to proper ANC (50.7% and 44.0%) and health facility birth (43.4%). Maternal educational status contributes the second highest inequality among all factors in accessing adequate maternal healthcare (29.5%). Adequate maternal healthcare presented the lowest coverage rate and opportunity index among all (approximately 24% with HOI=17.2).Conclusions We found that attained adequate maternal healthcare had the lowest coverage and widest dissimilarity, while wealth index, education and place of residence are the major factors that contribute to inequalities in accessibility to maternal healthcare services in Bangladesh. These findings underscore a need for pro-poor interventions to narrow the economic inequalities between the poor and rich in terms of accessibility to maternal healthcare services. The results indicate the need for the Bangladeshi government and its health department to strengthen their commitment to improving female education. Investments should be made in initiatives that facilitate the proximity of maternal healthcare services to women in rural areas