Contraceptive use among married women of reproductive age has showed a substantial progress over the last few decades in Bangladesh. This study explores the sociodemographic factors associated with contraceptive use among ever-married women of reproductive age in Bangladesh by utilizing the information extracted from three of the Bangladesh Demographic and Health Surveys (BDHSs) in 1993–1994, 2004 and 2014. Bivariate analysis was conducted by performing chi-squared test of independence to explore the relationship between selected sociodemographic factors and dependent variables. To know the adjusted effects of covariates, a popular binary logistic regression model was considered. Respondents’ current age, place residence, division religion, education, age at first marriage, family planning (FP) media exposure, ideal number of children and fertility preferences are the significant determinants according to the most recent survey, BDHS 2014. However, wealth index and a respondent’s current working status were found to be significant factors only in BDHS 2004. The results of the study strongly recommend efforts to increase the education level among poor people, particularly among women in Bangladesh. Program interventions, including health behavior education and family planning services and counselling, are especially needed for some categories of the population, including those living in rural areas, Sylhet, Chittagong and Dhaka divisions, as well as illiterate and Muslim ever-married women.

Background: Early child development is a crucial factor for children that controls health and well-being in later life. Aims: To determine the influence of sociodemographic factors on the Early Child Development Index (ECDI) among children aged < 5 years. Methods: The analysis was performed using cross-sectional survey data from 2019, 2017–2018 and 2018 Multiple Indicator Cluster Surveys from Bangladesh, Ghana and Costa Rica, respectively. We used the Chi-square test for bivariate analysis and binary logistic regression model for multivariate analysis for all 3 countries. All the statistical analyses were performed with IBM SPSS version 25 and R version 4.0.0. Results: Child age and sex, followed by maternal education level, economic status, child nutritional status, reading children’s books, and maternal functional difficulties had the greatest effect on ECDI. Children aged 36–47 months had lower odds of development than those aged 48–59 months, and boys had lower odds of development than girls in Bangladesh, Costa Rica and Ghana. Urban children had lower odds of development than rural children in Costa Rica but higher odds in Ghana. Conclusion: We recommend that governments should take the necessary steps to enhance children’s early development and well-being in all 3 countries by raising education, improving economic conditions and providing balanced nutrition.

Purpose Malnutrition is one of the serious public health problems especially for children and pregnant women in developing countries such as Bangladesh. This study aims to identify the risk factors associated with child nutrition for both male and female children in Bangladesh. Design/methodology/approach This study was conducted among 23,099 mothers or caretakers of children under five years of age from a nationally representative survey named Bangladesh Multiple Indicator Cluster Survey, 2019. This study used chi-square test statistic for bivariate analysis and multinomial logistic regression was used to evaluate the adjusted effects of those covariates on child nutritional status. Findings The prevalence of severely malnourished, nourishment was higher for males than females (5.3% vs 5.1%, 77.4% vs 76.8%) while moderately malnourished were higher for females (18.1% vs 17.4%). The findings from the multinomial model insinuated that the mother’s education level, wealth index, region, early child development, mother’s functional difficulties, child disability, reading children's books and diarrhea had a highly significant effect on moderate and severe malnutrition for male children. For the female children model, factors such as mother’s education level, wealth index, fever, child disability, rural, diarrhea, early child development and reading less than three books were significant for moderate and severe malnutrition. Originality/value There is a solution to any kind of problem and malnutrition is not an exceptional health problem. So, to overcome this problem, policymakers should take effective measures to improve maternal education level, wealth status, child health.

Background This study aimed to determine the impact of pulmonary complications on death after surgery both before and during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Methods This was a patient-level, comparative analysis of two, international prospective cohort studies: one before the pandemic (January–October 2019) and the second during the SARS-CoV-2 pandemic (local emergence of COVID-19 up to 19 April 2020). Both included patients undergoing elective resection of an intra-abdominal cancer with curative intent across five surgical oncology disciplines. Patient selection and rates of 30-day postoperative pulmonary complications were compared. The primary outcome was 30-day postoperative mortality. Mediation analysis using a natural-effects model was used to estimate the proportion of deaths during the pandemic attributable to SARS-CoV-2 infection. Results This study included 7402 patients from 50 countries; 3031 (40.9 per cent) underwent surgery before and 4371 (59.1 per cent) during the pandemic. Overall, 4.3 per cent (187 of 4371) developed postoperative SARS-CoV-2 in the pandemic cohort. The pulmonary complication rate was similar (7.1 per cent (216 of 3031) versus 6.3 per cent (274 of 4371); P = 0.158) but the mortality rate was significantly higher (0.7 per cent (20 of 3031) versus 2.0 per cent (87 of 4371); P &lt; 0.001) among patients who had surgery during the pandemic. The adjusted odds of death were higher during than before the pandemic (odds ratio (OR) 2.72, 95 per cent c.i. 1.58 to 4.67; P &lt; 0.001). In mediation analysis, 54.8 per cent of excess postoperative deaths during the pandemic were estimated to be attributable to SARS-CoV-2 (OR 1.73, 1.40 to 2.13; P &lt; 0.001). Conclusion Although providers may have selected patients with a lower risk profile for surgery during the pandemic, this did not mitigate the likelihood of death through SARS-CoV-2 infection. Care providers must act urgently to protect surgical patients from SARS-CoV-2 infection.

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.